At IBM Think in February, IBM made several announcements around the expansion of Watson’s availability and capabilities, framing these announcements as the launch of “Watson Anywhere.” This piece is intended to provide guidance to data analysts, data scientists, and analytic professionals seeking to implement machine learning and artificial intelligence capabilities and evaluating the capabilities of…
On a monthly basis, I will be rounding up key news associated with the Data Science Platforms space for Amalgam Insights. Companies covered will include: Alteryx, Amazon, Anaconda, Cambridge Semantics, Cloudera, Databricks, Dataiku, DataRobot, Datawatch, Domino, Elastic, Google, H2O.ai, IBM, Immuta, Informatica, KNIME, MathWorks, Microsoft, Oracle, Paxata, RapidMiner, SAP, SAS, Tableau, Talend, Teradata, TIBCO, Trifacta,…
Note: This blog is a followup to Amalgam Insights’ visit to the “Change the Game” event held by IBM in New York City.
On September 19th, IBM announced its launch of a portfolio of AI trust and transparency capabilities. This announcement got Amalgam Insight’s attention because of IBM’s relevance and focus in the enterprise AI market throughout this decade. To understand why IBM’s specific launch matters, take a step back in considering IBM’s considerable role in building out the current state of the enterprise AI market.
IBM AI in Context
Since IBM’s public launch of IBM Watson on Jeopardy! in 2011, IBM has been a market leader in enterprise artificial intelligence and spent billions of dollars in establishing both IBM Watson and AI. This has been a challenging path to travel as IBM has had to balance this market-leading innovation with the financial demands of supporting a company that brought in $107 billion in revenue in 2011 and has since seen this number shrink by almost 30%.
In addition, IBM had to balance its role as an enterprise technology company focused on the world’s largest workloads and IT challenges with launching an emerging product better suited for highly innovative startups and experimental enterprises. And IBM also faced the “cloudification” of enterprise IT in general, where the traditional top-down purchase of multi-million dollar IT portfolios is being replaced by piecemeal and business-driven purchases and consumption of best-in-breed technologies.
Seven years later, the jury is still out on how AI will ultimately end up transforming enterprises. What we do know is that a variety of branches of AI are emerging, including
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
Amalgam Insights (AI) recently attended IBM Interconnect under the Social Influencer program with the goal of understanding how IBM is planning to position itself in context of technology market changes, investor demands to increase revenue, and the challenges of embracing innovation as one of the largest enterprises on the planet.
In observing IBM over the past few years, AI investigators have noted in the past that IBM faces the challenge of needing to create billion-dollar businesses just to maintain existing revenue. It is not enough for IBM to create a single startup such as Pivotal or Airwatch that ends up becoming a market leader in analytic application development or enterprise mobility. To drive 80 billion+ dollars in annual revenue, IBM needs to grow enough businesses to maintain pace while simultaneously divesting cash cows and declining margin businesses that are not strategic to future growth. Over the past couple of years, this has meant selling off assets such as Salary.com and semiconductor chip manufacturing (and possibly its mainframe division) while investing deeply into systems and capabilities that will drive upcoming business capabilities.
At Interconnect, IBM provided its vision for upcoming success focused on three areas: IBM Cloud, Cognitive computing services highlighted by Watson, and the promise of Blockchain.