7 sure-fire ways to fail at data analytics

Bob Violino

This doesn't mean having to move all the data from their originating systems into a monolith, Nimeroff says. “Instead, we use one of the modern integration technologies to provide a unified view of the data while it rests in its current systems,” he says.


4. Eschew good data hygiene

If the data you’re analyzing is not accurate, up to date, well organized, etc., the value of the analytics can drop drastically.

"Garbage in, garbage out is a problem that is magnified by the volume and scope of raw business data,” Nimeroff says. “The best [data analytics] teams want quality to permeate. As such, building processes and leveraging technology that enforce quality standards is a winning combination.”

On the process side, ensuring repeatability of processes and then auditability of the results is important, Nimeroff says. On the technology side, deploying data quality tools including profiling, metadata management, cleansing, sourcing, and so on, help ensure better quality data, he says.

Organizations need to use tools to “clean out debris — incomplete and broken data — and massage data from different sources to make it compatible and comprehensible and to make it as easy as possible to analyze,” Tavakoli says. “Make the data as self-describing as possible so all members of the team understand the meaning of the various bits of data.”

High quality data “is the key fuel for generating useful insights,” says TP Miglani, CEO at Incedo, a technology services firm. “You need to build data warehouses and data lakes to bring in the structured and unstructured pieces of data together. Successful [organizations] make sure they improve quality of the data with cleaning, computing missing values, [and] labeling it accurately.”

Good data hygiene also means keeping data as current as possible. The data needs to be fresh and the “data universe” constantly expanding for companies to draw value from analytics, Nimeroff says.

“Data freshness requires having an understanding of the timeliness of your current data acquisition processes,” Nimeroff says. “Obviously, the more real-time a system is, the better the freshness. Freshness can also be supported by using third-party services to augment your existing technology and processes.”


5. Forgo executive sponsorship of analytics initiatives

As with any other type of major IT project, not having the blessings of senior executive leadership on data analytics projects can be a detriment to success.

“The objective of analytics teams is to generate insights by connecting the data with a company’s tactical and strategic decisions,” Miglani says. “One example of failure would be if a data science team did great data analysis, developed accurate predictive models, but the results were not implemented because it required changes in organization and culture.”

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