Data Platform Modernization
The pressure to leverage data as a business asset has never been stronger. Companies today are busy formulating data strategies that position their teams with capabilities able to create new market/product offerings and to create competitive advantages relative to their peers. The world is moving faster and operationalizing data platforms with actionable analytics solutions is a critical competency. The reality is companies are still bound by prior investments in technology, in-house skill sets, and available budgets but these must be overcome, using a thoughtful and pragmatic approach, to avoid being left behind.
Over the past few years it appeared as though open source-based big data computing frameworks like Hadoop and Spark were the way to go. Enterprise architects found them intriguing given their ease of deployment, scalability and cost efficiency but these frameworks have proven to be somewhat lacking in the administrative capabilities and true implementation success stories have been hard to identify. While all these big data/advanced analytics frameworks were being hyper-glamorized, “legacy” enterprise data warehouse (EDW) assets- many around for decades, have quietly continued to deliver enterprise analytics solutions, and associated business value.
However, the reality is that any data warehouse platform built 10+ years ago requires modernization, or at the very least an augmentation of a more operational/business agile data platform. Recent advances in high volume storage methods, licensing methods, and segregated compute/scalability issues, market leading platforms like Snowflake Computing, have addressed many of the most critical challenges. Organizations must take actionable steps to better understand the changing landscape for data platforming and consider strategies that combine, or replace, traditional EDW platforms with operational, and often business-team created, data lakes and cloud data platforms.