The Market

Data quality software products generally fall into two categories:

  1. Data Profiling - investigate and measure
  2. Data Cleansing - make corrections

These are covered in more detail on the comparison page.


The following quotes highlight the important role that data quality plays in CRM, business intelligence, data warehousing, supply chain management, and more.

(These quotes have been gathered from public sources. Any added emphasis is ours. No endorsement or even knowledge of DQ Now is implied. External links are preceded by link_out.)

Poor data quality is like dirt on the windshield. You may be able to drive for a long time with slowly degrading vision, but at some point, you either have to stop and clear the windshield or risk everything.

Ken Orr, Cutter Consortium
link_outThe Good, the Bad, and the Data Quality, November 2000

For the next several years, corporate buyers will be bent on reining in IT budgets; they'll look for technologies that address business problems directly, provide a near-term return on investment, and improve customer acquisition and retention, cost cutting, revenue, or profits. The word from the top is clear: Make this stuff work. [1]

There's an expectation that it's time to get a return on these investments. The way to do that is to increase the quality of the data so that you can analyze who your customers are and think more strategically. [2]

[1] David Kirkpatrick, Fortune, link_outBeyond Buzzwords, March 2002
[2] John Cassella, PricewaterhouseCoopers, link_outBlame It All on Bad Data, April 2001

Survey responses from more than 600 companies indicate that slightly more than 25 percent have purchased a data quality tool in the past three years to five years. An additional 25 percent say they are evaluating or purchasing a tool this year.

The Data Warehouse Institute (TDWI) Survey: Data Quality and the Bottom Line
Quoted in link_outThe High Cost of Dirty Data, February 2002

Fully 75% of respondents reported significant problems as a result of defective data. ... Little over 40% of them were 'very confident' in their own data quality -- despite the fact that most of them professed to be responsible for it. ... Only 15% of respondents professed themselves ‘very confident’ about the quality of other organisations’ data, suggesting a vast chasm of unease among respondents over the accuracy and integrity of the mass of data now passing between organisations.

link_outGlobal Data Management Survey 2001

Ask anyone who has built a data warehouse or who is responsible for managing one, and they'll tell you that ensuring data integrity and cleanliness is the most difficult step in the overall warehouse project. By far the largest 'unexpected' labor cost in data warehousing involves data cleanup and its associated data loading processes. In fact, organizations often report that as much as 70% of their effort went into supporting the data cleansing and transformation process.

Curt Hall, Cutter Consortium
link_outData Integrity and Cleansing: Tools and Techniques

Data warehouses follow the "garbage in = garbage out" (GIGO) equation very closely. In fact, the millions of dollars spent designing, building, and implementing will be wasted without good data quality.

Bab Angell
link_outMaximizing Your Database ROI, Part 1: Data Warehouses, November 2001

Reason #1 [for the failure of CRM projects]: Data is ignored. ... Enterprises must have a detailed understanding of the quality of their data — how to clean it up, how to keep it clean, where to source it, and what third-party data is required. ... Action Item: Have a data quality strategy. Devote one-half of the total timeline of the CRM project to data elements.

Scott Nelson and Jennifer Kirkby, Gartner
link_outSeven Key Reasons Why CRM Fails, September 2001

Without paying attention to the accuracy of the data, [business intelligence] quickly leads to misguided decision making.

Ted Friedman and Eric Hwang, Gartner
Data Quality: Don't Sacrifice the 'Intelligence' in BI, June 2001
(not available on the Web)

Almost one-third of companies across all business models have had to deal with corrupt data as a result of information sharing within supply chains.

Helen D'Antoni, Information Week
link_outData Exchanges Permeate Supply Chains, May 2001

Bombarding your business trading partners (both customers and suppliers) with poor data will be harder to sustain than bombarding one's own management and knowledge workers with poor data. Trading partners will quickly go to another partner.

Jim Highsmith, Cutter Consortium
Quoted in link_outThe Data Quality Challenge

Next step: skim the many terms used for different aspects of data quality.