Three take-aways about big data analytics from IBM’s recent big data announcement
Last week I attended the IBM Big Data at the Speed of Business Event at IBM’s Research facility in Almaden. At the event IBM announced multiple capabilities around its big data initiative including its new BLU Acceleration and IBM PureData System for Hadoop. Additionally, new versions of Infosphere Big Insights and Infosphere Streams (for data streams) were announced as enhancements to IBM’s Big Data Platform. A new version of Informix that includes time series acceleration was also announced.
The overall goal of these products is to make big data more consumable –i.e. to make it simple to manage and analyze big data. For example, IBM PureData System for Hadoop is basically Hadoop as an appliance, making it easier to stand up and deploy. Executives at the event said that a recent customer had gotten its PureData System “loading and interrogating data 89 minutes.” The solution comes packaged with analytics and visualization technology too. BLU Acceleration combines a number of technologies including dynamic in-memory processing and active compression to make it 8-25x faster for reporting and analytics.
For me, some of the most interesting presentations focused on big data analytics. These included emerging patterns for big data analytics deployments, dealing with time series data, and the notion of the contextual enterprise.
Big data analytics use cases. IBM has identified five big data use cases from studying hundreds of engagements it has done across 15 different industries. These high value use cases include:
- 360 degree view of a customer- utilizing data from internal and external sources such as social chatter to understand behavior and “seminal psychometric markers” to gain insight into customer interactions.
- Security/Intelligence- utilizing data from sources like GPS devices and RFID tags and consuming it at a rate to protect individual safety from fraud or cyber attack.
- Optimizing infrastructure- utilizing machine generated data such as IT log data, web data, and asset tags to a improve service or monetize it.
- Data warehouse augmentation- extending the trusted data in a data warehouse by integrating other data with it like unstructured information.
- Exploration- visualizing and understanding more business data by unifying data across different silos to identify patterns or problems.
(for more information on these use cases there is a good podcast by Eric Sall)
Big data and time series. I was happy to see that Informix can handle time series data (it has been doing that for several years) and that the market is beginning to understand the value of time series data in big data analytics. According to IBM, this is being driven in part by the introduction of new technologies like RFID tags and smart meters. Think about a utility company collecting time series data from the smart meter on your house. This data can be analyzed not only to compute your bill, but to do more sophisticated analysis like predicting outages. Now, it will be faster to analyze this data because BLU Acceleration will be used with IBM Informix. This is a case of a new kind of data being analyzed using new technology.
The contextual enterprise. Michael Karasick, VP of IBM Research talked about the notion of the Contextual Enterprise which is a new holistic approach of dynamically building and accumulating context at scale from disparate data sources to deliver client value. These utilize data from what IBM calls systems of engagement (sources such as email, social data, media) together with traditional data sources in a gather, connect, reason, and adapt loop.
There is definitely a lot to wrap your head around in these big data announcements. The bottom line though is that the goal of these new products is to provide ease of use and improvements in performance and capabilities which can help improve big data analytics. The products can help improve what companies have already been doing with analytics because it is now faster to do it or they can help companies to perform new kinds of analysis that they couldn’t do before. That is what big data analytics is about.
Posted by Fern Halper, Ph.D. on April 8, 2013