Advances in context computing automatically makes sense of diverse data sources
Understanding context is a true marvel of the human brain. In fact, our default behavior is to try to find sense or meaning in even the most trivial sources. Did you try and find animal shapes in the clouds when you were young? Do you rationalize a sudden noise inside your hotel room as a problem with the AC? Who can forget the famous sighting of a religious figure in a grilled cheese sandwich? Ever try to make sense of this text?
Though a seemingly simple and intuitive strategy, understanding context (a process that takes mere seconds for the human brain) is still something that a computer can't do as accurately. Humans needed to develop this capability in order to survive. We needed to pick up on environmental cues and scene context to avoid extinction, to find food and to avoid enemies. A recent publication in Science Daily, explores how the brain creates context.
Some are predicting context computing will be the next big IT trend. IBM has been investing heavily in this area, under the leadership of Jeff Jonas, recently named a "Wizard of Big Data" by National Geographic. IBM defines context computing as: integrating diverse observations (data) as they arrive, in real time. Information in context is the essential ingredient required to deliver high quality insights, whether these insights reflect opportunity (such as the ability to personalize customer offerings) or risk (for example, customers on sanction lists). Information in context also enables the discovery of new, more accurate, predictive models. In a nutshell, IBM is helping clients leverage context for more informed and faster decisions which lead to better business results.
So what are the market opportunities?
- Execute the next best action, based on up-to-the-second information coming from big data sources such as sensors, text and geo-spatial positions
- Create higher quality offerings and leaner, more agile processes by optimizing business operations, infrastructure (such as utilization, capacity, performance) and the client experience
- Expand brand image and improve customer intimacy by executing real-time marketing campaigns
- Streamline operations by storing less data while also analyzing more in real time
- Find where threats are hiding in the noise of big data, uncover threats, identify patterns and correlations, pinpoint vulnerabilities and respond faster
Any context computing solution should include the following design features:
- Scale. Make sense out of huge data lakes think zettabytes
- Sequence neutral. Every observation is a question itself asking if this new information changes a previous assertion, if it does, the assertion changes automatically
- Analytics. Multi-cultural sensitivity on names since names globally are very different
- Designed for real-time but capable of batch. You can make a real-time system operate like a batch system by queuing up records and shoving them in but you can’t make a batch system operate in real-time
- Privacy by design. Real-time, sequence neutral analytics at scale, while protecting privacy and security of sensitive data
As businesses we want to be able to harness all data to create the next generation of our products and services. With its context computing offerings, IBM is helping organizations constantly sense and analyze the world around us to intelligently optimize decisions, processes, systems and points of interaction in the business moment.
So what is the business value of context computing?
Talking with analysts, clients and IBM experts, I came up with the following list:
- Effective, tailored offerings to the market of one. More clients consuming more products and services, as well as greater loyalty.
- Faster identification of risk and greater insight into non-obvious relationships. Spot weak signals in the noise of big data.
- Better predictive models enhanced with rich context and pattern discovery. Establish cognitive models for deeper business insight.
To get ahead of the trend, follow Jeff Jonas, IBM Fellow and chief scientist of Context Computing.
Jeff recently wrote about the 7 top tips for sense making technologies. Read more