How to make “data-driven” more than jargon in your organization
Many organizations today describe themselves as “data-driven,” and it’s easy to understand why: Organizations are producing, and have access to, more data than ever before. It’s considered a competitive advantage and many customers are demanding it.
Further, advanced technologies like artificial intelligence (AI) and machine learning (ML) are more widely available to help make sense of this massive amount of data and improve business processes and functions like customer experience (CX).
But what does it really mean to be a data-driven organization? To some extent, the term “data-driven” has become marketing jargon, possibly because it’s being used to describe even the most basic data activities. But just because an organization collects data doesn’t mean it’s data-driven.
The bottom line: Being a data-driven organization means digging into the information readily available and making strategic business decisions based on the facts and insights that are uncovered.
Sounds simple, right? Not always.
In order to get to a place where organizations can confidently call themselves data-driven, here are some of the initial steps to successfully leverage data, draw out insights, and as a result, maximize ROI.
As our world becomes increasingly connected and digitized, we have a growing amount of unstructured data. The ability to effectively manage and analyze it will be imperative for organizations trying to enhance business processes and innovations.
The first and most critical step to successfully leveraging unstructured data is to organize it, which can be daunting. With data stored in a variety of repositories and formats, it’s important to first understand what the data actually is and how much of it is relevant to the business.
As data understanding increases, creating a data map and dictionary of what and where is crucial. Organizing data can be a long process, but by starting with the critical data and working down, your organization will quickly amass defined and trusted data.
Once it is clean and defined, move that data from the “swamp” of messy data to a clean data lake where it can be preserved and stored – a trusted place where the data is ready and available for analysis.
As the data is structured, begin experimenting and using it in existing processes and conducting analysis to inform new business decisions. Start small and work up to bigger, more critical decisions based on the data available.
At the outset, avoid swinging for the fences. It takes time to understand the relationships in clean organized data, and at the beginning it’s best to avoid unnecessary spend and cycles by taking too many risks.
This is also where AI and ML come into play. Understood and defined data can be used to train different algorithms and help streamline or automate processes and innovations.
As your organization expands its data-driven initiatives, it is important to mind the unavoidable newly discovered gaps in data. As organizations define and organize data, they often discover they are discarding relevant or highly useful data in old processes.
Every organization I have worked with discovers they are throwing away valuable data out of ignorance or inexperience. In one case, while building a data lake we discovered that we were purchasing data from an external source for a few relevant fields but throwing all the remaining data away.
