Non-data scientists may not go beyond a quick analysis that confirms their suspicions. its ability to make ad hoc reporting both easier and more accessible for employees) can increase the risk of poor-quality reports. In " Ad Hoc Analysis: Business Intelligence's Fast Food Problem," we note that the primary attraction of self-service BI (i.e. Democratizing analytics capabilities sounds wonderful until the organization realizes that everyone requires a license to do their own BI - individual departments purchasing different systems hurt the larger organization when it can't take advantage of larger bulk discounts. Like other services falling under the umbrella of shadow IT, self-service BI is not necessarily the most efficient option, especially when departments independently choose different BI vendors. Good BI requires solid data engineering and experience in properly interpreting results, something an expert could accomplish with sub-par tools if required but beyond the skills of the average self-service business user. From this viewpoint, self-service BI is not bad per se, but it might lull companies into a false sense of security. In " Self-Service BI: Barriers, Benefits, and Best Practices," Jim Gallo points out that the original driver of self-service BI was a desire by IT departments to free themselves from effectively doing ad hoc grunt work for different business units. Self-Service BI: Barriers, Benefits, and Best Practicesģ Best Practices for Becoming More Self-Sufficient with Self-Serviceĥ Rules for Successful Self-Service Analytics Most articles focus on the benefits of self-service BI without looking at potential downsides that companies need to watch out for, including: Self-service BI is a powerful tool, but like all tools, it can be misused, especially by people lacking proper training. Here are five considerations to keep in mind about a self-service BI program. Self-service BI promises data democratization and faster data-based decisions, but it's not without limitations.
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