The Evolution of IBM’s Cloud, Watson, and Analytic Consulting at IBM Vision

In mid-May, Amalgam Insights (AI) attended IBM Vision, an event focused on business performance, both as an attendee and a presenter. This has been my favorite IBM tradeshow for several years, as it focuses directly on key concerns that I have looked at throughout my career: financial management, enterprise governance, and compliance. Because everyone at this show is focused on some form of BI, performance management, or risk, it is easy to speak with a business user, consultant, or IBM professional at this show and to quickly find common professional ground.

This year, I took three key findings away from IBM Vision that should be of ongoing value for financial departments within the enterprise.

• The Maturity of IBM’s Cloud Analytics Capabilities
• Promontory Digital’s Role for IBM and IBM Watson
• IBM’s DataFirst Method for Analytic Consulting

The Maturity of IBM’s Cloud Analytics Capabilities

First, IBM has a complete analytics story in the cloud, but users are still not able to put all the pieces together. To describe what I mean, first step back and think about why analytics exists in the enterprise. It’s not because people like to play with numbers or that charts have any special meaning in and of themselves. That’s some Dilbert-level thinking right there…

"It must be true because it's pie"
It must be true because it’s pie

Instead, think of Simon Sinek’s framework of “Start with Why.” If you don’t know what this means, watch the TED talk. Go ahead and click! (Don’t worry, this blog will still be here after the talk…)

OK, so the point of Simon Sinek’s Golden Circle is to start with why you do something, then work your way out to How and then What. Now, consider this set of priorities in context of Watson Analytics, Planning Analytics, and Cognos Analytics.

In the IBM model, IBM Planning Analytics can serve as a hub of financial and operational analytics used to contextualize both the Why and the What. Both Planning Analytics and Cognos Analytics compete strongly against cloud-based standalone competitors while Watson Analytics continues to be a unique tool among enterprise software providers.

IBM Watson, Planning, Cognos Analytics in Context
IBM Watson, Planning, Cognos Analytics in Context

AI believes that IBM needs to take an additional step to complete its cloud analytics packaging by integrating Watson Discovery with these analytics products. Watson Discovery is IBM’s toolkit for data enrichment and relationship identification. Although it is positioned to focus on unstructured datasets, AI believes that this service could also provide significant value in supporting data quality, preparation, and definitions while reducing the ~50% of time spent by data analysts in wrangling and augmenting data prior to analysis.

By using Watson Discovery as a self-service tool to prepare business data for the Why, How, and What of business analytics, AI believes that IBM is on the cusp of providing an end-to-end analytics solution for the next generation of business users: in the cloud, governed self-service, and highly contextualized to provide business value.

Hierarchical Progression of Analytical Priorities
Hierarchical Progression of Analytical Priorities
Progression of Enterprise Analytics
Progression of Enterprise Analytics

(Note: I also presented at IBM Vision 2017 on the importance of “Increasing Analytic Value by Bringing Cognos Analytics and Watson Analytics Together” with a focus on using both historical and predictive analysis on enterprise data to increase the value of the data from an executive perspective.)

Promontory Digital’s Role for IBM and IBM Watson

Second, IBM’s engagement of Promontory Financial Group is an important gauge of the future of Watson and should be closely analyzed by both tech and investment analysts seeking to understand the growth potential of IBM Watson. AI believes that the Promontory acquisition is promising both for demonstrating IBM’s ability to support the CFO office and, more importantly, for the future of Watson as a monetizable asset.

When IBM first announced the acquisition of Promontory as a risk management and regulatory consulting firm in July 2016, the obvious connection was to assume that Promontory’s 600+ consultants would be an important specialized capability in IBM’s Global Business Services Unit. However, IBM also surprised some by explaining that Promontory would also be involved in training Watson on how to support regulatory compliance.

In the IBM Vision 2017 General Session, Marc Andrews, VP of Watson Regulatory Compliance Solutions, and Michael Dawson, Managing Director of Promontory Financial Group, took the stage near the end of the session to describe their roles in tracking an increasingly complex regulatory environment both as highly trained professionals and in training Watson to support controls, governance, and compliance. One of the topics they covered was Watson’s ability to ingest regulations and showcase relevant regulatory aspects associated with the document or business task in question.

Watson is fundamentally a technology that can learn about any topic, then provide value as a cognitive and consultative technology. IBM needs to figure out how to quickly onboard Watson in high-value areas ranging from healthcare to law to regulatory compliance in order to take full advantage of the unique nature of Watson as a technology. IBM has already supported a number of successful Watson deployments in healthcare, tax (in the case of H&R Block), and industrial settings, but the IBM Watson that memorizes information and provides consultative guidance is not yet a fundamental technology with the revenue of, say, a market leader in an enterprise application category such as ERP, CRM, or BI.

In this light, AI believes that the Promontory acquisition is actually a key turning point for monetizing Watson. If IBM can effectively use Watson to both enhance Promontory’s existing services and to replace the time-consuming aspects of regulatory compliance in a way that materially affects revenue, IBM will be able to make similar acquisitions across a wide variety of industries and unlock a latent multi-billion dollar market of Watson-guided consulting. The goal must be to both template Watson training for deeply detailed and morphing topics, such as compliance, as well as retain human subject matter expertise capability of upgrading Watson’s knowledge over time. Anybody keeping a close eye on the future of IBM should keep a close eye on the success of Watson Financial Services in delivering Watson for Regulatory Compliance over the next 24 months.

IBM’s DataFirst Method for Analytic Consulting

Finally, AI also learned about IBM’s DataFirst Method for providing analytics services. This method is a consistent series of stages that all IBM consultants across Global Business Services and Global Technology Services use in identifying and developing analytic services. The key aspect of this process that got my attention was at the outset, where the Briefing and Vision, Discovery Workshop, and Design and Validate aspects take place.

In particular, the time frame was especially interesting. The Discovery Workshop is designed to occur over a couple of weeks while the Design & Validate stage is designed to create a prototype and to assess the business value of the solution over a couple of months. In other words, an IBM-led analytics consulting engagement could simply be a 6 – 8 week sprint designed to define and prototype a use case rather than the multi-million dollar, long-term consulting engagements that one would expect an IBM, Accenture, or other top-tier consulting firm to conduct. And while AI is sure that IBM would not turn down long-term consulting engagements, it was enlightening to find that IBM is also becoming faster and more agile in its consulting engagements.

IBM DataFirst - Source: IBM
IBM DataFirst – Source: IBM

With these defined stages, IBM can start fast and either support ongoing SaaS purchases or set up long-term consulting engagements without locking a client into the commitment. AI will be interested in tracking the adoption of DataFirst-based consulting services over time and see how these initial stages convert to longer-term Run & Maintain contracts. This multi-stage method providing rapid and repeatable introductions to IBM’s cognitive journey, problem solving, and collaborative approach is a welcome change from traditional consulting approaches conducted by Big Four consulting firms.

One Final Note: Introducing IBM Think 2018

Overall, the trip to IBM Vision was very educational in seeing how IBM has evolved its analytics and performance management offerings over the past year. In that note, AI admits some sadness in finding that this is IBM Vision’s last year, as IBM announced that its events would be coming together into one megashow, Think 2018, scheduled for March 19-22 in Las Vegas. This consolidation is an evolution of IBM’s 2017 event scheduling when both InterConnect (focused on the Cloud) and Amplify (focused on Customer Enagement) were both in Las Vegas in the March 20 – 23 time frame. Having been in Vegas for InterConnect, AI found it difficult to break out and attend Amplify sessions associated with AI’s coverage of technology-augmented personalized customer relationships. Based on this year’s experience, AI wonders how Think 2018 will be designed to support executives with multiple business priorities.

Vision has been a personal favorite among IBM’s events both for its intimacy and its focus on business performance. AI hopes that IBM will maintain the feel and focus of IBM Vision, perhaps with some residual data discovery and management additions, and hold a “conference-within-a-conference” at Think 2018. This approach would provide a critical mass of shared interests, which often lead to the unexpected insights and eureka moments gained at conferences.

Overall, AI found IBM Vision to be both an interesting validation of IBM’s current ability to support analytics and financial planning and an important touchpoint in assessing IBM’s integration of cloud products as IBM races to aggregate and productize the individual component data and analytics products already in place. Based on this show and previous briefings and analysis, AI highly recommends IBM’s current cloud analytics capabilities and looks forward to the continued evolution of IBM’s cloud analytics value proposition.

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