First off, the DataWorks moniker is not new. It has been around for a while, initially used to describe IBM BlueMix Data Integration capabilities. But now DataWorks has been re-purposed as an all-inclusive headline for what IBM touts as the first cognitive data platform.

It’s more than just a technological platform, however, so let’s take a look at what DataWorks is comprised of.

The platform

DataWorks leverages two of IBM’s greatest strengths, plus an industry darling it has adopted: the BlueMix PaaS cloud, offering advanced services for application development; Watson, IBM’s spearhead in advanced analytics and AI capabilities; and Apache Spark, the dominant platform for Big Data analytics. IBM takes pride in being the #1 committer to Spark, while both BlueMix and Watson have been around for a while.

So there’s not much new here, except maybe for the way IBM promotes integration of analytics and AI capabilities in everyday applications. This is a trend that has been unfolding for a while, and IBM does not want to be left behind.

IBM says the norm for applications will soon be to incorporate 10-15 external data sources and to use them to offer embedded insights and functionality. So the claim is that by using the DataWorks stack, insights developed for example via an R model can be infused into an application developed on BlueMix, at the click of a button.

In order to do this, massive amounts of data have to be ingested. And this is where IBM makes another claim, that of being the fastest around: “from 50 to hundreds of Gbps”. Since there is no elaboration on the origins of these numbers (Big Data benchmarks exist, but are not overly popular) one has to speculate that this must be attributed to a combination of factors: IBM’s cloud alleged superiority over the competition, plus an array of advanced metadata and machine learning techniques applied at ingestion time, such as automated classification of data types and relationships, and deep learning to assist in ML model recommendation, iteration, evaluation, and deployment.

Thus IBM promises to assist in cases where clients may not even know what kind of data they have or what their relationships are, empowering citizen data scientists and facilitating collaboration between application developers and data scientists. But there’s also something aimed purely at data scientists under the DataWorks umbrella, namely the Data Science Experience (DSX). This can be described as a combination of a data science networking and discovery tool with a platform that facilitates tasks such as setting up Spark instances and Docker containers.

A method to the madness

IBM sees this not just as a technological journey, but one that entails broader organizational transformation including culture and processes. As such, it has also released a methodology on how to approach this journey, called DataFirst and based on four pillars: Efficiency, Modernization, Democratization, and Monetization. DataFirst is comprised of four corresponding tracks: Data Management, Data Lake, Data Science, and Data in Action. The methodology is the crystallization of experience accumulated via IBM’s service branch and is offered through a series of workshops.

Even though IBM’s own offering in terms of products and services is massive, the latter having in fact contributed to productization, it still can’t reach everyone. So this is where partnerships come into play. IBM has also announced a series of partnerships to go with DataWorks, including the likes of Continuum Analytics, Galvanize, Alation, NumFOCUS, and RStudio.

Where do we go from here?

So what to make of all this? IBM seems to making strides in the data landscape competition. When looking at the technology itself it may seem like there’s not much new there, except maybe DSX coming out of private beta and a number of under-the-hood automations and integrations on which there are not many details at this point. But that does not mean that there’s no value in there, even taken in isolation.

By having all of that under the same roof, pre-integrated and with a focus on deriving business value it seems like IBM really means it when it says that the value in data is shifting: it’s not about storage, but more about how data can be used with trust and efficiency. With IBM’s traditional foothold, it should be well positioned to make a compelling offer for enterprises.

As for the rest, it remains to be seen whether an ecosystem of partners and DataWorks’ technological underpinnings can help execute on an a-la-carte strategy to lure players who are not keen on buying the entire stack.

This article has been put together from publicly available information, including statements from IBM executives Armand Ruiz Gabernet, Rob Thomas, and Ritka Gunnar. IBM has not responded to ZDNet’s questions on topics such as licensing, partnerships, methodology, and data ingestion benchmarks by the time the article was published.

via IBM – Google News

September 30, 2016 at 04:39AM