Avoid Fire-Fighting Through Early Data Quality
To paraphrase a former US Supreme Court Justice, “good data quality is sometimes hard to define, but we all know it when we see it”. It’s even easier to see the effects of poor data quality on our business processes, our sanity, and our stomach lining. Without an enterprise focus on data quality, data quality issues tend to first become visible deep in the information structure, far from the data’s source and far from a point where a fix can be achieved quickly and efficiently.
When data quality problems are discovered too late, we have few choices, and none of them are ideal. We can let it go, leaving the problem unfixed for a later effort. We can go into fire fighting mode, cobbling together a fix that is neither optimal in result nor reusable in ongoing processes. We can begin the laborious process of tracing the error to its source, diagnosing the problem, and implementing procedures to correct and monitor the root cause for the future. (more…)
Bottom Up Data Quality
Data quality is driven by business rules, so why not push the business rules down to the business by having the business develop the rules in the first place using data quality software?
When that question is posed, there is a tendency to focus on the “not” rather than the “why” or better still the “how”. Let’s look at some common objections. (more…)
Loading ...