More organizations than ever before are putting the right data on the right platform for the right reason.
This new level of data efficiency greatly reduces – and, in the long run, may ultimately eliminate – the need to pull data in to a centralized source such as a data warehouse or analytic sandbox for the purpose of analytics.
Instead, given the distributed nature of connected devices and the explosive growth of IoT infrastructures, more organisations will look to execute analytics at the edge, and, as a result, the ability to push analytic capabilities to (and run them directly at) the source of data will become paramount. Applying a predictive model and running the analytics where the data lives eliminates the time, bandwidth and expense required to transport the data, enabling immediate action to be taken in response to the insight.
The growth of IoT, in particular, will spur this movement of analytics out to the edge. We now have the ability to harness and use IoT data at the speed of business in an economic way, such that in some instances, transporting that data back to a centralized core is both inefficient and untimely. The power of IoT ultimately lies with the ability to analyze data and move at the real-time speed of a specific workflow. Analytics at the edge makes that possible.
We are starting see a new breed of analytics users cropping up throughout organizations everywhere. Citizen data scientists — or every day, non-technical users — are going to play an even greater role in the analytics revolution as platforms will begin incorporating technologies and capabilities that help these users consume analytics in an easily digestible way.
This new wave of business-savvy users will also present challenges: Citizen data scientists will experience a learning curve in wrangling data, running the optimized analytic and presenting the outcome of those insights. They’ll also put the onus on vendors to deliver quick-start analytics template and reusable workflows. Once the learning curve is overcome and the right capabilities are delivered, citizen data scientists will be the driving force behind the use of analytics to drive innovation.
One could already argue that the ROI of advanced analytics is highest when applied to targeted, vertical market use cases. This will continue to be the case in 2016 and beyond, with manufacturing – particularly regulated manufacturing – leading the way.
Within regulated manufacturing, not only are there numerous processes that can greatly impact the precision and quality of a given production run, but outcomes often need to be validated and proven to meet the regulations of the industry being served. As such, advanced analytics platforms will be increasingly relied on not only to uncover insights that help optimize processes, but to verify and validate those insights in accordance with regulatory requirements.
So, for example, a pharmaceutical manufacturer might leverage advanced analytics to optimize the drug creation process and avoid a catastrophic batch loss, while also using advanced analytics tooling to confirm that its processes have been tested and validated as required by its governing regulatory body.