Everybody likes a pithy definition. Marketers describe them as “sticky,” meaning that they’re easy to remember. Of course, that doesn’t always mean they’re useful or completely accurate. Information Management has a couple. Metadata is almost universally described as “Data About Data,” but I’d be willing to bet that you rolled Read more…
For those who don’t want to click from page to page, here are all five articles in the Data Chasm series. In it, we explore the question of why we continue to see overwhelming numbers of analytics, artificial intelligence, machine learning, information management, and data warehouse project failures despite the Read more…
Relating the challenge of launching a new corporate process to a flywheel is a tired, overused metaphor. Probably because it so often applies. Like here. It’s been hard getting started. Probably several false starts. Progress has been agonizingly slow. But now you’ve found a new ally. Hopefully several. They showed Read more…
If you’re just joining, welcome. Before launching in at the middle, it might be useful to go back and read Part 1, Part 2, and Part 3. I’ll wait. Recapping very briefly: It seems that most everybody has become comfortable with the notion that the overwhelming majority of data warehouse, Read more…
This tale may sound familiar to companies that have not yet crossed the Data Chasm (and maybe even to some that have). A project team was modernizing a major operational system. One that had been written decades earlier. One where nearly all the developers and subject matter experts with knowledge Read more…
It all seems so easy. Companies are recognizing that the accuracy of artificial intelligence and machine learning applications is directly related to the quality of the data used to train the models. Obviously, you want to improve the quality of your company’s decisions, so it makes sense to improve the Read more…
This is the first in a series of articles that explores the question of why we continue to see overwhelming numbers of analytics, artificial intelligence, machine learning, information management, and data warehouse project failures despite the equally overwhelming availability of resources, references, processes, SMEs, and tools…and what can be done Read more…