Table of contents for IBM Big Data Analytics Analyst Insights 2013
- Driving Transformation with Big Data Analytics #BDA13
- Business Value of Big Data and Analytics #BDA13
- Big Data and Analytics: Fueling Competitive Advantage #BDA13
- Using Analytics for Competitive Advantage #BDA13
- Big Data and Analytics Use Cases #BDA13
- IBM Watson Engagement Advisor #BDA13
- An IBM Innovation Panel #BDA13
- Enhancing the Client Experience #BDA13
- Addressing the Analytics Skills Gap #BDA13
- Big Data Inspires Analytics Innovations at IBM #BDA13
- Speed of Analytics: Why Infrastructure and Platforms Matter #BDA13
- IBM Solutions for Insight Driven Marketing #BDA13
- IBM Counter-Fraud Point of View #BDA13
- Closing thoughts on IBM Big Data Analytics #BDA13
Another customer panel followed focused on big data and analytics use cases with a particular focus on streaming data, data in motion. Customers were University of Ontario Institute of Technology and Aginity.
Dr Carolyn McGregor from UOIT is someone I have blogged about before. Her focus was on using analytics to process the data from medical devices (rather than using paper charts). She became an early user of what has since become the InfoSphere Streams environment to show how analytics can identify, from streaming data, that a premature baby is developing an infection before the NICU staff would see any symptoms.
Aginity meanwhile has a focus on customer interaction and on making interactions more personalized by spotting patterns, matching them to customers and taking appropriate actions in different marketing systems to maximize customer value. Their retail customers are gaining the ability to stream in data about a customer while they are in the store and push messages or offers to customers in real-time. This is making analytics for marketing more complex and driving towards real-time stream processing.
There’s also a strong privacy/opt-in angle. After all, as they point out, you can’t thank someone for doing something unless you are REALLY sure they did it! You also want to be personalized without being creepy. Privacy and data management are an issue for the UOIT work too as this is personal data that they want to be able to use for research etc. In both cases, of course, the data is still very valuable even if anonymous as long as the data representing a single person (or a single cell phone for instance) can be linked over time. For instance a retailer wants to know if the same person comes back but they don’t necessarily need to know who that person is.
UOIT’s platform approach allows them to expand their focus from one problem with premmies to multiple ones. They have embedded additional algorithms in InfoSphere streams to handle various issues. Some of these are clearly being developed through data mining and analytic work while others involve a mix of data analysis and embedding best practices in the code (the reason decision management always involved both rules and analytics). Various head to head medical trials are underway while the platform is being used outside of neo-natal facilities and even outside of the hospital, picking up events coming in from home and remote monitoring devices.