Rapid Business Growth and Data Quality

My colleagues and I just completed a webinar for Trillium Software that discusses rapid business growth and it's negative effects on data quality. It was called "Improving Data Quality in SAP Netweaver Environments with Trillium Software". In hindsight, it was probably a poor name. The event focussed more on how rapid growth sets up a company for huge data quality problems and how you can use re-usable data quality processes to tackle the problem. We had only about half of the attendees that we usually get.
But, wow! Where there were shortcomings in picking a name, we must have struck a chord with the content.
I had some great follow-up calls directly after the event to follow-up and ask questions. A pharma company called and wanted to chat about doctor's data. You know, doctor data can be quite challenging in that they tend to work in multiple facilities. So, in order to match up duplicate doctors, you have to go off of non-name and address components and compare things like tax id number or provider number.
Another company, who I'll describe as retailer, called to talk about some of the professional services that I mentioned in my webinar. More and more, staffing a data quality initiative is a problem, but we can usually help out with our professional services team. The company wanted to go back and clean up some legacy data for re-use in a marketing campaign. They were asking for advice on how to get started. Since Trillium SW now has strategic planning services, run by some talented folks like my colleague Jim Orr, we were able to help.
That was just two of the eight or so follow-up calls I got. Great projects from some great companies.
As for the webinar, if you get a chance, please check it out and let me know what you think. The idea is that as companies grow, the fact that they are trying to grow rapidly, often with little regard to information quality, can be a major problem. Not only silos of data crop up, but silos of information quality processes appear. Everyone begins to put their own unique spin on what the data should look like, and before you know it, the information quality problem gets worse.