Business Intelligence Has Changed – Welcome to the Age of Advanced Revenue Analytics

BI (Business Intelligence) was a great innovation in the 90’s and people still use it to this day. Why not? It’s the devil you know; highly trained people are available for hire for excessive salaries, BI tools are all over the place, and everyone sings praises around their perceived virtues.

I should know; I’ve created multiple BI software companies to help customers get key information that was very difficult, if not impossible to do, with normal tools. There isn’t a BI path I haven’t been down in the name of innovation. My previous ventures provided great value to my customers and open source communities. My teams provided simple visualization tools from detect and alert to Big Data Analytics on Hadoop/NoSQL.

With each previous venture, I was fortunate to learn what really mattered (and hopefully, help make things better for the market and our customers). And so Corvana was born in 2016 and today is now part of SugarCRM, one of the true visionaries in the customer experience (CX) space. We needed to make the entire analytics process much easier to get rolling (it should take less than a day), widen the audience, instill ease of use, and automate the analytics themselves (the biggest issues with BI adoption mirror those of CRM: technology, training, and disciplined usage.)

Any tool can create thousands of reports and dashboards…but that’s a waste. Who uses them, who monitors them, who maintains them? For example: a mid-to-enterprise customer ends up hiring a team of two to four people who extract CRM data, cleanse it, build a data model, and then layer a BI tool on top of it. The result? Even then the historical analysis is tough at best. Then, we train people on how to use BI tool and maintain it. And so more reports and dashboards are built.

Does this sound familiar? A small company uses existing resources to periodically extract CRM data into Excel, create formulas to do custom calculations, then pastes those results into PowerPoint.

Lather, rinse, repeat.

Except the result isn’t bountiful data; it’s the opposite: extremely limited historical analysis. This is done in different departments across the company and the numbers will never align.

So how about a fresh approach? After all, it’s 2019 (where’s my jetpack?)

Let Sugar Discover automatically access your data, create an Analytical Data Mart, monitor it with machine learning algorithms, notify you when you should be made aware of something, and how you the results and associated insights/root cause impacts. And if you like to create reports and dashboards, that’s cool, too. We still let you do that, but only after using the automated analytic capabilities of Sugar Discover.

We’re just getting started; expect rapid innovation in this space as part of our No-Touch Information Management pillar. Stay tuned for more details and be sure to view today’s press release as well!

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About the Contributor
Richard Daley
Richard Daley

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