The Common Pitfall When Buying Data Analytics Technologies
It’s an exciting time to be a part of the data and analytics community. The pace of innovation seems only to be increasing as analytics unlocks new value for organizations, who then pour that value back into funding or purchasing more cutting-edge technologies. Yet amid any boom there are always a few regrettable investments made.
Among Chief Data Officers, VPs of Analytics, and other purchasers of new analytics tools, we’ve seen a common error that usually causes headaches for the business down the road. The understandable desire is to keep up with the pace of innovation and equip data architectures to handle streaming, graph, location, machine learning, and emergent analytics that may be advantageous. However, the more one-off, specialized tools an organization tries to incorporate, the more the cost and complexity of its analytics operation will rise. While working to modernize, many a business leader has ended up way over budget and with more troubleshooters than they’d ever dreamed they’d need.
Evaluating advanced technologies against an organization’s future needs is of course no simple task. But one approach that can help cut costs and reduce complexity is to consolidate various analytics workloads into fewer technologies wherever possible. At Kinetica, we’ve seen great traction with customers who like that they can integrate their analytics into the single Kinetica platform. This not only reduces their total cost of ownership across their stack, but also decreases their time-to-value for new analytics applications they’d like to build, because they can perform various workloads on a single copy of the data, without having to manage complex pipelines to move data around.
Interested in reading more about how Kinetica helps address the common headaches of business leaders? Check out the links below:
Why a Streaming Data Warehouse?
Andrew Wooler is global marketing manager at Kinetica.
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