The 3 Waves of Analytics: From Experts to Business Users

January 17, 2019
Kevin Smith's picture
VP, Product Marketing
Kevin Smith is Vice President of Product Marketing for GoodData. Prior to GoodData, Kevin was responsible for delivering consulting services such as analytic product strategy, data monetization, and go-to-market services at NextWave Business Intelligence. He is the author of numerous ebooks, articles, and webinars on embedded analytics and building data products. In addition to NextWave, Kevin has held leadership positions heading analytics teams, designing SaaS products, and performance and managing product teams for both small start-ups and Fortune 500 companies such as SAP, ServiceSource, and Qwest Communications. Kevin holds a B.S. in Finance, as well as an M.B.A. in Quality/Process Management, both from the University of Maryland, College Park.

In 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 as well. Who are analytics for? How are insights going to be delivered? And who’s going to be making decisions with those insights? The evolution of 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 are we talking about when we refer to the three waves of analytics?

1. First Wave: Analytics performed by experts with expert tools

The first wave of analytics saw their use focused on experts, data scientists,  using expert-level tools. Those experts would then use their tools to answer common questions like “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 experts and asking them to conduct another analysis. Consequently, this process was slow and required finding—and keeping—highly trained analytics experts. These experts were the only ones capable of finding answers to questions and it made them a bottleneck. Also, people often didn’t know what questions they could even ask so would ask very elementary questions with no idea how to delve deeper into the data. With the pace of business change rapidly accelerating, the stage was set for the second wave of analytics.

2. Second Wave: Analytics performed by 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, less complex, and the average analyst could use them on their own instead of relying on experts as they had before. However, the same problem soon surfaced: Requests for custom insights were pouring in from other users within the company. Because the average business person didn’t have the technical background necessary to perform the analysis themselves, the reporting/analytics power users were spending a ton of time analyzing data and creating reports for each unique inquiry. However, the person who requested the insights also oftentimes didn’t know what to do with the information they were presented with. The bottleneck had lessened a bit, but there were still major delays when it came to getting insights from the data at hand.

3. Third Wave: Analytics performed by business users at the point of work

We’re now in the third wave of analytics, which builds on the lessons learned from the first two waves. In today’s world, all employees need access to advanced analytics capabilities quickly and easily—no more bottlenecks. They need to get insights in near real-time, in context, and 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. Embedded insights—designed for the average business person—has enabled the third wave of analytics, and it makes it much easier 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.