Rare is the organization that is 'fully fluent' in its ability to analyze, use, share, discuss, and act on data. In most cases, there is at least one component of the data fluency quadrant that lags — whether it is the skills on staff to communicate effectively (upper right in the figure below) or the tools in place to make data accessible (lower right).
Shutterstock may be an exception that proves this rule.
Last week I had an opportunity to chat with David Cohen, Director of Information at Shutterstock. He’s been with the company four years and has helped transform the culture, build the analytical talent, and put processes that place data at the center of the company’s decision making.
The impetus for the conversation was a data communication program that Shutterstock had instituted called the Daily Dose of Data. Every day, a short e-mail is sent company-wide with an interesting analysis or view of data. Here’s a recent example showing weekly customer searches for the term Ebola:
The purpose of the Daily Dose program is simply to get people thinking. Some messages are interesting factoids while others may spark substantial debate in the company as new people are exposed to realities of the business. Like many good ideas, Daily Dose started small. It originated as an email to a select group of colleagues, but once the company’s founder and CEO caught wind of it, he saw an opportunity to build a more data fluent culture.
David had a lot more to share about how Shutterstock had successfully incorporated analytics into how decisions are made. I was particularly intrigued by his description of how analysts are integrated into the engineering and business teams.
On the one hand, new analysts are required to spend their first three months learning the business, much of that time with the engineering teams to get a deep understanding of the data sources, systems, and metrics at the core of the digital imagery and music marketplace. In Data Fluency, We had emphasized a similar point about building a shared understanding of metrics and where the data comes from. Without this understanding, people don’t know what data they can depend on and what it actual actions and events it represents.
Shutterstock analysts are then moved to product and business teams. David explained the unique incentive structure that he feels makes this arrangement successful. Analysts are partially compensated based on achieving measurable business impacts through analytical findings. Which is to say: analysts get rewarded if they can find an area for improvement, convince the business to make a change, then track and validate the results. For Shutterstock’s case, this incentive instills productive behaviors:
- analysts need to communicate their analytical findings effectively to product managers;
- analysts need to look for opportunities that product managers can actually act on (i.e. theoretical concepts need not apply);
- analysts need to establish test and control models to evaluate the impact of their change.
As an added benefit, Shutterstock analysts often are able to move beyond the worst part of their job: report order taking. By working closely with the business team, they are able to teach others how to access and understand data on their own.
Through training, incentives, and hiring skilled analysts, Shutterstock has been able to bridge the divide between the data-have’s and the have-not’s and build a culture that gets people working together using data as a language of business.
I'd love to hear more stories about how companies like Shutterstock. Send me a note.