20 Encounters of the Information Management Kind – #7 Data Conversion Strategies is Where Quality Counts
Jun 17, 2013 by Joyce Norris-Montanari
I don’t believe you can commit to the success of a data conversion without addressing quality (or lack thereof). Do you agree? If so, then why are there so many conversion projects that just move data from one place to another?
Are you embarking on a data migration in the near future? If so, there is one nagging question that will loom across every stage leading up to the final moment of truth as your data finally lands in the target system:
“Will the migrated data be able to support our business functions post-migration?”
What makes a great data scientist? It’s an interesting question and, to be sure, an increasingly important one now that that we’ve entered the era of Big Data.
An organization’s perspective on data quality is often revealed by its data auditing practices. Some organizations practice data quality ignorance by not performing data audits, assuming that if they don’t check it or hear anyone screaming about it, their data quality must be good enough.
Last time we started to look at methods used in setting product prices, and I asked whether knowledge of customer type would contribute to the determination of a “fair” price for an item that might change in relation to customer type.
Jun 10, 2013 by Joyce Norris-Montanari
There are two cases, that I can think of, where you may have to consider whether to convert history in a data warehouse. They are:
Jun 07, 2013 by Dylan Jones
Do you want to know what one of the single largest causes of bad data is?
Irrelevant data has no value or place within your business, yet for many reasons it is still being maintained (badly).
Sheryl Sandberg’s new book has, as expected, ignited a debate over women in the workplace. Sandberg contends that many women don’t negotiate very well, and this inability is largely responsible for “the gender gap.”
One of my least favorite phrases in the data quality industry is “getting data right the first time, every time.” It’s not that I disagree with the premise of defect prevention. Even though it’s impossible to truly prevent every defect before it happens, defect prevention is highly recommended because the more control enforced where data originates, the better the overall quality will be for enterprise information.