In a blog post called JUDGEMENT DAY FOR DATA QUALITY published yesterday Forrester analyst Michele Goetz writes about the future of data quality tools.
Michele says:
“Data quality tools need to expand and support data management beyond the data warehouse, ETL, and point of capture cleansing.”
and continues:
“The real test will be how data quality tools can do what they do best regardless of the data management landscape.”
As described in the post Data Quality Tools Revealed there are two things data quality tools do better than other tools:
- Data profiling and
- Data matching
Some of these new challenges I have worked with within designing tomorrow’s data quality tools are:
Point of capture profiling- Searching using data matching techniques
- Embracing social networks
Point of capture profiling:
The sweet thing about profiling your data while you are entering your data is that analysis and cleansing becomes part of the on-boarding business process. The emphasis moves from correction to assistance as explained in the post Avoiding Contact Data Entry Flaws. Exploiting big external reference data sources within point of capture is a core element in getting it right before judgment day.
Searching using data matching techniques:
Error tolerant searching is often the forgotten capability when core features of Master Data Management solutions and data quality tools are outlined. Applying error tolerant search to big reference data sources is, as examined in the post The Big Search Opportunity, a necessity to getting it right before judgment day.
Embracing social networks:
The growth of social networks during the recent years has been almost unbelievable. Traditionally data matching has been about comparing names and addresses. As told in the post Addressing Digital Identity it will be a must to be able to link the new systems of engagement with the old systems of record in order to getting it right before judgment day.
How have you prepared for judgment day?
Liliendahl on Data Quality 