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Analytics 3.0: Beyond Big Data

Think you need a data scientist to glean actionable insights from big data? Then you haven’t read Big Data at Work, the newest book by Tom Davenport, who is widely acknowledged as one of the world’s leading analytics experts.

Analytics 1.0: The Data Analyst in the Closet

Davenport said the days when analysts sat in back rooms working with data for weeks, if not months, and then preparing the results of their findings for end users they’ll never meet are long gone.

This is a good thing because making decisions based on stale data is like driving your car while looking in the rearview mirror.

Analytics 2.0: Win the Attention of the Data Scientist

Though data scientists can deliver amazing insights, there aren’t enough of them. So if you’re a manager who needs to make a data-driven decision right now, chances are you‘ll have to go without the big data and with your gut. Sorry you weren’t the top priority.

Analytics 3.0: Democratizing Big Data

Davenport said we’re entering the Analytics 3.0 era where analytics are part of everyone’s job (not just data scientists and analysts) and that insights can be delivered where and when you need them via any device.

Not only that, but that many decisions can be made and acted upon at scale and with speed, automatically (no human interaction required). This capability not only gives those Enterprises who leverage Analytics 3.0 a competitive advantage, but it also is the raw material from which new products and services can be built.

Are Leading-Edge Analytics Experts on the Same Page?

Is Analytics 3.0 a pie-in-the-sky idea, or is it something that Enterprises can take advantage of now? We asked three leading Analytics providers Datameer, Platfora and Alteryx about Analytics 3.0 and if their companies were doing anything to make the more democratized vision a reality. Here is what they told us:

Karen Hsu, Senior Director of Product Marketing, Datameer:


Analytics 3.0 has been built on the descriptive and predictive foundation from analytics 1.0 and 2.0. Instead of just describing what happened or predicting what will happen, now analytics has evolved to prescribe what needs to done to reach the desired result.  For example, analytics 3.0 can prescribe what Businesses need to be doing to influence customer behavior. To achieve this, businesses are going beyond answering simple questions based on small amounts of data.

At Datameer, we agree that analytics 3.0 is about making a business more agile, lending it to quicker and more accurate decisions. However, in order to maximize this potential, the information needs to be accessible to those closest to the information, which is typically the business analyst. In the past, these analysts have had to rely on IT for integration and changes.  Analysts also tend to be more comfortable with visualizations. As such, they have to wait until the final visualization in the big data analytic workflow before they can prove their hypothesis. This approach is time consuming and hemorrhages resources that could be better spent elsewhere.

Our goal is to give power to the user by equipping them with an end-to-end big data analytics workflow.  We’re really excited to have announced Datameer 4.0 earlier this week. The new solution allows for visual insights at every step of analysis. We’ve instituted a new “flip side” view to our spreadsheet interface so business analysts can get instant visual feedback at any point in the analytics process. This allows them to check initial results and adjust appropriately as they go further, shortening time to insight and freeing up time for teams to tackle bigger challenges.  Businesses can only be successful with analytics 3.0 if they open up the analytics workflow beyond the traditional internal stakeholders. That means implementing systems and tools that play to the strengths of their team whether that means numbers or visualizations."

Ben Werther, CEO of Platfora:

Ben Werther

In analytics 1.0 and 2.0, we could only access about 10 percent of the organization’s total data. Anything that wasn’t structured in a database usually could not be found or analyzed. More challenging yet, the time and money it took to extract, transfer and load (ETL) data into analytical systems was often so great that only the most critical projects were authorized. And these projects required specialized teams of programmers, statisticians and analysts to deliver the data to the line of business professionals who requested it. 


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