Insights from IBM Insight

crowded-productsMy dad was a programmer for IBM in the 1970s, but I doubt he’d recognize the company today. Heck, I hardly recognize the company today. In a conversation with an IBM executive on the Analytics business unit, I was asked today, “What is IBM missing?” My response was, “just the story to tie it all together.” Even setting aside Watson, this is a company with an (over-)abundance of analytics tools, BI products, and mature databases. Combine that portfolio with huge investments into Spark, Hadoop, an analytics-as-a-service, and a healthy roadmap for all, and it’s not a question of gaps but one of overlaps.

I’m particularly impressed with the progress in three areas:

1. Spark. IBM has 15+ products already leveraging Spark as the versatile analytics engine. Plus 35 and counting Spark contributors. Plus training of hundreds of thousands of aspiring data scientists. Plus an available open source machine learning module. This all adds up to more than a press release, rather this is significant development of the Spark space.

2. Cloud Data Services. IBM has an undersung cloud platform with BlueMix and Cloudant, and may never achieve the cloudy reputation of AWS, Azure, or Google. Yet for a solid offering, it seems ready to go. Did I mention Spark-as-a-service above? Oh, yeah, IBM does that too. Big data is going to increasingly be done in the clouds. (Fun fact: GM Derek Schoettle and I graduated together from high school, but hadn’t spoken for 24 years until I saw him here today!)

3. Internet of Things. IBM offers four layers of value around IoT: industry transformation (business models), applications (for optimizing operations), platforms (and platforms of platforms!), and devices and networks (for connectivity). While it feels like these aren’t unified in a meaningful way yet, it does represent a lot of ways to engage with their customers and build custom IoT solutions.

While other players have made a lot of noise about big data revolutions, IBM has quietly been getting on with developing real-world solutions for real-world customers. This may well be the real story, with the company focused on using its industry expertise to hand-pick the right elements for each prospective customer and use case. Watson itself represents the future with cognitive, learning systems augmenting human knowledge, and Watson Analytics makes this tangible at a personal level.

IBM will need to act as concierge and consultant to get the right pieces in place, but arguably there is very little they can’t do today, and more in the future.

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Topics: Data Platforms, Analytics, & AI