Quality is not a law, and organizations do not have to subscribe to the principles of it. However, for those that choose to embrace and invest in data quality, it pays off – and often significantly! An organization' s commitment to data quality attracts customers, generates revenue, and builds and maintains an organization’s reputation. Plus, an organization’ s global standard of quality supports regulatory compliance as well.
Customers notice all of this. After all, quality is what defines an organization. For an organization to fully realize the benefits of a global standard of quality, its commitment to quality must stretch throughout and across its value chain. This requires strict process controls maintained throughout the quality system. The International Organization for Standardization (ISO) is recognized worldwide as the authority on quality management. The benefits to achieving ISO certification are many, including improved efficiencies, increased revenue, risk mitigation and process consistency.
Data quality involves two factors – validity and reliability. Validity means that the answers we receive apply to the information we are seeking preciously, while reliability describes the ability of an instrument to demonstrate consistent results. Having strong quality measures and standards in place allows researchers to trust the data that's being generated. It enables market intelligence, clinical, health outcomes and real-world evidence researchers to develop and make quality, evidence-based decisions for their internal and external stakeholders.
The highest quality data in healthcare research is clinical grade data. This data is collected in a well-controlled environment and put through stringent validation processes. In market intelligence, data quality measurement is different, as opinion can change with data subjects responding to the same question differently even on different days. To assure data quality, validation and verification of data must be addressed.
For verification of work, all data entry must be keyed verbatim as recorded on the questionnaire, and built-in logic checks should be utilized where possible. For data quality validation, records are required and actions must be taken if problems are identified. This is typically performed by one of three methods: conducting validation independently by two different coders; applying system validation where it checks the coding results performed by the original coder; and utilizing self-validation, which is based on the experience and competence of the coder.
Finally, applying the right quality standards and quality procedures to research projects is done in consideration of the study type and research area, as well as in accordance with international and local regulations. This is also based on ethical principles and published quality standards. Today, more than ever before, a company’s brand, its reputation, and its commercial success are forever linked to the integrity and trustworthiness of data.
At Kantar, a global leader in healthcare market research, we have a proven system in place for managing our proprietary Quality Framework. We employ a Global Compliance and Quality Director, who leads Kantar’s quality program, as well as designated, local Quality Representatives assigned with a wide range of responsibilities. These responsibilities include implementing and monitoring the quality procedures in their unit, conducting internal audits, maintaining local documentation, and managing local vendors. Our active monitoring and management of our Quality Framework, which involves consultation with Kantar’s senior management team, ensures that project management processes are always operating effectively.