Q&A: Third Wave of Analytics

January 24, 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.

Over the years, those of us who are deeply involved in the world of big data have picked up the terminology, history, and general knowledge that are unique to our industry. However, the terms used every day at companies like GoodData may not be so clearly understood by others, who may have only recently begun learning about analytics or embedded analytics. In a recent webinar that we held with Michael Lock—SVP of Research, Aberdeen—we discussed the third wave of analytics and embedded analytics. At the end, we held a Q&A session with the participants. I thought I’d share some of what we discussed, which can be helpful for anyone looking to cement their understanding of key analytics terms.

What do we mean by “embedded analytics”?

First, let’s define what we mean by “traditional” or “standalone” analytics. These are the tools that you run up as a dedicated desktop or web-based application to help you with specific analytic queries—something like creating a bar chart of revenue by company segment before a board meeting. These are separate tools for specific analysis purposes. Embedded analytics are completely different. Here, we’re talking about data that has been inserted into an application or perhaps an enterprise portal. They might be insights that reside right alongside your ERP workflow.  Or maybe your work involves logistics in shipping. Placed right next to your forms for tracking packages and ordering materials, you’d have insights showing the number of packages you’ve shipped, how many you have left, or your average handling time.

What are some differences between embedded and standalone analytics?

One of the key differences is that, unlike with standalone tools, embedded analytics exist within your normal workflow. You don’t leave what you were doing to start up an analysis tool. The analytics could be represented as a dashboard in a tab in your application or, you might see individual analytics placed within your workflow. In the second scenario, analytics elements — like bar charts, infographics, or tables are situated alongside the workflow for the user. No switching modes or leaving the application. If you want insights like rate of work, speed of processing, or what’s remaining to work today, it’s right there for you to see. It’s a great and highly effective way to make sure that the analytics contribute the maximum value to the business.

Who are embedded analytics best suited for?

Embedded analytics are great for any customer-facing application where data is being generated which is to say, about everything that exists today. And, they’re perfect for any business user who needs to understand how much, how fast, how often, or how well they’re executing their tasks. Again — just about everyone. The idea is to take insights out of the back room—where only a few analysts had access to the information—and bring them front and center to the people making business decisions all day long. How can you improve your workflow or your business processes and impact the bottom line if you don’t have access to insights that are contextual, relevant, timely, and easy to understand? With analytics embedded into your application’s workflow, decisions and ultimately actions, are made faster and more effectively than if delivered periodically by analysts sitting farther from the work being performed.

Why are embedded analytics so important?

There are plenty of reports out there explaining the benefits of business intelligence, but what makes embedded analytics in particular so valuable? The world we live in has more people than ever in jobs that require them to interact with some form of software on a daily basis. Is the average person—who has, up until this point, been doing their job perfectly fine without an embedded analytic solution—really going to take time away from their other priorities to learn a new analytics tool? My experience shows that it’s unlikely.  A far better approach to get those capabilities into users’ hands is to deliver it within the context of a process they already know and use on a daily basis. That’s the value of embedded analytics: more users making more and faster decisions based on data.

I always love talking with people about their biggest questions when it comes to big data and business intelligence—a subject that I’m obviously passionate about. This webinar was exactly that: an opportunity to share some of Aberdeen’s exciting new research and to get closer to those people who want to know more about all embedded analytics have to offer.

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