How To Build a Data-Driven Culture

Image credit: iStockphoto/jesadaphorn

The impact of data science grows every day as more employees understand the value and importance of the business insights coming from their data science teams. I know from my work as a data leader at Fivetran that proper research and interpretations of results can lead to radical changes in business models. The companies that embrace data literacy will move fast and lead the next generations. Ultimately, I believe that the thoughtful use of data will revolutionize business and help humanity achieve a higher level of consciousness.

But expanding awareness of data science also necessitates additional education across the business. Data scientists often have to help non-technical colleagues understand how the domain works. Sales or Marketing may see the value of a business intelligence dashboard, but moving beyond this phase and building a data-driven culture throughout the company can be more complex.

Initially, you want everyone from junior staff to senior leadership to be able to interpret visualizations and dashboards and make decisions based on that data. Junior data analysts and individual technical contributors should be able to create new data models, visualizations, and dashboards. Then save the hardcore tasks like integrating new data sources for the domain experts on the analytics team. This will help define roles and levels of detail appropriate for each business group so that you can get the best returns from a data science program.

Large enterprises in the finance industry already understand how data science can grow their business. At J.P. Morgan, I helped set up the Business Intelligence Group, which launched large-scale data analytics projects that reported all the way up to the CEO, Jamie Dimon. But if data isn’t an obvious daily revenue driver, getting traction and understanding across the broader organization can take a bit of work.

To create a data-driven culture that is valuable to non-technical teams, start with these steps to build a strong foundation.

Start with the cloud

The popularization of cloud computing and SaaS software offerings has made it easy to improve data integration capabilities without deploying new hardware. Companies can take advantage of cloud data warehousing and integration with other SaaS tools and spread the cost across each month. And more importantly, cloud services, with their potential for scalability and automation, can be the first step to unlocking data assets down the road.

Having your data available on demand is crucial for digital transformation. Startups are especially well-situated to think about how they will capture and process large volumes of data over time. At Fivetran, we’ve seen this leap to the cloud provide game-changing capabilities for smaller companies - it’s much easier to start running analytics and integrating your data when your infrastructure is cloud-native from the beginning.

Find your fans

The potential scale of data analytics can be daunting. That’s understandable given the rapid changes and the complexity of state-of-the-art data models. Finding an executive advocate is crucial for the long-term success of your program.

Look for the early adopters among your executives; find the people most open to new ideas. They don’t have to be senior — just enthusiastic about data, well-informed and detail-oriented. Identify your power users, your best advocates, and find out the problems they face and how better business intelligence would solve those problems. Then leverage those success stories with the next cohort of workers to expand peer influence across your company.

I really enjoy days when I uncover an aspect of a business that is crucial but hard to understand with traditional business intelligence products. When that happens, I give people visibility they didn’t have before.

Uncover the gold

Sometimes the insights you’re looking for are easier to see in aggregate than piecemeal. I once worked with a stellar product manager named Alexa, who created a dashboard showing the discounts each sales manager was giving to customers. Typically companies will measure their sales team on the total number of deals or revenue volume for each quarter, not necessarily on the price charged for products.

Alexa realized there was an opportunity to grow revenue by looking across the sales team and making small changes that could add up to a significant impact. In this case, she found that the number of deals was increasing by looking at total revenue each quarter, but revenue per deal wasn’t keeping up. The company was making less money per deal.

She created a dashboard of sales discounts for the VP of Sales and was able to see instances of sales reps being too generous and leaving money on the table. By looking at the deals altogether and then finding the individual outliers, she could increase revenue without dramatically changing how business was done.

It's much easier to convert people to data-driven thinking when you can give people a smarter way to do business with easily visible information.

Spread the word

To expand your data-driven culture, you’ll have to build a culture of data lovers across the business. You need to give employees visibility into the metrics that matter to them professionally and use this data to make changes that will scale.

At Fivetran, we focus on surfacing visibility to information that matters for each employee group. The more access someone has to those analytics, the more visibility for the business and the faster it is to adjust to a changing market.

In the long term, your most significant signal of success will come when your CEO and head of business prioritize the findings from analytics in their metrics. Looking back 20 years ago, the big opportunity was in the power of software. 10 years ago, it was the power of mobile. Now what matters is how you’re using your data, what you’re doing with it, and how fast you’re iterating and driving the business. You want your executives to understand the tools they have in data science to embrace this opportunity.

If you start from a consolidated foundation of information, then determine and demonstrate how non-technical teams can benefit from this data, and use these insights to convert people across your company, you can build a culture of data literacy that brings new value to all the stakeholders throughout your company.

This article is by Veronica Zhai, principal analytics technical product manager at Fivetran.

The views and opinions expressed in this article are those of the author and do not necessarily reflect those of CDOTrends. Image credit: iStockphoto/jesadaphorn