IBM Think in San Francisco was a huge technology event with approximately 27 000 attendees, thousands of different sessions, presentations and keynotes held in different venues in San Francisco.
Due to the size of the conference we wanted to focus on certain key areas: AI, machine learning and analytics. There were about 500 data and analytics presentations to choose from. Topics covered areas such as data science, AI, business and planning analytics, hybrid data management, governance and integration. IBM Cloud Private for Data alone had 18 sessions where this new product was presented.
Solita has a strong expertise in the area of analytics
Solita has a strong expertise in the area of analytics (Cognos Analytics & Planning Analytics) and we wanted to strengthen our competence and learn about upcoming releases of those products. We had a chance to meet IBM’s offering management and discuss new features and give feedback. There were also several hands-on labs where one could test upcoming features of products.
Although Planning Analytics (PA) was a bit of a sidekick compared to buzzwords like AI and Blockchain, the PA sessions provided good information about the new features and on-going development. In addition, there were several different client presentations providing insights into their CPM solutions. Interestingly, many of those presentations were still focusing on TM1 technology and not on Planning Analytics even though TM1 support will end on 30th of September 2019.
AI and data science were strongly present on IBM Think agenda. Success stories on AI implementations were told for example by Carrefour (retail chain who wanted to optimize existing and new supermarket investment decisions), Nedbank (bank that used predictive maintenance to optimize AMT services), Red Eléctrica de España (electrical company that wanted to predict generation and optimize production) and Daimler (truck manufacturer using AI to comprehend the complexity of product configurations).
Also AI project best practices were shared in many of the sessions.
Also AI project best practices were shared in many of the sessions. Best practices included starting with a quick-win use case to gain buy-in from management and business, having a business sponsor for the project, measuring clear KPIs and business impact and, good quality data, creating effective teams, choosing the right tools, etc. These are all principles we definitely agree on and that are already now implemented in Solita data projects.
What else did we learn in IBM Think 2019? Deep dive into learnings coming up!