Data Quality Poll Results: Edition #1

Thanks!

Whenever people participate in a polling exercise they provide a valuable perspective to the poller.  With that in mind, I'd like to thank all that voted in the first set of The Data Quality Chronicle Polling exercise.  It's been an exciting exercise that I'm looking forward to expanding on in the future.

Basic Poll Design

A polling section of the blog was setup in order to solicit information from the readers on various topics. In an attempt to gain a picture of the larger data quality picture, the first edition of the polling contained very basic questions. The questions were as follows:

1. What is the biggest challenge in data quality?


2. What is the biggest opportunity in data quality?


3. What is your data quality tool of choice?

Data Quality Challenges

With regard to the biggest challenge in data quality, three answers were pre-defined along with an "other" option for poll participants to fill in their own answer. The three pre-defined answers were as follows:

1. Access to the data


2. Comprehensive domain expertise of all the data involved


3. Duplication of critical data

Poll Results

The results of the polling regarding data quality challenges are depicted in Figure 1 below.

Figure 1 Biggest Data Quality Challenge Results

Data Quality Opportunities

With regard to the biggest opportunities in data quality, three answers were pre-defined along with an "other" option for poll participants to fill in their own answer. The three pre-defined answers were as follows:

1. Increased accuracy and confidence in critical data


2. Compliance improvements


3. De-duplication of master data

Poll Results

The results of the polling regarding data quality opportunities are depicted in Figure 2 below.

Figure 2 Biggest Data Quality Opportunities Results

Data Quality Tool of Choice

With regard to the data quality tool of choice, three answers were pre-defined along with an "other" option for poll participants to fill in their own answer. The three pre-defined answers were as follows:

1. Trillium


2. Informatica Data Quality


3. DataFlux

Poll Results

The results of the polling regarding data quality tool of choice are depicted in Figure 3 below.

Figure 3 Data Quality Tool of Choice Results

Conclusions

Even though the sample size was relatively small, I feel like there are some strong conclusions that can be drawn when reviewing this simple polling exercise.

The two strongest themes regarding data quality challenges seem to be that data quality is an important aspect of enterprise data management and that critical data to the enterprise is often duplicated.  This seems to be a common theme in the writings I have observed from many of the leading data quality professionals.

When it comes to summing up the biggest opportunity in implementing a data quality initiative the strongest theme was an increased accuracy and confidence in critical data.  This result seems to correlate strongly with the results regarding data quality challenges where data quality is defined as an important aspect of enterprise data management. 

In reviewing the results of the data quality tool of choice, my observations of the data quality software market were reinforced.  With so many vendors offering data quality solutions, no one tool dominates the market.  While Trillium  received the most votes; IBM, Omikron, and Datanomic were also popular choices.  For that matter the decision to "build over buy" was just as popular.

Again, I'd like to thank those that participated in this first edition of data quality polling at The Data Quality Chronicle! 

Keep checking back to participate in our second edition coming soon ...