Big Data. Big Decisions
InformationWeek
Special Coverage Series


IBM's Big Data Strategy: Combine And Conquer

Big Blue is gobbling up big data companies at a steady clip. What's the method behind the madness?

 Big Data Analytics Masters Degrees: 20 Top Programs
Big Data Analytics Masters Degrees: 20 Top Programs
(click image for larger view and for slideshow)

IBM has been on a big data buying binge in recent years, acquiring more than 11 companies in the data analytics space since 2008, including six firms over the past 12 months. Just last week Big Blue announced it had completed yet another acquisition: StoredIQ, an Austin, Texas-based developer of analysis and management software for managing litigation and regulation requirements.

More Insights

Webcasts

More >>

White Papers

More >>

Reports

More >>

IBM's big data buys since February 2012 include:

-- Emptoris (2/1/12), a provider of strategic supply, category spend and contract management software.

-- DemandTec (2/15/12), maker of cloud-based software for pricing, promotion and assortment planning.

-- Variscent (5/23/12), a developer of analytics software for compensation and sales performance management.

-- Vivismo (5/29/12), a provider of federated discovery and navigation software.

-- Star Analytics (2/1/13), a business analytics software company.

According to IBM director of big data strategy Nancy Kopp, the acquisitions will continue in 2013.

[ Want to know how IBM's big data computing is influencing healthcare? IBM's Watson Could Be Healthcare Game Changer. ]

"Our goal is to offer the broadest and biggest depth and breadth of any vendor in the marketplace," Kopp told InformationWeek in a phone interview. "As we fill up this portfolio, we're really filling in the holes that we had in (our) integration."

With its recent acquisitions, IBM has focused on companies and products that can help it deliver "smarter analytics," including improved simplicity and integration of various software components that mix well with the company's big data offerings.

"When you think about the challenges of big data, you have to drive back to what is motivating people to invest in more data structures, and that's really the goal of smarter analytics," said Kopp.

IBM's big data tools, including InfoSphere Data Explorer, which is based on technology from the company's Vivismo acquisition, as well as Star Analytics, another recent purchase, are designed to make data easier to access and manage.

"StoredIQ, for example, "has a very strong focus on simplifying storage administration in the world of big data," said Kopp. The product also helps companies automate the elimination of data that they're not going to leverage.

Kopp claimed IBM's big data products are superior to those of its top competitors.

"When you look at how we compare to, say, Oracle or Teradata, they are starting to build out some capabilities, but not to the degree that we have within (our) ecosystems," she said.

Specifically, Kopp said the data virtualization capabilities and explorations of IBM's Vivismo, as well as the integration features of its InfoSphere DataStage, provide enterprises with a more comprehensive big data solution.

Customers "not only need all the parts and pieces, but they also need the parts and pieces integrated," she added. "That's the key."

IBM is addressing the growing need for data scientists and big data applications as well, Kopp said.

"We're working with several universities to drive better curriculums, because these people (data scientists) aren't born. They're going to have to be created," she said

For instance, IBM has increased its efforts to bring big data to academia. In the past 18 months, the company has built new partnerships with Michigan State, Northwestern University, Yale and the University of Southern California. The company's' goal is to help develop courses that provide the analytics skills required for big data platforms.

On the applications side, Kopp named IBM's InfoSphere BigInsights as a good example of a software tool that simplifies Hadoop and big data app development.

"There's a very strong focus on managing the simplicity of the system," said Kopp of BigInsights. "You can get immediate value with some visualization around the data -- without the data scientist."

InformationWeek is conducting a survey on the state of database technology in the enterprise. Take our InformationWeek 2013 Database Technology Survey now. Survey ends Feb. 15.



Related Reading




Currently we allow the following HTML tags in comments:

Single tags

These tags can be used alone and don't need an ending tag.

<br> Defines a single line break

<hr> Defines a horizontal line

Matching tags

These require an ending tag - e.g. <i>italic text</i>

<a> Defines an anchor

<b> Defines bold text

<big> Defines big text

<blockquote> Defines a long quotation

<caption> Defines a table caption

<cite> Defines a citation

<code> Defines computer code text

<em> Defines emphasized text

<fieldset> Defines a border around elements in a form

<h1> This is heading 1

<h2> This is heading 2

<h3> This is heading 3

<h4> This is heading 4

<h5> This is heading 5

<h6> This is heading 6

<i> Defines italic text

<p> Defines a paragraph

<pre> Defines preformatted text

<q> Defines a short quotation

<samp> Defines sample computer code text

<small> Defines small text

<span> Defines a section in a document

<s> Defines strikethrough text

<strike> Defines strikethrough text

<strong> Defines strong text

<sub> Defines subscripted text

<sup> Defines superscripted text

<u> Defines underlined text

BYTE encourages readers to engage in spirited, healthy debate, including taking us to task. However, BYTE moderates all comments posted to our site, and reserves the right to modify or remove any content that it determines to be derogatory, offensive, inflammatory, vulgar, irrelevant/off-topic, racist or obvious marketing/SPAM. BYTE further reserves the right to disable the profile of any commenter participating in said activities.

Disqus Tips To upload an avatar photo, first complete your Disqus profile. | View the list of supported HTML tags you can use to style comments. | Please read our commenting policy.

Follow InformationWeek

By The Numbers

What Are Your Primary Concerns About Using Big Data Software?

Base: 417 respondents at organizations using or planning to deploy data analytics, BI or statistical analysis software
Data: InformationWeek 2013 Analytics, Business Intelligence and Information Management Survey of 541 business technology professionals, October 2012

What Do You Think?

What's your attitude about SQL analysis on top of Hadoop?
We want fast, standard SQL analysis capabilities on Hadoop ASAP
Hadoop is for unstructured data; SQL is for relational databases
We'll give SQL on Hadoop a try, but relational DBs will remain the mainstay
Given strong SQL support on Hadoop, we'd nix the data warehouse
We're not interested in Hadoop
No opinion



Related Content

From Our Sponsor

Five Big Data Challenges and How to Overcome Them with Visual Analytics

Five Big Data Challenges and How to Overcome Them with Visual Analytics

Business leaders often need a visual snapshot of data to quickly grasp and use it. This paper identifies five challenges in presenting data and how visual analytics can resolve them. Solutions are suggested to overcome the challenges of: speed, data clarity, data quality, displaying meaningful results, and dealing with outliers.

Game-Changing Analytics: How IT Executives Can Use Analytics to Create Innovation and Business Success

Game-Changing Analytics: How IT Executives Can Use Analytics to Create Innovation and Business Success

Today's competitive advantage requires a deeper understanding of your business, your market and your customers. As an IT executive, you can drive that knowledge transformation. In this white paper, learn how to make decisions as a strategic business leader and three steps to begin an analytics initiative within your enterprise.

Data Visualization Techniques: From Basics to Big Data with SAS Visual Analytics

Data Visualization Techniques: From Basics to Big Data with SAS Visual Analytics

High-performance data visualization turns sophisticated analyses into meaningful graphics, leading to faster and smarter decision making. In this white paper, learn how visual analytics can transform big data, with additional features such as real-time functionality, mobile compatibility, robust applications for technical groups and accessibility for nontechnical users.

Big Data: Lessons from the Leaders

Big Data: Lessons from the Leaders

Financial performance, competitive advantage, operational efficiency, strategic decision making - every business goal can extract value from big data, and the time for doubt or inaction has long passed. In this Economist Intelligence Unit report, in-depth interviews with data pioneers reveal the link between the effective use of big data and the bottom line among other results.

Decision-Driven Data Management: A Strategy for Better Decisions with Better Data

Decision-Driven Data Management: A Strategy for Better Decisions with Better Data

Which came first, the data or the decision? This white paper makes the case for having a decision in mind, then tailoring big data's volume, variety and velocity to achieve business results such as overcoming customer dissatisfaction or creating well-informed strategies in real time.

Informationweek Reports

Research: The Big Data Management Challenge

Research: The Big Data Management Challenge

The challenge of big data is real, but most organizations don't differentiate 'big data' from traditional data, and nearly 90% of respondents to our survey use conventional databases as the primary means of handling data. We'll help you understand what constitutes big data (it's not just size) and the numerous management challenges it poses.