Over the last three months we’ve seen an unprecedented amount of demand for projects that we broadly define as “data stack modernization.” These kinds of projects are not new, but the influx of interest we’ve seen recently is clearly an indicator that more and more enterprises are finding their current data & technology stacks, lacking, and out of tune with their future direction and needs.
So why now? What’s driving a mass reflection on current infrastructure versus future needs? We see three key motivations and developments driving the trend, two that are more fundamental, and one that is truly transformational and generational.
The Rise of Cloud Databases and Composable Architecture
The first key motivator is the rise of cloud databases and composable architecture. Enterprises have spent the last 10+ years moving from legacy on-prem databases to more nimble and flexible cloud-based architecture. As that’s happened, there’s been an accompanying advancement and proliferation of utilities built on top of these cloud-based environments. The composable architecture model allows enterprise to centralize the data that matters in one cloud-based hub, and then plug-in best-of-breed functionality on top. It eliminates the redundancies and the overhead of multiple databases, duplicate data storage, bills, etc., and addresses many of the privacy implications of moving data across entities as well. With the focus on cloud-based data hubs and composable functionality, enterprises are reevaluating their current sass tool sets – and the constructs of their data environments – to retool their systems for composable world.
Drive For Cost Savings
The second major driver of these evaluations is the current market focus on efficiency (i.e. cost savings). Technology and data leaders are looking with a more critical eye at licensed software platforms to identify redundancies. When they observe two or three platforms in their data stack all performing the same function, they are inclined to evaluate whether each of those platforms are, in fact, necessary.
Eliminating redundancies is a consistent theme in our tech and data stack modernization engagements. And while cost savings is a goal, it’s not always the primary goal. In fact, many organizations are more concerned with creating operational efficiency as they try to consolidate data fluency and proficiency across departments and teams. They want a common language and a common toolset, and stack consolidation facilitates that development.
The Rise of AI
Third, and lastly, the most fundamental change in the technology landscape in the last year has been a rise of AI, and the applications that AI-driven approaches will have on every aspect of the data and technology stack. Enterprises are looking seriously at how they can deploy AI tooling in their current environments, and what updates and enhancements they need to effect in the future to make AI applications a valuable part of their tool set. We are in the early days of evolution come for sure, but the potential is real, and getting serious attention among the customers with whom we interact.
Our informed opinion is that the influx of demand we’ve observed is just the beginning, and the compounding motivations of cloud-based, composable environments, the need for efficiencies, and AI-tooling and evolution will continue to drive “data stack modernization” demand for the foreseeable future.
