IBM Insight 2015: The Cognitive/Analytics Continuum

By Joe Clabby, Clabby Analytics

“Insight” is an annual conference sponsored by IBM that promotes the use of cognitive computing and analytics. Attendees can attend “big tent” super-sessions that articulate then company’s business, market and product strategies; they can go to product break-out sessions; they can listen to customers present cognitive and analytics use case scenarios; they can attend hands-on labs and technical sessions — and they can also visit the expo floor to talk with IBM and 3rd party product experts and business partners.

The key message that IBM delivered last week at Insight 2015 echoed last year’s message: enterprises need to transform from traditional labor-intensive, slow development, best guess operations into more agile/cognitive/analytics-driven businesses. By doing so, they will gain new insights that can improve competitive positioning – and decisions can be made based on analytical fact. IBM has even coined a name for this transition: “the Cognitive Era”.

According to IBM, there are 5 steps that an enterprise must take to build a cognitive/analytics-driven computing foundation:

1) formulate a cognitive strategy;

2) build robust data and analytics capabilities;

3) use cloud services that are optimized to provide industry-specific, data and cognitive services;

4) tune IT infrastructures for cognitive workloads; and,

5) implement security that can serve the needs of cognitive systems.

Each of these points bears closer scrutiny.

Step 1: Build a Cognitive Strategy

IBM opened the conference with a focus on the benefits of implementing a combined cognitive computing/analytics strategy, including greater organizational efficiency, better decision making and stronger competitive positioning. Further, the company reinforced its view by inviting several customers on stage to describe their respective journeys into these areas, along with the resulting benefits.

The opening keynote featured Bob Picciano, Senior Vice President, IBM Analytics, talking about the new “Insight Economy” — the evolution of business toward being insight-driven to better service customers and to gain advantages over competitors. Picciano was followed by Mike Rhodin, Senior Vice President, IBM Watson(Watson is the brand name of IBM’s cognitive systems and IBM Watson Health, talking about how cognitive systems work: they can understand, they can reason and they keep learning. Rhodin introduced Watson Ecosystem partner “Wines4me”, a maker of an application that uses cognitive services from the “Watson Developer Cloud”) to help customers select wines that match their individual tastes. He then introduced customer StatSocial, a vendor that uses social data and analytics to build consumer profiles (this company offers profiles and buyer behavior data on over 650,000 consumers to its customers).

Rhodin also introduced “Pepper”, a small robot that was hooked up to a voice driven Watson back end cognitive system. Rhodin and Pepper carried on a logical discussion — illustrating how cognitive systems can be used as voice-driven query engines today. Finally, The Weather Company (which IBM just announced that it has acquired) took the stage and spoke about how it analyzes vast amounts of data to provide answers to 15-26 billion queries per day. All of these examples served to illustrate how new business models that exploit cognitive and analytics technologies are evolving and profiting.

Comment: At these Insight shows, I like to strike up conversations with customers and business partners to check-out their progress up the cognitive/analytics ladder. I spoke with several customers who have already formulated their analytics strategies, as well as a few that had already integrated cognitive computing/analytics strategies. It was clear to me that IBM’s cognitive era message is being well received, and that customers are actively moving ahead with these strategies.

Step 2: Build Robust Data and Analytics Capabilities

Step 2 relates to implementation. It essentially calls for customers to better integrate and manage data across their organizations in order to make analytics research more accurate.

Comment: During my discussions with customers and business partners, things got really interesting… In one of several trips to the demo floor, I noticed that IBM’s Information Management and Governance booth consistently attracted large crowds. I listened to customer questions there, and what I heard were requests for more information about how to govern data within Big Data Hadoop environments and across hybrid clouds.

That focus on data governance intrigued me, so I talked with IBM systems architects and a few business partners who all told me that this year’s Insight sparked a bunch of activity in data rationalization, cleansing and governance. One IBM business partner, Prolifics, also pointed out that compliance requirements are driving enterprises to better govern their data. Still in research mode, I attended a technical break-out session on governance where attendees asked numerous questions about cloud and data governance. I finished this line of inquiry by attending a governance super-session where IBM executives and customers described their activities in master data management, data integration and governance. This session was packed – standing room only.

My conclusions after talking and listening to end-users, IBM and its business partners are these:

  1. Many of this year’s Insight attendees are on the second rung of their journey to becoming cognitive/analytics-driven enterprises, focusing on how to structure data such that it can be best consumed and exploited by analytics software.
  2. Many questions involved data governance within Hadoop Big Data environments. This tells me that many customers are familiar with running analytics within the Hadoop environment — and are perhaps ending proof-of-concept projects and moving into more formal deployments. And these customers know that data within these new production environments must be properly governed.
  3. Regulators may be putting increased pressure on enterprises to better govern their data.

Step 3: Use Cloud Services That Are Optimized To Provide Industry-Specific, Data and Cognitive Services

IBM is in the midst of a quantum shift from being a provider of traditional data center systems, software and services to acting as a services company — delivering applications and services on-premises and/or across the cloud. IBM’s journey to become a provider of cognitive, analytics, mobile and social services began several years ago — and its portfolio is now rich with industry-specific service solutions, data management tools and Watson-driven cognitive offerings. Further, a whole ecosystem has evolved to expand the base of cognitive and analytics offerings for enterprise customers.

Comment: Attendees at both IBM’s Insight and Interconnect trade shows indicated to me that they understand this transition. IBM has spent billions of dollars in R&D as well as in acquisitions in order to expand its analytics portfolio. And the company is spending billions more building out its cognitive computing architecture and related solutions. IBM is now positioned as a major supplier of cognitive, analytics and mobile service solutions that can be delivered both as traditional on-premises (at the customer’s site) solutions – and/or directly from IBM as managed services. IBM offers hundreds of service solutions today — a number that will rise to thousands in years to come. For those who have not gotten the message, the cognitive era is all about deploying service solutions that can be made more efficient and accurate using cognitive and analytics technologies.

Step 4: Tune IT Architecture for Cognitive Workloads

There is a major shift taking place in configuration of workloads and system/storage/network management as machines are becoming more intelligent and as analytics algorithms are becoming more accurate. The most interesting element of this shift is that computing systems are becoming more capable of managing themselves — identifying the sources of problems and addressing them according to automated scripted policies and procedures. Systems are now able to heal themselves – or at least inform systems managers “where it hurts”.

And just as interesting, systems are now able to look at reams of Big Data systems management data and predict potential future failures – leading to greatly-reduced downtime. Further, by using machine driven cognitive/analytics software, enterprises are now able to increase service level delivery (because downtime will be quickly overcome or avoided); fewer and lower skilled individuals are now required to manage systems and workloads (helping avoid human errors and driving down costs); and operational management troubleshooting/configuration/tuning workloads can now be decreased (because systems using cognitive computing monitoring large amounts of Big Data such as log files will be able to troubleshoot and fix problems more quickly and easily, thus reducing troubleshooting backlogs).

At Insight 2015, IBM demonstrated products related to this, including its Operations Analytics – Log Analysis and IBM Operations Analytics – Predictive Insights offerings. These offerings are constantly being improved with better and better algorithms, helping to eliminate false positives and accordingly identify the root causes of problems more quickly and accurately. Further, these technologies are becoming more proficient at tuning systems to deliver maximum performance. Plus, libraries of policies and procedures are automating more and more functions currently being performed by humans.

Comment: IBM is in an ideal position to change the systems management, configuration, tuning and workload automation marketplaces with cognitive/analytics-driven management offerings. I see no other company with the cognitive depth that IBM is delivering with Watson and its related services/ecosystem; nor do I see any other major competitor positioned to compete with IBM in depth and breadth of analytics offerings. IBM is well positioned to change the industry with its optimization offerings — but I’m not sure information technology executives understand quite yet how innovative and compelling the company’s combined cognitive/analytics automated offerings are. I’ll be better able to judge whether IBM’s infrastructure optimization message is resonating after attending InterConnect in February, 2016.

The good news is that cognitive/analytics solutions have arrived for systems management; the bad news is that the market has yet to figure out that cognitive systems with analytics facilities are about to fundamentally change the way systems are configured, managed and tuned in the future. IBM has the products to drive this change – and IT executives are on the cusp of understanding the major impact the company’s efforts will have on their IT environments.

Step 5: Implement Security That Can Serve the Needs of Cognitive Systems

Imagine if cognitive systems could analyze vast amounts of Big Data in real time to more quickly identify insider as well as external threats. Now consider this – IBM is using cognitive computing and analytics to build highly-responsive security systems that can react much more quickly to threats and invasions that people can. The potential exists to immediately negate attacks and threats using machine intelligence to outwit and prevent insider threats and black hat attacks.

Comment: With its very deep security portfolio, a very deep and broad analytics portfolio — and with unique cognitive intelligence assets, IBM is extremely well positioned to lead the industry in the creation of next generation, intelligent security response and exposure prevention systems.

Summary Observations

IBM gave technology research analysts who attended Insight 2015 a guide that showed five foundational steps that need to be taken by enterprises in order to enter the cognitive era (these were described on the first page of this report). As I look at these five steps, I see more Insight attendees than ever before with partially or fully formulated cognitive/analytics strategies. This year, I witnessed a strong movement toward cleansing, organizing, preparing, managing and governing data — indicating that many enterprises are moving their cognitive and analytics strategies out of proof-of-concept mode into full production.

I also see IBM, its partners and its customers very significantly growing their solutions portfolios with service-oriented solutions that can be deployed on-premises or via the cloud. Given the breadth of its services portfolio and related growth of its services ecosystem (with numerous offerings that now exploit Watson cognitive technology) the company’s transformation from selling traditional data center systems, software and professional services company to providing cognitive, cloud, analytic and industry-specific services appears to be gaining traction.

And finally, with the use of cognitive intelligence blended with analytics, IBM is very well prepared to shake up the systems optimization, management and security marketplaces. Global earnings performance and costs related to transforming IBM into a services company have frustrated IBM’s earnings performance for the past few quarters. But with the company’s current positioning and its wealth of innovative and unique new products, I believe IBM is extremely well positioned for solid growth as more and more enterprises enter the evolving “cognitive era”.

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