Data Culture Founders Interview

Data Culture
5 min readDec 22, 2020

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Data Culture Co-Founders Leah Weiss and Gabi Steele on their first year in business and the future of data.

Image courtesy of Mosiah Rashaad; Gabi Steele (left), Leah Weiss (right)

I’m curious how you would describe “Data Culture” and what it means to both of you?

L: It’s an important question, because “Data Culture” isn’t necessarily a destination where one day you’ll have arrived, it’s a process you undertake. Your data will never be 100% perfect, and you will always be iterating. But no matter how mature you are as a data organization, in a data-driven culture, employees at every level should be able to surface opportunities for more-informed decision making, and then use data to quickly create solutions. Some of that is tooling and access, but most of it is mindset.

G: Generally speaking, we’re always trying to bridge the gap between an organization’s data capabilities and how the business actually acts on that data. Technology and data talent can’t change culture, you need a group of inspired team members and leaders that genuinely value data-driven thinking and decision making.

The Highs and Lows of Starting a Business (in 2020)

Image courtesy of Christina @wocintechchat on Unsplash.

We’re almost at the one year mark of Data Culture’s founding. What are some of the highlights that stand out to you from your first year in business?

L: One of the great pleasures of this year was developing a data science curriculum for Kode with Klossy. We ran our first inaugural camp this past summer with 26 amazing scholars. We were really motivated to inspire a whole new generation of data scientists in a field that desperately needs their perspective. In reality, they inspired us! Our scholars were some of the most thoughtful and inquisitive people we’ve ever had the pleasure to work with. If they are the future of our field, we are in capable hands.

What advice would you give other young entrepreneurs?

G: Find the people who make you feel like you have no choice but to take a leap. We’ve always said that anything is possible once you’ve defined the problem. If you have a well-defined problem and a partner or a team or an advisor or anyone who believes in you, you’re bound to do something great.

I wanted to touch on some of the more challenging aspects. There’s a level of uncertainty attached to any early stage of business, but this past year, you faced an added layer with Covid-19 and the economic fallout of the pandemic. How have you managed uncertainty when starting a business, especially in 2020?

G: We’ve learned that if we deliver good work, more work will follow. So if we just focus on one step at a time, the uncertainty seems less daunting.

The Future of Data & Data Culture

Image courtesy of Looker; Leah Weiss (left), Gabi Steele (right)

You’ve both been immersed in the field of data for almost 10 years and experienced first hand the changes that have occurred in that time. Based on what you’ve seen in the past, where do you think the future of data is going and what do you predict the next big trends to be?

L: There’s an emerging awareness that data can really help decision making in industries that haven’t had access to data & analytics tools. We’ve talked to companies in construction, law, and real estate who are trying to use data more effectively. Data culture will be even more important as that trend continues. Everyone will start evaluating tools that promise machine learning and AI capabilities, but very few people know how to apply that into their product or their organization to really drive value.

We also hope data ethics and bias will be at the forefront of every conversation about data. Data is a powerful tool that can be used to innovate, but without very intentional intervention, it can reinforce inequity and injustice.

G: Another positive shift we’re seeing is the field of data engineering and data science becoming much more multidisciplinary. Tools in the modern data stack have made what used to be the most painful parts of setting up your data stack completely seamless. Creating data pipelines and storage solutions are managed by amazing tools like Fivetran and Snowflake, which allows data professionals to be a little less specialized and develop a more holistic view of the businesses. This is a great opportunity for companies to bring their data talent closer to the business. It’s also a great opportunity for new data talent to emerge from non-traditional career paths and change the field for the better.

Speaking of new and exciting facets within the field of data, Gabi, what do you predict for the future of data visualization in particular?

G: Data visualization is still such a specialized discipline in many ways. I’m simplifying things a bit, but there’s Excel and BI tools for business/data analysts (those tools aren’t going anywhere!) and then there’s D3.js, which has stayed mainly in the hands of frontend developers and data visualization engineers. I hope those two worlds will start to converge a bit more, and more people will have access to sophisticated visualization tools without having to code fully custom experiences.

Based on everything you’ve accomplished in only one year, what do you hope to accomplish in your second year of business?

G: First of all, we’re hiring! So if you or someone you know is as passionate about building data-driven teams as we are, please reach out.

We’ve done a variety of things in our first year, including infrastructure and engineering work, training and workshops, and data strategy. We want to take all of our learnings from this year and build scalable solutions to problems we see repeating across companies and industries. Whether that evolves into a product solution or establishing new verticals, we are very open to what 2021 will bring us!

Interested in learning more about how to scale data at your company? Reach out to Leah and Gabi at Data Culture.

Conversation moderated by Mikaela Ergas-Lenett.

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Data Culture
Data Culture

Written by Data Culture

We help organizations build data capabilities and get value from their data.

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