When organizations seek the benefits of a data-driven culture, they require more efficient approaches to uncovering answers and insights. Self-service analytics can help address that need for speedy understanding. Self-service analytics provides data access to more people within a company—along with the autonomy to explore connections between disparate data sources. But that doesn’t mean self-service is a completely do-it-yourself data experience. Instead, it requires new ways of collaborating with IT, to help ensure accuracy and security don't suffer in the pursuit of efficiency.
“Self-service done right is about digitalizing the workplace. Data needs to be fully accessible to a wide audience and customizable to any context, but at the same time it has to meet quality standards and be protected against fraudulent use,” says Jean-Michel Franco, director of product marketing for Talend. "Self-service enables a win-win situation wherein workers can do their jobs more efficiently while IT can still have governance over the data.”
Today's business landscape changes rapidly and companies need to stay nimble. Self-service analytics can help in that regard, as Yahoo discovered. As we explore in detail in our new report Self-Service Analytics, some of Yahoo’s more complex reports used to take several months to complete because those requests were sent to the company’s central reporting team. But then the company switched to a self-service model that focused on ease-of-use and found that employees could generate their own customized reports in seconds.
That’s just one of the cases covered in the report, but it also reveals how several other organizations have maximized the benefits of a self-service approach. Their experiences highlight the importance of metadata, ideas for creating a culture of data literacy, and ways to ensure employees have the appropriate analytics tools available.
When workers have more autonomy to explore data and delve deeper into the realm of “what if,” organizations reap vital insights.But more freedom to create customized analysis doesn’t mean a free-for-all. That’s why a thorough assessment of data governance should precede any foray into self-service analytics. Most organizations find they have plenty of room for improvement in data governance. It’s better to learn that before employees dive into oceans of data, because self-service environments can create significant risks in terms of report accuracy, as Kurt Schlegel, research vice president at Gartner, advises. One approach he recommends is to build a cultural understanding that ad hoc reports and analyses are not meant to be used as systems-of-record to run the business. This means enabling users to create their own reports and share them in public folders, but only if they share a fundamental understanding about the data quality. Until the reports go through a rigorous validation process (usually carried out by the central BI team), they should treat the results as preliminary.
Download the free O’Reilly report Self-Service Analytics: Making the Most of Data Access for an overview and case studies on instituting self-service analytics in several different types of organizations.
This post is part of a collaboration between O’Reilly and Talend. See our statement of editorial independence.