Note: I’ve had the eminent thought leader Esteban Kolsky, founder and managing principal of ThinkJar, doing guest posts before on this blog. Time and again, the guy simply nails what the core of contemporary thinking is and how to approach it.
This time, he goes to the heart of how the business world is evolving and what it takes to have a transformative success – and that means ecosystems and platforms.
This post is the first of two that he will have here. (Part two comes next week.) The idea for these posts grew out of research that Esteban just finished for Radius, a company that characterizes itself as providing Customer Data Platforms (CDP) for B2B revenue teams. This research inspired more than simply a post with market data; this is significant thinking on where data platforms are going in a world that has solved (more or less) big data.
Thanks, Paul, for letting me use your blog to spout on data and data platforms. I want to split the research I did in two posts (for easier consumption). First one (this one) on the evolution of data, and the second one (next one) on the evolution of data platforms.
There has been a lot of discussion recently on the “thought leadership interwebz” about what is the best way to aggregate data. We talk about data lakes, swamps, BI, MDM, CDP, and much, much more — but none of this provides a simple solution to the problem of how to optimize data use in a digitally transformed organization.
The problem has recently risen to the executive level, where I am having conversations about the differences between all of them. Where did all this problem start? Glad you asked.
By 2025, the volume of all data created will top 163ZB (zettabytes). Enterprises will experience a 50-fold increase in data they must manage. This is what we started using the last five to six years under the name of big data. As with all technology-only solutions, they quickly became “solutions” looking for problems to solve — not the solution to existing problems.
What is available today is focused on the sheer amount of data available (big data), and how to store it, rather than finding value from it. If we only wanted to process data, the big data movement would’ve been fine, but since we want more (actionable insights became the holy grail of data processing shortly after big data started, and the origin of digital transformation), we need to find different value propositions for that tidal wave of data.
In the last 10 years, we saw slow progress from simple, demographic data-in-storage to multi-dimensional data-in-use: We moved away from creating huge electronic storage areas for data, and we began to use it in real-time; unfortunately, most enterprise data today is still stored in disparate systems waiting to be processed. Value comes from aggregating the right data from myriad sources and using it efficiently and effectively to solve business quandaries and optimize processes — and to do that, we need to understand what the data shows, not just the data itself.
We don’t have a problem finding data, we can find more than we need. The problem comes down to appropriately using it.
Enterprises are beginning to understand the concept of data-driven, outcome-focused, customer-centric operations, and the need for digital transformation (ensuring that data flows easily and fluidly across the enterprise). Most of them have early strategies and operations in place.
The biggest problem remains understanding how data affects transactions and processes (what data is and how to use it to achieve intended business outcomes) and not being able to learn from past results. This is where the “gold in them hills” is — in using the lessons learned to engender continuous optimization, not just one-time improvements. The correlation between digital strategies and existing data is what necessitates data platforms, but first, we need to fix serious operational problems.
