Take a minute to increase awareness and understanding of the terminology, context, and application of data and analytics.
Part two of a series.
I really don’t want you to be data-driven. This statement is probably not something you would expect from me, a data and analytics professional. It may even be blasphemous in some circles. And yet, this is an accurate statement.
The Challenges of a Data-Driven Mindset
In this day of big data hype, there are a lot of companies investing in data scientists and data analysts (which is a wonderful thing). In their pursuit of being data-driven, they also invest in data lakes, new open source storage capabilities like Hadoop, and a whole array of new and changing tools. Again, not necessarily bad things at all.
The challenge comes when the goal of being data-driven results in the exclusive focus of driving data into a “Data Lake” or some other central repository. Business intelligence pursuits and other analytic efforts are often temporarily halted so that resources can focus on filling the new repository with data.
After several months, however, picture this scenario:
- The business continues to hear about the new data lake without seeing business results.
- Data scientists appear busy, but they seem unaware of business priorities.
- Teams become frustrated, budgets vanish, and everyone wonders what happened.
- The goal of being “data-driven” has lost its luster. Many wonder if being data-driven even makes sense.
If this is your situation, there’s hope. Being data-driven can result in a ton of business value if done correctly.
How To Be Data-Driven
Perhaps my earlier statement should read: “I really don’t want you to be data-driven UNTIL you understand the business priorities and metrics you want to impact.”
In other words, before moving all that data into a lake (or into any other kind of central repository), define the scope of data that matters:
- Frame the business context so that you and your data scientists can target the right data and put the effort in the right place.
- Help data scientists understand the context of the problem.
- Determine the potential impact on business processes or skills to understand if the ideal outcomes are actionable.
This approach helps define small steps and iterate through problems in pieces. This way, you can adapt to data insights and potential solutions as you find them. You can also quickly uncover and address data quality and cleansing challenges.
Because the work is completed in smaller chunks, problem resolution is handled in pieces and is not as overwhelming, so value is recognized much earlier in the effort.
As value is recognized, the entire team becomes more energized, driving better decisions and better data. And data-driven becomes a value-driven mantra across the business.
Seek to understand business priorities and metrics first so you can set the scope for the data you need. With this approach, you can work iteratively and focus on the business challenges most pertinent to the organization.
Not only is there is no need to wait for “all of the data,” there is no need to waste time moving or analyzing data that simply doesn’t impact your business. Instead, your business-driven focus helps you address the right business problem with the right data, rather than simply filling a data lake.
So, next time someone tells you that you should be data-driven, you may want to clarify.
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