Written by Roman Stanek |
If you’re new to analytics and business intelligence, it can be difficult to keep track of all the terms that are flying around: BI, cloud BI, embedded BI, embedded analytics, just to name a few. Because there are so many terms, it can also be tempting to use them interchangeably, when there are actually enormous differences between each of these technologies. So let’s start with the basics. What is BI, and what is embedded analytics?
What is BI?
BI is a broad term that refers to the applications, infrastructure, and tools that make the analysis of complex data possible, usually for the purpose of improving business processes and performance. You’re likely already familiar with BI in some capacity; maybe your company uses dashboards and reports to display information about sales data, for example. The key differentiator here is that traditional BI creates dashboards and reports that are then housed in a different program than the one you’d be using to move through your daily workflows. To continue that sales example, maybe a salesperson is generating a list of potential customers to target, but they navigate to the dashboard to find out which regions or industries have historically proven to be most profitable.
While BI was groundbreaking when it was first introduced—and it’s certainly a big step up from not using any form of analytics at all—it’s clear there’s still room for improvement. In fact, I’ve even compared traditional BI to pagers, which have obviously fallen out of favor as more advanced technology like smartphones enter the market. Unlike pagers, however, traditional BI is still widely used, even though better technologies are available.
What is embedded analytics?
One of those better technologies is embedded analytics, which builds on the groundwork laid by traditional BI. Embedded analytics still collects and analyzes data that is turned into dashboards and reports, but those dashboards and reports are now embedded into the program you’re using instead of a separate one. Where you’d previously have to disrupt your workflow to find what you were looking for, the process is now a lot more seamless.
Over the years, I’ve talked a lot about the value that embedded analytics delivers to the end user, like an improved experience and contextual information that makes decision making easier. For a long time, embedded analytics represented the peak of what it was possible to do with analytics, but lately it seems to be losing its luster. First, when it comes down to it, all embedded analytics has done is to change the location of certain dashboards and reports, while the data and information that the platform provides remain the same.
Second, part of the problem comes down to the meaning of the term “embedded analytics” becoming diluted as more and more providers and platforms pop up. It seems that, in an attempt to capitalize on what’s viewed as a tech buzzword, every analytics provider offers an “embedded” solution, but what is actually embedded varies widely by provider and can cover any numbers of solutions. Is the term referring to a widget, a static dashboard, reports, or something else entirely? That “something else entirely” is where the future lies, in a new field referred to as insights delivery. But what is insights delivery, and how does it differ from embedded analytics? I’ll dive into that in my next blog post.
Written by Roman Stanek |