Taking the Next Steps To Build a Data-driven Business

You finally did it. After successfully convincing the board and senior management that the future belongs to agile, data-driven businesses, your organization has hired some data analysts and established a center of excellence for analytics. What’s next?

Building a data-driven business takes more than hiring specialists and acquiring some new tools. Here are some next steps to building a successful data-driven business.

Establish a data-driven approach from the top

The absence of a strong culture that embraces data is often cited as a top challenge faced by chief data officers. Unlike relatively niche areas such as creating a better mobile app, creating a customer loyalty reward system, or improving efficiency at a manufacturing plant, a data-driven culture must be introduced across the entire organization to work.

Getting the ball rolling on a data-driven culture must start from the top, with the onus on top executives and senior managers to drive a policy of data-driven decisions. This entails actively pursuing a course away from a place where decisions are made by gut decisions, to one where everything is quantifiable with data.

And while reducing wastage or improving service resolution time can offer short-term improvements to profitability, the real benefit of a data-driven culture stems from how it offers a continuous loop of feedback and decisions leading to sustained, long-term benefits across the entire organization. This can only happen when driven from the top.

Finally, as employees are immersed in an environment where metrics and data-driven decisions are now the norm, they become increasingly familiarized with the nuances of data. And as I noted last month, familiarity builds confidence, slowly but surely culminating in a culture where the use of data is actively discussed during office hours, and employees at all levels make a conscious attempt to further incorporate the use of data.

Keep proof of concept simple

While analytics offer the tantalizing potential for huge gains, they must always be anchored in explicit, quantitative levels of outcomes, notes David Sweenor, a senior director at Alteryx in a recent contributed opinion piece. But organizations should keep proofs of concept simple and sturdy, he said.

“[Employees] see the recommendations, they create projects, they make a proof of concept, and then it’s [nixed]. It’s so disheartening, especially if the data supports it. Not taking action when there was a compelling opportunity to do so can negatively affect psychological safety, foster mistrust in employers, and stop that feedback loop that is so vital to establishing a data-driven culture,” explained Sweenor.

Organizations should focus on supporting proofs of concepts that are viable. This might be a simple project, which can be enhanced later into a more elaborate system. In a nutshell, get viable projects going even if the returns are not spectacular. Focus on instilling confidence and a sense of achievement at the start, and future projects will reach the stars.

Don't neglect your tools

A recent call I had with a former data analyst struck me on the importance of data lineage. He was relating how it could take hours or as much as half a day to investigate and respond to users’ reports of anomalous data. The reason is due to the need to trace the flow of information through multiple systems, he shared.

While there is no straightforward answer here, data solutions with built-in data lineage now offer much greater visibility and ways to trace errors back to the root cause in a data analytics process. In the same vein, a new generation of automated tools can help with data wrangling, while others can seamlessly connect disparate data repositories and pipe them to a data warehouse.

The point here is this: the right tools can greatly ease the journey towards a data-driven future by improving productivity and vastly simplifying data management. While you might not require the most sophisticated tools from the get-go, it makes sense to periodically review the organization’s requirements, and acquire them as necessary.

Ultimately, transitioning to a data-driven organization isn’t simple, nor a particularly fast process. But get it right, and you set in place the foundation for the next leap ahead for the business.

Paul Mah is the editor of DSAITrends. A former system administrator, programmer, and IT lecturer, he enjoys writing both code and prose. You can reach him at [email protected].​

Image credit: iStockphoto/Radachynskyi