“We have data – why can’t we use it?” To answer that question, I would say, “What do you want to do with your data?”
Lately, whomever I talk to answers with a general desire for their data – to gain insights or to make data-driven decisions – and then they launch into an explanation of the tools they’ve invested in that should help make it all happen. Unfortunately, no matter what tool they add, they still only “have data.” Using it is off the table.
In fact, I hear some variation of “we have data – why can’t we use it?” everywhere I go. When I pull that question apart, it’s the “have” that trips me up every time. Having data is the old paradigm. An organization collects data as it becomes attached to the tools and services the organization uses. In a desperate bid to unlock the true meaning of that data, they add more tools and services. This only makes the problem worse. Organizations have data, and they continue to “have” it until having it is meaningless.
It is essential to think about data differently – a new paradigm for approaching data. Let’s explore this shift in data thinking.
Companies Are Trapped in an Unsustainable Loop Trying to Be Their Own SI
The volume and frequency of data requires companies to upgrade tools and applications frequently. Adding legacy systems to this mix, companies face a complex ecosystem that requires massaging and problem-solving to work. Instead of extracting insights, they waste an extraordinary amount of time simply trying to establish communication between their tools. In fact, according to IDC
- 95% of organizations are integrating data across hybrid and multi-cloud environments.
- 77% of organizations are integrating up to five different types of data in pipelines.
- 65% of organizations are using at least 10 different data engineering and intelligence tools to integrate data coming from or going to up to 10 different Data Management technologies.
Thanks to this increasingly complex data environment, companies cannot scale their data solutions – they can’t even keep up with what they have now. The Bureau of Labor Statistics predicts the ever-growing demand for data scientists will continue to increase by 28% over the next few years as companies continue to create technical debt that they won’t be able to fix.
Organizations Can’t Just “Have” Data and Think That’s All
The products and concepts that make up these complex data environments met the needs of businesses at that specific moment in time, providing some insight into the organization’s beliefs about data. Whether the data solutions were right or not isn’t the point. These static products must evolve as we understand more.
Think about it like this.
For hundreds of years, medicine understood the importance of blood in the body but completely missed how it worked. So, centuries of medicine treated blood as something bodies “have” but in a balance of four humors. An imbalance in these humors caused any number of ailments, and the treatment was bloodletting, the removal of blood to allow the body to function harmoniously (i.e., correctly) once again.
We know now that having blood isn’t the point. The continuous movement of blood throughout the body’s organs and parts carries critical components that allow us to live. We don’t just have blood; it’s constantly in motion, performing its fundamental job of keeping our bodies full of the things we need. When we lose too much blood, it’s not the loss of blood itself that kills us. It’s the lack of fundamental building blocks carried by the blood.
Data is the same. Having it is not the point, but we’ve artificially divided data up like those four humors and missed the fundamental context of what data should be to our organization. Because of this old way of thinking, we’ve locked it up in silos, invested in tools that allow small siphons of data to power the company’s decisions, and blocked it from doing the job it’s truly meant to do.
No wonder we can’t use the data we have.
Don’t Forget: Data Is an Organization’s Core IP
Data is more than columns and values. It’s all this plus the knowledge around it – provenance, quality, who uses it and why, etc. It’s the associated business logic and governance parameters. It’s even the decisions currently being made with the data, however accurate or imperfect.
All of this information is buried in the data infrastructure. When organizations continue to approach data as something to have and they leave out the core context, it means that someone else owns their core IP. Even in cases where data isn’t inextricably tied up in vendor lock-ins, it’s still far too attached to the infrastructure and tools. Companies become too tool-centric for data to flow freely and provide what the business needs to thrive.
With a product- or tool-centric strategy like this, it’s no surprise that enterprises struggle to operationalize data. They have it, but they’re too locked in to use it the way data was meant to be used. Instead of investing in yet another product, they need a decision-based solution.
The new paradigm decouples a business’s core IP – its data – from the underlying infrastructure. The Data Management space must be redefined by facilitating data availability in a fully governed manner, allowing the entire organization to infuse data into every process and collaborate fully based on data-driven methods.
Instead of worrying about data’s “balance of humors,” organizations should be free to bring any tool they need to the table, not as a final life-changing solution, but as a flexible and composable piece of the infrastructure. When the tool outlives its usefulness, companies can shift to something new without losing the inherent context associated with that tool.
Organizations Need a Decision Solution, Not Another Point Solution
The takeaway is this: Product- and tool-centric approaches to data are neither scalable nor sustainable. Companies need a decision product, a data operating system, instead of hoping for a single data product to solve their entire infrastructure challenge. A data operating system unites their existing tools, platforms, and services while providing native governance and core observability across the data lifecycle.
It operationalizes data and prioritizes it, not just to have but to use. Now, data is the lifeblood of the organization, one that’s fully understood and integrated into the enterprise as a whole. Until companies can take this approach, they’ll never have control over their own data or understand its real purpose.
It’s time to take a new approach and allow data to connect each part of the enterprise and activate insights in real time. The old, fragmented approach to data will lead only to frustration. This new paradigm ensures the real value of data. We no longer “have” data – we use it.