Punto Data

Don’t love solutions, love problems!

Victoria Orozco — Tue, 26 Oct 2021 02:09:41 +0000

Have you seen ‘solutions in search of problems’? I bet you have! This happens when in teams that are gathered to tackle a specific issue, we observe dynamics early on that move the group towards the design of a particular solution without having a clear understanding of the problem. From that point forward, all the analyses and conversations revolve around developing the solution that one or more team members were promoting. However, the team never performed a basic diagnostic of the issue, or the analyses were skewed from the beginning. It was always ‘a solution in search of a problem’.

Sometimes I see this type of approach in my work at the intersection of data analysis and policymaking. It also happens in every other field. I have even found myself collecting data and evidence in a way that responds to specific solutions –because we all can fall in that trap–. This need to sort things out and jump right to the end is certainly inherent to our human nature. We are all in search of cognitive closure.

After almost 10 years of working in this field, I still need to make conscious efforts to go against my need to quickly reach concrete explanations to complex issues. However, with the years, I transformed those conscious efforts into mechanisms that guide my daily work and don’t allow solutions to take too much space too early on.

Solution-guided processes to explore data take us directly into a self-reinforcing loop. It is a type of confirmation bias that locks us in a tendency to seek out information that supports something the group already believes. Good diagnostics to inform good solutions are underestimated, and it’s easy to believe that problems are self-evident.

So what are the mechanisms that we can use to identify those flaws early on in our work and prevent us from falling into that trap? Here are a few ones that I use.

Categories, cover them all

When studying a topic, I create categories (a.k.a. frameworks) for my analyses. Creating categories to segment data analyses forces us to explore different dimensions of the same issue. In the last few months, I have been studying business ecosystems in the United States and how we can manage to create more inclusivity in those spaces. One of the focuses of my work is the performance of businesses owned by people of color. Last year with my team at Drexel, we came up with 3 categories that we always look at to assess the performance of those businesses: number, size, and sector. They are not 100% comprehensive of what entails to be a business owned by people of color, but believe me, they are the bulk. And every time I start studying a business ecosystem in a different region, it’s certainly a useful exercise to cover all those three categories and understand what’s going on in each dimension. Caveat: it’s beneficial to create categories, but when those categories do not fit your information anymore, it’s time to change them. Besides, if they are not grounded in research and a certain level of expertise, those categories could end up narrowing our scope. Start creating and using categories only if you are willing to let them go when time arrives!

Guided brainstorming

Last week, I read an article about mind maps, and I believe they are a great example of guided brainstorming. You start with a big piece of paper, write down your question or goal in the center, and start to grow the tree with ideas directly connected to each other, from the center node outwards. I realized I used mind maps many times but without having a name for them. I usually take a piece of paper (for my next mind map I will take a big one!), and I start writing down ideas and connecting them. I keep pushing the boundaries of the network. Sometimes I give myself a few hours between new iterations of the mind map. Sometimes, a few days. As I see this, it’s a creative process. And the word ‘guided’ is important here. We need to know our objective and posing that objective at the center it’s certainly a smart move.

Open-ended efforts

Finally, in the early stages of a project, it is intellectually rewarding to set up a few days for researching without a north star (I believe productivity is misunderstood or overrated!). I give myself and my team a few days without clear direction in the initial stages of those endeavors where we need to come up with something new. A framework that does not exist, a platform that has not yet been created, a process that has not been mapped before. However, as simple as this strategy looks like, it takes courage to implement this approach in teams. Researching without a north star forces team members to deal with uncertainty and frustration. Still and all, this approach paves the way for new ideas to emerge.

The post Don’t love solutions, love problems! appeared first on Punto Data.

Step #1 for developing a data platform for the common good: the 3-Question Rule

Victoria Orozco — Mon, 23 Aug 2021 15:51:27 +0000

A data platform is an additional tool that we all should have in our policy toolkit. Usually, it is not the final goal, but one of the means to achieve an objective. As I recently wrote, when deciding whether a data platform is the right tool, keep in mind its value-added: capturing people’s attention, bridging gaps of knowledge, and providing flexibility in the analysis of data. Data platforms take time, effort, and resources. Before embarking on this journey, be sure that the foundations are solid enough. Here we offer a road map for this initial stage.

I have seen many frameworks to take the first step towards building a data platform. Most of them are similar in essence, trying to identify what is at the core of the message that you want to convey. For me, the 3-Question Rule presented in Communicating Data with Tableau captures very well the 3 questions that we, policymakers, should be sure to answer before moving to the building phase. It is simple and to the point.

Between us: This is the step where an interdisciplinary approach is needed the most. As an economist and a policy practitioner, I am equipped to identify policy questions, and I have knowledge on several tools and techniques that can give birth to a data platform, from data engineering to visualization tools. However, I will never be able to replace the value of an interdisciplinary team looking at different angles of the same issue. And here is where I think data platforms face important chances of falling short. Keep an eye on this! Economists, data scientists, and software engineers are as needed in this process as people on the frontline.

The 3-Question Rule

What is the policy question that you are trying to answer?The first step is crafting the question/s that you want your data platform to answer. This may seem straightforward, but it is certainly one of the most challenging tasks. The Opportunity Atlas does a terrific job here. It starts, from the beginning, with the question that the platform attempts to answer: “Which neighborhoods in America offer children the best chance to rise out of poverty?”. It is an incredibly sophisticated data platform that starts with a simple question, setting the scene for 2 key stakeholders: the team that builds the platform and the users. For the team, this question is like the north start that guides their decisions throughout the product development. For the users, it delineates the type of information that they will encounter. In our case (I led the design and development of the Small Business Equity Toolkit), we were very clear from the beginning that the question “How are black-owned businesses performing?” was the north start of our project. However, this was a not-good-enough question. What do we understand by performance? Where are the Black-owned businesses that we are assessing with our data located? Several iterations left us with the following question: “How do black-owned businesses in your metropolitan area perform in number, size, and sector?” It may look simple, but there is policy and theory behind this question: identifying the importance of assessing these three dimensions (and not two or four) took the team several iterations.
Why do you want users to know this information? In our project, our second question became: “Why do we want users to know how black-owned businesses are performing in terms of number, size, and sector?”. We wanted to capture and rank the performance of Black-owned businesses to promote a clearer understanding of the small business ecosystem and its gaps, encourage goal setting and provide useful comparison points to economic decisionmakers at all levels. Having this in mind was key to craft the functionalities of our data platform, and the way in which we needed to present data and visualizations. Another good example appears in the Spatial Equity Data Tool from the Urban Institute. This data tool is also great in making very clear the distinction between the “What” and the “Why” questions. The “What” question: “How can cities ensure that resources are equitably distributed to all residents?”. And then, the “Why” explanation: “to help city officials, community organizations, and residents quickly assess spatial and demographic disparities in their cities”.
Who is your intended audience?Data platforms for public good tend to have a broad audience, from policymakers to academic researchers to citizens. Although it may not be possible to identify only one type of user, each audience has its own requirements and, ultimately, your data platform should be tuned accordingly. For example, platforms such as the Growth Lab’s Viz Hub are popular among researchers and a technical audience (e.g., the economic development team of a local government), and many of its features reflect that trend.

Your takeaways: If you decide that creating a data platform is the right path, then start with the 3-question rule: (1) What is the policy question that you are trying to answer?; (2) Why is your question relevant?; (3) Who is the intended audience for your data platform?

If you have feedback or thoughts about this content, please reach out and let us start an exchange of ideas!

The post Step #1 for developing a data platform for the common good: the 3-Question Rule appeared first on Punto Data.

Data Platforms for the Common Good: Are these tools the right ones?

Victoria Orozco — Mon, 26 Jul 2021 23:06:03 +0000

In March this, the Small Business Equity Toolt, a data platform that I designed, was publicly launched. The goal of this data analytics and visualization platform is to capture and rank the performance of Black-, Women-, and Latino-owned businesses across metropolitan areas and cities in the U.S. Our ultimate purpose is to help close the racial, ethnic, and gender gap in the business landscape.

This is one milestone of a journey that started from scratch several months ago. Recent years have certainly seen an explosion of data tools, and the policy sphere has not been the exception. From The Opportunity Atlas to Data USA to the Growth Lab’s Viz Hub, data platforms have come to stay in our policy toolkits.

An additional tool for your policy toolkit: data platforms

It was June 2020. In the afternoon of my first day of work, the Lab’s Director shared with us a new database that had recently been released by the U.S. Census Bureau: the Annual Business Survey. It is a national survey that provides granular information of employer businesses in the U.S., disaggregated by sector, size, geographical areas and, even more important, by race, ethnicity, and gender. It is among the few national databases that provide business data disaggregated by demographics, and many local policymakers and stakeholders rely on this data to assess the inclusiveness of their business ecosystems.

Coming with a lot of experience in business data in my country (Argentina), I saw an opportunity right away and decided to take action. I processed the data with a statistical software, created relevant metrics, and developed a sophisticated Excel model, which enabled the user to conduct many relevant and insightful analyses. After 3 days of work, I had my first official deliverable as an employee of the Lab (and I was very proud of myself!).

Yet data does not mean knowledge. What for me were easy to read and intuitive dynamic tables in a spreadsheet, were almost illegible numbers for people who are not used to working with large amounts of data. A deck would not have fit the bill because the purpose of the data analysis was exploratory (and not explanatory), and we needed the flexibility of changing geographies and population groups in our analyses. Although I had at hand knowledge and access to software to create data dashboards such as Tableau, I hadn’t even considered using these kinds of tools. In this first attempt, my polished tables failed to capture my team’s attention, convey key insights, and lack flexibility (pivot tables and macros in an Excel cannot make all the trick!). So, in our journey to address the shortfalls of the initial Excel analysis, a new data platform was born: the Small Business Equity Data Tool.

What is the value added of a data platform?

I see three key strengths in data platforms:

They capture people’s attention. A good data platform can make the difference between encouraging policymakers and citizens to keep exploring certain issues and losing them in the first row of a table. It does not need to be a complex data platform, but it needs to be meaningful, memorable, and visually “sticky”. Visualizations work from a human perspective because we process visual data better than any other type of data. And there is no triviality here: how you present your data matters, what is the type of charts that you create matters, the colors that you choose matter.
They build a bridge between science and action. Data platforms are tools to highlight relevant findings or angles of a problem, translate complex data relationships into accessible analyses, and tell stories. By doing this, they have the power to bridge knowledge gaps and bring together the producers of content and those in the field absorbing knowledge to turn it into action.
They provide flexibility to analyze a topic. From user-friendly dynamic tables to insightful charts and visualizations, data platforms offer the possibility of strategically combining these elements to create knowledge from the data and analyze as many angles of the issue as it is relevant to do (and all this without overcrowding a screen with tables!).

A data platform is an additional tool that we all should have in our policy toolkit. Usually, it is not the final goal, but one of the means to achieve an objective. When deciding whether a data platform is the right tool, keep in mind its value-added: capturing people’s attention, bridging gaps of knowledge, and providing flexibility in the analysis of data. So, let me reframe the initial question: are data platforms the right tools given your objective and the value they can bring? You tell me.

If you have feedback or thoughts about this content, please reach out and let us start an exchange of ideas!

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