Why do most self-service analytics tools fall flat?


"Self-service" was long considered the holy grail of data and analytics. It's quite a compelling proposition: instead of needing technologists to build out individual reports and dashboards to answer the questions the business is asking, why not just structure the data in a manner that lines up with how the business works?, Then,and then give analysts simple and intuitive tools to find what they need on their own.

Why not? Because this is all much easier said than done. Real-world business data is voluminous and complex, and the tools to harness that complexity for years were largely either too simple to consistently find the answers sought or too complex to be leveraged by any but the most technological-adept analysts.

In recent years, the tools for business users have come to maturity; but there areeven more complex and voluminous data than ever before! The result for many organizations is that analysts are cranking out professional-looking reports in their self-service tools that  in some circumstances may be unintentionally meaningless or misleading. (This isn't conjecture--Gartner has estimated that blah blah % of self service projects fail due to inadequate data governance).

PMsquare has seen the market evolve and has figured out established a set of methodologies to enable data self-service and cutting-edge data discovery aided by AI without sacrificing data quality. We'd love to discuss how to bring this to reality, enabling you and your team to bask in the glory of finally bringing self-service analytics to life for your organazation.

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