I hope we can all agree that investments in data capabilities and solutions must deliver business value. Maybe this is stating the obvious, but it is not always clear to me that major corporations have clearly articulated their business goals and expected outcomes as it relates to investments in data capabilities beforethey make these investments.
How many times have I met with data and technology leaders and listened to enthusiastic endorsements of the data architectures and platforms they have implemented, only to meet with business leaders who profess that they just don’t trust the data they are seeing – the quality of the data is questionable, the timeliness of the data is inadequate, the relevance of the data to the business questions that are most critical is questionable?
This may seem like a caricature of the current state of data in the business world today, but from my experience, exaggerated or not, there is truth to be found in this dynamic. Most organizations continue to struggle to manifest the holy grail of becoming data-driven – just 26.5% of leading companies identify as being data-driven organizations.
There is no question that efforts to capture, sanitize, organize, and analyze data require committed effort and expertise. Companies have been working hard at this for decades, with evidently mixed results – even though data is an asset that flows through any business from production to consumption, just 39.7% of companies say they are managing data as a business asset.
So, it is against this backdrop of an ongoing struggle to gain value from data, and to ensure that investments in data solutions, tools, and capabilities translate into some form of measurable business value, that we witness ongoing perplexity as it relates to how to select the right tools and technologies in ways that deliver plain old-fashioned business value.
I recall the story from many years ago of a data executive who met with the corporate CEO to request a multi-million-dollar investment “to build a corporate MDM capability.” The response of the CEO was to deny the investment, lecturing the data executive that until they could articulate the need in terms of business value and benefits, rather than “technology jargon”, no investment would be forthcoming. When the data executive related this story to me, I asked with a straight face whether MDM meant master data management or metadata management. The data executive looked at me like I had two heads. I asked because I wasn’t really sure.
This brings me to the state of data tools and technologies today. We all know that data continues to proliferate, in both quantity and form. We also know that the evolution of computing power has enabled companies to organize and analyze data to an extent that might have been unimaginable just a couple decades ago. There are so many wonderful new data tools and capabilities available today, bringing value to data leaders and enabling the emergence of “citizen data analysts”.
In spite of this hard-won progress, the proliferation of technical data jargon continues to frustrate and sometimes antagonize business leaders, as well as sow confusion among data leaders. Presumably, data literacy and data democratization must be good. How could they not be? Clearly, we want data to be healthy, and it would be very helpful if we could understand what data it is that we have in our possession for purposes of analysis by having an accurate data catalog. It would make sense that we have rules, policies, standards, practices, and procedures to manage data, so it would be helpful to have some form of data governance, though presumably not data fascism.
