by Rick Skriletz –
Businesses are inundated with data but have less success turning it into information quickly. To become data-driven requires managing enterprise data as an asset and reducing the time needed to collect and analyze it so it approaches zero.
You Must Deal with the Six Degrees of Separation that Keep You from Data
“In this world of so-called digital transformation and cloud computing that drives our always-on über-connected lifestyles, surely it would be useful to understand the what, when, where and why of data on our journey to then starting to appreciate the how factor.” (From Forbes’ The 13 Types of Data)
Companies are working to become data driven. Being data-driven means operational actions are “determined by or dependent on the collection or analysis of data.” The challenge is the dependency on “collection or analysis of data” – it becomes a time-consuming effort to get data needed and analyze it. As the business world rapidly becomes real time, the time available for data collection and analysis continues to shrink.
In a digital business, the value of data correlates with the speed with which it is put to use. Making data into a business asset requires rethinking enterprise data and working to get value from it. Being data-driven means data needs must be identified and data collected, analyzed, utilized and, to meet the demands of digital business, processed in real time.
What Is Meant by ‘Enterprise’ Data?
At a simplistic level, enterprise data is all the data in an enterprise. After all, data is used in applications to keep a business operating. While this is true, enterprise data is often thought of as “data that is shared by the users of an organization, generally across departments and/or geographic regions.” Shared data makes enterprise data different than application and user data but doesn’t address the need for enterprise data to be correct and consistent everywhere it is used in a digital, real-time business.
The path to becoming data-driven requires working through the accumulation of data practices, including data marts, enterprise data warehouses, data virtualization, Big Data environments, analytic platforms, data fabrics, data meshes, and more. These trace the evolution of data in business and, while each approach has a kernel of validity, that evolution has created a hodge-podge of legacy data “solutions” that continue to coexist in companies today.
It is necessary to consider these technical approaches as we look at the evolution of data and its use, and what is needed to overcome factors that separate data from its use.