When it comes to scaling analytics at a large organization, a lot of focus has been placed historically on successfully creating clean, consistent data that feeds well-defined, consistent analytical processes. This focus was necessary and valuable. However, as data governance continues to mature and data literacy comes evermore to the forefront, it is time to ensure that focus is also being placed on the other side of the coin: Namely, the consistent and accurate usage and application of data and analytics within an organization. In this blog, I will explain this distinction and why it is important.
Major Focus: Consistent Data and Analytical Processes
Without good, clean data, there is no point in worrying about what analytics might be created or how they are used. A focus on acquiring, cleaning, and standardizing data is a natural and needed first step in the journey to being a data-driven organization. For years now, the pursuit of a “single source of truth” has been commonplace. It is just as common today in the era of cloud as it was back when data warehousing was the dominant approach.
While no organization ever truly achieves the always-elusive goal of a literal “single source of truth,” many organizations can get far enough along to enable powerful and wide-ranging analytical processes to be built and utilized. Years of work to identify, capture, and standardize data means that, today, the state of data is probably as positive as it has ever been for many organizations. However, even if an organization manages to achieve the nirvana of having a true single source of the truth, there is still an additional battle to fight. Let’s look at that next.
Often Shortchanged: Consistent and Accurate Usage of Data and Analytical Processes
So much energy is put into getting data into good shape that the usage of that data is often not given the attention it deserves – and sometimes it is nearly neglected. It is important to remember that having clean, consistent data will not matter if people make use of that data in inconsistent ways or if they have differing interpretations of the analytics created with the data.
You can’t have different parts of the organization computing metrics in different ways; building models inconsistently from one another; and running the business using entirely different analytical philosophies. This gets to the heart of why data literacy is such a focus today. As I wrote about not long ago, current struggles with data literacy are only possible due to the availability of so much more data and analytics than ever before. Given that achievement, it is now time to ensure the consistent understanding and usage of that data and the analytics based upon it.
The core theme to understand from this blog is that there can be no mistake: Misusing clean data and analytics is just as destructive as trying to properly use bad data and analytics. This second side of the coin must also be given significant attention, or the other efforts are for naught. Strong governance protocols come into play here, another area getting much recent attention.
The Real Endgame: Better Decisions
Many organizations misunderstand what their goal really should be. Clean data and consistent analytics, as well as the consistent usage of them, are means to an end, not the end. The real endgame is to have people across the organization trust the data (and the analytics generated from it) and then consistently make faster, better, more consistent business decisions based upon them.
Without the data in place and consistent approaches to analyzing it, that trust won’t exist, and those better decisions won’t materialize. Similarly, without proper and consistent usage of the data and analytics, decisions will not improve materially, and scale will not be achieved. However, even when both sides of the coin are well-addressed, the actions of the business determine whether an organization will scale the impact of its data and analytics capabilities.
As you focus on both sides of the coin, never forget that the real endgame is to change behaviors and decisions. Getting your data and analytics house in order and ensuring a strong level of literacy are necessary precursors to achieving that ultimate goal. However, the final mile must be traveled by the business partners who are the recipients of the properly governed fruits of your labor.
Originally published by the International Institute for Analytics