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 . . . → 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 . . . → Read More: Big Data: 3 Criteria for Invaluable Analytics
Actually working with Big Data versus hearing the hype of Big Data and NoSQL vendors continues to amaze me. If I see Volume, Variety, and Velocity as the description of Big Data one more time I won’t be able click away from the web page fast enough. So below are three main questions you should . . . → Read More: Big Data: 3 Questions to Ask When Comparing Relational to NoSQL Databases
As I get ready to travel to IDUG Berlin I look back at the Information on Demand (IOD) conference and remember the importance of all the components that enabled the conference theme, Big Data Analytics.
The first components are the front-end products Cognos and the new QMF. While sharing a cab to the airport . . . → Read More: Big Data after the IOD Conference Thoughts
It was a good Information on Demand conference with Information Management, Business Analytics and Big Data taking their turns as the most important subject. Even the thoughts I mentioned in last week’s blog about management by magazine indicated by the latest Harvard Business Review cover story on big data and an interior article describing the . . . → Read More: DB2 10 Rocks Big Data