Data quality management deserves further attention simply because many companies have yet to ­master it; few companies, in general, seem to have data that meet their quality standards. Life-sciences companies have always sought to manage the quality of their master data — records on customers, products, territory alignment, sales performance, and other metrics that are used throughout the organization — so why reintroduce the subject now? What makes the perennial issue of data quality especially important today? Because changes are taking place in how the business views the importance of these assets. Consider that: » Merger and acquisition activity is on the rise. » Regulatory requirements place new ­demands on master data completeness and accuracy. » Companies are outsourcing responsibility for master data management through a software-as-a-service model. » Increasingly, master data are being used globally. » The need for accuracy in sales reporting for compensation purposes has never been greater. Against this backdrop of change, master data remain a key corporate asset that are essential to managing business effectively and to building lifetime customer relationships. The benefits, in particular, of having a valid, unified view of your customer include better decision making, cost savings, risk reduction, and improved effectiveness. Limited Progress to Date Data quality management also deserves further attention simply because many companies have yet to master it; few companies, in general, seem to have data that meet their quality standards. The statistics on internal data quality, while not specific to the pharma sector, suggest that there’s room for considerable improvement: » A benchmark study by the American SAP User Group reported that 93% of ­companies experienced data problems in their most recent projects. » In research with 100 companies engaged in a quality initiative, nearly 40% of all company data was found to be ­inaccurate. Even the best-in-class ­companies were using inaccurate data to make decisions 23% of the time, and ­others discovered that 43% of their data were “bad." » A general-industry survey by Gartner found that 38% of ­respondents couldn’t even estimate how much poor data was costing them, while for some, the figure went as high as $100 ­million a year. How to Implement a Data ­Quality Management Program 1. Lay the Foundation with Data Governance and Stewardship Prior to developing a data quality management program, companies should create data governance and data stewardship policies, procedures, and structures that will underpin all data-related activities. 2. Determine Data Attributes and Dimensions to be Measured The uses to which information will be put should determine the data attributes (or elements) that will be measured in a quality program, so selecting those attributes is an important early step. 3. Define and Document Data Quality Standards The next step in the process is to determine “what good data look like," which, in essence, is the standard against which data quality will be measured. The intended use of the data will determine what constitutes an acceptable level of quality on the selected attributes. Acceptable quality is, in effect, a sliding scale that should be adjusted to the type of data and its use. 4. Establish Reporting Classifications So that the results of a quality assessment can have meaning for stakeholders, it is important to classify the different levels of data quality and explain their ramifications on data use. Below is one basic type of classification scheme: Prime: Data are without major defects; high confidence in data integrity. Reporting: Data contain known issues that have only minor business impact. Directional: Data contain issues that may result in inaccurate or incomplete reporting results. Recommended: Data contain significant issues related to quality, timeliness or completeness. These classifications clearly relate to, and stipulate, the data intended use. Data used for compliance reporting may need to be prime, whereas data used for sales reporting may be acceptable if it is only directional. 5. Develop Data Quality Scorecards The “health status" of each data source should be measured using specific metrics across the dimensions deemed relevant to that data/use (such as completeness, accuracy, timeliness, and availability). These metrics should be published for stakeholders in a “Data Quality Scorecard" along with comments from data stewards on the factors that are driving the health of the data. 6. Perform an Annual Data Quality Audit Data quality programs are dynamic and must be periodically reviewed to ensure that they are doing what was intended at every level. A proper audit will: » Compare the data values to a “golden" standard to establish a baseline measure of data quality and identify areas for ­improvement. » Examine all work instructions and ­standard operating procedures across the program for relevance and support of ­program goals. » Result in recommended changes to reflect current best practices. For this reason, the audit is best performed by a third party. IMS Health is a leading information, services, and technology company dedicated to making healthcare perform better. By applying cutting-edge analytics and proprietary application suites hosted on the IMS One intelligent cloud, the company connects more than 10 petabytes of complex healthcare data on diseases, treatments, costs, and outcomes to enable its clients to run their operations more efficiently. { For more information, visit imshealth.com.

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