The case of Divvy’s data
Create business value with publicly available data and tools you already own.
Jason Drucker & Jay Breeden | October 1, 2015
The Internet is a vast expanse of information available to anyone with a connected device. Data that was once private or proprietary is now organized and stored in the public domain.
Despite all we’ve learned about using this available data, we haven’t yet extended this authority to the business realm. There’s immense untapped value there. Using tools you likely already own, it’s possible to improve your business analysis by combining your existing data with information publicly available on the Internet.
Take the case of Divvy’s data, for example.
Divvy, a Chicago bike rental and subscription company, recently posted stats on each ride taken by its customers. The information shows ride time and duration, starting and ending points, payment type (subscription or single-ride), and high-level demographic information.
Here we demonstrate a few examples of the type of analysis possible with this baseline data.
Data mash: weather’s impact
It’s clear that subscribers ride in greater numbers during commute hours. However, single-ride customers steadily increase throughout the mid-afternoon and decline into the evening.
Using this information, we can determine when people were out riding Divvy bikes, and when they’re not. What’s harder to understand is why.
Divvy sells an outdoor product, so we can find insights on consumer purchasing behavior by combining the base data set with weather conditions at the time of the ride. For example, we can scrape weather conditions from the web, and mash that with the provided Divvy ride stats in Excel and use free add-ins (PowerPivot, Power Query, and Power View) to find correlations.
Now, let’s add an extra dimension to this analysis: bad weather. Divvy’s commuter base are undeterred—they still ride and follow similar ride patterns. Trips taken by single-ride customers, however, fall off drastically.
This information could be used to help Divvy crews more strategically place bikes in popular commuter locations during bad weather, or lead Divvy to offer free ponchos to tourists for rainy rentals. Divvy could also boost sales by targeting single-ride customers on non-rainy afternoons with onsite marketing at high-traffic stations.
Data analysis: maintenance ramifications
Because Divvy knows that its most loyal subscribers are willing to brave the elements, it could use its data to perform maintenance checks on bikes used heavily during thunderstorms and snow. This proactive maintenance could contribute to equipment longevity and fewer breakdowns. Using a tool like Microsoft Power BI Q&A, anyone—from an analyst to a maintenance worker—could type questions in plain English and receive instantaneous answers about which bikes need servicing.
Tapping into publicly available data to complete the picture
Data can also help businesses understand how to expand operations. The core of Divvy’s business are bike stations in downtown Chicago. To determine where to expand, Divvy could send scouts to beyond the heart of the city to conduct surveys and make observations—or they could turn once again to freely-available information.
The City of Chicago Data Portal contains many data sets (crime statistics, health trends, etc.), including a listing of all public bike racks. By overlaying a map of existing Divvy bike stations and traffic patterns with the locations of large public bike racks, the company could determine potential new sites.
Using Microsoft Power Maps, we were able to visualize a flyover of Chicago that suggests prime growth opportunities in the city, assuming a high concentration of public bike racks indicates heavy cyclist traffic. These visualizations tell a powerful story, full of actionable insights.
The concept of augmenting organizational data is not limited to operationally focused companies. Many industries can benefit from the vast amount of freely available and openly disseminated information.
Imagine an emerging real estate developer overlaying trends in residential listings with homes selling below market value to determine the most profitable location for their next project. A startup trying to disrupt the traditional education structure by projecting student performance data on its target markets to identify school districts with the biggest impact potential. Or a large food manufacturer analyzing world food import/export patterns to determine which new-product proposals to pursue.
There are nearly endless sources of public information—and equally endless ways to use it effectively. Need help getting started? Contact us.
Jay Breeden is no longer with Slalom.
Jason Drucker is a practice area lead in Slalom’s Chicago office. He is an expert in end-to-end business intelligence solution architecture, ETL design and development, end user reporting and dashboard delivery, data integration, data modeling, business analysis, and project management.
Jay Breeden is a data management principal consultant in Slalom’s data & analytics practice. Jay is passionate about helping clients understand how to use their data to make better business decisions.