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During the Big Data DAMA-NCR Meeting in Washington D.C. this week, I heard from Svetlana Sicular, Research Director of Data Management Strategies at Gartner Group; Raul F. Chong, Senior Big Data and Cloud Program Manager at IBM; and John Adler and Madina Kassengaliyeva from Think Big Analytics. Their insights into Big Data projects were quite . . . → Read More: Big Data Day Recap — 5 Very Interesting Items As I talked recently in my blog “Big Data: 5 Considerations for Disaster Recovery,” Big Data disaster recovery drives companies to intense analysis of the big data usage and its importance. Getting the business to discuss and define the Recovery Time Objective (RTO) and Recovery Point Objective (RPO) for Big Data is a very . . . → Read More: 3 Big Data Disaster Recovery Performance Factors Big Data drives the issue of disaster recovery to the front of the discussion. How critical or how much benefit your Big Data business application is to the bottom line of the business is directly proportionate to your Big Data disaster recovery planning efforts. The following five considerations will help your evaluation of the data . . . → Read More: Big Data: 5 Considerations for Disaster Recovery Big Data analytics are all in fashion these days, but there are many issues with how analytics are used, and how developing the appropriate analytics takes a correct and thorough data model. As I talked about in my previous Big Lies, Big Damn Lies and Statistics blog post, there are many different ways analytics . . . → Read More: Big Data: Leverage the New Fantasy Football Data Model? The last two weeks I talked about evaluating reporting tools, the different tools criteria categories, and determining the particular important sub topics that are unique to your company. This week we are going to talk about the Big Data itself, and four ways to determine the best areas to cover in your analytical research for . . . → Read More: Big Data: Four Ways to Identify Domains Areas for Analysis The following are the remaining five criteria for your Big Data analytics reporting tool. As I mentioned last week, weightings for each criteria category should be discussed, along with adding your company’s sub-topic considerations, to calculate the total best score. By using these criteria and attributes as a starting point your company can quickly understand . . . → Read More: Big Data: Ten Criteria for Evaluating Analytics Reporting Tools (Part 2) Last week I talked about how to get involved in the latest conversations about Big Data analytics. When working with the Big Data analytics, the end business users reporting tools are critical. Your older tools may not be up to today’s Big Data analytics capabilities, such as delivering answers to the “bring your own device” . . . → Read More: Big Data: Ten Criteria for Evaluating Analytics Reporting Tools (Part 1) Last week I talked about five new data management perspectives that are driving Big Data. In Part Two of Big Data New Data Management Perspectives I would like to focus on the Big Data analytics. In my blog over the years I have mentioned how business analytics are making the difference between winning companies and . . . → Read More: Big Data: Five Ways to Get Analytics Started With each new trend there is always the hype cycle and the technology readiness level. The new technology or new capability provides a unique way to solve or analyze a business problem. The hype cycle is the sales and marketing by magazine crowd. The technology readiness level evaluation provides more a tested and realistic point . . . → Read More: Big Data: Part1 – New Data Management Perspectives In last week’s blog post on company and government analysis of our digital footprints, I mentioned several aspects of the Big Data digital horizon. The last point that I made in that post about the other data aggregators was highlighted in the movie preview of “Terms and Conditions May Apply” that details these data . . . → Read More: Big Data: 3 Criteria for Invaluable Analytics |    | ||
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