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Gartner indicated that by 2017, 33% of the largest global companies will experience an information crisis due to their inability to adequately value, govern and trust their enterprise information. In addition, data quality impacts business driven initiatives by only achieving 40% of the anticipated business value from projects and impacts labor productivity by 20%. To address information quality, usage, and maximize the value of information assets, leadership roles such as Chief Data Officers (CDO) and Chief Analytics Officers (CAO) are tasked to raise information-related business competencies within, across and external to their entire organization.
As White House author Todd Park explained, we are witnessing the emergence of a data-powered revolution in health care supported by digital data, clinical data, payment policies and rewards, and proactive care giving. With all these new avenues of data in healthcare and focus on value based care model, governing data quality plays a critical part in healthcare for data driven decision making. I have been implementing data quality for payers using Informatica tools. The common avenues of implementing data quality from a data governance decision making standpoint is as follows:
New enrollment of large and small group employers
Inbound and Outbound data file exchanges
Core systems such as Facets for claims and enrollment processing
Data quality for specific data domains in enterprise data warehouse
Analytical systems providing reports to business users
Meaningful data allows better and efficient decision making. Good quality data means higher performance, lower re-processing, and trusted reports for decision making. Having a set of processes that are formally managed through data governance to improve quality will have a positive impact in organizational data management. In addition, having people accountable for quality data can avoid adverse events and provide action plans for better data quality.
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