Data quality will determine success with self-service analytics

As organizations look to provide data analytics to more users, self-service capabilities are becoming more important—and data quality will influence success.

“There is a growing demand to disintermediate the middleman between the data ‘action taker’ and the data,” says William McKnight, president of McKnight Consulting Group. “This demand is exponential in progressive, achieving organizations.”

The main driver is “keeping a train of thought alive long enough to get to action,” McKnight contends. “If the path to a solid action is fraught with IT delay, the action will not be taken. Or, assuming the data is there, will be taken without access to it. When competing on analytics, an organization absolutely must adopt self-service analytics.”

William McKnight horizontal.jpgDepending on the level of data science in the organization, a company that leverages self-service analytics can expect to see an infusion of intelligence into its operations and decision-making processes, McKnight says. “True intelligence in the form of analytics takes a company closer to its goals.”

If self-service is provided to data of poor quality, poor-performing data or hard-to-understand data, however, self-service analytics can actually bring more harm than good to an organization.

“Some users, if they feel like they’re suddenly ‘on their own’ after years of a different arrangement, could actually go backwards in their use of data,” McKnight believes. “Self-service analytics is a program. It’s not suddenly dropping a responsibility. It’s understanding [that] the responsibility all along was to get data to users and taking processes closer to that ideal.”