On Analytics, Data Platforms and Smart Applications
IBM displayed two sides of the company at its annual analyst forum last week: the mighty tech titan and the agile new design-driven Big Blue. Here’s my take on a company in transition.
IBM is a mighty tech titan that can make new markets by sheer force of will. It’s also a practical, agile and focused company that can dream up innovative products and services that give customers exactly what they need.
Both of these IBMs were heard from at last week’s annual IBM Analyst Forum in Stamford, Connecticut. In the mighty vein, analysts heard all about how cognitive computing, meaning IBM Watson, will set the company apart. We also heard executives talk about a new Big Blue that is moving beyond CIO/IT-oriented selling and delivering more actionable solutions with more hands-on technical guidance for line-of-business buyers.
A key component of the new Big Blue story is the embrace of design thinking, which The New York Times last week boiled down to “building what customers want, rather than building something and then trying to convince people that they want it.” After hearing about IBM’s many plans and initiatives last week, I’d say some plans and initiatives seemed design-thinking inspired while others sounded like big, market-making bets. Here, then, is my take on the two IBMs.
IBM Analytics on Spark
Analytics has long been a cornerstone for IBM, but it’s an area that’s understandably in transition due to larger market forces moving to open source, distributed computing options. IBM is adapting to increasingly popular open-source platforms including Hadoop and, more recently, Apache Spark. IBM embraced Spark in a big way in June, and it’s now porting plenty of IBM software to run on that open source framework.
IBM said last week that it’s embracing Spark for the same reasons it embraced Linux in 1999. I take that to mean it’s hoping to leapfrog competitors (like SAS and integrators like Accenture) by leading a wave of open source technology adoption. IBM even described Spark as “the analytics operating system,” and executives predicted Spark will have the same level of market impact as did the IBM 360 computer and Linux.
Spark has seen plenty of vendor adoption and support, but IBM insists it’s now at the head of the pack, poised to become the top contributor and committer to Spark. The company also pledged it will train more than one million data professionals on Spark. That will happen through the IBM-sponsored Big Data University community as well as through 11 DataPalooza events the company will hold around the globe over the next year. The three-day DataPalooza events show students how to develop data products using the new IBM Analytics for Apache Spark on IBM Bluemix. The stated goal is to “start a movement to create a belief that IBM is a trusted partner for data science success.”
MyPOV on IBM’s Spark Bet: Considering that it formally embraced Spark less than six months ago, IBM has managed to grab lots of attention. In fact, the buzz far outstrips the number of IBM-led Spark-related deployments out there in the real world. Far more numerous are customers using the company’s more mature analytics offerings around the IBM BigInsights Hadoop distribution, which include BigSQL, BigSheets and Big R.
Spark is a long-term bet for IBM. For now it’s sounding like the mighty tech titan touting “the largest investment in Spark of any company in the world.” But for this Spark bet to pay off, we need the story line to evolve and start getting into the compelling services and capabilities the company will offer. IBM says it has at least 16 products and services that now run on Spark, including IBM IoT on Bluemix, IBM Dataworks on Bluemix, IBM Swift Object Storage, the IBM SPSS Analytic Server and the IBM SPSS Modeler.
IBM is clearly trying to wear a white hat and win new customers with its training options, but I’m more interested in hearing about how customers will use (and are using) these new, reborn and refactored products and services on Spark. In my mind, Spark is definitely a destination for data scientists workloads, but I’m not sure big data types see it as an analytics “operating system” on which they want to run commercial tools.
IBM Cloud Insight Services
The new design-driven Big Blue came through in last week’s discussion of IBM Insight Cloud Services. When these services were discussed at last month’s IBM Insight event, I came away wondering how much custom app-development and consulting work would be required to put them into action. At the Analysts Forum I was pleased to learn that these are very purpose-built services designed to bring actionable insights into well-defined work environments.
A Social Merchandising Insight Cloud Service, for example, is aimed at common questions asked by retail merchandizers. These individuals have to gauge weather patterns, social comments, topical news, and internal supply chain forecasts. This cloud service analyzes data on all of the above to help merchandizers trigger targeted promotions, spot affinity opportunities, and optimize inventories in a timely way.
In another example IBM talked about a new Fan Insight Cloud Service developed for sports and entertainment organizations, and it described how the service is being used by the Ottawa Senators NHL hockey team, an early customer. The service helps marketers and ticketing teams make sense of fan attitudes and behavior trends so they can boost brand awareness and fan interaction and better target renewal, upsell and incentive offers.
IBM says it will deliver more than 20 of these cloud services. It’s using high-scale, high-speed infrastructure licensed from the soon-to-be-acquired Weather Channel to deliver the data, and it’s also weaving that company’s weather data into many of the cloud services.
MyPOV On Insight Cloud Services: These services are far more targeted than I expected, down to user roles and integrations with tools typically used. That’s good news and in line with a line-of-business-oriented selling approach discussed by several executives last week.
IBM has long excelled in selling to the CIO and IT. Here’s where the mighty IBM – trust us, we have one of everything – style of selling has worked for years. But tech buying is shifting to LOB decision makers. These buyers are far more focused on quickly implementing products and services that address their specific problems, so IBM says it’s bringing more technical depth and architectural understanding into selling situations. This is a positive sight that IBM’s field sales approach is evolving.
MyPOV On The Two Faces of IBM
For years we’ve heard IBM boast of investing billions of dollars here and billions of dollars there to go after new businesses. The company conjures up stats about how many zillions of zetabytes are being created or how many millions of documents are being published, suggesting that only IBM has the heft to solve such problems. But instead of emphasizing how tough the problems are and how enormous the investments have been by IBM, why not focus on the clever, practical and approachable solutions IBM has dreamed up for real-world, well-defined problems?
The scenario of being overwhelmed by information is solid selling point for cognitive computing. IBM is promoting Watson as an advisor to doctors who can’t possibly keep up with all the latest medical journals and clinical trials each year. It’s also developing Watson to serve as a financial advisor, assisting professionals who can’t possibly keep up with all the stocks, funds and dynamics in the market. Both of these problems are very real, but these advisor roles don’t exist today. IBM is trying to create new markets.
In other cases cognitive is being aimed at existing roles. Last week, for example, exec Mike Rhodin passingly mentioned cognitive as a tool for automating call centers to cut costs by as much as 40%. “That’s real money,” he said, almost apologizing for such a practical use of cognitive computing. IBM has announced pilot deployments in this call center role, and I’d love to know whether those early customers are ready to talk about proven cost savings? In this same vein, Wipro is training its Holmes cognitive computing technology to automate manual data-collection-and-analysis steps required in anti-money-laundering “Know Your Customer” compliance processes. Sounds like Wipro is less shy about focusing on more practical uses of cognitive computing.
Given IBM’s imperative to drive revenue growth, I would think the focus would be on low-hanging fruit where companies see near-term benefits. To my mind, the focused, agile IBM that’s applying design thinking to focused customer challenges is compelling. The mighty IBM that spends $1 billion here and $1 billion to go after grand challenges and forge all-new markets seems out of step. I’m hoping the new Big Blue is the one that’s more in evidence in the coming year.