The term “Self-Service Analytics” gets thrown around a lot these days and for good reason. It’s what a driven-driven organization strives for, and at InterWorks, it’s the goal we help our clients achieve. I don’t know if there is a specific definition for self-service analytics, but I tend to think of it this way:
In writing, that translates to an organization’s people intrinsically using data to answer their questions and inform their decisions. It sounds simple, but a lot goes into reaching that point. Self-service analytics is a concept. You can’t buy a software and install self-service analytics. The right tools and data architecture are critical, but they only get you so far — you need to address the human elements too. Establishing a culture, providing education, earning trust and building confidence are just as vital to self-service analytics as a warehouse or analytics platform.
The reality is that everyone faces obstacles on their path to self-service analytics. Those obstacles tend to look a little different from place to place, but we’ve found they often stem from a handful of core challenges. As you probably know, many InterWorkers previously worked in the industries we serve. Our team is a global collection of data geeks from industries like education, finance, restaurants, retail, hospitality, big tech, biotech and many, many more. Over the next several weeks, we’ll take advantage of that institutional knowledge as our experts describe how the biggest challenges to self-service analytics manifest in different situations and offer guidance for overcoming them. To kick things off, we’d love to hear from you, the reader, about the core challenges to self-service analytics that you’ve experienced. If you’d like to help us out, fill in the brief survey below, and we’ll tailor this blog series to you, and all the other readers of the InterWorks blog:
