IBM’s Five Lenses for Big Data Analytics


Last week, IBM brought industry analysts to its famed Almaden Research Center, where the company outlined its big data analytics strategy and introduced a number of new innovations. Big data is no new topic to IBM, which has for decades helped organizations store and use data. But technology has changed over those decades, and IBM is working hard to ensure it is part of the future and not just the past. Our latest business technology innovation research into big data technology finds that retaining and analyzing more data is the first-ranked priority in 29 percent of organizations. From both an IT and a business perspective, big data is critical to IBM’s future success.

On the strategy side, there was much discussion at the event around use cases and the different patterns of deployment for big data analytics. Inhi Cho Suh, vice president of strategy, outlined five compelling use cases for big data analytics:

  1. Discovery and visualization. These types of exploratory analytics in a federated environment are a big part of big data analytics, since they can unlock patterns that can be useful in areas as diverse as determining a root cause of an airline issue or understanding relationships among buyers. IBM is working hard to ensure that products such as IBM Cognos Insight can evolve to support a new generation of visual discovery for big data.
  2. 360-degree view of the customer. By bringing together data sources and applying analytics to increase such things as customer loyalty and share-of-wallet, companies can gain more revenue and market share with fewer resources. IBM needs to ensure it can actually support a broad array of information about customers – not just transactional or social media data but also voice as well as mobile interactions that also use text.
  3. Security and intelligence. This area includes areas around fraud and real-time cyber security, where companies leverage big data to predict anomalies and contain risk. IBM has been enhancing its ability to process real-time streams and transactions across any network. This is an important area for the company as it works to drive competitive advantage.
  4. Operational analysis. This is the ability to leverage networks of instrumented data sources to enable proactive monitoring through baseline analysis and real-time feedback mechanisms. The need for better operational analytics continues to increase. Our latest research on operational intelligence finds that organizations that use dedicated tools to handle this need will be more satisfied and gain better outcomes than those that do not.
  5. Data warehouse augmentation.  Big data stores can replace some traditional data stores and archival systems to allow larger sets of data to be analyzed, providing better information and leading to more precise decision-making capabilities. It should be no surprise that IBM has customers with some of the larger data warehouse deployments. The company can help customers evaluate their technology and improve or replace existing investments.

Prior to Inhi taking the stage, Dave Laverty, vice president of marketing, went through the new technologies being introduced. The first announcement was the BLU Accelerator – dynamic in-memory technology that promises to improve both performance and manageability on DB2 10.5. In tests, IBM says it achieved better than 10,000x performance on queries. The secret sauce lies in the ability to do column store data retrieval, maximize CPU processing, and provide skipping of data that is not needed for the particular analysis at hand. The benefits to the user are much faster performance across very large data sets and a reduction in manual SQL optimization. Our latest research into business technology innovation finds that in-memory technology is the technology most planned for use with big data in the next two years (22%), ahead of RDBMS (10%), data warehouse appliance (19%), specialized database (19%) and  Hadoop (20%).

vr_bigdata_obstacles_to_big_data_analytics (2)An intriguing comment from one of IBM’s customers was “What is bad SQL in a world with BLU?” An important extension of that question might be “What is the future role for database administrators, given new advancements around databases, and how do we leverage that skill set to fill the big data analytics gap?” According to our business technology innovations research, staffing (79%) and training (77%) are the two biggest challenges to implementing big data analytics.

One of IBM’s answers to the question of the skills gap comes in the form of BigSQL. A newly announced feature of InfoSphere BigInsights 2.1, BigSQL layers on top of BigInsights to provide accessibility through industry-standard SQL and SQL-based applications. Providing access to Hadoop has been a sticking point for organizations, since they have traditionally needed to write procedural code to access Hadoop data. BigSQL is similar in function to Greenplum’s Pivotal, Teradata Aster and Cloudera’s Impala, where SQL is used to mine data out of Hadoop. All of these products aim to provide access for SQL-trained users and for SQL-based applications, which represent the predominance of BI tools currently deployed in industry. The challenge for IBM, with a product portfolio that includes BigInsights and Cognos Insight, is to offer a clear message about what products meet what types of analytic needs for what types of business and IT professional needs. In addition further clarity from IBM on when to use big data analytics software partners like Datameer who was on an industry panel at the event and part of IBM global educational tour that I have also analyzed.

Another IBM announcement was the PureData System for Hadoop. This appliance approach to Hadoop provides a turnkey solution that can be up and running in a matter of hours. As you would expect in an appliance approach, it allows for consistent administration, workflow, provisioning and security with BigInsights. It also allows access to Hadoop through BigSheets, which presents summary information about the unstructured data in Hadoop, and which was already part of the BigInsights platform. Phil Francisco, vice president of big data product management and strategy, pointed out use cases around archival capabilities and the ability to do cold storage analysis as well as the ability to bring many unstructured sources together. The PureData System for Hadoop, due out in the second half of the year, adds a third version to the BigInsights lineup, which also includes the free web-based version and the Enterprise version. Expanding to support Hadoop with its appliances is critical as more organizations look to exploit the processing power of Hadoop technology for their database and information management needs.

Other announcements included new versions of InfoSphere Streams and Informix TimeSeries for reporting and analytics using smart meter and sensor technology. They help with real-time analytics and big data depending on the business and architectural needs of an organization. The integration of database and streaming analytics are key areas where IBM differentiates itself in the market.

Late in the day, Les Rechan, general manager for business analytics, told the crowd that he and Bob Picciano, general manager for information management, had recently promised the company $20 billion in revenue. That statement is important because in the age of big data, information management and analytics must be considered together, and the company needs a strong relationship between these two leaders to meet this ambitious objective. In an interview, Rechan told me that the teams realize this and are working hand-in-glove across strategy, product development and marketing. The camaraderie between the gentlemen was clear during the event, and bodes well for the organization. Ultimately, IBM will need to articulate why it should be considered for big data, as our technology innovation research finds organizations today are less worried about validation of a vendor from a size perspective (23%) compared to usability of the technology (64%).

IBM’s big data platform seems to be less a specific offer and more of an ethos of how to think about big data and big data analytics in a common-sense way. The focus on five well-thought-out use cases provides customers a frame for thinking through the benefits of big data analytics and gives them a head start with their business cases. Given the confusion in the market around big data, that common-sense approach serves the market well, and it is very much aligned with our own philosophy of focusing on what we call the business-oriented Ws rather than the technology-oriented Vs.

Big data analytics, and in particular predictive analytics, is complex and difficult to integrate into current architectures. Our benchmark research into predictive analytics shows that architectural integration is the biggest inhibitor with 55 percent of companies, which should be a message IBM takes to heart about integration of its predictive analytics tools with its big data technology options. Predictive analytics is the most important capability (49%) for business analytics, according to our technology innovation research, and IBM needs to  show more solutions that integrate predictive analytics with big data.

H.L. Mencken once said, “For every complex problem there is an answer that is clear, simple and wrong.” Big data analytics is a complex problem, and the market is still early. The latent benefit of IBM’s big data analytics strategy is that it allows IBM to continue to innovate and deliver without playing all of its chips at one time. In today’s environment, many supplier companies don’t have the same luxury.

As I pointed out in my blog post on the four pillars of big data analytics,vr_predanalytics_predictive_analytics_obstacles our research and clients are moving toward addressing big data and analytics in a more holistic and integrated manner. The focus shift is less about how organizations store or process information than how they use it. Some may argue that the IBM’s cadence is reflective of company size and is actually a competitive disadvantage, but I would argue that size and innovation leadership are not mutually exclusive. As companies grapple with the onslaught of big data and analytics, no one should underestimate IBM’s outcomes-based and services-driven approach, but in order to succeed IBM also needs to ensure it can meet the needs of organizations at a price they can afford.

Regards,

Ventana Research

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