How many times have you gone onto a website, put a few things in a shopping cart, and then exited the Internet? I do it all the time. Sometimes when I log on to that site during my next visit, those same items are still in my cart – ready for purchase. I find
Modernization. It’s a hot topic for organizations in all types of industries that are looking for ways to streamline hardware and software footprints while gaining control and insights from the data deluge. In the data integration space, this means we have to look beyond a traditional ETL approach to one
In my prior two posts, I explored some of the issues associated with data integration for big data and particularly, the conceptual data lake in which source data sets are accumulated and stored, awaiting access from interested data consumers. One of the distinctive features of this approach is the transition
I believe most people become overwhelmed when considering the data that can be created during event processing. Number one, it is A LOT of data – and number two, the data needs real-time analysis. For the past few years, most of us have been analyzing data after we collected it,
As we enter the era of “everything connected,” we cannot forget that gathering data is not enough. We need to process that data to gain new knowledge and build our competitive advantage. The Internet of Things is not just a consumer thing – it also makes our businesses more intelligent. Whenever
(Otherwise known as Truncate – Load – Analyze – Repeat!) After you’ve prepared data for analysis and then analyzed it, how do you complete this process again? And again? And again? Most analytical applications are created to truncate the prior data, load new data for analysis, analyze it and repeat
In my last post, I pointed out that an uninformed approach to running queries on top of data stored in Hadoop HDFS may lead to unexpected performance degradation for reporting and analysis. The key issue had to do with JOINs in which all the records in one data set needed
Since now is the time when we reflect on the past year and make resolutions for next year, in this post I reflect on my Data Roundtable posts from the past year and use them to offer a few New Year’s data resolutions for you and your organization to consider in
Henrik Liliendahl Sørensen recently blogged about the times when a HiPPO (Highest Paid Person’s Opinion) outweighs data in business decision-making. While I have seen plenty of hefty opinions trump high-quality data, those opinions did not always come from the highest paid person. The stubborn truth is that we all hold our
In my previous post, I used the book Mastermind: How to Think Like Sherlock Holmes by Maria Konnikova to explain how additional information can make us overconfident even when it doesn’t add to our knowledge in a significant way. Knowing this can help us determine how much data our decisions need to be driven
