At the start of any significant IT hype cycle, predictions abound. No matter what the trend, a seemingly endless array of vendors, experts and analysts alike line up to place their bets as to how this new movement will forever change the IT and business landscape.
Big data was no exception.
As an explosion of immense new unstructured datasets gave root to the big data hype cycle that now dominates so much of the ongoing IT conversation, scores of experts chimed in with opinions on the many ways in which this new megatrend would change the way companies do business.
While a handful of oft-predicted outcomes of the big data trend have in fact come to pass -- notably the emergence of analytics as a key IT spending priority, an increase in the prevalence of data-driven decision making at the executive level, and the rise of the CMO as a key power broker in the new data economy -- many of the most commonly predicted outcomes never come to fruition.
In some cases, not only have those outcomes failed to materialize, but what actually has occurred is the complete opposite of what was predicted. In many ways, it’s those unexpected outcomes that represent big data’s most significant impact on the IT landscape. Let’s take a closer look at four unexpected impacts of big data playing out today.
1. Revitalization of the Data Warehouse
Conventional wisdom at the start of the big data hype cycle was that the introduction of Hadoop and other modern unstructured databases would signal the beginning of the end for the traditional data warehouse. Companies were expected to significantly divest in what seemed like an aging relic compared to these emerging and more modern platform options. But rather than being the source of its demise, big data has actually helped breathe new life into aging data warehouses, driving new investment and spurring new uses cases in the process.
Rather than ushering traditional data warehouses out, organizations have instead focused on blending the old with the new, and as such, data warehouses are being revamped and revitalized, enabling organizations to lower TCO while achieving greater ROI. Companies are reexamining their data warehouse to become smarter about which portions need to be kept, and which portions can be released for archiving or jettisoned altogether.
New opportunities to replicate data warehouses to secondary structures in order to fuel business analytics are being explored. Many organizations have even taken a hybrid approach in which their data warehouse serves as the analytic sandbox, with Hadoop serving as more of an archive environment. Whatever the case, reports of the data warehouse’s demise were greatly exaggerated.
2. Resilience of Traditional Relational Databases
The data warehouse wasn’t supposed to be the only casualty of big data. The same fate was expected to befall traditional relational databases. Believing that the maturation of Hadoop and other NoSQL sources would spur widespread migration of data away from traditional systems, experts rushed to predict a major decline in license revenue for the big relational database vendors. Traditional databases were purported to have no place in a big data world.