Blog Administrator | Analyzing Data Quality, Data Cleansing, Data Integration, Data Management, Data Quality | American Health Information Management Association, Data Quality Act, Information Quality Act, Melissa Data, real costs of poor data quality, track data flows

By Elliot King

In 2001, the U.S. Congress added two lines to its major
appropriations act that required that the Office of Management and Budget
“provide policy and procedural guidance to Federal agencies for ensuring and
maximizing the quality, objectivity, utility, and integrity of information
(including statistical information) disseminated by Federal agencies.” These two
lines have come to be known as the Data Quality Act (DQA) or the Information
Quality Act.

While the DQA applied to federal agencies, it also put a marker in the ground.
Regulatory agencies could demand that data given to them meet criteria set by
the Congress. Government data had to be accurate, objective and have integrity
as a matter of law.

The same notion has spread to corporate America and other specific industries.
In one of the most high profile examples, the Dodd-Frank Wall Street Reform and
Consumer Protection Act passed in the wake of the financial meltdown of 2008
established the Office of Financial Research with a mandate to improve the
quality of financial data accessible to regulators.

One of the primary challenges for financial institutions is to insure that the
information they supply to regulators is consistent across all their divisions.
Companies are also going to be able to track data flows and usage and develop
chains of custody that can be audited. The net result should be that all users
of the data will see consistent information. And that may not be easy when data
is siloed in different operating entities, which is often the case in those
too-big-to-fail financial behemoths.

The regulatory pressure for data quality is being felt elsewhere as well. The
American Health Information Management Association, which promotes the
technological advancement of health information management systems, has noted
that for electronic health records (EHR) to have the positive impact on overall
health care that their proponents anticipate, data quality can no longer be a
reactive process based on auditing but must be proactively focused on data
capture. Standards to ensure that result will be built into the requirements for
EHRs.

While many folks in IT chafe at government regulations. However, the Federal
government often has been a leader in IT innovation. Think about the Internet.
Federal IT initiatives have also been inept–think about the FBI case management
system or the Air Traffic Control system. While burdensome, the regulatory
demand for higher quality data could easily have a positive ripple effect that
will spread widely.