Agile businesses embrace self-serve analytics
In an ever-accelerating information age, the companies most likely to succeed are the ones that not only glean the most profitable insights from their data, but do it faster and more nimbly than their competitors. In fact, according to ESG, companies who have successfully transformed their internal data cultures operate with 18x greater confidence: 72% versus 4% report their company almost always makes better and faster data-driven decisions.
Not surprisingly, vendors are increasingly developing self-service analytics products that allow business users to directly access and utilize data that’s aggregated from a range of sources, without requiring them to have a background in technology. When every department is able to apply their own unique expertise to BI and make faster, more reliable data-driven decisions, organizations can reach a whole new level of agility and competitiveness.
But most companies can’t optimize self-service analytics because of the 3 D’s (distributed, dynamic and diverse) of data
Unfortunately, many companies today are unable to implement a self-service analytics culture despite the products available. This is because of the fragmented state of their data. They have many different types of data in many different formats, scattered across multiple and disparate systems and servers. Some data is in the cloud, some is in on-premises servers, and it is often in varying formats and governed by different policies and security practices. Under these circumstances, it is difficult to locate, access, and integrate data for analysis. And if you have incomplete data, or if it’s out of date, the results of your analysis could be unreliable.
Furthermore, most enterprise-level companies have already invested a considerable amount of money in a number of different BI tools. For example, one department might use Tableau while another prefers Microsoft Power BI or Excel. Different BI tools use a range of query languages and display data in slightly different ways. When data with incongruent definitions are combined without being normalized, costly errors in analysis can occur, even when the underlying data is the same.