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----
-layout: page
-title: 1. Define
-permalink: /define/
----
-## Play 01. Define: goals & objectives
-
-This play is focused on helping Departments facilitate a high-level conversation on some of the most pressing issues facing the Department and what data solutions might be available to address the issue or problem.
-
-The objective is to get staff to think about how and what we can do to improve the programs we administer. We want to help change the way we think about the programs we work on–not only about how we administer the programs day-to-day, but how do we make them better, more efficient and more effective.
-
-
-
->Continue to refine the value proposition and expand it after assessing data assets.
-
-1. What problem are we trying to solve?
-2. What outcomes do we want to achieve?
-3. How will data work to meet our mission?
-4. What kinds of data do we need access to?
-5. Who are the main stakeholders and how do we engage them?
-6. How are we going to track, assess and monitor progress?
-7. Can we pilot a few projects and start small? What can we learn from starting small and building out?
-8. Do we have the right workforce in place?
-9. Have we received buy-in from leadership and across teams?
-10. Are we delivering on the needs of our customers? How do we know?
-
->Use best practice resources to build a use case around a particular **opportunity, issue or problem**.
-
-## What is the problem you are trying to solve?
-
-This Play helps Departments develop a use case; define the customer/stakeholder who would benefit from implementing the use case; and assess the impact after the implementation.
-
->As a **[user role]** I want to do **[scope]** in order to achieve **[measurable objective]** to serve **[stakeholder group]**.
-
-This Play can also help Departments navigate the Stage 1 Business Analysis.
-
-
-
-Consider [creating a Logic Model](https://www.practicalplaybook.org/resources/develop-logic-model){:target="_blank"} to demonstrate how a program achieves an intended outcome or impact and where data-driven interventions can further program goals.
-
-There are dozens of ways each Department can use data, and the applications can seem endless. It will be important to pick one core mission problem, and then gradually build and grow programs based on lessons learned.
-
-[
-]({{ site.baseurl }}/action_items)
-
-[]({{ site.baseurl }}/resource_library)
-
-
-
diff --git a/1_plan.md b/1_plan.md
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+---
+layout: page
+title: Planning your Data Project
+permalink: /plan/
+---
+## Section 1: Planning for your Data Project
+
+
+### Table of Contents:
+### Part A. [Determining Goals and Strategy](#goals-strategy)
+### Part B. [What Data you will Need](#collect)
+### Part C. [Where to Find the Data you Need](#find)
+
+___
+
+**Taking the time to plan your project is essential.**
+Whether you’re managing a team, analyzing or cleaning a portion the data, or drawing conclusions from your findings, completing **any portion of the project** requires a great deal of thought and planning. In the following section, we’ll provide a clear, step-by-step guide to the entire planning process, including everything you need to know about **creating goals, determining a plan, and getting your data.** It is our hope that you leave this section with a detailed and specific plan, and the confidence that you have the tools to carry out a successful project.
+
+>### This section will:
+> * Help you identify the right questions and **goals** to guide your data collection process
+> * Describe **what data you will need** to successfully complete your project
+> * Provide helpful frameworks to jump-start your **strategic planning process**
+
+| For Managers | For Analysts |
+| ----------- | ----------- |
+| - Setting KPIs and measuring performance | - Getting the data you need — list of Useful contacts |
+| - Assessing your department’s data organization and data strategy | - All things public data |
+| - Assessing Readiness and Resources | - Review of Data Sharing Agreement and Simplifying Interdepartmental Access to Data |
+
+## Part A: Determining Goals and Strategy
+
+## Step 1: Identify your Guiding Questions and Set your Goals
+
+It is important to decide your **vision** and **purpose** behind your project, and identify what you hope your data project will accomplish. Be thoughtful — what impact do you hope to have? What changes are you trying to bring about? It is worth taking the time to write down your answers to the broader **Guiding Questions**, as they will be the foundation of your goals and strategic plan.
+
+First, a bit about goals: for your project to execute smoothly, it is best to choose SMART Goals, of goals that are **specific, measurable, achievable, realistic, and timely**. Look at our summary of the [SMART Goal checklist](https://www.mindtools.com/pages/article/smart-goals.htm) below:
+
+
+
+| **Hint:** Goals often fall into one of the following categories: |
+|---------|
+| - **Increase** something (e.g. increase healthy behaviors in a population) |
+| - **Make** something (e.g. produce a mapping/visualization of all COVID-19 cases across California) |
+| - **Improve** something (e.g. improve living conditions of a population) |
+| - **Reduce** something (e.g. reduce number of smokers in California) |
+
+## Step 2: Develop a Strategic Plan
+
+Now that you have identified your goals, you must develop a strategy for achieving your desired outcomes. A Strategic Plan is first and foremost a **Roadmap to Success** – the more care and thought you put into your plan, the more likely you are to produce a successful data project.
+
+| Did you know? |
+|--------|
+| CHHS has its own [Strategic Plan](https://chhsdata.github.io/dataplaybook/documents/CHHS%20Information%20Strategic%20Plan%202016.pdf) that summarizes our vision and goals for every department’s products and services. While not a substitute for your strategic plan, it can give you ideas and and helps you ensure your strategy aligns with CHHS’ guiding principles and mission statement. |
+
+Utilize a **strategic planning framework** such as the use case diagram or a logic model. These frameworks will help you explicitly define each step necessary to achieve your goals as well as anticipate what challenges you may face throughout your project.
+
+>### **Strategy Tip:** Find the *action words* that best describe the work you’ll do:
+>
+>Action words are verbs that describe how you will approach each task in this project. They don’t describe your intended outcome (i.e. increase and reduce are not action words); rather, they describe roles you will take throughout your project to assure a successful outcome.
+>
+>If you are creating a product:
+> * Update, Upgrade, Develop, Create, Implement, Evaluate, Produce
+>If you are managing a project:
+> * Oversee, coordinate, supervise, manage, plan, support, transition
+>If you are implementing the specifics of a project:
+> * Write, process, provide, maintain, reconcile, direct, administer
+
+
+Example Use Case Diagram ([lucidchart.com](https://www.lucidchart.com/pages/uml-use-case-diagram))
+
+## The Use Case Diagram
+
+This framework is most helpful for projects where you intend to **build some sort of system** (e.g. website, smart phone app, etc.) that your users must interact with. You also must use a **Business Use Case** for any data you request using the Data Sharing Agreement form.
+
+>#### A Use Case Diagram will…
+> * **Identify** the goals of system-user interactions
+> * Define and organize **functional requirements** in your system
+> * Specify the **context** and **requirements** of a system
+> * **Model** the basic flow of events in a use case
+
+### Instructions for building a Use Case Diagram:
+
+| **Step 1:** Start by defining your actors, or the users that interact with your system. they can be anything from a person to an organization or outside system that interacts with your product.
**Note**: Think broadly -- your users may include institutions both within and outside of CHHS as well as specific populations of the public |
+| **Step 2:** For each user, list all the ways they can interact with your system (these are the “use cases”)
**Note**: Ensure you consider alternate/undesirable courses of events and use cases that aren’t obvious |
+| **Step 3:** Draw lines between use cases to reflect commonalities or relationships among them.
**Note**: Identify the use case with the greatest number of relationships/associations -- the most common use cases represent the functions in your project that should be essential. |
+
+Also check out this [Online resource](https://online.visual-paradigm.com/diagrams/solutions/free-use-case-diagram-tool/) to build your own Use Case Diagram.
+
+## The Logic Model
+
+The logic model framework focuses on **visualizing the relationship** between **inputs**, **outcomes**, and **costs** associated with your project. It is a **graphical model** where each component (or “phase”) of your project relates to a list of intended effects in **an implicit, ‘if-then’ way**.
+
+
+
+|The seven "components" you’ll consider are:|
+|-----------|
+| **1. Inputs:** The resources you need for your project |
+| **2. Activities:** What the staff or the program does with those resources |
+| **3. Outputs:** Tangible products, capacities, or deliverables that result from the activities |
+| **4. Outcomes:** Changes that occur in other people or conditions because of the activities and outputs |
+| **5. Impacts:** The most distal/long-term outcomes |
+| **6. Assumptions:** Your beliefs about the program and the resources involved (including how successful you you think it will be or the challenges you may face) |
+| **7. Moderators:** Contextual factors that are out of control of the program but may help or hinder your efforts. These may influence participation, implementation, achievement of your outcomes. |
+
+> **Example:**
+>e.g. **If** I hire more staff for my project (input), **then** I can collect more data about who would benefit from my service (activity). **If** we have more data, **then** our predictive model will be more accurate (output). **If** our model is more accurate, **then** we can increase outreach to populations who are more likely to benefit from our service (outcome) and so on.
+
+To begin, simply **create six headers** as is shown in the diagram above — this can be done by hand, with sticky notes, or online.
+
+>### Guiding Questions:
+>1. Identifying Impact: What measurable change are you seeking to achieve in the long-term?
+>2. Identifying Outcomes: What measurable changes are you seeking to achieve in the short-term?
+>3. Identifying Outputs: What tangible outcomes can you measure immediately following the implementation of your product/project?
+>4. Identifying Activities: What are some high-level steps you must take to complete your project?
+
+List everything that comes to mind when you answer the those **guiding questions** above, drawing a box around each entry. Finally, draw arrows between boxes to signify the ‘if-then’ relationship.
+
+
+A Sample Logic Model from [CDC.gov](https://www.cdc.gov/dhdsp/docs/logic_model.pdf)
+
+>[Click for Back to Top](#top)
+
+___
+
+## Planning Part B: What Data You Will Need
+
+ With your goals and strategy successfully outlined, you can now think about what data or measurements you need to collect to answer your guiding questions, as well as the data you need to determine if you are ready to proceed with data collection.
+ If you’re a manager, you’ll also need to define your outcome measures and performance/self-assessment metrics to maintain the integrity of your project and ensure you’re supporting your team and stakeholders as best you can.
+
+## For Analysts
+
+Before proceeding, you should go through a **Readiness Checklist** to ensure you’ve considered your own strengths, weaknesses, and that of your manager and team. Get the support or learning you need now to prevent misunderstandings or frustrations later in the process.
+
+### Step 1: Readiness Checklist
+
+Ask yourself: Do you have the **Support, Knowledge, and Resources** to Complete your Project?
+
+| - Do my managers/directors have the bandwidth to support me? |
+| - Do I/my team have enough expertise to complete this project? |
+| - Who is my department’s **Data Coordinator**? (The individual responsible for knowing the data assets held by your department)
- Contact CHHS@osi.ca.gov to find your Departments Data Coordinator |
+| - Do I have access to the data I need to complete the project? |
+| - Do I know the statistical methods required to analyze my data? |
+
+### Step 2: Review Your Program Data
+
+Your **program data** is the core data of this project — it’s the specific measurements that you need to collect in order to answer the project’s **guiding questions**. As a review, your guiding questions are **the purpose** of this project as a whole, and spending some time thinking about your project’s **purpose statements** will help you determine what data you need and how you should collect it.
+
+>**Example Purpose Statements:**
+> * I need to decide **how to allocate** resources to different programs based on which is the most successful
+> * I want to **improve or refine** an existing program or model to be more effective
+> * I want to **create** product or service that positively impacts a community
+> * I want to **look at existing data** to find trends and patterns that people care about
+
+It can be useful to review all your data assets with these questions in mind. Contact your department’s data coordinator for more information about the types of program data you collect in your department by emailing CHHS@osi.ca.gov.
+
+## For Managers:
+
+ Managing a team at CHHS is challenging — in addition to setting and working toward your own personal goals, you must also assess the performance of your team and support their continuing learning; set the broader goals that guide larger initiatives, programs, or departments; and work toward capacity building in analytics, data literacy/governance, and much more.
+ The following section is written for a wide range of manager roles, including the larger cohort of managers who supervise analysts and technical employees (SSM1s) to the smaller cohort of branch-level directors or managers working on capacity, vision, and strategy of their department.
+
+### Part 1: Assessing Capability
+
+As a manager, you may be in charge of managing the overall performance and strategy of the project or program; you also may need to assess the performance of the team itself, and the department’s resources. This requires defining and measuring **outcome data**, monitoring your team’s or program’s **performance**, and assessing your department’s **current data assets and analytic capabilities**.
+
+| The following section contains a number of frameworks and resources to assess your Team’s Capabilities… |
+|----------|
+| …related to projects and programs |
+| - **Assessing Readiness**: considering the scope, risks, limitations of your data project
- **Measuring Performance**: Setting Key Performance Indicators (KPIs) for the project and your team
- **Determining outcome measures**: benchmark, baseline, and comparative data |
+| …at the department level |
+| - **Strategic Use of Data**: how effectively does the department utilize data to inform decisions and strategy?
- **Capacity Building**: Improving internal capacity, assessing management strategy & organization
- **Data Governance & Management**: Management & Security of Data, Improving Data Literacy, data de-identification guidelines |
+
+### Managers of Projects and Programs: Assessing Readiness
+
+Before planning your data collection, go through the following **readiness checklist** to ensure you are capable of successfully carrying out this data project. You should catalog your **assets** and **resources** regularly throughout your project to identify areas of weakness or gaps in resources.
+
+>#### **The Readiness Checklist:**
+>* How do programs or stakeholders use data currently? What do they do with it? How do they use it to make decisions or produce products for external stakeholders?
+>* What are limits to either the data or the implementation solution?
+>* What are the risks and issues with the current data? What value is not being realized?
+>* Identify the current workflow for collecting, processing, and publishing data. Are there dependencies to collecting, processing, and publishing the data?
+
+Remember, if you do not have the resources you need, **you and your team will likely encounter problems in your data project**. Address weaknesses early and be on the lookout for areas you can improve throughout your project.
+
+### Measuring Performance and Outcome
+
+This is the data you need to collect **after deploying your product or service** to determine whether or not it met your goals and was successful. A useful framework to reference is the Key Performance Indicators (KPIs) framework described [here](http://kpilibrary.com/). KPIs measure your performance relative to your goals.
+ * Check out [this resource](https://kpi.org/KPI-Basics) to learn all about KPIs: what they are, why they work, and how to set them effectively.
+
+### Managers of Departments: Strategic Use of Data
+
+It is imperative for managers to regularly assess and improve how effectively they use their data assets to inform their strategic planning and organizational structure, as well as improve their offered programs and services. We will root our assessment in Harvard’s [Strategic Use of Data Self-Assessment Guide](https://sdp.cepr.harvard.edu/files/cepr-sdp/files/sdp-rubric-self-asssessment.pdf), a useful framework for understanding how strategically your department uses data and how to improve. A few examples from the guide:
+
+>* Effective **Budgeting** and **Financial Planning** practices driven by data
+>* Assessing **organizational strategy** and **goal-setting**
+>* Measuring **accountability** at all levels of your team
+
+### Building Analytic Capacity
+
+For managers interested in these types of assessments, check out additional resources on building Capability and Capacity in your department (such as the [Analytics Capability Assessment for Human Service Agencies](https://chhsdata.github.io/dataplaybook/documents/APHSA-Analytic-Capability-Roadmap-1-0-for-Human-Services-Agencies.pdf).
+
+>**Note**: For more concrete recommendations to build analytic capacity, check out this [Roadmap to Capacity Building in Analytics](https://chhsdata.github.io/dataplaybook/documents/APHSA-Roadmap-to-Capacity-Building-in-Analytics-White-Paper.pdf). It will cover:
+>* All staff/Human Resource Needs for a successful team
+>* Executing a successful Program or Initiative
+>* How to integrate best practices in Data Governance
+>* Training Resource Topics to Provide to Analysts (Data Processing Methods)
+
+You may also be tasked with assessing the quality of your department’s data management and data governance, or working on capacity-building frameworks to improve data literacy and analysis skills.
+
+Harvard’s [Strategic Use of Data Self-Assessment Guide](https://sdp.cepr.harvard.edu/files/cepr-sdp/files/sdp-rubric-self-asssessment.pdf) has specific questions to identify where departments can better use data at the organizational and strategic level
+
+Harvard’s [Strategic Use of Data Self-Assessment Guide](https://sdp.cepr.harvard.edu/files/cepr-sdp/files/sdp-rubric-self-asssessment.pdf) has specific questions to identify where departments can better use data at the organizational and strategic level
+
+
+>[Click for Back to Top](#top)
+
+___
+
+## Part C: Where to find your data
+
+ The final step of the planning process is also **the most important** and **crucial** to successful execution of your project: determining what data you need and where you will find it.
+ This step can be time-consuming and frustrating, but the effort you put in will pay itself back ten-fold when you find yourself sitting down to start data analysis. Accurate, validated, and comprehensive data is the cornerstone to any data-driven initiative. It is critical to prioritize reliability and integrity of the data in order to ensure the legitimacy of your findings.
+ In most data-driven companies, the **“80/20 Rule”** applies to data projects: 80% of your work will be spent finding, retrieving, cleaning, and organizing your data, and only 20% spent on actual data analysis. So don’t be surprised if this process seems daunting, and don’t rush through it.
+ In this section, you’ll find information on **accessing Internal Data (both within your department and in others)** as well as **External Data** (data owned by some outside agency/organization, and typically publicly available). Use the **Process Flow Chart** on the following page to choose which resource — the Data Sharing Agreement, the Open Data Portal, your department’s stored data, or publicly available data— is appropriate for each of your data sources.
+
+
+
+## Option 1: Program Data
+
+In most cases, you’ll be working with your **Program Data** — data that is owned by your department and collected by or for your program. This data resides within your department, and is easily accessible through your department’s **Data Coordinator**, who is your first resource to seek out when you need help thinking of what data to source for your project or where to find it. Please email CHHS@osi.ca.gov for help with contacting your department’s data coordinator.
+
+## Option 2: Data in Other Departments
+
+ In a few cases, you may find that your department does not have enough data for you to proceed with data analysis. To ensure you have a sufficient amount of data to being your analysis, you are encouraged to look to other departments’ data assets and determine if they'd be appropriate for your project.
+ Your first step to finding data in other departments is to check the CHHS [Open Data Portal](https://data.chhs.ca.gov/), our database for all CHHS data that is publicly-available.
+
+### Data Sharing Agreement:
+
+ Accessing private data in other departments is dictated by the **CHHS Data Sharing Agreement**, a [legal document](https://chhsdata.github.io/dataplaybook/documents/datasharing/CHHS Data Sharing - Legal Agreement.pdf) that entitles any department to accessing another’s data assets through a [Business Use Case Proposal](https://chhsdata.github.io/dataplaybook/documents/datasharing/Business Use Case Proposal - Form.docx). Only proceed with this section if you’ve (1) decided that some of the data you need is **not already available through your department** and (2) is **NOT found on the [Open Data Portal](https://data.chhs.ca.gov/),** then this is your next step.
+
+>**Note:** Read the Data De-Identification Guidelines (in Section 2, Part 1: Cleaning/De-Identifying your Dataset) before sharing any data from your department.
+
+The goals of the **Data Sharing Agreement** are the following:
+ * Establish a legal framework for data initiatives
+ * Maximize appropriate sharing to increase positive outcomes and customer service
+ * Ensure privacy and security protections
+ * Reduce risk and use of duplicative resources
+ * Standardize data use agreements among CHHS Departments and offices
+ * Reduce contracting and data use agreement redundancies
+ * Track activity for better understanding of common data sharing needs between CHHS departments
+
+To get data via the Data Sharing Agreement, you must **contact your department’s Data Coordinator** and submit a [Business Use Case Proposal](https://chhsdata.github.io/dataplaybook/documents/datasharing/Business Use Case Proposal - Form.docx); this ensures proper documentation of what data you need, why you need it, and your commitment to several requirements, such as preserving the shared dataset in the form it was given to you. For more detailed instructions, visit the Business Use Case [instructions](https://chhsdata.github.io/dataplaybook/documents/datasharing/Business Use Case Proposal - Instructions.pdf) or view the [FAQ](https://chhsdata.github.io/dataplaybook/documents/datasharing/CHHS Data Sharing - FAQs.pdf).
+
+
+
+## Option 3: Externally (Publicly-Available Data)
+
+In the past decade, public interest in big data and data-driven projects has skyrocketed. As a result, there is a **wealth of data available** for free that may help you contextualize your results, find baseline measurements, or contribute to your findings. This section showcases **some of our favorite sources** of publicly available data.
+
+> * [USAFacts.org](USAFacts.org) — A data-driven portrait of the American population, our government’s finances, and government’s impact on society that uses federal, state, and local data from over 70 sources.
+> * [datacatalogs.org](datacatalogs.org) — DataCatalogs.org aims to be the most comprehensive list of open data catalogs in the world. It is curated by a group of leading open data experts from around the world - including representatives from local, regional and national governments, international organizations such as the World Bank, and numerous NGOs.
+> * [HealthData.gov](HealthData.gov) — Dedicated to making high value health data more accessible to entrepreneurs, researchers, and policy makers in the hopes of better health outcomes for all.
+> * LOGD Dataset Catalog — The Linking Open Government Data (LOGD) project investigates opening and linking government data using Semantic web technologies. We are translating government-related datasets into RDF, linking them to the Web of Data and providing demos and tutorials on mashing up and consuming linked government data.
+> * [CIA World Fact Book](https://www.cia.gov/library/publications/the-world-factbook/) — Provides information on the history, people, government, economy, geography, communications, transportation, military, and transnational issues for 267 world entities.
+> * [openFDA](https://open.fda.gov/) — Makes it easier to get access to publicly available FDA data. FDA’s goal is to make it simple for an application, mobile device, web developer, or researcher to use data from the FDA.
+> * [Census Reporter](https://censusreporter.org/) — A Knight News Challenge-funded project to make it easier for journalists to write stories using information from the U.S. Census bureau. Place profiles and comparison pages provide a friendly interface for navigating data, including visualizations for a more useful first look.
+> * [CalEnviro Screen](https://oehha.ca.gov/calenviroscreen) — A mapping tool that helps identify California communities that are most affected by many sources of pollution, and where people are often especially vulnerable to pollution’s effects.
+> * [California Healthy Places Index](https://healthyplacesindex.org/) — A tool to explore community conditions that predict life expectancy. It contains user-friendly mapping and data resources at the census tract level across California.
+> * [CHHS Open Data Portal](https://data.chhs.ca.gov/) — Offers access to standardized data that can be easily retrieved, combined, downloaded, sorted, searched, analyzed, redistributed and re-used by individuals, business, researchers, journalists, developers, and government to process, trend, and innovate.
+
+>[Click for Back to Top](#top)
+
+
+
diff --git a/1_plan_a.md b/1_plan_a.md
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+---
+layout: default
+permalink: /plan_a_goals/
+---
+## Planning Part A: Determining Goals and Strategy
+
+### Table of Contents:
+### 1. [Identify Questions and Goals](#goals "goals & objectives")
+### 2. [Develop a Strategic Plan](#strategicplan "Strategic Plan")
+### 2a. [The Use Case Diagram](#usecase "Use Case Diagram")
+### 2b. [The Logic Model](#logicmodel "Logic Model")
+
+___
+
+## Step 1: Identify your Guiding Questions and Set your Goals
+
+It is important to decide your **vision** and **purpose** behind your project, and identify what you hope your data project will accomplish. Be thoughtful — what impact do you hope to have? What changes are you trying to bring about? It is worth taking the time to write down your answers to the broader **Guiding Questions**, as they will be the foundation of your goals and strategic plan.
+
+First, a bit about goals: for your project to execute smoothly, it is best to choose SMART Goals, of goals that are **specific, measurable, achievable, realistic, and timely**. Look at our summary of the [SMART Goal checklist](https://www.mindtools.com/pages/article/smart-goals.htm) below:
+
+
+
+| **Hint:** Goals often fall into one of the following categories: |
+|---------|
+| - **Increase** something (e.g. increase healthy behaviors in a population) |
+| - **Make** something (e.g. produce a mapping/visualization of all COVID-19 cases across California) |
+| - **Improve** something (e.g. improve living conditions of a population) |
+| - **Reduce** something (e.g. reduce number of smokers in California) |
+
+## Step 2: Develop a Strategic Plan
+
+Now that you have identified your goals, you must develop a strategy for achieving your desired outcomes. A Strategic Plan is first and foremost a **Roadmap to Success** – the more care and thought you put into your plan, the more likely you are to produce a successful data project.
+
+| Did you know? |
+|--------|
+| CHHS has its own [Strategic Plan](https://chhsdata.github.io/dataplaybook/documents/CHHS%20Information%20Strategic%20Plan%202016.pdf) that summarizes our vision and goals for every department’s products and services. While not a substitute for your strategic plan, it can give you ideas and and helps you ensure your strategy aligns with CHHS’ guiding principles and mission statement. |
+
+Utilize a **strategic planning framework** such as the use case diagram or a logic model. These frameworks will help you explicitly define each step necessary to achieve your goals as well as anticipate what challenges you may face throughout your project.
+
+>### **Strategy Tip:** Find the *action words* that best describe the work you’ll do:
+>
+>Action words are verbs that describe how you will approach each task in this project. They don’t describe your intended outcome (i.e. increase and reduce are not action words); rather, they describe roles you will take throughout your project to assure a successful outcome.
+>
+>If you are creating a product:
+> * Update, Upgrade, Develop, Create, Implement, Evaluate, Produce
+>If you are managing a project:
+> * Oversee, coordinate, supervise, manage, plan, support, transition
+>If you are implementing the specifics of a project:
+> * Write, process, provide, maintain, reconcile, direct, administer
+
+
+Example Use Case Diagram ([lucidchart.com](https://www.lucidchart.com/pages/uml-use-case-diagram))
+
+## The Use Case Diagram
+
+This framework is most helpful for projects where you intend to **build some sort of system** (e.g. website, smart phone app, etc.) that your users must interact with. You also must use a **Business Use Case** for any data you request using the Data Sharing Agreement form.
+
+>#### A Use Case Diagram will…
+> * **Identify** the goals of system-user interactions
+> * Define and organize **functional requirements** in your system
+> * Specify the **context** and **requirements** of a system
+> * **Model** the basic flow of events in a use case
+
+### Instructions for building a Use Case Diagram:
+
+| **Step 1:** Start by defining your actors, or the users that interact with your system. they can be anything from a person to an organization or outside system that interacts with your product.
**Note**: Think broadly -- your users may include institutions both within and outside of CHHS as well as specific populations of the public |
+| **Step 2:** For each user, list all the ways they can interact with your system (these are the “use cases”)
**Note**: Ensure you consider alternate/undesirable courses of events and use cases that aren’t obvious |
+| **Step 3:** Draw lines between use cases to reflect commonalities or relationships among them.
**Note**: Identify the use case with the greatest number of relationships/associations -- the most common use cases represent the functions in your project that should be essential. |
+
+Also check out this [Online resource](https://online.visual-paradigm.com/diagrams/solutions/free-use-case-diagram-tool/) to build your own Use Case Diagram.
+
+## The Logic Model
+
+The logic model framework focuses on **visualizing the relationship** between **inputs**, **outcomes**, and **costs** associated with your project. It is a **graphical model** where each component (or “phase”) of your project relates to a list of intended effects in **an implicit, ‘if-then’ way**.
+
+
+
+|The seven "components" you’ll consider are:|
+|-----------|
+| **1. Inputs:** The resources you need for your project |
+| **2. Activities:** What the staff or the program does with those resources |
+| **3. Outputs:** Tangible products, capacities, or deliverables that result from the activities |
+| **4. Outcomes:** Changes that occur in other people or conditions because of the activities and outputs |
+| **5. Impacts:** The most distal/long-term outcomes |
+| **6. Assumptions:** Your beliefs about the program and the resources involved (including how successful you you think it will be or the challenges you may face) |
+| **7. Moderators:** Contextual factors that are out of control of the program but may help or hinder your efforts. These may influence participation, implementation, achievement of your outcomes. |
+
+> **Example:**
+>e.g. **If** I hire more staff for my project (input), **then** I can collect more data about who would benefit from my service (activity). **If** we have more data, **then** our predictive model will be more accurate (output). **If** our model is more accurate, **then** we can increase outreach to populations who are more likely to benefit from our service (outcome) and so on.
+
+To begin, simply **create six headers** as is shown in the diagram above — this can be done by hand, with sticky notes, or online.
+
+>### Guiding Questions:
+>1. Identifying Impact: What measurable change are you seeking to achieve in the long-term?
+>2. Identifying Outcomes: What measurable changes are you seeking to achieve in the short-term?
+>3. Identifying Outputs: What tangible outcomes can you measure immediately following the implementation of your product/project?
+>4. Identifying Activities: What are some high-level steps you must take to complete your project?
+
+List everything that comes to mind when you answer the those **guiding questions** above, drawing a box around each entry. Finally, draw arrows between boxes to signify the ‘if-then’ relationship.
+
+
+A Sample Logic Model from [CDC.gov](https://www.cdc.gov/dhdsp/docs/logic_model.pdf)
+
+
+
+
diff --git a/1_plan_b.md b/1_plan_b.md
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+---
+layout: default
+permalink: /plan_b_collect/
+---
+## Planning Part B: What Data You Will Need
+
+### Table of Contents:
+### 1. [For Analysts](#analysts)
+### 2. [For Managers](#managers)
+
+___
+
+ With your goals and strategy successfully outlined, you can now think about what data or measurements you need to collect to answer your guiding questions, as well as the data you need to determine if you are ready to proceed with data collection.
+ If you’re a manager, you’ll also need to define your outcome measures and performance/self-assessment metrics to maintain the integrity of your project and ensure you’re supporting your team and stakeholders as best you can.
+
+## For Analysts
+
+Before proceeding, you should go through a **Readiness Checklist** to ensure you’ve considered your own strengths, weaknesses, and that of your manager and team. Get the support or learning you need now to prevent misunderstandings or frustrations later in the process.
+
+### Step 1: Readiness Checklist
+
+Ask yourself: Do you have the **Support, Knowledge, and Resources** to Complete your Project?
+
+| - Do my managers/directors have the bandwidth to support me? |
+| - Do I/my team have enough expertise to complete this project? |
+| - Who is my department’s **Data Coordinator**? (The individual responsible for knowing the data assets held by your department)
- Contact CHHS@osi.ca.gov to find your Departments Data Coordinator |
+| - Do I have access to the data I need to complete the project? |
+| - Do I know the statistical methods required to analyze my data? |
+
+### Step 2: Review Your Program Data
+
+Your **program data** is the core data of this project — it’s the specific measurements that you need to collect in order to answer the project’s **guiding questions**. As a review, your guiding questions are **the purpose** of this project as a whole, and spending some time thinking about your project’s **purpose statements** will help you determine what data you need and how you should collect it.
+
+>**Example Purpose Statements:**
+> * I need to decide **how to allocate** resources to different programs based on which is the most successful
+> * I want to **improve or refine** an existing program or model to be more effective
+> * I want to **create** product or service that positively impacts a community
+> * I want to **look at existing data** to find trends and patterns that people care about
+
+It can be useful to review all your data assets with these questions in mind. Contact your department’s data coordinator for more information about the types of program data you collect in your department by emailing CHHS@osi.ca.gov.
+
+## For Managers:
+
+ Managing a team at CHHS is challenging — in addition to setting and working toward your own personal goals, you must also assess the performance of your team and support their continuing learning; set the broader goals that guide larger initiatives, programs, or departments; and work toward capacity building in analytics, data literacy/governance, and much more.
+ The following section is written for a wide range of manager roles, including the larger cohort of managers who supervise analysts and technical employees (SSM1s) to the smaller cohort of branch-level directors or managers working on capacity, vision, and strategy of their department.
+
+### Part 1: Assessing Capability
+
+As a manager, you may be in charge of managing the overall performance and strategy of the project or program; you also may need to assess the performance of the team itself, and the department’s resources. This requires defining and measuring **outcome data**, monitoring your team’s or program’s **performance**, and assessing your department’s **current data assets and analytic capabilities**.
+
+| The following section contains a number of frameworks and resources to assess your Team’s Capabilities… |
+|----------|
+| …related to projects and programs |
+| - **Assessing Readiness**: considering the scope, risks, limitations of your data project
- **Measuring Performance**: Setting Key Performance Indicators (KPIs) for the project and your team
- **Determining outcome measures**: benchmark, baseline, and comparative data |
+| …at the department level |
+| - **Strategic Use of Data**: how effectively does the department utilize data to inform decisions and strategy?
- **Capacity Building**: Improving internal capacity, assessing management strategy & organization
- **Data Governance & Management**: Management & Security of Data, Improving Data Literacy, data de-identification guidelines |
+
+### Managers of Projects and Programs: Assessing Readiness
+
+Before planning your data collection, go through the following **readiness checklist** to ensure you are capable of successfully carrying out this data project. You should catalog your **assets** and **resources** regularly throughout your project to identify areas of weakness or gaps in resources.
+
+>#### **The Readiness Checklist:**
+>* How do programs or stakeholders use data currently? What do they do with it? How do they use it to make decisions or produce products for external stakeholders?
+>* What are limits to either the data or the implementation solution?
+>* What are the risks and issues with the current data? What value is not being realized?
+>* Identify the current workflow for collecting, processing, and publishing data. Are there dependencies to collecting, processing, and publishing the data?
+
+Remember, if you do not have the resources you need, **you and your team will likely encounter problems in your data project**. Address weaknesses early and be on the lookout for areas you can improve throughout your project.
+
+### Measuring Performance and Outcome
+
+This is the data you need to collect **after deploying your product or service** to determine whether or not it met your goals and was successful. A useful framework to reference is the Key Performance Indicators (KPIs) framework described [here](http://kpilibrary.com/). KPIs measure your performance relative to your goals.
+ * Check out [this resource](https://kpi.org/KPI-Basics) to learn all about KPIs: what they are, why they work, and how to set them effectively.
+
+### Managers of Departments: Strategic Use of Data
+
+It is imperative for managers to regularly assess and improve how effectively they use their data assets to inform their strategic planning and organizational structure, as well as improve their offered programs and services. We will root our assessment in Harvard’s [Strategic Use of Data Self-Assessment Guide](https://sdp.cepr.harvard.edu/files/cepr-sdp/files/sdp-rubric-self-asssessment.pdf), a useful framework for understanding how strategically your department uses data and how to improve. A few examples from the guide:
+
+>* Effective **Budgeting** and **Financial Planning** practices driven by data
+>* Assessing **organizational strategy** and **goal-setting**
+>* Measuring **accountability** at all levels of your team
+
+### Building Analytic Capacity
+
+For managers interested in these types of assessments or improving the current heat the of their data structures, check out additional resources on building Capability and Capacity in your department (such as the Analytics Capability Assessment for Human Service Agencies.
+
+>**Note**: For more concrete recommendations to build analytic capacity, check out this [Roadmap to Capacity Building in Analytics](https://chhsdata.github.io/dataplaybook/documents/APHSA-Roadmap-to-Capacity-Building-in-Analytics-White-Paper.pdf). It will cover:
+>* All staff/Human Resource Needs for a successful team
+>* Executing a successful Program or Initiative
+>* How to integrate best practices in Data Governance
+>* Training Resource Topics to Provide to Analysts (Data Processing Methods)
+
+You may also be tasked with assessing the quality of your department’s data management and data governance, or working on capacity-building frameworks to improve data literacy and analysis skills.
+
+
+
+
+
+
diff --git a/1_plan_c.md b/1_plan_c.md
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+---
+layout: default
+permalink: /plan_c_find/
+---
+## Planning Part C: Where to find your data
+
+### Table of Contents:
+### 1. [Program Data](#program)
+### 2. [Data in Other Departments](#departments)
+### 3. [External Data](#external)
+
+___
+
+ The final step of the planning process is also **the most important** and **crucial** to successful execution of your project: determining what data you need and where you will find it.
+ This step can be time-consuming and frustrating, but the effort you put in will pay itself back ten-fold when you find yourself sitting down to start data analysis. Accurate, validated, and comprehensive data is the cornerstone to any data-driven initiative. It is critical to prioritize reliability and integrity of the data in order to ensure the legitimacy of your findings.
+ In most data-driven companies, the **“80/20 Rule”** applies to data projects: 80% of your work will be spent finding, retrieving, cleaning, and organizing your data, and only 20% spent on actual data analysis. So don’t be surprised if this process seems daunting, and don’t rush through it.
+ In this section, you’ll find information on **accessing Internal Data (both within your department and in others)** as well as **External Data** (data owned by some outside agency/organization, and typically publicly available). Use the **Process Flow Chart** on the following page to choose which resource — the Data Sharing Agreement, the Open Data Portal, your department’s stored data, or publicly available data— is appropriate for each of your data sources.
+
+
+
+## Option 1: Program Data
+
+In most cases, you’ll be working with your **Program Data** — data that is owned by your department and collected by or for your program. This data resides within your department, and is easily accessible through your department’s **Data Coordinator**, who is your first resource to seek out when you need help thinking of what data to source for your project or where to find it. Please email CHHS@osi.ca.gov for help with contacting your department’s data coordinator.
+
+## Option 2: Data in Other Departments
+
+ In a few cases, you may find that your department does not have enough data for you to proceed with data analysis. To ensure you have a sufficient amount of data to being your analysis, you are encouraged to look to other departments’ data assets and determine if they'd be appropriate for your project.
+ Your first step to finding data in other departments is to check the CHHS [Open Data Portal](https://data.chhs.ca.gov/), our database for all CHHS data that is publicly-available.
+
+### Data Sharing Agreement:
+
+ Accessing private data in other departments is dictated by the **CHHS Data Sharing Agreement**, a [legal document](https://chhsdata.github.io/dataplaybook/documents/datasharing/CHHS Data Sharing - Legal Agreement.pdf) that entitles any department to accessing another’s data assets through a [Business Use Case Proposal](https://chhsdata.github.io/dataplaybook/documents/datasharing/Business Use Case Proposal - Form.docx). Only proceed with this section if you’ve (1) decided that some of the data you need is **not already available through your department** and (2) is **NOT found on the [Open Data Portal](https://data.chhs.ca.gov/),** then this is your next step.
+
+>**Note:** Read the Data De-Identification Guidelines (in Section 2, Part 1: Cleaning/De-Identifying your Dataset) before sharing any data from your department.
+
+The goals of the **Data Sharing Agreement** are the following:
+ * Establish a legal framework for data initiatives
+ * Maximize appropriate sharing to increase positive outcomes and customer service
+ * Ensure privacy and security protections
+ * Reduce risk and use of duplicative resources
+ * Standardize data use agreements among CHHS Departments and offices
+ * Reduce contracting and data use agreement redundancies
+ * Track activity for better understanding of common data sharing needs between CHHS departments
+
+To get data via the Data Sharing Agreement, you must **contact your department’s Data Coordinator** and submit a [Business Use Case Proposal](https://chhsdata.github.io/dataplaybook/documents/datasharing/Business Use Case Proposal - Form.docx); this ensures proper documentation of what data you need, why you need it, and your commitment to several requirements, such as preserving the shared dataset in the form it was given to you. For more detailed instructions, visit the Business Use Case [instructions](https://chhsdata.github.io/dataplaybook/documents/datasharing/Business Use Case Proposal - Instructions.pdf) or view the [FAQ](https://chhsdata.github.io/dataplaybook/documents/datasharing/CHHS Data Sharing - FAQs.pdf).
+
+
+
+## Option 3: Externally (Publicly-Available Data)
+
+In the past decade, public interest in big data and data-driven projects has skyrocketed. As a result, there is a **wealth of data available** for free that may help you contextualize your results, find baseline measurements, or contribute to your findings. This section showcases **some of our favorite sources** of publicly available data.
+
+> * [USAFacts.org](USAFacts.org) — A data-driven portrait of the American population, our government’s finances, and government’s impact on society that uses federal, state, and local data from over 70 sources.
+> * [datacatalogs.org](datacatalogs.org) — DataCatalogs.org aims to be the most comprehensive list of open data catalogs in the world. It is curated by a group of leading open data experts from around the world - including representatives from local, regional and national governments, international organizations such as the World Bank, and numerous NGOs.
+> * [HealthData.gov](HealthData.gov) — Dedicated to making high value health data more accessible to entrepreneurs, researchers, and policy makers in the hopes of better health outcomes for all.
+> * LOGD Dataset Catalog — The Linking Open Government Data (LOGD) project investigates opening and linking government data using Semantic web technologies. We are translating government-related datasets into RDF, linking them to the Web of Data and providing demos and tutorials on mashing up and consuming linked government data.
+> * [CIA World Fact Book](https://www.cia.gov/library/publications/the-world-factbook/) — Provides information on the history, people, government, economy, geography, communications, transportation, military, and transnational issues for 267 world entities.
+> * [openFDA](https://open.fda.gov/) — Makes it easier to get access to publicly available FDA data. FDA’s goal is to make it simple for an application, mobile device, web developer, or researcher to use data from the FDA.
+> * [Census Reporter](https://censusreporter.org/) — A Knight News Challenge-funded project to make it easier for journalists to write stories using information from the U.S. Census bureau. Place profiles and comparison pages provide a friendly interface for navigating data, including visualizations for a more useful first look.
+> * [CalEnviro Screen](https://oehha.ca.gov/calenviroscreen) — A mapping tool that helps identify California communities that are most affected by many sources of pollution, and where people are often especially vulnerable to pollution’s effects.
+> * [California Healthy Places Index](https://healthyplacesindex.org/) — A tool to explore community conditions that predict life expectancy. It contains user-friendly mapping and data resources at the census tract level across California.
+> * [CHHS Open Data Portal](https://data.chhs.ca.gov/) — Offers access to standardized data that can be easily retrieved, combined, downloaded, sorted, searched, analyzed, redistributed and re-used by individuals, business, researchers, journalists, developers, and government to process, trend, and innovate.
+
+
+
diff --git a/2_analyze.md b/2_analyze.md
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+---
+layout: page
+title: Analyzing your Data
+permalink: /analyze/
+---
+## Section 2: Analyzing Your Data
+
+
+### Table of Contents
+### Part A: [Cleaning your Dataset](#cleaning "Cleaning & De-Identifying your Dataset")
+### Part B: [Learning Resources](#resources "Learning Resources")
+### Part C: [Review — Facts, Stats, and Trends](#review "Statistics Review")
+
+___
+
+## Part A: Cleaning & De-Identifying your Dataset
+
+A dataset that has duplicate entries or misspelled words can skew your outcomes. Check out Microsoft Office’s [Top Ten Ways to Clean your Data](https://support.office.com/en-us/article/Top-ten-ways-to-clean-your-data-2844b620-677c-47a7-ac3e-c2e157d1db19) before starting your analysis.
+
+### CHHS Data De-Identification Guidelines
+
+ Every single employee of CHHS — whether working with data in a technical capacity, as a manager, an analyst, or otherwise -- is responsible for ensuring **no personal or redacted information is ever shared throughout their project**. The [De-Identification Guidelines](https://chhsdata.github.io/dataplaybook/documents/CHHS-DDG-V1.0-092316.pdf) will walk you through this process.
+ As Departments classify data tables and catalog their publishable state data, they should be mindful of legal and policy restrictions on publication of certain kinds of data. The CHHS Data Subcommittee put together the following guidelines to ensure a standard of data governance across CHHS.
+
+ The CHHS Data De-Identification Guidelines support CHHS governance goals to reduce inconsistency of practices across Departments, align standards used across Departments, facilitate the release of useful data to the public, promote transparency of state government, and support other CHHS initiatives, such as the CHHS Open Data Portal.
+See the full guidelines [here](https://chhsdata.github.io/dataplaybook/documents/CHHS-DDG-V1.0-092316.pdf).
+
+### De-Identification Considerations:
+
+1. The CHHS Data De-Identification Guidelines are the default policy for CHHS departments. If a CHHS Department wants to customize the guidelines, it must have appropriate references to departmental processes and must file a copy of their guidelines with the Office of the Agency Information Office.
+2. While most state agencies are covered by the California Information Practices Act (IPA), some are also covered by or impacted by HIPAA, the United States Health Insurance Portability and Accountability Act. Unlike the IPA, which applies to all personal information, HIPAA only applies to certain health or healthcare-related information. HIPAA requirements apply in combination with IPA requirements.
+3. For Departments covered by HIPAA, de-identification must meet the HIPAA standard. The CHHS Data De-Identification Guidelines serve as a tool to make and document an expert determination consistent with the HIPAA standard.
+
+>[Click for Back to Top](#top)
+
+___
+
+## Part B: Learning Resources
+
+### Learning Resources
+
+If you don’t have much experience with data analysis, it may be helpful to review some of the key concepts in statistics and math to make sure you understand what you are actually looking at during data analysis.
+ * [Central Tendency](https://statistics.laerd.com/statistical-guides/measures-central-tendency-mean-mode-median.php)
+ * [Correlation](https://www.excelfunctions.net/excel-correl-function.html)
+ * [Paired t-test](http://www.real-statistics.com/students-t-distribution/paired-sample-t-test/)
+ * [Regression Analysis](https://www.qimacros.com/hypothesis-testing/regression/)
+ * [Multiple Regression in Excel](https://www.businessinsider.com/understand-excel-multiple-regression-2014-10)
+
+Other Resources:
+ * [Coursera](https://www.coursera.org/courses?query=beginner data analysis) almost always has free online classes for new or experienced data analysts
+ * [Khan Academy](https://www.khanacademy.org/computing/ap-computer-science-principles/data-analysis-101) is one of the most popular online learning tools if you have more specific questions about data analysis.
+ * [Lynda](https://www.lynda.com/Tableau-tutorials/Tableau-9-Essential-Training/386886-2.html) has a number of Tableau Learning Tutorials
+
+>[Click for Back to Top](#top)
+
+___
+
+## Part C: Review — Facts, Stats, and Trends
+
+Data analysis can be daunting for the first-time data analyst. To simplify the vast work of data science, we’ll stick to the “facts-stats-trends” framework:
+>1. **Facts** are counts, sums, and numbers.
+>2. **Stats** are basic descriptive statistics (mean, median, mode, distribution)
+>3. **Trends** are found by comparing data between two points in time or groups (e.g. percent change, percent different)
+ * You can compare **across time** in the same group (*longitudinal*), or **between groups** in the same time period (*cross-sectional*)
+
+### Facts
+
+>Facts are useful for providing a **high-level summary** of your data or methods. (*e.g. How many participants completed your program? What was the average calorie intake of participants at 4 weeks?*)
+
+ * Facts can **contextualize** your data as percentages, fractions, or rates (*e.g. 100 participants completed the program out of 150 = 67% completion rate*)
+ * If you ever had benchmark measurements or metrics to set a baseline for your data, you can compare facts with those to assess **whether some intervention was successful.** (*E.g. Only 80/150 participants completed the program prior to our change in outreach. Thus, we increased completion by nearly 14% through this project.*)
+
+### Stats
+
+>Gathering meaningful statistics about your data is essential to gaining a deeper understanding into the impacts it had on your users. Even basic statistical transformations can reveal surprising patterns in your data.
+
+ * Simply put, stats present high-level summaries and suggest implications of your data
+ * Often the basis of simple charts and tables
+ * **Central Tendency** is one simple but powerful statistical measure
+ * Mean, median, and mode are all measures of central tendency — that is, how each data point relates to the average
+ * Excel can [calculate](https://access-excel.tips/excel-central-tendency-mean-mode-median/) this for you
+ * It can be useful to **compare the central tendency of different populations** in your study (*Example: did individuals who completed your program experience fewer days of unemployment on average than those who did not complete the program?*)
+ * **The Distribution** of your dataset — or how each data point relates to the set as a whole — can tell you a lot about your data through **visualizations** or **graphs**
+ * You can easily mistake a skewed dataset for a positive/negative outcome if you only rely on central tendency (*e.g. out of 100 participants who completed the program, one reported 300 days of unemployment compared to an average of 14-20 days for the remaining 99. Since the 50 who did not complete the program experienced an average of 20-30 days of unemployment, __you underestimated the effectiveness of your program__ due to the presence of this outlier.*)
+
+### Trends
+
+>Trends are what result when you combine your **facts** with your **stats** — they reveal broader patterns that describe your program’s impact and answer your guiding questions.
+
+Some examples trends and how to find them:
+ * Compare the average rate of completion of your project over time (How many participants completed your program on average at three different timepoints?)
+ * How did the percent of people who reported high satisfaction with the program change over time? (Was the percentage higher in at one time? Why?)
+ * Was one group more likely to experience unemployment one year after completion than another?
+
+>**Note**: When comparing distributions or central tendencies between two populations over time, you must prove any differences are statistically significant — that is, **it is more than 95% likely** that the differences you found between populations is actually due to your intervention and **not to random chance**. You can do this using something called a [t-test](http://www.real-statistics.com/students-t-distribution/paired-sample-t-test/). Read more about statistical significance [here](https://hbr.org/2016/02/a-refresher-on-statistical-significance).
+
+>[Click for Back to Top](#top)
+
+
+
diff --git a/2_assess.md b/2_assess.md
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-layout: page
-title: 2. Assess
-permalink: /assess/
----
-## Play 02. Assess: tools & capabilities
-
-This play focuses on assessing a Department’s current assets and capabilities—including technology and human resources–to leverage their data to help formulate program or policy solutions. The goal is to look at existing capabilities to address the problems or issues.
-
-* What data do you currently collect?
-* What technology can you leverage?
-* What data analytics tools do you have available within the Department or Agency?
-* What established processes can you leverage?
-* What are the training needs?
-
-Identify staff development and training opportunities. See the Selected Training Resources in the Resource Library.
-
->Assess the Department's readiness by **identifying the gaps and needs** in order to operationalize the data strategy.
-
-The capability assessment will help facilitate a conversation within the Department to help catalog the various resources available in order to address the problems identified in the objective assessment.
-
-1. How do **programs or stakeholders** use data currently? What do they do with it? How do they use it to make decisions or produce products for external stakeholders?
-2. What are **limits** to either the data or the implementation solution?
-3. What are the **risks/issues** with the current data? What value is not being realized?
-4. Identify the current workflow for **collecting, processing, and publishing** data. Are there dependencies to collecting, processing, and publishing the data?
-
-
-
-Consider applying an existing data capacity framework to your Department.
-* [Strategic Use of Data Rubric](https://sdp.cepr.harvard.edu/strategic-use-data-rubric/){:target="_blank"} – A resource that provides direction and support to education organizations to transform their use of data. The rubric establishes a common language and framework to more clearly illustrate what effective data use at the system-level looks like.
- * The California Department of Public Health has adapted the data rubric to their needs. [Download the Public Health data rubric here](https://github.com/chhsdata/dataplaybook/raw/gh-pages/documents/Public-Health-Strategic-Use-of-Data-Rubric-09-04-18.docx).
-* [Analytics Capability Roadmap for Human Service Agencies]({{ site.baseurl }}/documents/APHSA-Analytic-Capability-Roadmap-1-0-for-Human-Services-Agencies.pdf){:target="_blank"} – Helps assess current analytic capability and developing an analytic strategy to help meet their organizational objectives and measure outcomes across programs.
-* [Roadmap to Capacity Building in Analytics]({{ site.baseurl }}/documents/APHSA-Roadmap-to-Capacity-Building-in-Analytics-White-Paper.pdf){:target="_blank"} – Provides information on (1) analytical capabilities required for successful analytical efforts, (2) skillsets as well as governance structures and change management processes for such efforts, and (3) practical examples of existing solutions across public and private health and human service sectors.
-
->To give an even deeper look at these strategies, Departments share how they’ve put them to use in different scenarios and for different efforts.
-
-[]({{ site.baseurl }}/action_items)
-
-When looking for data, start with the [Open Data Portal Catalog](https://data.chhs.ca.gov/dataset/dataset-catalog/resource/2d60ad30-db63-43c8-a4b6-0861f27856ff){:target="_blank"} of public datasets available. You can use the [dataset priority scoring tool](https://github.com/chhsdata/opendatahandbook/raw/gh-pages/documents/CHHS-Open-Data-Priority-Scoring-Template.xlsx) to inventory department data for your analysis.
-
-[]({{ site.baseurl }}/resource_library)
-
-
-
diff --git a/3_communicate.md b/3_communicate.md
new file mode 100755
index 0000000..b99f7a8
--- /dev/null
+++ b/3_communicate.md
@@ -0,0 +1,97 @@
+---
+layout: page
+title: Communicating Your Results
+permalink: /communicate/
+---
+## Section 3: Communicating Your Results
+
+
+### Table of Contents
+### Part A: [Crafting your data narrative](#narrative "Crafting your data narrative")
+### Part B: [Designing Effective Visualizations](#visualize "Designing Effective Visualizations")
+### Part C: [Sharing with Others](#sharing "Sharing with Others")
+
+___
+
+## Part A: Crafting your data narrative
+Sharing your findings with the world is just like telling any good story — sometimes it’s more about the storyteller than the story itself.
+
+All too often, truly meaningful and interesting data projects **fall through the cracks** because they **lack a cohesive narrative** or don’t convince the audience **why they should care**. Remember, it’s up to you to decide how to best leverage your data to tell your story in a way that compelling, interesting, and true to you. Here are some guiding questions to get you started:
+
+### Who is your audience?
+
+Your data story can and should change based on your intended audience. The contextualizing information you provide, anecdotes you share, or images you include in a professional journal would be completely different from those you’d choose to share to a group of high schoolers interested in pursuing a career in STEM. Consider the following questions:
+
+>* **What’s your relationship to your audience?**
+> * Are you their peer? Did you used to be in their shoes? Do you have anything in common?
+>* **What can you do to understand your audience?**
+> * Create an audience profile for one of your readers/users
+> * Have you interviewed them? Learned their likes/dislikes?
+>
+>* **What is your ideal medium?**
+> * Your ideal medium is the format through which you implement your product or disseminate your findings, such as:
+> * *Digital* (web, smart phone applications, social media, etc.)
+> * *Formal Print* (reports, conferences, PowerPoint/Keynote presentations)
+> * *Informal Print* (staff meetings, flyers, etc.)
+> * *Video*
+>* **What do you want them to take away?**
+> * Is your purpose to share something generally exciting (informational) or do your results inform a specific decision or action (decisional)?
+> * If *informational*: highlight the findings that are most shocking/interesting to you and your audience
+> * If *decisional*: present the findings in a way that obviously supports some change or recommendation
+> * This often requires you to contextualize your information — what else should your audience know to reach your conclusion?
+
+| General Tips |
+|-------|
+|- Use a word editing app like [Hemingway](http://www.hemingwayapp.com) to **improve** the readability of your writing
- Hemingway will highlight lengthy or run-on sentences, remove overly dense writing, offer alternatives for weak adverbs and phrases as well as poor formatting choices. |
+|- **Connect** to your audience emotionally — how can you make this more personal? |
+|- **Visualize** your story with a storyboard (see MIT’s [guide](https://datatherapy.org/activities/activity-finding-a-story-in-data/) to finding a story in your data) |
+|- Find the right **balance** between words/explanation and figures/tables/images
- This will largely depend on who your intended audience is and what medium you are using — digital products should be more visual while reports or prints should rely more on words |
+|- Similarly, balance your **quantitative data** with **qualitative data** — too much dry facts or too many numbers may work against a compelling data story
- Anecdotes, stories, and contextualizing comments also count |
+|- **Start** with your ultimate **goal**: What message do you want the audience to walk away with? |
+
+>[Click for Back to Top](#top)
+
+___
+
+## Part B: Designing Effective Visualizations
+
+Finding the ‘best’ way to visualize your data takes time and experience — if you’re a beginner, **focus your efforts** on learning from others and refining your methods to master the art of **translating data to diagrams.**
+
+|If you just need a quick chart or table, check out these online tools — they are simpler to use than the advanced data visualization guides and may be more appropriate for your specific project: |
+| - [Google Charts](https://developers.google.com/chart/) (interactive charts & simple data tools)
- [DataWrapper](https://www.datawrapper.de) (charts, tables, and maps)
- [Infogram](https://infogram.com) (beginner-friendly, collaborative, focuses on design thinking principles) |
+
+For more complex data projects, choosing the right visualization is more than just deciding between a pie chart vs. a bar graph — it’s about understanding your audience’s learning style and design preferences, leaning in to your creative side, and asking for lots of feedback.
+
+>Here are some resources to help you understand **all types of data visualization**, **how to create** them, and **which choices are most appropriate** for your data:
+> 1. **Beginner**: This [article](https://www.qlik.com/us/data-visualization) summarizing general Data Visualization strategies and common methods used in different professions and sectors.
+> 2. **Beginner**: Tableau’s [Data Visualization for Beginners](https://www.tableau.com/learn/articles/data-visualization): a Definition & Learning Guide with helpful examples
+> 3. **Beginner:** This Step-by-Step Guide to Data Visualization and Design written for beginners
+> 4. **Beginner-Intermediate**: Kaggle’s [Data Visualization Course](https://www.kaggle.com/learn/data-visualization) teaches you how to implement some more basic, powerful data visualization techniques (line charts, scatter plots, and distributions) and how to choose the right one.
+> 5. **Intermediate-Advanced**: The [Data Visualization Catalogue](https://datavizcatalogue.com/search.html) has a comprehensive list of charts that are separated by what data visualization function they employ.
+> 6. **All levels**: Coursera often has free online [Data Visualization Courses](https://www.coursera.org/search?query=data visualization&) — check to see if one is available!
+
+>[Click for Back to Top](#top)
+
+___
+
+## Part C: Sharing with Others
+
+Getting your message out there requires you to **actively share** and **distribute** what you discovered or created.
+
+> **Important Note:** While it may seem as if we believe success is a necessary requirement to any “good” data project, this could not be further from the truth. No data scientists is free from failure, and data projects with less-than-ideal or confusing outcomes — besides being incredibly common — are immeasurably valuable to share with others. As a community, we will never learn from each other’s experiences if we do not communicate our failures.
+
+### Building the Data Community at CHHS
+
+Across the agency, there are a few existing groups and initiatives that exist to **help you leverage your department’s resources** to publicize your findings. Take advantage of the resources available to you, ask for help from those who’ve done this before, and be proud of yourself for completing your project!
+
+ * There are a number of “**Data Showcase Teams**” across the agency. They organize events to build a shared understanding of data, celebrate successes and failures, and lear from each other’s projects.
+ * Your department or program may have an **established visual and brand style** that provides credibility to your data analysis, thus increasing its chances of publication. These styles standardize color themes, fonts, and citation formats across agency publications.
+ * A repository of **CHHS data assets** is currently underway to streamline creation, maintenance, and sharing of each department’s resources.
+
+>[Click for Back to Top](#top)
+
+
+
diff --git a/3_implement.md b/3_implement.md
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+++ /dev/null
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----
-layout: page
-title: 3. Implement
-permalink: /implement/
----
-## Play 03. Implement: plan & strategy
-
-This play will help Departments move from conversation to action. It will provide resources that will allow the Departments to succeed. This might include: roles and responsibilities; governance structures; and data standards.
-
-This play includes resources on project management and change management to help both staff implement and leadership enforce the importance of this work.
-
->This Play moves away from the **theoretical** and drives toward the **tactical**.
-
-By detailing all critical steps before starting the project, the Department can anticipate factors they otherwise would not consider until encountered and identify potential problems and challenges on the front end.
-
-The planning becomes proactive instead of reactive, which allows best practices to be used and ensures that energy and time are spent on implementing a high-quality, well‐thought‐out project rather than "putting out fires."
-
-
-
-The planning and implementation processes will allow any person working on the project, regardless of his or her level of involvement, to fully understand the goal of the project and how it is to be accomplished. It ensures that everyone working on the project is on the same page and that any discrepancies are resolved before they become costly to the project or population served.
-
->This play includes various **resources designed** to help with project management and data management.
-
-
-
->Departments will work to identify Data Sharing and Data De-Identification best practices and lessons learned.
-
-The **Project Management Resources** will include resources on: agile planning and development; resource management; training; communication; and governance.
-
-The **Data Management Resources** will include resources on: data access and publishing; data standards; data documentation; and data tools and analytics.
-
-[]({{ site.baseurl }}/action_items)
-
-
-### CHHS Data De-Identification
-
-CHHS collects, manages and disseminates a wide range of data. As Departments classify data tables and catalog their publishable state data, they should be mindful of legal and policy restrictions on publication of certain kinds of data. The CHHS Data Subcommittee commissioned the development of Agency-wide guidelines to assist Departments in assessing data for public release.
-
-The [CHHS Data De-Identification Guidelines]({{ site.baseurl }}/resource_library#datade-id "CHHS Data De-Identification Guidelines") support CHHS governance goals to reduce inconsistency of practices across Departments, align standards used across Departments, facilitate the release of useful data to the public, promote transparency of state government, and support other CHHS initiatives, such as the [CHHS Open Data Portal](https://data.chhs.ca.gov/ "CHHS Open Data Portal"){:target="_blank"}.
-
-See the full guidelines [in the Resource Library]({{ site.baseurl }}/resource_library#datade-id "CHHS Data De-Identification Guidelines").
-
-De-Identification Considerations:
-1. The CHHS Data De-Identification Guidelines are the default policy for CHHS departments. If a CHHS Department wants to customize the guidelines, it must have appropriate references to departmental processes and must file a copy of their guidelines with the Office of the Agency Information Office.
-2. While most state agencies are covered by the California Information Practices Act (IPA), some are also covered by or impacted by HIPAA, the United States Health Insurance Portability and Accountability Act. Unlike the IPA, which applies to all personal information, HIPAA only applies to certain health or healthcare-related information. HIPAA requirements apply in combination with IPA requirements.
-3. For Departments covered by HIPAA, de-identification must meet the HIPAA standard. The CHHS Data De-Identification Guidelines serve as a tool to make and document an expert determination consistent with the HIPAA standard.
-
-### CHHS Data Sharing
-
-[Data sharing at CHHS]({{ site.baseurl }}/resource_library#datasharing "CHHS Data Sharing Framework") is governed by the CHHS Data Exchange Agreement. The CHHS Data Exchange Agreement is bifurcated into two parts—one master agreement with general legal boilerplate language and subordinate "Business Use Case Proposals" containing the specific business case to document each data exchange under the master agreement. The Business Use Case Proposal includes information such as data elements, intended use, etc. The master agreement, when coupled with the Business Use Case Proposal, forms the complete, standardized, legally-compliant data sharing agreement.
-
-The goals of data sharing at CHHS are to:
-
-* Establish a legal framework for data initiatives
-* Maximize appropriate sharing to increase positive outcomes and customer service
-* Ensure privacy and security protections
-* Reduce risk and use of duplicative resources
-* Standardize data use agreements among CHHS Departments and offices
-* Reduce contracting and data use agreement redundancies
-* Track activity for better understanding of common data sharing needs between CHHS departments
-
-See the CHHS Data Exchange Agreement, Business Use Case Proposal, and related data sharing materials [in the Resource Library]({{ site.baseurl }}/resource_library#datasharing "CHHS Data Sharing Framework").
-
-Data Sharing Tips:
-1. The CHHS data sharing process encourages collaboration between departments. Start by requesting a meeting with the data provider to talk through your business use case. You will likely find the data provider has important insights about the data you are requesting.
-2. The CHHS data sharing process requires data providers to work with the Departmental Data Coordinator in the development of a Business Use Case Proposal. Data coordinators will help programs refine Business Use Case Proposals so that they can be successful. If the two parties cannot come to agreement, the data coordinators will assist in taking the request to the Risk Management Subcommittee to help moderate the dispute.
-3. The CHHS Data Exchange Agreement and associated resources govern intra-agency data sharing between CHHS departments. Considering using these resources as templates for data sharing agreements with California government agencies, local governments, and universities as well.
-
-[]({{ site.baseurl }}/resource_library)
-
-
-
diff --git a/4_evaluate.md b/4_evaluate.md
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--- a/4_evaluate.md
+++ /dev/null
@@ -1,31 +0,0 @@
----
-layout: page
-title: 4. Evaluate
-permalink: /evaluate/
----
-## Play 04. Evaluate: outcomes & impacts
-
-This is an important play as it will help us validate outcomes and determine successes. It also will help identify lessons learned, which will grow our toolbox and provide us with better intelligence. This ultimately will allow us to generate new content and additional best practices to help other Departments across the Agency.
-
->**“Driving towards continuous
-process improvement.”**
-
-**Process Evaluations** – assess the processes involved in organizing and/or implementing the project. The focus here is on evaluating organizational and project capabilities rather than results.
-
-**Impact Evaluations** – assess short term objectives, which suggest that your larger goals are being achieved. Impact evaluations are much easier to measure because they consider benefits in terms of changes in beliefs and attitudes, skills, behavior and/or policies, structures and systems.
-
-**Outcome Evaluations** – assess how effective you have been in meeting big picture goals. The difficulties associated with outcome evaluations include: attributing change to any one particular project; long periods between the project and being able to see change; and finding reliable and valid ways of gathering this type of information.
-
-
-
-Establishing an evaluation process will ensure that the benefits anticipated by the implementation of any particular program or policy change are realized and an assessment can be made of the project’s overall success.
-
->**What are the lessons learned?** How will you iterate on the current solution? What are the next steps?
-
-[]({{ site.baseurl }}/action_items)
-
-
-
diff --git a/4_maintain.md b/4_maintain.md
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--- /dev/null
+++ b/4_maintain.md
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+---
+layout: page
+title: Concluding your Project
+permalink: /maintain/
+---
+## Section 4: Concluding Your Project
+
+
+### Table of Contents
+### Part A: [From Data Projects to Data Products](#projects)
+### Part B: [The Product Lifecycle](#lifecycle)
+### Part C: [Automating the Maintenance of Your Dataset](#automate)
+### Part D: [Retrieving and Implementing User Feedback](#feedback)
+
+___
+
+## Part A: From Data Projects to Data Products
+
+ Until now, we’ve used the words “data product” and “data project” interchangeably, but the two concepts are worth differentiating before this section. A **project** might come to mind when you think of any enterprise or initiative related to engineering, science, and data, such as building a bridge or conducting a research study. A **product** is more often associated with business, markets, and consumer satisfaction.
+ Projects have a **short-term scope** (i.e. a set start and end-date), are meticulously **planned** through use of budgets, schedules, and deadlines, and are comprised of a team of **technical experts** (such as engineers, physicists, mathematicians, etc). They are intended to deliver an **output** timely, safely, and effectively.
+
+
+
+ **Products** are different from projects mostly because they have a circular lifecycle — that is, a “product” never ends. This is because a product is **anything intended to meet some need** of the customer, which is often a moving target that morphs and changes over time. Where projects are concerned with budgets and schedules, products rely on **markets, customers**, and **trends**, and require a team of flexible, creative, and intuitive individuals to effectively understand how to address the needs of the customer.
+ Since projects are so narrow in scope, they are often left unmaintained and unmanaged after completion. This is a huge waste of the time and resources that went into that project: what lessons did they learn? How could their strategy be utilized elsewhere? Even small efforts toward maintaining your project — such as getting stakeholder feedback every six months — transform your project's impact.
+ What’s more, the product mindset can teach you to be adaptable, flexible, and creative; three skills necessary to building longer-lasting and more innovative solutions. The product design principles in this section will give some strategies for improving your project’s outcome/service over time by constantly seeking feedback and maintaining your data.
+
+>[Click for Back to Top](#top)
+
+___
+
+## Part B: The Product Lifecycle
+
+When planning for long-term maintenance of a your project, it can be helpful to look at the principles behind long-term maintenance of a product. The product lifecycle is the natural process of conception and decline undergone by any product. It is made up of **four stages**:
+
+>1. Introductory Stage:
+> * Also the "Market Development” stage; preliminary need of the user has been identified, but not proven demand exists
+>2. Growth Stage
+> * This is the stage of highest growth; demand is high, size of market & competition expands rapidly
+>3. Maturity Stage
+> * Demand levels off, product becomes dated
+>4. Decline Stage
+> * The product becomes obsolete or maintenance is stopped
+
+
+
+With these stages in mind, **ask yourself the following questions:**
+
+| - How can you predict each stage of your project?
- Think about your market: anything technology, for example, typically has quickly changing trends and faster life cycles. Medical devices or healthcare services are the opposite. |
+| - How can you determine what stage you are in? |
+| - How can you use this knowledge to change your strategy **now?** |
+
+Your answers will inform how you choose to maintain your product or project over time, and will help you anticipate the natural rise and fall that will occur.
+
+>[Click for Back to Top](#top)
+
+___
+
+## Part C: Automating the Maintenance of Your Dataset
+
+A number of incredibly valuable research projects and compiled/cleaned datasets **become useless** over time because **they are not routinely updated with the latest data**. If your project required you to compile a dataset from publicly-available, regularly updated datasets (such as census data), then consider automating your data’s year-to-year updates so it remains valuable for years to come. This is a straightforward process that any technical employee can do.
+
+ First, see the [Data De-Identification Guidelines](https://chhsdata.github.io/dataplaybook/documents/CHHS-DDG-V1.0-092316.pdf) to ensure your dataset is de-identified and all redacted information removed. Next, go to the [CHHS Open Data Portal](https://data.chhs.ca.gov/). [OpenGov.com](OpenGov.com), the host of the data portal, runs the site on an open-source data platform called [CKAN](ckan.org) which provides a FileStore API that enables **automation of dataset updates**. See Python documentation [here](https://docs.ckan.org/en/latest/maintaining/filestore.html#filestore-api).
+
+>[Click for Back to Top](#top)
+
+___
+
+## Part D: Retrieving and Implementing User Feedback
+
+Understanding iterative product development — or the the process of prototyping, delivering, assessing, and adjusting your output— is foundational to understanding product design principles. By continually asking for feedback and implementing changes, your product will continue to be tailored to the needs of your customer despite changing markets, attitudes, and trends.
+
+### Use the following framework to guide your feedback requests:
+
+>1. **Ask** your customers about your product
+>2. **Categorize** their feedback
+>3. **Act** on their feedback
+
+### Step 1: Understanding overall trends in customer satisfaction over time
+
+To identify **trends** in customer satisfaction, ask feedback at regular intervals, and track them over time.
+Some popular ways to measure overall customer satisfaction trends are:
+* [Net Promoter Score (NPS)](https://blog.hubspot.com/customer-success/what-is-nps): A single question, answered by with a scale of 1 to 10 “How likely are you to refer us as a product or service?”:
+* **Customer Satisfaction Score (CSAT)** is a measurement of how satisfied a customer is with a specific interaction with a company: “Please rate the quality of service provided to you”
+* **Social media monitoring**: use google tracking to keep up with what people are saying about your product or service
+
+### Step Two: Categorizing Customer Feedback
+
+When it comes to organizing your feedback, it generally depends on the product or program you’re working on. Some common buckets are **product** and **customer service:**
+
+1. **Product feedback:**
+ * **Major bugs/points of frustration**. These are extremely urgent issues that prevent users from getting the core value out of your product.
+ * **Minor bugs/points of frustration**. These are minor issues that don’t distract from the core product value.
+ * **Requests**. Important to ask for feature requests, and prioritize them based on a mixture of volume of requests, potential impact of building that feature, and opportunity costs associated with each choice.
+2. Customer service feedback:
+ * **Consider designing an automated survey** if your product is digital and has some sort of customer assistance chat
+ * **Include surveys after phone calls with customer service**
+
+### Step Three: Act on Feedback
+
+The most important part of getting feedback is **actually acting on it** — get your team together to brainstorm ways to meet the developing needs of the customer, and improve it in any way you can.
+
+>[Click for Back to Top](#top)
+
+
+
diff --git a/5_share.md b/5_share.md
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--- a/5_share.md
+++ /dev/null
@@ -1,44 +0,0 @@
----
-layout: page
-title: 5. Share
-permalink: /share/
----
-## Play 05. Share: progress & results
-
-It will be important to recognize that we will have successes and failures and it will be equally important to communicate our failures as much as our successes in order for us to learn from both.
-
-
-
-**Storyboard** – Create a data story to share the results of your data project. Think about how your data analysis contributes to your Department or program missions and goals. Create or leverage a Department mailing list to get the word out.
-
-The [MIT Center for Civic Media developed a guide](https://datatherapy.org/activities/activity-finding-a-story-in-data/){:target="_blank"} for finding and creating various types of data stories: connection, comparison, change, persona, and factoid stories. [Access the "finding a story" worksheet](https://datatherapy.files.wordpress.com/2014/07/finding-a-story-worksheets-v1-1.pdf){:target="_blank"}.
-
-**Meetings and Showcases** – Don’t underestimate the power of leveraging the people network. Organize a “data showcase” for your team, or participate in one of emerging across the Agency. Use these events to build a shared understanding of data, celebrate successes, and learn from your peers.
-
->**How do we share lessons learned and create a feedback loop?**
-
-[](https://letsgethealthy.ca.gov/)
-
-**Documentation** – The completion of an “Information Architecture Assessment” provides standard documentation about the characteristics, linkages, and dependencies of all the relevant data for each use case.
-
-There’s an opportunity to use this documentation to build a repository of Agency data assets that captures the Agency’s “Information Architecture” for the most critical and valuable Agency data assets. Consider how the creation, maintenance, and sharing of such documentation can add to a greater Agency repository of resources.
-
-[]({{ site.baseurl }}/action_items)
-
-**Data Publication and Presentation** – Your Department or program may have an established visual and brand style that can provide greater credibility to your data analysis. Aspects such as color, font face, and citation format can add consistency.
-
-**Consider the Audience** – Consider the audience for the message of your data story. Is the audience the general public, a program manager, or a technician? What is their degree of data literacy?
-
-Many think of data in terms of large reports with many charts and graphs, but we can’t expect all audiences to digest long reports. It’s better to offer increasingly deeper levels of detail. For example, lead with an impactful figure or statistics to entice the reader. Then, offer a single graph or short fact sheet with more detail. You can further link the audience to a full report or interactive visualization.
-
-Use a text analyzer tool, such as [the Hemingway Editor App](http://www.hemingwayapp.com/){:target="_blank"}, to ensure your narrative is impactful, clear, and tailored for your audience. Be sure to also consider your product’s accessibility to a wide array of users.
-
-[]({{ site.baseurl }}/resource_library)
-
-Check out the examples of CHHS data-driven success in the [Success Stories section]({{ site.baseurl }}/success_stories/).
-
-
-
diff --git a/6_action_items.md b/6_action_items.md
deleted file mode 100755
index 1e25be6..0000000
--- a/6_action_items.md
+++ /dev/null
@@ -1,38 +0,0 @@
----
-layout: page
-title: Action Items
-permalink: /action_items/
----
-
-*Adapted from the [GovLoop Big Data Playbook for Government](https://www.govloop.com/wp-content/uploads/2015/01/Big-Data-Playbook-.pdf?utm_source=Website&utm_medium=Button&utm_campaign=Guide){:target="_blank"}*
-
-#### 1. SET CLEAR AND MEASURABLE OUTCOMES
-
-Be sure that you are measuring success and thinking critically about what your success metrics will be. You must have clear and actionable goals that you want to achieve with your data project.
-
-
-#### 2. DEFINE YOUR OWN MEANING OF DATA
-
-Everyone is going to define data differently. Start by understanding what data means for your Department. It also will be important for you to prioritize your data. You must know which data are the highest value to your organization.
-
-#### 3. START SMALL
-
-You’re going to want to be sure to start small. Running a few pilots around data can’t hurt; this will help you get a better understanding of the lay of the land, what you can improve with data, and how you can identify the gaps.
-
-#### 4. IDENTIFY STAFF TO MANAGE DATA
-
-Every employee will need different kinds of accessibility, so make sure that your data systems maps to these needs and is not providing unauthorized access to information.
-
-#### 5. FOCUS ON EDUCATION AND TRAINING
-
-This will help staff clearly see the impact of the project and how data can improve effectiveness and efficiency. Including the Playbook in a new employee orientation could further enable staff and foster a culture of data within a Department.
-
-#### 6. EXPLORE SHARED SERVICES MODELS
-
-Don’t have access to the IT services you need? Maybe there is a shared service you can use with other Departments, or there are easier ways to get access to contemporary technologies. It’s also possible that you could iteratively re-engineer your existing IT infrastructure to gradually meet emerging needs.
-
-
-
diff --git a/7_resource_library.md b/7_resource_library.md
index ca9c4d3..0ffd332 100644
--- a/7_resource_library.md
+++ b/7_resource_library.md
@@ -3,126 +3,115 @@ layout: page
title: Resource Library
permalink: /resource_library/
---
+Below you can explore different data resources. Click on each heading to navigate to each section of resources.
+
+### Table of Contents
+### 1. [Internal CHHS Strategies and Guidelines](#internal)
+### 2. [Goal Setting](#goals)
+### 3. [Data Sources](#data)
+### 4. [Introductory Statistics Tools](#stats)
+### 5. [Data Visualization (Beginner to Advanced)](#visualize)
+### 6. [Presenting your Data](#present)
-1. [CHHS Information Strategic Plan](#strategicplan "CHHS Information Strategic Plan")
-2. [CHHS Open Data Handbook](#opendatahandbook "CHHS Open Data Handbook")
-3. [CHHS Data Sharing Framework](#datasharing "CHHS Data Sharing Framework")
-4. [CHHS Data De-Identification Guidelines](#datade-id "CHHS Data De-Identification Guidelines")
-5. [Select Public Data Resources](#publicdataresources "Select Public Data Resources")
-6. [U.S. Digital Services Playbook](#USDSplaybook "U.S. Digital Services Playbook")
-7. [Select Training Resources](#trainingresources "Select Training Resources")
-8. [Select Technical Resources](#technicalresources "Select Technical Resources")
+___
-
-### 1. CHHS Information Strategic Plan
+## Internal CHHS Strategies and Guidelines
-[CHHS manages a diverse portfolio](http://www.chhs.ca.gov/ "CHHS.ca.gov"){:target="_blank"} of programs and technical infrastructure that requires a complex level of management, operational capabilities and vendor support to effectively meet the expectations of its clients and stakeholders. Aligning the programmatic and operational strategies of the Agency and its Departments is an immense undertaking, considering:
+If you are ever stuck, contact your Department’s data coordinator for information on how to find and exchange CHHS data.
-* The scope and complexity of CHHS programs and initiatives;
-* The diversity of project management and technical expertise amongst Departments; and
-* The current state of the technology environment and the historical approach to siloed designs.
+* [CHHS Information Strategic Plan]({{ site.baseurl }}/documents/CHHS%20Information%20Strategic%20Plan%202016.pdf "CHHS Information Strategic Plan")
+* [CHHS Master Data Management Strategy]({{ site.baseurl }}/documents/CHHS-Master-Data-Management-Strategy.pdf "CHHS Master Data Management Strategy")
+* [CHHS Open Data Portal](https://data.chhs.ca.gov "CHHS Open Data Portal")
+* [CHHS Open Data Handbook](https://chhsdata.github.io/opendatahandbook "CHHS Open Data Handbook")
+* Data sharing materials:
+ * [CHHS Data Sharing - Process Flow]({{ site.baseurl }}/documents/datasharing/CHHS%20Data%20Sharing%20-%20Process%20Flow.pdf "CHHS Data Sharing - Process Flow")
+ * [CHHS Data Sharing - Legal Agreement]({{ site.baseurl }}/documents/datasharing/CHHS%20Data%20Sharing%20-%20Legal%20Agreement.pdf "CHHS Data Sharing - Legal Agreement")
+ * [CHHS Data Sharing - Frequently Asked Questions (FAQs)]({{ site.baseurl }}/documents/datasharing/CHHS%20Data%20Sharing%20-%20FAQs.pdf "CHHS Data Sharing - Frequently Asked Questions (FAQs)")
+ * [Business Use Case Proposal - Form]({{ site.baseurl }}/documents/datasharing/Business%20Use%20Case%20Proposal%20-%20Form.docx "Business Use Case Proposal - Form")
+ * [Business Use Case Proposal - Instructions]({{ site.baseurl }}/documents/datasharing/Business%20Use%20Case%20Proposal%20-%20Instructions.pdf "Business Use Case Proposal - Instructions")
+* [CHHS Data De-Identification Guidelines]({{ site.baseurl }}/documents/CHHS-DDG-V1.0-092316.pdf "CHHS Data De-Identification Guidelines")
+* [U.S. Digital Services Playbook](https://playbook.cio.gov/ "U.S. Digital Services Playbook")
-The evolution of technology has provided CHHS, its Departments, local government partners and providers with a unique opportunity to transform the operational paradigm from one focused on the autonomy of individual Departments or programs to one governed in a way that maximizes benefit to the Agency as a community. This convergence of business and technology, when coordinated and managed appropriately, will better enable more client-centric services, more efficient programmatic execution and better fiscal responsibility. The Agency strives to provide a strategic direction that will evolve the manner in which stakeholders within the Agency collaborate to drive better informed investment decisions, resulting in a more effective utilization of assets and human capital. As the costs and risks of technology have grown, the necessity for a more effective approach to the management of these expensive and critical systems has become a focal point of both the state and the federal government.
+>[Click for Back to Top](#top)
-[Download the Strategic Plan]({{ site.baseurl }}/documents/CHHS%20Information%20Strategic%20Plan%202016.pdf "CHHS Information Strategic Plan"){:target="_blank"}
+___
-[Download the Master Data Management (MDM) Strategy Addendum]({{ site.baseurl }}/documents/CHHS-Master-Data-Management-Strategy.pdf "CHHS Master Data Management Strategy"){:target="_blank"}
+## Goal Setting
-
+* [SMART Goal checklist](https://www.mindtools.com/pages/article/smart-goals.htm)
+* [lucidchart.com](https://www.lucidchart.com/pages/uml-use-case-diagram) will help you create a use case diagram
+* [Visual Paradigm](https://online.visual-paradigm.com/diagrams/solutions/free-use-case-diagram-tool/) will also help you create a use case diagram
+* For managers, [key Performance Indicators](http://kpilibrary.com/) (KPIs) are also a great framework for measuring performance relative to your goals.
+* Check out [this resource](https://kpi.org/KPI-Basics) to learn what they are, why they work, and how to set effective KPIs
+* Data readiness: Harvard’s [Strategic Use of Data Self-Assessment Guide](https://sdp.cepr.harvard.edu/files/cepr-sdp/files/sdp-rubric-self-asssessment.pdf) offers a useful template.
+* [Analytics Capability Assessment for Human Service Agencies](https://chhsdata.github.io/dataplaybook/documents/APHSA-Analytic-Capability-Roadmap-1-0-for-Human-Services-Agencies.pdf) gives more specific data readiness guidance.
+* [Roadmap to Capacity Building in Analytics](https://chhsdata.github.io/dataplaybook/documents/APHSA-Roadmap-to-Capacity-Building-in-Analytics-White-Paper.pdf) - This document is dense, but gives great insight into what you need to carry out a successful product.
-### 2. CHHS Open Data Handbook
+>[Click for Back to Top](#top)
-The CHHS Open Data Handbook provides guidelines to identify, review, prioritize, and prepare publishable CHHS data for access by the public via the [CHHS Open Data Portal](https://data.chhs.ca.gov "CHHS Open Data Portal"){:target="_blank"}—with a foundational emphasis on value, quality, data and metadata standards, and governance.
+___
-The handbook focuses on general guidelines and thoughtful processes but also provides tools and resources that operationalize those processes. CHHS and its Departments and offices will use this handbook in their work as they consider various perspectives involved in governing business processes, data, and technology assets.
-
-
-[Access the Handbook](https://chhsdata.github.io/opendatahandbook "CHHS Open Data Handbook"){:target="_blank"}
-
-
-
-### 3. CHHS Data Sharing Framework
-
-Data sharing at CHHS is governed by the CHHS data exchange agreement. The CHHS Data Exchange Agreement is bifurcated into two parts—one master agreement with general legal boilerplate language and subordinate "Business Use Case Proposals" containing the specific business case to document each data exchange under the master agreement. The Business Use Case Proposal includes information such as data elements, intended use, etc. The master agreement, when coupled with the Business Use Case Proposal, forms the complete, standardized, legally-compliant data sharing agreement.
-
-Contact your Department’s data coordinator for information on how to find and exchange CHHS data.
-
-Download the data sharing materials:
-
-* [CHHS Data Sharing - Process Flow]({{ site.baseurl }}/documents/datasharing/CHHS%20Data%20Sharing%20-%20Process%20Flow.pdf "CHHS Data Sharing - Process Flow"){:target="_blank"}
-* [CHHS Data Sharing - Legal Agreement]({{ site.baseurl }}/documents/datasharing/CHHS%20Data%20Sharing%20-%20Legal%20Agreement.pdf "CHHS Data Sharing - Legal Agreement"){:target="_blank"}
-* [CHHS Data Sharing - Frequently Asked Questions (FAQs)]({{ site.baseurl }}/documents/datasharing/CHHS%20Data%20Sharing%20-%20FAQs.pdf "CHHS Data Sharing - Frequently Asked Questions (FAQs)"){:target="_blank"}
-* [Business Use Case Proposal - Form]({{ site.baseurl }}/documents/datasharing/Business%20Use%20Case%20Proposal%20-%20Form.docx "Business Use Case Proposal - Form"){:target="_blank"}
-* [Business Use Case Proposal - Instructions]({{ site.baseurl }}/documents/datasharing/Business%20Use%20Case%20Proposal%20-%20Instructions.pdf "Business Use Case Proposal - Instructions"){:target="_blank"}
-
-
-
-### 4. CHHS Data De-Identification Guidelines
-
-The CHHS Data De-Identification Guidelines describe a procedure to be used by Departments and offices in the Agency to assess data for public release. As part of the guidelines, specific actions that may be taken for each step in the procedure are described. These steps are intended to assist Departments in assuring that data is de-identified for purposes of public release that meet the requirements of the California Information Practices Act (IPA) and the Health Insurance Portability and Accountability Act (HIPAA) to prevent the disclosure of personal information.
-
-The CHHS Data De-Identification Guidelines are focused on the assessment of aggregate or summary data for purposes of de-identification and public release. Aggregate data means collective data that relates to a group or category of services or individuals. The aggregate data may be shown in table form as counts, percentages, rates, averages, or other statistical groupings.
-
-[Download the Guidelines]({{ site.baseurl }}/documents/CHHS-DDG-V1.0-092316.pdf "CHHS Data De-Identification Guidelines"){:target="_blank"}
-
-
-
-### 5. Select Public Data Resources
+## Data Sources
Public data resources are available from a number of online sources, including the federal government and non-profit organizations. Following is a partial list of select data resources that can help contribute to data projects and analyses.
-* [USAFacts.org](http://usafacts.org/ "USAFacts.org"){:target="_blank"} – A data-driven portrait of the American population, our government’s finances, and government’s impact on society that uses federal, state, and local data from over 70 sources.
-* [Healthdata.gov](https://www.healthdata.gov/ "Healthdata.gov"){:target="_blank"} – Dedicated to making high value health data more accessible to entrepreneurs, researchers, and policy makers in the hopes of better health outcomes for all.
-* [CIA World Fact Book](https://www.cia.gov/library/publications/the-world-factbook/ "CIA World Fact Book"){:target="_blank"} - Provides information on the history, people, government, economy, geography, communications, transportation, military, and transnational issues for 267 world entities.
-* [openFDA](https://open.fda.gov/ "openFDA"){:target="_blank"} – Makes it easier to get access to publicly available FDA data. FDA’s goal is to make it simple for an application, mobile device, web developer, or researcher to use data from the FDA.
-* [Census Reporter](https://censusreporter.org/ "Census Reporter"){:target="_blank"} – A Knight News Challenge-funded project to make it easier for journalists to write stories using information from the U.S. Census bureau. Place profiles and comparison pages provide a friendly interface for navigating data, including visualizations for a more useful first look.
-* [CalEnviro Screen](https://oehha.ca.gov/calenviroscreen "CalEnviro Screen"){:target="_blank"} - A mapping tool that helps identify California communities that are most affected by many sources of pollution, and where people are often especially vulnerable to pollution’s effects.
-* [California Healthy Places Index](https://healthyplacesindex.org/ "California Healthy Places Index"){:target="_blank"} - A tool to explore community conditions that predict life expectancy. It contains user-friendly mapping and data resources at the census tract level across California.
-* [CHHS Open Data Portal](https://data.chhs.ca.gov/ "CHHS Open Data Portal"){:target="_blank"} - Offers access to standardized data that can be easily retrieved, combined, downloaded, sorted, searched, analyzed, redistributed and re-used by individuals, business, researchers, journalists, developers, and government to process, trend, and innovate.
+* [USAFacts.org](http://usafacts.org/ "USAFacts.org") – A data-driven portrait of the American population, our government’s finances, and government’s impact on society that uses federal, state, and local data from over 70 sources.
+* [Healthdata.gov](https://www.healthdata.gov/ "Healthdata.gov") – Dedicated to making high value health data more accessible to entrepreneurs, researchers, and policy makers in the hopes of better health outcomes for all.
+* [CIA World Fact Book](https://www.cia.gov/library/publications/the-world-factbook/ "CIA World Fact Book") - Provides information on the history, people, government, economy, geography, communications, transportation, military, and transnational issues for 267 world entities.
+* [openFDA](https://open.fda.gov/ "openFDA") – Makes it easier to get access to publicly available FDA data. FDA’s goal is to make it simple for an application, mobile device, web developer, or researcher to use data from the FDA.
+* [Census Reporter](https://censusreporter.org/ "Census Reporter") – A Knight News Challenge-funded project to make it easier for journalists to write stories using information from the U.S. Census bureau. Place profiles and comparison pages provide a friendly interface for navigating data, including visualizations for a more useful first look.
+* [CalEnviro Screen](https://oehha.ca.gov/calenviroscreen "CalEnviro Screen") - A mapping tool that helps identify California communities that are most affected by many sources of pollution, and where people are often especially vulnerable to pollution’s effects.
+* [California Healthy Places Index](https://healthyplacesindex.org/ "California Healthy Places Index") - A tool to explore community conditions that predict life expectancy. It contains user-friendly mapping and data resources at the census tract level across California.
+* [CHHS Open Data Portal](https://data.chhs.ca.gov/ "CHHS Open Data Portal") - Offers access to standardized data that can be easily retrieved, combined, downloaded, sorted, searched, analyzed, redistributed and re-used by individuals, business, researchers, journalists, developers, and government to process, trend, and innovate.
-
+>[Click for Back to Top](#top)
-### 6. U.S. Digital Services Playbook
+___
-The American people expect to interact with government through digital channels such as websites, email, and mobile applications. By building better digital services that meet the needs of the people that use our services, we can make the delivery of our policy and programs more effective.
+## Introductory Statistics Tools
-One way to advance smarter digital service delivery is by putting the right processes and practices in place to drive outcomes and accountability and allow people and companies to do their best work. [The US Digital Services (USDS)](https://www.usds.gov/ "United States Digital Service"){:target="_blank"} Playbook documents these best practices and processes.
+Here are some key concepts and help integrating them into Excel
+* [Central Tendency](https://statistics.laerd.com/statistical-guides/measures-central-tendency-mean-mode-median.php)
+* [Correlation](https://www.excelfunctions.net/excel-correl-function.html)
+* [Paired t-test](http://www.real-statistics.com/students-t-distribution/paired-sample-t-test/)
+* [Regression Analysis](https://www.qimacros.com/hypothesis-testing/regression/)
+* [Multiple Regression in Excel](https://www.businessinsider.com/understand-excel-multiple-regression-2014-10)
+* [Statistical Significance](https://hbr.org/2016/02/a-refresher-on-statistical-significance)
-The Digital Services Playbook identifies a series of “plays” drawn from successful best practices from the private sector and government that, if followed together, will help government build effective digital services. The plays outline an approach to delivering services that increases our ability to be flexible, iterative and, most importantly, to focus on the needs of the people that use our services.
+>[Click for Back to Top](#top)
-[Access the Digital Services Playbook](https://playbook.cio.gov/ "U.S. Digital Services Playbook"){:target="_blank"}
+___
-
+## Data Visualization (Beginner to Advanced)
-### 7. Select Training Resources
+If you just need a quick chart or table, check out these online tools — they are simpler to use than the advanced data visualization guides and may be more appropriate for your specific project:
+ * [Google Charts](https://developers.google.com/chart/) (interactive charts & simple data tools)
+ * [DataWrapper](https://www.datawrapper.de) (charts, tables, and maps)
+ * [Infogram](https://infogram.com) (beginner-friendly, collaborative, focuses on design thinking principles)
-There are a number of training resources available from reputable governmental and non-governmental sources. Following is a partial list of select training resources that can help contribute to data projects and analyses.
-* [Strategies for Collecting Data, Analyzing Data, and Reporting the Results](http://www.calhr.ca.gov/Training/Pages/course-description.aspx?class=Strategies%20for%20Collecting%20Data,%20Analyzing%20Data,%20and%20Reporting%20the%20Results){:target="_blank"} – Data analysis is the process of describing and interpreting quantitative information. This introductory seminar will guide students through the three basic steps involved in conducting research: collecting data, analyzing data, and reporting the results. **Provided by:** CalHR
-* [How to Lead with Data](http://www.calhr.ca.gov/Training/Pages/course-description.aspx?class=How%20to%20Lead%20with%20Data){:target="_blank"} – As government leaders, we all know that we need to do more to take advantage of the power of data to improve the communities we serve. One of the biggest challenges to realizing this goal is to understand how government leaders value data in advancing strategic priorities, optimizing operations, and building trust with their customers. **Provided by:** CalHR
-* [Communicating with Data](http://www.calhr.ca.gov/Training/Pages/course-description.aspx?class=Communicating%20with%20Data){:target="_blank"} – This course will help you present numerical data to managers, decision makers, or the general public so they can readily understand the data. You will learn concepts, conventions, and mechanics behind the effective use of tables, charts, and graphs. **Provided by:** CalHR
-* Microsoft Excel – Training to help you create, format, and analyze Excel data tables. **Provided by:** CalHR
- * [Excel Level 1](http://www.calhr.ca.gov/Training/Pages/course-description.aspx?class=Excel%20Level%201){:target="_blank"}
- * [Excel Level 2](http://www.calhr.ca.gov/Training/Pages/course-description.aspx?class=Excel%20Level%202){:target="_blank"} **·** [Pivot Tables, Charts, and Filters](http://www.calhr.ca.gov/Training/Pages/course-description.aspx?class=Excel%20Level%202%20(Pivot%20Tables,%20Charts%20and%20Filters)){:target="_blank"} **·** Formulas
- * [Excel Level 3](http://www.calhr.ca.gov/Training/Pages/course-description.aspx?class=Excel%20Level%203){:target="_blank"}
-* Tableau Business Intelligence and Analytics – Training to help your work with Tableau Desktop and other Tableau tools.
- * [Tableau Desktop and Web Authoring Help](https://onlinehelp.tableau.com/current/pro/desktop/en-us/default.htm){:target="_blank"}. **Provided by:** Tableau.com
- * [Tableau Essentials Training](https://www.lynda.com/Tableau-tutorials/Tableau-9-Essential-Training/386886-2.html){:target="_blank"}. **Provided by:** Lynda.com
- * [Data Visualization and Communication with Tableau](https://www.coursera.org/learn/analytics-tableau/){:target="_blank"}. **Provided by:** Coursera
+More sophisticated guides are listed below:
+* **Beginner:** [databasic.io](https://databasic.io/) – A suite of easy-to-use web tools for beginners that introduce concepts of working with data. These simple tools make it easy to work with data in fun ways, so you can learn how to find great stories to tell.
+* **Beginner**: This [article](https://www.qlik.com/us/data-visualization) summarizing general Data Visualization strategies and common methods used in different professions and sectors.
+* **Beginner**: Tableau’s [Data Visualization for Beginners](https://www.tableau.com/learn/articles/data-visualization): a Definition & Learning Guide with helpful examples
+* **Beginner:** This Step-by-Step Guide to Data Visualization and Design written for beginners
+* **Beginner-Intermediate**: Kaggle’s [Data Visualization Course](https://www.kaggle.com/learn/data-visualization) teaches you how to implement some more basic, powerful data visualization techniques (line charts, scatter plots, and distributions) and how to choose the right one.
+* **Intermediate**:[Vega](https://vega.github.io/vega/) – A visualization grammar, a declarative language for creating, saving, and sharing interactive visualization designs. With Vega, you can describe the visual appearance and interactive behavior of a visualization in a JSON format and generate web-based views using Canvas or SVG.
+* **Intermediate-Advanced**: The [Data Visualization Catalogue](https://datavizcatalogue.com/search.html) has a comprehensive list of charts that are separated by what data visualization function they employ.
+* **Advanced:** [D3.js](https://d3js.org/) – Data-Driven Documents D3 is a JavaScript library for manipulating documents based on data. D3 helps you bring data to life using HTML, SVG, and CSS. D3’s emphasis on web standards gives you the full capabilities of modern browsers without tying yourself to a proprietary framework, combining powerful visualization components and a data-driven approach to DOM manipulation.
+* **All levels**: Coursera often has free online [Data Visualization Courses](https://www.coursera.org/search?query=data visualization&) — check to see if one is available!
+>[Click for Back to Top](#top)
-
+___
-### 8. Select Technical Resources
+## Presenting your Data
-There are a number of technical resources available from reputable governmental and non-governmental sources. Following is a partial list of select technical resources that can help contribute to data projects and analyses.
-* [databasic.io](https://databasic.io/){:target="_blank"} – A suite of easy-to-use web tools for beginners that introduce concepts of working with data. These simple tools make it easy to work with data in fun ways, so you can learn how to find great stories to tell.
-* [D3.js](https://d3js.org/){:target="_blank"} – Data-Driven Documents D3 is a JavaScript library for manipulating documents based on data. D3 helps you bring data to life using HTML, SVG, and CSS. D3’s emphasis on web standards gives you the full capabilities of modern browsers without tying yourself to a proprietary framework, combining powerful visualization components and a data-driven approach to DOM manipulation.
-* [Data Visualization Catalogue](https://datavizcatalogue.com/index.html){:target="_blank"} – A library of different information visualization types, how each method works and what it is best suited for.
-* [Vega](https://vega.github.io/vega/){:target="_blank"} – A visualization grammar, a declarative language for creating, saving, and sharing interactive visualization designs. With Vega, you can describe the visual appearance and interactive behavior of a visualization in a JSON format and generate web-based views using Canvas or SVG.
-* [Color Contrast Grid](http://contrast-grid.eightshapes.com/){:target="_blank"} – Test many foreground and background color combos for compliance with WCAG 2.0 minimum contrast.
+* [Color Contrast Grid](http://contrast-grid.eightshapes.com/) – Test many foreground and background color combos for compliance with WCAG 2.0 minimum contrast.
+* Use a word editing app like [Hemingway](http://www.hemingwayapp.com) to **improve** the readability of your writing. Hemingway will highlight lengthy or run-on sentences, remove overly dense writing, offer alternatives for weak adverbs and phrases as well as poor formatting choices.
+* **Visualize** your story with a storyboard (see MIT’s [guide](https://datatherapy.org/activities/activity-finding-a-story-in-data/) to finding a story in your data)
-
+>[Click for Back to Top](#top)