Introduction

I have been in love with Data from the time I first laid eyes on her. (Is data gender-specific?) But I am not formally trained in Data Management or Data Quality. However, through various career moves, I now manage a data management team and almost all my work involves data management and data quality.

My formal training is as an Actuary. And my 13-year career has been entirely working in the insurance industry, mostly involved with pricing of insurance policies. To determine the prices, we have to look at how the past has performed to try to project the future. There is definitely a lot of data that is involved in these projections.

Because of the sheer amount of data needed for pricing decisions, I had been doing some form of data quality throughout my career, though very informal. Either it would be reconciling data with other sources, checking it for reasonableness or checking to see if certain business rules were followed. (I did not call them business rules at that time.)

Last year, for the first time, I embarked on my first formal data quality project. It was met with some successes and MANY learning opportunities (I don't believe in failures). I learned a lot from this first project and have applied that knowledge to help build a process for my current projects.

Since I was not formally trained on Data Quality, I have had to do a lot of research and I have found a lot of interesting sources on the web which have been invaluable. One such resource is DataQualityPro.com. I saw a tweet the other day about "20 simple tips to spice up your data quality blog" at http://www.dataqualitypro.com/data-quality-home/20-simple-tips-to-spice-up-your-data-quality-blog.html. I had been reading other blogs, especially those by Dylan Jones on DataQualityPro.com, Daragh O'Brien at iaidq.org and Jim Harris at ocdqblog.com. These blogs have given my some good resources to use for my own work.

I was reading through the 20 tips that Dylan had provided and started thinking of ideas for my own blog. My perspective on data quality will be as an Actuary and from the insurance industry. It is a different perspective than what is seen in much of the data quality literature. I will be expanding on it in future blogs.

I look forward to the insights from the experts (and others who just have opinions) since I still consider myself a novice in the world of data quality.