Much has been made about the value of data, but data is really only useful insofar as it can power some decision, process, or application. The problem is that legacy data analytics architectures have become so convoluted that they’re preventing organizations from taking all of their data into account. Harvard Business Review studies have shown that on average, an organization uses less than half of its structured data to make decisions — with less than 1% of unstructured data considered at all.
Analyzing your entire collection of data demands both scalability and simplicity from your architecture. You must be able to perform unified, petabyte-scale analysis across your enterprise and its many different data streams, while also ensuring you reduce the need for copying data or moving it around, by conducting all your crucial analytical operations within one technology. Implementing a Streaming Data Warehouse into your stack can help achieve scalability, simplicity, and also performance gains, because you won’t be dealing with the network data transfers and extra computational cycles that come along with bloated analytics stacks.
Andrew Wooler is global marketing manager at Kinetica.