The "install, run and ... poof" magic

I spent many years on the enterprise software side, hardly aware of the existence of something called “data quality.” In fact, like many people in the ERP realm, I probably contributed to the problem, because I was focused on slotting data into the right fields without stopping to consider the actual content. Today, we’re hearing a lot more about data quality, particularly when it comes to customer data, and increasingly when it comes to product data.

It’s not really surprising that the term means different things to different people and is used for varying purposes. Data quality has become more of a marketing slogan than a well structured and defined concept, with many consultants and software companies jumping onto this amorphous bandwagon.

I’ve spent the last three years in the development of computerized systems and best practices to solve the mess we helped to generate over many years. It is tough, complex, and requires a lot of experience and know-how as well as a profound understanding taxonomy and in many technical domains.

Here’s the bad news: there are no real simple solutions to this very complex problem; furthermore, crappy data created by over the years can’t be automatically solved by a magic tool: “Install, run and… poof!”

But here’s the good news: experience and know-how, best practices and methods, suitable software tools and hard work can solve the problem and bring the quality of the data to the right level.

Lately I’ve been seeing more and more promises of magic wands and tools that automatically and painlessly fix all the data quality problems and live happily ever after. Well, I too am looking for such a magical spell book!

In the meanwhile, I thought it will be more practical to share my thoughts with those involved in the data quality realm, bring the complex issue of PDQ down to earth, and maybe save some growing pains.

I do not pretend to be objective – I am biased. I develop systems and practices, run projects all over the world and am confronted with new challenges every day. But I am going to write about the real world, without marketing hot air and without delving into the realm of theoretical concepts.

I will be happy to receive your comments and to publish your thoughts regarding the data quality domain.