Written by Kevin Smith, Cassie Lee |
In all the years I’ve been working with business intelligence, analytics, and insights, I’ve seen many trends come and go. I’ve also seen the way we think about and approach data evolve over time as well. Who are the analytics for? How are insights going to be delivered? And who’s going to be making decisions with those insights? The evolution from traditional analytics to embedded analytics is what has defined the three “waves” of analytics, the subject of a recent webinar we hosted along with analyst firm Aberdeen. But what exactly are we talking about when we refer to the three waves of analytics?
First Wave of Analytics: Experts With Expert-Level Tools
The first wave of analytics consisted of only data scientists with specialized analytical expertise.. Those experts would then use their expert-level tools to answer common questions such as, “What was total revenue for Q3?” However, finding the answer to any follow-up questions (What’s the breakdown of revenue by region? How does Q3 revenue compare to Q2, or maybe compared to Q3 from the previous year?) required going back to those same experts and asking them to conduct yet another analysis. Consequently, this process was tedious and required finding — and retaining — highly trained analytics experts. These data science experts were the only ones capable of finding answers to questions — which made them a bottleneck. Also, people often didn’t know what type of questions they could even ask, so they would ask very elementary questions with no idea how to delve deeper into the data and what it means. With the pace of business change rapidly accelerating, the stage was set for a second wave of analytics.
Second Wave of Analytics: Reporting Specialists with Power-User Tools
In the early 2000s, analytics transitioned from being the responsibility of a few experts, to select groups of power users who specialized in reporting and analytics. Here we see the rise of the data scientist. The tools had become less specialized and less complex, where the average analyst could use them on their own instead of relying on experts as they had before. However, the same problem soon resurfaced: Requests for custom insights were pouring in from other users within the company. Because the average business person didn’t have the strong technical background necessary to perform the analysis themselves, the reporting and analytics power users would analyze data and create reports for each unique inquiry, which was a time consuming procedure. Additionally, the person who requested the custom insights also oftentimes didn’t know what to do with the information they were presented with. The bottleneck had lessened a bit, but still remained an issue — there were still major delays when it came to getting actionable insights from the data at hand.
Third Wave of Analytics: Non-Technical Business Users at the Point of Work
We’re now in the third wave of analytics, which builds on from the lessons learned in the first two waves. In today’s world, all employees, not just the data science experts, need access to advanced analytics capabilities quickly and easily — no more bottlenecks. They need to get insights in near real-time, with context, that are relevant to the business process or problem at hand. When insights are embedded within business processes and deliver tailored insights to each user, it reduces the friction between the user and custom insights. With an embedded solution, there’s no need to learn a new tool, no real need to be trained, and no need to pick up new terms and language. Instead, non-technical business users are empowered to make decisions on their own and examine custom insights without the need to consult an expert Embedded insights — designed for the average business person — has enabled the third wave of analytics, making it much easier and faster to scale the use of analytics to multiple job roles, functions, and capabilities.
I had a wonderful time co-hosting this webinar, which presented a lot of valuable research regarding the third wave of analytics. We also had a lively Q&A session at the end where I answered a number of questions from the attendees. I’ll go over some of the highlights of that session in my next post.
Written by Kevin Smith, Cassie Lee |
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