Table of contents for IBM Big Data and Analytics 2014
- IBM Big Data & Analytics: Transforming Industries with Data
- IBM Big Data & Analytics: Customer Panel
- IBM Big Data & Analytics: Redefine the experience
- IBM Big Data & Analytics: Deepen Business Relevance
- IBM Big Data & Analytics: Transform adoption with cloud
- IBM Big Data & Analytics: Watson and Cognitive Computing
- IBM Big Data & Analytics: Analytics and Fighting Fraud
- IBM Big Data & Analytics: Predictive Customer Intelligence
- IBM Big Data & Analytics: Internet of Things and Predictive Maintenance
Glenn finch and John Murphy talked about deepening business relevance. The challenge, he says, with much of the work around analytics and data is how easily it drops into discussions of security, integration, scalability, performance etc. What clients care about though is business relevance – business value – being created by the use of this plumbing. To make data, or analytics, relevant takes channels and strategy not just technology. This is why IBM has created its new Strategy and Analytics group.
This kind of business relevance is a journey – it takes time, multiple projects,new or changed processes and organizational change. Enterprises can be transformed one business area at a time as part of a journey to being an analytic enterprise. Where IBM has invested is in getting to these more analytical processes fast – 30 days to change a manual process to a more automated, analytical one. This is where the Watson Foundation is applied, using Watson technology to learn how to make this decision quickly.
This kind of solution focus involves bringing multiple IBM products to bear, often leveraging cloud deployments to keep costs and time to deploy minimized. Plus the solution focus allows for pricing based on results not software as well as for more incremental pricing – delivering value quickly. These solutions often pull lots of data sources, structured and unstructured, to deliver analytic value. Resolving inconsistencies, rapidly integrating disparate types of data and making this available is critical to analytic solutions.
IBM has packaged up various products into solutions like its Predictive Customer Intelligence and Counter Fraud solutions. These are then specialized into industry-specific patterns or applications for rapid deployment and consumption – increasing business relevance.Technologies ranging from predictive analytics to entity analysis (from unstructured data), to decision management (to deploy),reporting and more. Indeed IBM sees a pattern of patterns here, where multiple data sources of various types are integrated, analyzed, used for decision-making and followed-up on with process or case management.