Big data and analytics technology can reap huge benefits to both individuals and organizations—bringing personalized service, detection of fraud and abuse, efficient use of resources and prevention of failure or accident. So why are there questions being raised about the ethics of analytics, and
When it comes to talking about big data technology, there's a big bowl of alphabet soup to digest: noSQL, EDW, HBASE, ETL, BI, ERP, JAQL and many others. Don't "Pig" out on the soup to the point where you break out in "Hives"—join us as we discuss the technology issues and help you understand what'
This is part 13 of our series on the findings and text from the IBM Institute for Business Value’s latest study and paper “Analytics: A blueprint for value - Converting big data and analytics insights into results” from my colleagues Fred Balboni, Glenn Finch, Cathy Rodenbeck Reese and Rebecca
Patient matching requires a solution that includes people, process and technology. There is no silver bullet such as a national patient identifier. The ONC Patient Identification and Matching Final Report reflected this long-held belief. Enjoy some highlights from this report below:
Seeing is believing. In this short demonstration, you will see the innovations announced in the prior session in action. You will see integration and governance applied to one of the most popular big data use cases: an extended 360 degree view of the customer.
Last Wednesday, February 26, the Big Data & Analytics Hub, which is where you're reading this post right now, and Business Analytics software at IBM ran a Twitter chat entitled, “Innovations in Analytics" with special guest, Seth Grimes (@sethgrimes). Seth is a leading industry analyst covering
Organizations know that there are insights hidden within data that can be the key to uncovering new sources of competitive advantage. Some of the data is proprietary and some is public, or quasi-public. Some of that data is traditional structured data, and some is unstructured, unformatted. At the
The era of big data is the era of messy data. This is the big data paradox: larger volumes and variety of new sources are inherently complex, and that complexity can actually lower confidence. Organizations have created an entire role to raise confidence in big data: the chief data officer.
There have been a number of high-profile missteps with big data in the news. One recent example is the story of OfficeMax and the “Dead Daughter” marketing mailing (read more about it here http://onforb.es/1hhjVCL). OfficeMax accidently sent a mailing with “Mike Seay – Daughter Killer in Car Crash
2014 has been hailed as the year when big data adoption goes mainstream. As adoption accelerates, I’m starting to see a change in the thinking about big data. More and more business and government leaders seem to be taking a step back and asking questions like these:
How can I understand the
As it was well explained by David Linthicum, building a business case begins with the definition of the problem domain. The drivers outlined in the previous section define most common high-level problem domains for MDM.
If your ROI and NPV estimates require a more detailed list of problem domains
Without a doubt, data integration is essential to the success of big data projects. However, some folks in the big data vendor community, including data warehouse, Hadoop and data integration vendors, are telling a very confusing story about the fitness of Hadoop as a data integration platform.