Click to learn more about author Chris Lynch.
As the CEO of a technology startup and a recovering venture capitalist, I’ve developed a love/hate relationship with technology during the last few decades. I had the incredible opportunity to be at the forefront of change and explosive growth in the early networking days, was present for the birth of the web and its commercialization (post Al Gore’s invention), and am now an active participant in the mainstream use of data and analytics. While the earlier two experiences were what I consider foundational and enabling, analytics has the potential to create step function changes in every market, ecosystem, and society in general – a complete transformation of how we engage, interact, and develop.
One of the principal drivers in that change is the rise of apps (i.e., enterprise SaaS and mobile applications). Think about it: No matter what you want to do, where you want to go, who you would like to meet, there’s an app for that. What else comes along with the app, you ask? (Or maybe you haven’t asked yet, but I’m going to tell you anyway.) Data – and an intense desire to understand the why, what, how, and who is driving your engagement. The proliferation of apps isn’t limited to personal experience or engagement. They are at the heart of businesses of all types and sizes; the commoditization of infrastructure, compute, and storage has dramatically simplified the path to application commercialization.
This commoditization has also ushered in a new way of consuming software and business services via the cloud. It’s far easier to begin using business applications when you aren’t responsible for procuring infrastructure, spinning up machines, or installing software packages. While it’s created a far easier medium for businesses like ours (and many others) to run critical systems (CRM, accounting and general ledger, engineering management), it’s made the analysis of these processes and systems much more challenging.
The forces of digital transformation have been in overdrive this past year. As we begin to emerge from this pandemic, and organizations race to re-establish their operating cadence and standard operating procedures, modern SaaS applications will take an even more important role in operations. The massive trove of data they collect will represent enormous competitive advantage for those teams that figure out how to quickly leverage it.
Quickly leveraging it is the most important, yet somewhat elusive, aspect of amassing these stockpiles of data. In my experience, traditional ETL (extract, transform, and load) processes leveraged with legacy stacks directly inhibit doing anything quickly! Over the last decade or so, as the data platforms became more performant, ELT (extract, load, and transform) became more popular, as it reduced the time between generating the data in the source system and getting it into the analytic database for analysis. This was one of the concepts we employed at Vertica when I was the CEO, as it enabled us to capture significantly more data (increasing our revenues) while accelerating the time to insight. This was a step in the right direction for our customers, but still more closely aligned with the traditional approach to application development and data analysis. These are the boundaries driving organizations to the cloud, en masse. Time, simplicity, and ease of use/consumption are the focal points of creating a closed loop, data-driven culture = action -> evaluation -> calibration -> iteration. Long story short, the traditional approaches to Data Management and analysis are under siege.
While radically simplifying the adoption of powerful business processes and productivity tools, this “SaaS-ification of the enterprise” has created a need for a “connective tissue” between the various applications, and a common data language that can be used across an organization’s business application stack. The goal is to ease the burden on individuals looking to analyze this decentralized data, while maintaining consistency in data vocabulary. Ideally, individuals across the organization can strive to make smarter, data-driven decisions based on “analysis-ready” sets.
There is a window of opportunity over the next few years for organizations to embrace this data-driven disruption and charge ahead of their competition. The key is organizing around processes that will increase the velocity of data-driven business insights.