Data Science Hot Spots in the US: And What to Expect Post COVID-19

October 1, 2020 Estevan McCalley

Here’s a story about data science. Turn the lights off in 2020. And If data scientists glowed, you might see a map like this.

This map and the interpretation that follows build upon the analysis shown in the infographic of data science activity by sector. While the infographic focuses on the distribution of data scientists across business sectors of the Fortune 500, this map provides a visual geographic snapshot of data science activity centers across the contiguous United States.

This geographic representation may be useful for a variety of purposes. It may, for instance, provide a useful starting point for organizations who wish to take a data-driven approach to understand, engage, and participate in this community. For some, it may also be helpful if there is a need to allocate and deploy location-based resources in proportion to where the action is.

Data Science Activity in the US: Map Guide

Data Points

Each circle on the map is located at a US headquarters city of a Fortune 500 (2020) company that has at least one, self described “Data Scientist” on their team. Circle size and color saturation increase in direct proportion to the number of data scientists the company currently employs. No other company information like revenue or number of employees are considered as factors that influence circle features (such as size and color).

Data Scientist Numbers

The total number of data scientists for each company is the output of a LinkedIn search for “Data Scientist” as the root phrase for current job title for each of the Fortune 500. This includes titles like “Data Scientist Manager”, or “Lead Data Scientist”. Company subsidiaries are also considered. The number of data scientists for subsidiaries (when they exist) is included in the total data scientist count for the parent company. For example, data scientists who list either “Victoria’s Secret”, or “Bath & Body Works” as their current employer, are included in the total number of data scientists for parent company “L Brands”.

It is perhaps noteworthy, that 84 (17%) of the largest 500 companies in the US (by revenue) in 2020 may not employ a single data scientist. The overwhelming majority of data scientists are found within these top 500 companies, and seldom are found in small to medium sized businesses and organizations.

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Interpretation of Data Scientist Distribution: And What to Expect Post COVID-19

Data Science Activity Centers in the United States

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As might be expected, data science activity is heavily focused within data native & data rich sectors like Technology & Financial Services and their associated geographies. Most other sectors barely register their presence by comparison.

It’s clear that sectors outside of Technology & Finance are not effectively adopting data science & machine learning approaches. This could be for a number of reasons. While there is a shortage of data scientists, there are more systemic and foundational issues.

Some suggest an 85% failure rate of big data projects. A McKinsey report suggests a struggle to demonstrate ROI. And according to a 2019 Deloitte survey “Most executives do not believe their companies are insight-driven” and that culture might be a culprit.

Based on our experience working directly with many of the Fortune 500, we agree with these findings. Creating a data-driven, and data literate culture is foundational. Next, analytics leaders and their technology partners must tie data projects to measurable business outcomes. They also need to be able to effectively communicate this value to business partners. Without any of these critical components, data science programs may struggle to get off the ground.

So where are we headed next? And what will be the impact of these efforts in the wake of economic recession left by the COVID-19 pandemic?

The MITSloan Management Review predicts that that ability to demonstrate ROI will become even more important in a post COVID-19 world. As businesses face a more challenging economic environment, analytics teams who can demonstrate ROI may see increased investment, while teams who cannot may face cutbacks.

We are at an inflection point for data science, and now is the time to act. Some analytics executives are embracing this challenge as an opportunity. For example, Jill Canetta, the Chief Data Officer of Experian Marketing Services is focused on building a strong data culture and prioritizing business critical projects. As she puts it, "We understand that data can educate us and inform business decisions, not to mention, the value it can have in making our lives easier". And "With that in mind, we have to constantly remain hyper-focused on our key priorities to be most effective.”

Canetta goes on to explain and suggest that “Every person is at a different point in the COVID-19 lifecycle and businesses need to be relevant and find ways to address consumers’ most pressing needs.”

When we turn the lights off in 2021, what will our data science map look like? It may likely change faster than it has in previous years. How it will change depends more than ever on the actions we take right now.

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