Over the past few posts we've looked at developing an integration strategy to enable the rapid alignment of candidate business processes with the services provided by a master data environment. As part of a preparatory step, it is valuable to at the very least understand the implementation requirements to meet the
I recently discovered this technical white paper on SAS’ customer support site called Data Modeling Considerations in Hadoop and Hive, written by one of SAS’ R&D teams. I was intrigued by the team’s findings, so in this post, I want to share its highlights – without getting into the technical
It is easy to consider data migration as a movement problem. After all, we need to get our data from A to B with as little effort and cost as possible. With this viewpoint, many practitioners commence mapping and linking the source and target systems together to form an elaborate
As others have pointed out, 2013 may well go down as the year of Bitcoin, the first "mainstream" form of cryptocurrency. It's easy to dismiss Bitcoin as a fad, but other events from the previous year suggest that privacy is making a comeback. Exhibit A: Temporary photo and message app Snapchat, arguably the
My previous post was inspired by what Andrew McAfee sees as the biggest challenge facing big data: convincing people to trust data-driven algorithms over their expertise-driven intuition. In his recent VentureBeat blog post, Zavain Dar explained that the real promise of big data is that it will change the way
Let’s say that you have successfully articulated the value proposition of incorporating a master data capability into a customer’s business application. Now what? If you are not prepared to immediately guide that customer in an integration process, the probability is that a home-brew solution will be adopted as a “temporary”
How well do you know big data in the retail industry? Want to find out? Read the following statements and pick which one is false: In the retail industry, big data is still five years away from becoming mainstream. In 2013, large billion dollar retailers spent an average of $75,000, or
Dylan Jones says one way to improve data accuracy is to increase the frequency and quality of reality checks.
While not quite at the level of big data, data discovery is attracting a good bit of attention these days. I explore both topics in The Visual Organization and Too Big to Ignore. It's only fair for people to ask if their legacy reporting tools support big data and data discovery. In short, the
“As the amount of data goes up, the importance of human judgment should go down,” argued Andrew McAfee in his Harvard Business Review blog post about Convincing People NOT to Trust Their Judgment, which is what he sees as the biggest challenge facing big data. “Human intuition is real,” McAfee
