If you're involved in the world of businesses intelligence in any way, then you've most likely noticed the rise in prominence of embedded analytics over the past few years.
Sitting within the broader umbrella of business intelligence, embedded analytics systems allow businesses to integrate and visualize reports, dashboards, and third-party analytics services like never before. Being able to collate key information from various sources into a single platform is a huge benefit to BI professionals looking to streamline processes and deliver unique insights as quickly as possible.
Yet despite a growing understanding of the advantages of embedded analytics, we're also seeing the same misconceptions crop up again and again regarding its general function and application. That's why we've put together this handy guide to delve a little deeper into these myths and hopefully shine a stronger light on the reality.
Myth 1: All Data Roles Are Created Equal
You're starting to get a handle on your data sources and decide that it's worth bringing a skilled data professional on board to help run that side of the show. The next logical step is to decide which type of person and which particular skillset you need to fill the gap.
Here's where a lot of employers take a wrong turn. It's easy to conclude that there's little difference in specific data roles — a data engineer and a data scientist, for example — and the accompanying skillsets they each possess. This couldn't be further from the truth. For instance, an engineer is far more geared toward extracting raw data as well as constructing and maintaining an effective structure to do so. A data scientist, on the other hand, is much more concerned with turning raw data into insights and insights into action.
It might seem like splitting hairs, but specialization is absolutely essential if you're going to mine and translate key data points into unique insights that benefit the business.
Myth 2: Personalization Isn't That Important
The ability to personalize and customize BI dashboards might seem like an unnecessary extra, but it's actually incredibly helpful and vital to maintain consistency and branding at every turn. By making good use of white label analytics, businesses can keep a tight handle on the look, feel, and color palette of their reports and platforms, making information easily relatable and digestible for every stakeholder involved in a project.
BI specialists such as Sisense now offer scalable white label solutions as standard with their embedded analytics systems. With this advanced yet easy-to-implement technology, companies ultimately save themselves a lot of time, effort, and investment compared with the task of building their own branded data architecture from scratch.
Myth 3: Reliance On BI Puts Human Jobs At Risk
Perhaps the most common and long-standing myth across various industries is that advances in technology will soon render many human jobs obsolete in the not-too-distant future. It's a belief that seems to stem from too many Hollywood blockbusters about AI and robots turned evil.
Fortunately for us, this isn't quite the case in the real world. It's true that top companies across the board are investing more and more in embedded analytics systems, yet that in itself has created a huge increase in demand for skilled BI professionals. Certain job roles within certain industries will change, of course, which means there's going to be an even greater need to retrain and upskill employees over the course of the next decade. Even so, by combining human know-how with BI capabilities, businesses stand themselves the best chance of streamlining operations, providing a better service for customers and delivering growth.
We hope you enjoyed this quick guide, and the next time somebody mentions one of these embedded analytics myths, you know exactly where to send them.
