Introduction to Data for Decision-Making
- Learn how data are used in everyday life, and understand the importance of data collection, analysis, and sharing.
- Understand the concepts and terms around data for decision-making.
- Learn basic questions to ask and how to define problems or needs within the participants’ community or organization.
- Understand how to be a consumer of data, and what questions to ask in order to use the data for decision-making.
Begin by greeting the class and introducing yourself. Provide time to discuss your background, any experience you have with data, and any other relevant information you wish to share.
Outline the structure of the day (two modules with a break in between for lunch), and provide time to address any other administrative issues that need to be brought up before class begins.
Pause to ask if anyone has questions so far.
Critical Thinking15 minutes
Next, introduce the name of the workshop, taking time to write “Data for Decision-Making” on the whiteboard (if available). Underscore the word “data”, or verbally highlight it.
- Ask the class to work with a partner and discuss what they think of when they hear the word “data”. Have the class write down 5 words that they associate with data. If they are having trouble thinking of words, feel free to gently guide them by providing a few words: numbers, words, etc. If you provide this information, ask the participants not to use these words as their answers. (5 minutes)
- Ask for volunteers to report back on what they wrote. If possible, write their answers on the board. What did they write? Did some people write similar answers as others? Do people agree with other people’s answers? Discuss their answers with the class. (5 minutes)
Before transitioning, make sure to underscore to the participants that you are going to jump right in and define “data”, “decision-making”, and “data for decision-making” and then outline course goals and objectives.
Introduction to Key Concepts20 minutes
Note to instructor: This section comes before “Defining Workshop Goals and Outcomes” in order to provide context for the workshop.
After the class has shared their thoughts on what data are and what they associate data with, offer a formal definition. On the board, write:
At the most basic level, data are facts and information. Data may be represented as numbers, words, measurements, observations, descriptions, images, audio, and much more.
If I didn’t eat breakfast and am hungry, I could ask my colleagues where the best place to get Mohinga is nearby. When I ask that question and receive responses, I collect information.
Ask the class what type of information I could collect in the above example. . Is it price? How the breakfast tastes? How close the breakfast place is to the office? How long it will take to eat the breakfast?
The information that I collect are data.
Questions to ask:
Now that data has been re-framed as simply information, ask the class what kinds of data (information) they collect in their everyday life. If the class is quiet and has not provided a response, feel free to nudge them along with a few more examples, such as do they ask their families about their days when they get home? If they want to buy a new cell phone, do they ask for recommendations?
On the board write:
After collecting data (information), you can make a decision. When you make a decision, you can use the data you collected as evidence to make an informed decision.
For instance, in the previous breakfast example, if I hadn’t asked my colleagues for their suggestions for breakfast options, I run the risk of eating a bad breakfast. When I collect data from my colleagues, I can use that information (their responses) as a solution to my problem (the fact that I am hungry) in order to make an informed decision that leads to a great breakfast.
The more people I ask, the more data (information) I have in which to make my decision.
Questions to ask:
Take some time to relate this information back to the participants’ own lives. Ask the class how they’ve used data to make informed decisions in their own lives. As some guiding questions, ask would you rather eat at a location recommended by a colleague or risk a location on which you have no data (information)? If you want to buy a new cell phone, would you rather have information about the qualities and features of a phone or just buy the first phone you see? Gathering information and using it to make an informed decision is a fact of life that happens on a daily basis for most people.
Once the discussion has finished, underscore that data are information that is all around us, and that we are all willing or unwilling consumers of data.
- Putting it all together: Data-driven decision-making is the practice of basing decision on the analysis of data rather than using your intuition, guess, or estimate.
Take a moment to ask the class why they think it is important to base decisions on evidence.
Once finished, relate data-driven decision-making back to organizations or local governments, saying that using data to make organization or policy decisions allows us to improve programs, respond more effectively to organization and community needs, create solutions to pre-existing problems, and if the data are publicly available, allows the public to use data to make decisions.
Take a moment to answer any questions from the participants. Then, transition by mentioning that with this greater context and understanding of data for decision-making, we can move on to defining the goals and outcomes for this workshop.
Defining Workshop Goals and Outcomes10 minutes
Say to the class: The goal of this 3-day workshop is to equip you with ability to understand issues and resources around data and data collection, and learn the importance of using the data to make informed, evidence-based decisions. This workshop is meant to help you and your organization better understand, use, and manage data for decision-making.
Say to the class: By the end of this workshop you will be able to:
- Use a basic set of processes and questions to assess the data environment at your own organization.
- Critically examine data visualization approaches for different audiences.
- Understand data lifecycle theory, basic skills when working with data, and the resources needed to support working with data.
- Be able to create a data project plan that is specific to your own organization.
Take a moment to walk the participants through the structure of the course. Each day will be structured as two, three-hour workshops with a one hour break for lunch in between that focus on addressing key outcomes and goals.
- Day 1: Introduction to Data for Decision-Making (morning), Assessing your own organization's data culture (afternoon)
- Day 2: Building data skills and resources
- Day 3: An applied project using your new skills in which you will use the data available to you to solve a problem or assess a need at your organization.
Pause before moving on and allow time for the participants to ask any clarifying questions.
Case Study: Myanmar30 minutes
Provide context for the participants about how decisions made with data can lead to innovative, evidence-based practices for organizations and governments. Take a moment to introduce the following case study (adapt or update as appropriate) to the participants.
Guiding questions before beginning:
- How can data be used to solve a problem?
- Do you know of any examples of governments using data to make decisions?
- Do you know of any organizations that use data to make decisions?
Many countries around the world are using data to make decision on a daily basis, and are seeking ways to solve existing problems using new forms of data.
Provide context by briefly outlining the case study from Myanmar Information Management Unit’s (MIMU) use of cell phone data and GPS coordinates to create and update baseline data on schools across Myanmar:
- In Myanmar, smartphones have become increasingly accessible and popular since foreign investors entered the country in 2014. Most individuals now have a smartphone and a data-enabled SIM card.
- MIMU wanted an easier and more accurate way to compile and update baseline data on school locations across Myanmar. Data collection where individuals visit each school and mark the location is time consuming and costly. Accurate and up-to-date baseline data are important to developing more sector-specific data. Information regarding the number and location of schools is also important to help communities identify where available schools are, which schools close, and where more schools are needed. In 2015, MIMU decided to utilize the growth of cell phones and mobile data to facilitate baseline data collection on schools. This was cost effective, fast, and allowed for data to be collected and updated more frequently.
- They created the “School Location Collector” tool, which collects the GPS location of schools. School officials use the service to get the GPS location and save it to the system’s database. They can then send the GPS coordinates over the internet or by SMS.
What was the problem MIMU was trying to solve? How did they use data collection to solve it? How did they use new data to solve the problem? How can they now use that data to make a decision?
Myanmar natural disaster case
Now, allow each participant the time to read the following case. On their own, have them write down their answers to the following questions on a sheet of paper:
- What was the problem being solved?
- How are new forms of data collection used to solve it?
- How can new data now be for decision-making?
This example is a hypothetical case of how data could be used for decision-making in Myanmar:
Natural disasters in Myanmar threaten both crop production and populations in affected areas. Both the cyclone in 2008 and the excessive flooding in 2015 resulted in many deaths, displaced persons, and crop and paddy destruction. The new parliament is working to create new policies to address concerns following future natural disasters. They want to understand how crops’ export potential and the displacement of local populations are affected by these natural disasters. In order to do so, they need to draw from several data sources to evaluate the effect of natural disasters on crop production and on local populations:
- Annual rice surveys
- Monthly exports
- Weather data
- National population data and UN data on internally displaced persons
Using these data, policymakers determine which townships were most affected by the cyclone in 2008 and the rainfall and flooding in 2015. They then analyze the population data published yearly by the Central Statistical Organization to identify if there was a significant population decrease in these townships and increases in neighboring townships. They also use the population data to identify if populations returned to the affected areas by observing any growth in affected townships. The politicians can then identify if the UN data on internally displaced persons aligns with the affected townships.
To evaluate the effect on crop production, politicians use annual rice surveys to identify any decreases in rice production in the states and regions with affected townships. The politicians can also identify if rice production remained low in these areas in following years, which could indicate more permanent damage to crop fields. They also use data on the amount of rice exported and changes in the price of rice to identify the effect of natural disasters on the nation’s economy. To identify if natural disasters in Myanmar have a larger effect on populations or crop production, politicians can then compare their findings.
Dismiss the class for a ten-minute break.
Activity 1.1: Defining a Data Problem Within an Organization35 minutes
- Learn how to address a challenge at your organization by defining a problem that you can use data to solve.
- Understand how to think through the problem, what types of data may already be available to you, and how you can collect new data to address that problem.
- Think critically about how to ask the right questions, collect the right data, and how that data can inform the decisions you make at your organization.
A key piece of using data for decision-making is learning how to describe how you would like to use your data as evidence, and what questions you would like to answer with your data. This begins with being able to define a problem or need your organization wants to address.
Defining a problem or challenge that you wish to solve using data can help you allocate time and resources while you maximize your project returns. With a clearly defined problem, you can build your own capacity to collect only the data that you need to answer your own questions.
This approach can help identify the key challenge you are trying to address, and discover what data already exist relating to the challenge. It will push you to think specifically about how better measurement or more complete information can help solve the problem. Perhaps you will realize additional decisions that need to be made, or ways you can more efficiently come to decisions.
As you complete the activity, you should ask yourself what data you already have relating to the problem. You should also think where you might find existing data from previous projects in the same problem area.
Before you begin, think critically about an issue, challenge, or problem you have in your organization. Then, work through the process provided on the next page (20 minutes).
Participant Guide: Problem Definition
Directions: Use this guide to help you think through a key issue you are trying to address, the factors that contribute to that issue, the people it affects, existing data related to the issue, potential sources of new data, and how to use those data to make a decision.
- What is the issue, challenge, or problem you want to address? What are the decisions that need to be made to solve the issue? Why is the issue important to your organization?
- What is preventing this issue from being solved? What is the political, social, and economic environment in which the issue is situated?
- What communities and individuals are most the directly impacted?
- What is currently being done, if anything, to address the problem? Who is involved?
- How up-to-date are these data? What do they focus on? What are the gaps in the data?
- How can you use these data to solve your problem?
Presentations and Debrief (15 minutes)
After group work has concluded, ask for volunteers to present. After each presentation, ask some guiding discussion questions for the participants, such as: What was the problem the participant was trying to solve? How did they use new forms of data collection to solve it? How can they now use those data for decision-making?
Exploring Different Forms of Data10 minutes
Explain to the class that data exist in many forms and can be looked at in multiple ways.
- Qualitative vs. Quantitative Data
Ask the class if they know the difference between the two types of data. Then, provide the following definitions for each:
Qualitative: information that cannot be captured or expressed as a number or quantified. Qualitative data include descriptive data such as the color of your house, a description of what the beach smell like, or the someone’s perception of how good government services are.
Quantitative: information that can be counted or measured.
Application: Show the class a picture of this dog (either on screen or a hard copy)
Ask the class how can you use qualitative data to provide information about the dog? (For example, he has short hair, he is brown and black, he is standing, etc.)
Now ask the class how can you use quantitative information to provide information about the dog? (For example, he has 4 legs, he has two ears, the temperature is 0 degrees.)
Tell the class that the primary focus of this curriculum is on quantitative data since that is the type of data most governments and organizations use to make decisions. However, qualitative data is also very important and should not be ignored. Other courses can teach ways to help incorporate qualitative data more effectively into organizational decision-making.
- Primary vs. Secondary Data
Ask the class if they know the difference between primary and secondary data. Then provide the following definitions for each:
Primary: Data that have been collected from an original source for a specific purpose. For example, if an organization wanted to know what their communities wanted, they would question the community members directly.
Secondary: Data that are not originally collected by a group. For example, finding out the average age of a community by using national census data.
Introduction to Key Concepts15 minutes
The following are ways that data can be applied. Similar to the format above, pause and ask the class for definitions of each word before providing the definitions in this instructor’s guide.
Data table: Data selected and arranged in rows and columns. Has the class seen a data table before? What do their data tables look like? How are these data tables stored? Explain to the class that we can also refer to data tables as datasets.
Take time to pass around several sample datasets. In groups of 2-3, have the participants look at each dataset. What information is provided? Is it qualitative or quantitative? What does this information show? Who can use it/what is it for?
Documentation: documents that provide proof or evidence of something, or are a record of something. What are some examples of documentation in the participants’ everyday lives? If the class is quiet, feel free to provide some sample answers (i.e. a passport, birth certificate, proof of residency, etc.)
Data visualization: a way to help people understand the significance of data by placing them in a visual context. Data visualization is the presentation of data in a pictorial or graphical format. It enables decision-makers to see analytics presented visually, so they can grasp difficult concepts or identify new patterns.
Pass around example visualized charts or project these images on the screen:
For each visualization, ask the class what information is provided. Is it qualitative or quantitative? What does this information show? Who can use it/what is it for? Are there any limitations to the visualizations? Is there information missing that would help a reader better understand the visualization?
Note to the instructor: Students may uncover (or you may point out) the fact that the Myanmar Electricity Sources donut chart does not indicate the units of measurement and that there's no indication over what period of time the units are measured.
Stakeholders: a person with an interest or concern in something. For example, who are the stakeholders in this class? Who are the stakeholders at a restaurant? Who are the stakeholders at a football match?
Activity 1.2: Understanding and Working with Data Visualizations35 minutes
Adapted from Visualization Best Practices, Gengo, 2017 & Visualization, the Medicine for Big Data, East Africa Business Daily 2017
- Understand the importance of visualized data and why visualization matters.
- Learn how to interpret and use visualized data.
- Learn how to be consumers of data visualizations, who can use the data in the visualization, and how the information found in the data could be applied to answer different questions.
- Data visualization print-outs
- Datasets printed out for participants
- Data visualizations
Introduction: (Use the following information to introduce and explain the activity to the class)
Remind the class that data visualization is a way to help people understand the significance of data by placing them in a visual context. Patterns, trends, and correlations that might go undetected in text-based data can be exposed and recognized easier with data visualization.
Pass around this image, or show it on the screen
Only allow each person to look at information for 30 seconds. What information is provided? Is it qualitative or quantitative? What does this information show? Who can use it/what is it for? The participants may not have complete answers to this.
Now show the class the same dataset, but visualized:
Allow the class 30 seconds once more. Now, have them answer: What information is provided? Is it qualitative or quantitative? What does this information show? Who can use it/what is it for? The participants may not have complete answers to this.
Because of the way the human brain processes information, using charts or graphs to visualize large amounts of complex data are easier than looking at datasets or report. Data visualization is a quick, easy way to convey concepts in a universal manner.
Visualizing data allows us to help you or the person using your data easily understand your results and quick pull out information that is relevant to them.
Now ask the class, how would they visualize these data for different stakeholders. How would they visualize it for those working in the employment bureau? What about that work at an NGO? How would the public want to view these data?
In this activity, the participants will be broken into groups of 2-3. They will each be given a data visualization. Working together, they should answer the following questions:
- What are the data that were used to make this visualization? It is not provided, so think critically about what information was used to make the chart.
- Who is the intended audience, or audiences that could use it?
- How could they frame these data a different stakeholder? For example, if their answer to the above question was the government, how should these data be visualized for the public?
- What problem are these data visualization trying to show?
- What information can you gather from the visualization?
- What conclusions could you make from the visualization?
- Using the conclusions you made, what are additional questions that you could as about the information provided in the data visualization?
- What kinds of decisions could be made from them?
Once the class is finished, have each group present their findings to class. When the presentations are over, debrief with the participants. You might ask the following questions:
- Did you like one visualization more than the other?
- How can you use data visualizations to make insightful conclusions?
- How can you use these conclusions to make decisions?
- How can these decisions improving programming within communities or organizations?
- What should you consider when visualized data for the public, the government, or an NGO?
It’s important to discuss with that class that:
- good data visualizations require good data. If the data that underlie a visualization are faulty, the result will be misleading and incorrect conclusions from the visualization.
- The processes described in Activity 1.1 of defining a data problem, audience, and goal, are also necessary to take for visualizations. As we move forward throughout this class, we’ll return to these topics. It’s always critically important to remember that for every data project, you need to keep in mind the your audience and what you are trying to accomplish with the data.
For more advanced classes, use the following questions for participants to answer in their presentations:
What other data could be provided to make this more meaningful?
- Different percentages? Total populations? Other...
- Why do we want to tell this
- Is there anything misleading or easily misinterpreted?
- What other information is necessary for context?
- What assumptions are made about the graph?
- Comparisons provided matter
- Data quality/completeness
Briefly review key concepts identified, pause to answer any questions, and dismiss the class for lunch.