At IBM Connect, Blue Hill had an interesting discussion with David Brooks, CTO of IBM Watson Work, who provided insights on how IBM is differentiating. As a CTO who has brought large-scale applications to market, Brooks seems to be a perfect fit for the likes of Facebook and Google, which are also known for their massive scale. In hearing about how IBM is reacting to this new paradigm of computing, Blue Hill heard how IBM was embracing open standards to accelerate business.
More interesting, Brooks spoke about his team was able to move from code to production in a matter of hours. This Fail Fast, Move Fast mentality is an interesting change for IBM compared to the IBM that many of us encountered earlier in our tech careers. However, this combination of open, API-based, rapid-code, and design-based culture is a necessary step forward both for IBM and other large organizations seeking to effectively compete with startups. The fact that IBM has taken this step forward in its Watson Work organization is an encouraging sign of developing true next-generation solutions.
Blue Hill also saw interesting innovations coming from the IBM Research Innovation Lab, which provided three especially interesting examples: a Network Data API (Visualization: Network Data API and Q&A Confusion Explore), Video Scene Detection (Video Scene Detection: Enriching Video Content), and a taxonomic demonstration of Skills within Your Enterprise (What Skills are within Your Enterprise?).
The Network Data API provides a networked visualization of questions related to each other. The example demonstrated was to show questions asked through the Watson Talent solution related to onboarding and benefits. By showing which questions were related to each other, the network data API provided guidance on when questions might be semantically similar or related. Blue Hill believes that networked or nodal analytics has been highly underused in the enterprise compared to the potential value and that this demonstration and similar networked visualizations should be closely tracked by analytics professionals seeking to understand the next key trends in seeing analytic relationships.
The Video Scene detection solution provided an automated solution for separating multi-scene videos based on color contrast. By itself, the solution provides a useful and direct method of separating scenes. But the real value will come from combining this capability with existing IBM Watson Video and analytic capabilities including facial recognition, automated transcription, and sentiment analysis to provide greater automated guidance into video. With this tool, IBM comes one step closer to independently categorizing and understanding video without direct human intervention.
But the most important research project that Blue Hill saw was the skills taxonomy created by IBM Research. By exploring the corpus of IBM Research documents, IBM was able to categorize the types of skills within their organization through a bottoms-up approach. This scientific method of understanding skills within the organization is going to have repercussions across recruiting, learning and development, resource management, and project management as companies gain the ability to use this bottoms-up approach and stop guessing whether a job or resource requisition is correct or not. IBM has created a research-based solution to eliminate this problem based on existing documentation. Blue Hill is eagerly anticipating the progression of this research project into IBM Kenexa/Watson Talent in the near future.
This set of conversations and demonstrations provided Blue Hill with significant guidance on the future direction of IBM Watson Work innovation and progress. Based on this event, Blue Hill anticipates future product announcements based on increased agility, taxonomic analysis, nodal relationship analytics, and video analytics in the near future.