IBM claims it's ushering in a new era of analytic creativity in the enterprise — one that lets analysts ask bigger and more complex questions and get answers gleaned from larger quantities of data back in a flash.

What exactly does “in a flash” mean?

That a complex query that used to take an entire day to run can now take as few as four seconds. And that’s a business differentiator in today’s world.

It’s also the promise of dashDB Enterprise MPP, IBM’s fully managed cloud data warehousing service that provides the performance and simplicity of on-premises appliances, while offering the distributed power and scalability only available with a cloud-based architecture.

Or in simpler terms, you can ingest more data, more quickly and process it faster using IBM’s MPP (Massively Parallel Processing (MPP) engine and apply analytics to it to glean insights sooner. Not only that, but you can also do it without much hassle because it’s available as-a-Service.

Next Generation Solution

This is the kind of thing that many other data warehouse vendors aspire to, or already claim to do, but their approaches are a generation behind the one IBM announced today.

At least that’s what Adam Kocoloski, CTO of IBM’s Cloud Data Services (CDS), told me in an interview Friday.

He said IBM’s solution is “memory optimized” rather than simply in-memory, that it scales and can handle higher data volumes, that it leverages cluster architecture and  built-in Netezza analytics libraries, and is integrated with Watson Analytics, R, Cognos and third party BI toolsets like Looker, Aginity Workbench and Tableau.

Better yet, it can process both structured and unstructured (JSON) data when it also leverages IBM’s NoSQL DBaaS, Cloudant.

This means that large volumes of all kinds of data ranging from the Internet of Things (IoT), website data, and conventional data can be stored in the same place, analyzed in its entirety and operationalized from there.

Get Excited

“This is huge,” said Keenan Rice, VP of Technical Alliances at BI vendor Looker. It has the potential of unveiling capabilities as dramatic as providing the insights needed to identify a shopper’s favorite music before she walks into the store and have it playing as she walks in the door.

That’s a hypothetical, an off the wall example, mind you. But it’s indicative of the kind of digging a data scientist or even an algorithmically talented business analyst can do in short order.

From a more practical point of view, IBM’s dashDB Enterprise MPP when integrated with a product like Looker or Tableau, allows business users to ask complicated questions and get answers faster from more, and more kinds of data, over and over again.

The big questions that have yet to be answered are whether it actually outperforms Microsoft Azure’s SQL Data Warehouse, Amazon’s Redshift or those who come from a number of startups which promise similar things using their own technologies and whether it’s cost prohibitive.

“IBM has done a lot of work on pricing and delivery,” said Kocoloski.

If he’s right about that, then dashDB’s combination of accessibility, processing and analytical power, connectivity to a breadth of data sources, databases and analytical tools, could generate a hit for IBM. And the enterprise-worthiness that comes, as a given, from Big Blue might encourage hesitant enterprise managers to venture into 3rd platform waters.