Yesterday’s BI tools just don’t cut it. In today’s world the margin of time between data and decision needs to be instantaneous, or as close to that as we can get. The longer it takes to process information and glean insight from it, the more you can lose.

Data can do more than help companies win sales. It can also save lives.

But it can only do this effectively if a data scientist or data analyst can process all of the information, gather all of the necessary data and make it useful, or present it to decision makers visually so that a viz can speak for the data, tell the story and make decision making easier.

Disappearing Software

Now mind you, we’re not bashing the BI tools of yesteryear (that many analysts still use). It’s just that they were built for another era when data storage was expensive (and you were bound by the size of the data warehouse), processing power was slow and bar charts and overloaded Excel sheets were the standard. Not just that, but analysts had to submit requests to IT and wait to get data they could work with.

Today’s tools are different. If you were take a walk through the exhibit hall at Tableau’s Destination Data conference this week, you’d see a whole new set of user friendly solutions that were built by companies that are, for the most part, less than six years old. They promise to help workers and analysts engage with their data in short order. Their interfaces are so well designed that, as Tableau CEO Cristian Chabot puts it, “the software disappears.”

Imagine that, data analysts can now work with data, not software.

And there’s not just that, but in the age of big data, analysts have access to more data and more data sources than ever imaginable. This presents a new challenge. How do you make disparate data sets work together? (Take geographical, transactional and streaming data, for example.)

Need a Drink?

Data blending is the answer. If you’ve heard the term before, you’re probably reaching for an aspirin or a drink right now. But there’s no need to do that. It’s not the slow, painful drag that it used to be.

And if data blending is new to you, you’re in luck. We’ve got an introduction to share. We worked with our friends at Alteryx, which provides tool for data blending, to give you an overview from the Tableau conference floor. (Note, this is the first of many analytic concepts we’ll introduce you to this year).

First, we set off to ask a few conference attendees a simple question: What is data blending?

Here’s Matthew Hughes, manager of Inventory and analytics, Sager Creek Vegetable Company, on the subject:

Next Chris Love, consultant at the Information Lab:

And finally, John Myers, managing director at Enterprise Research Associates:

Did They Get It Right?

More or less. At least if you compare their answers to the one Brian Dirking, director of product at Alteryx provides. He says that data blending is “the process of combining data from multiple sources to create an actionable analytic dataset for business decision making (such as retail site selection or multichannel profiling) or for driving a specific business process (such as packaging data for sale by data aggregators).

Dirking goes on to explain that this matters because one of the most significant elements to effective analytics is the ability to incorporate the right data. He adds that it’s key that data analysts not only incorporate the right data, but also ensure the cleanliness and quality of that data. Needless to say, he’s convinced that Alteryx solves that problem.

That matters because the time from data to decision needs to be as short as possible. And one of the ways this is actualized is by expressing the data in Tableau which lets the viz tell the story.

How hard is it to use Alteryx and Tableau together?

We’ve got video.

Here’s Jimmy Garrett, a sales engineer from Alteryx:

The BI tools of yesteryear look nothing like this, right?

Digging BI's Grave

As we’ve already mentioned getting data ready to go in short order is a competitive advantage. Ditto for being able to presenting it visually, where it can be easily manipulated and the number of questions you can ask it are seemingly limitless.

It’s because of new technologies like these that old school BI is dead. Long live analytics.