Brewing Insights on Community-Informed Data: A Coffee with Erik Strand, Senior Associate, Data, at Karp Strategies

We grabbed coffee with Senior Associate, Data, Erik Strand, to talk about the importance of community-informed data analysis. With a background in urban planning and analytics, Erik has a unique blend of academic insight and practical experience in economic and workforce development. As a Senior Associate at Karp Strategies, he has worked across New York and the Northeast, helping city governments advance affordable housing, transit infrastructure, and community engagement through data-driven strategies. 

Erik’s expertise spans market analysis, community development, and the economic impacts of urban planning, and his approach is grounded in an understanding of both quantitative data and the lived experiences of the communities he’s worked with. He believes that for data to be truly meaningful, it must be informed by the communities it aims to serve. This principle guides his work at Karp Strategies, where he helps bridge the gap between data and everyday lived experiences. 

In this conversation, Erik shares his insights on how data can reveal opportunities, highlight disparities, and make a real difference in building equitable urban spaces. He discusses how data is most powerful when coupled with qualitative engagement, the challenges of working with imperfect or incomplete datasets, and the importance of involving communities in every step of the analysis process. Join us for this conversation as we dive deeper into how data, when used responsibly and transparently, can foster meaningful change.

How do you like your coffee? 

Black. If it’s hot. And with cream and Splenda, if it's iced. 

How did your career lead you to join Karp Strategies? 

After completing my undergraduate degree, I went straight into graduate school. I had done internships in the public and non-profit sectors, but I wanted to explore the private sector. At Columbia’s Graduate School of Architecture, Planning and Preservation (GSAPP), Rebecca Karp, CEO of Karp Strategies, was teaching a project management class while I was there, which I took during my first year. I appreciated her insights into the critical work the planning field is doing to make cities better for people, but also areas where organizations like Karp Strategies see opportunities to influence change in our systems and communities. These perspectives made Karp Strategies a great environment for my first full-time job during and after graduate school, as it gave me the opportunity to do rigorous work across a variety of clients and communities that also align with my core values.  

How much do urban planners like yourself rely on quantitative data in your work? What types of inferences and/or predictions do you make about this data?

That is the question we’re always trying to improve upon. Broadly, we analyze quantitative data to help us understand what is happening in the real world, whether that be social, economic, or market conditions. This process of marrying data with outreach and other qualitative research is a bit of a chicken-and-egg scenario: the data helps us identify opportunities and gaps in the first place, and also provides backing for conclusions we might come to through qualitative analysis. The order and balance of these quantitative and qualitative approaches vary from project to project, but it is really vital to have both. Without data, it’s very difficult to extrapolate broad trends or make our work sufficiently concrete. But qualitative engagement and research are also critical to validate the data with people’s lived experiences and draw out nuances that can get lost in the numbers. 

Our job as analytical planners is to iteratively improve upon this balance, which is challenging. On the one hand, the data provides a grounding in something concrete and is broadly applicable, allowing you to draw conclusions and make compelling arguments. An example of this is from a recent Lightcast ‘Ready Workforce’ conference Karp Strategies attended, where they discussed using, among other things, data that was used to demonstrate the economic and workforce value of immigration to the United States. By relying on numbers rather than personal opinion, data can help build persuasive cases, even to those who might not be sympathetic to the cause. However, the downside is that no data is 100% accurate, especially when working with smaller communities where models may become less reliable. The way we try to get around that is by using vetted sources. We try to engage with communities and clients directly, who know what is actually happening, to validate or challenge what we’re seeing through our data. 

What is your experience with private (non-governmental) data, and how do you handle concerns around bias?

Private data sources can be extremely valuable to fill gaps in government data. We frequently use data from universities, research organizations, advocacy groups, and industry associations to gain important insights into specific sectors and communities. For example, we often analyze small business databases to identify opportunities and gaps in the business ecosystems for specific regions and industries—this level of specificity can be hard to gather from public data. 

At the same time, we recognize that all data can be biased, regardless of where it comes from. Private data sources do have some unique bias concerns, such as high access costs, opaque data collection methodologies, and looser requirements around fairness and impartiality. But the main concern around data bias in general, particularly with people-centered data, is that even the best sources often have incomplete or inaccurate information about some of the real-world populations they are meant to represent. We saw this with the most recent Census—research shows real concerns with this count, particularly for marginalized populations, including children, people of color, and immigrants. 

Working with any data—public or private—requires thoughtful triangulation that draws on perspectives and validation from the people that it aims to represent. 

How does the Karp Strategies team avoid these bias errors? 

Clearly stating what the numbers do and don't mean is crucial. It’s rarely intentional that data is presented incorrectly on purpose. This involves being very clear about what the numbers mean, their limitations, and the assumptions made to reach conclusions. It's important to be transparent about model inputs and scope and to pull reviewed documentation about the data sources. Identifying built-in limitations is key. We try to build these disclaimers into all of our deliverables so that our clients and partners understand the limitations. But even there, there’s an instinctual human tendency to assume that a number presented is correct. In many cases, you have to be a bit imprecise by opting instead to highlight trends or round values to stay within a more reasonable range and reduce the chance of being completely wrong. At Karp Strategies, we also present quantitative work in the context of community engagement or other research to provide additional validation, making it easier to trust the numbers when supported by external sources.

How important is it for Karp Strategies to include the community in the data collection process? 

It is vital! At Karp Strategies, we begin our process with an assumption development stage before diving into quantitative analysis. This involves identifying and clearly listing assumptions to frame the model we aim to build. We prioritize having intentional conversations with clients and any other project partners to ensure everyone shares a mutual understanding. After completing the first round of analysis, we validate our findings by consulting experts and reaching out to as many stakeholders as possible. Based on partner feedback, we revisit and reframe the assumptions to refine our approach.

Can you explain the process that you have on a project where the data was not complete, and you had to create or collect such data? 

In our Blue Highways Workforce Assessment project and similar initiatives, Karp Strategies is working to address the challenge that the ecosystem we’re analyzing doesn’t yet exist in New York. Specifically, when discussing workforce implications, we’re often referring to jobs that haven’t even been created yet. The challenge is how to gather data on workers who aren’t yet in the workforce. To navigate this, we combine typical workforce data from Lightcast and government sources, which provide occupation and industry-level characteristics. Additionally, we are intentional about using research and community engagement to identify gaps in the existing data and ensure their analysis reflects a more complete picture of future workforce needs.

Fast Facts:

Last TV show I binge-watched: Severance (still going!)

Restaurant (delivery) I’d recommend to close friends: Dokodemo (in the East Village)

Best concert I’ve ever experienced: Zhu

Book that changed me: Pedagogy of the Oppressed by Paulo Freire… I read this at a very pivotal moment early in my undergraduate studies, and it pushed me toward planning as a career.

Movie I’d pay to see again and again: Interstellar

Most memorable KS project: NEC Connect C37

Favorite planning course in graduate school: Exploring Urban Data (taught by Dr. Boyeong Hong)

My heroes are… my parents!