KANTAR HEALTH BLOG

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Like Apples and Oranges!

by Jessica Santos | Jun 29, 2018

In the age of Big Data, data availability is no longer the biggest problem. Instead, the leading concerns of healthcare market research practitioners today focus on data quality. For this, we face a new set of challenges – ones that include reducing noise and bias, and eliminating false positives – for the production of quality data to satisfy a wide spectrum of healthcare stakeholders, including patients, caregivers, healthcare professionals, payers and regulators.

To maintain your corporate reputation and to gain deeper insights into areas such as awareness, perceptions, expectations, trends, predictions and epidemiology, constant attention must be paid to how data is gathered, verified, processed and utilized. This will protect the integrity and trustworthiness of the data and optimize its value in making better clinical and commercial decisions and better treatment decisions for patients.

Primary vs. Secondary Data – Very Different Challenges
While there are many standards in place for information collected from subjects directly, including ISO standards for data verification that I discussed in a previous blog, the challenges for achieving and maintaining primary and secondary data quality are very different – almost like apples and oranges!

That’s because with primary data, practitioners are challenged by people’s memories, inconsistency of moods, and changes of attitude, while with secondary data, such as databases, data subjects are no longer available for verification.

With secondary data, practitioners should be prepared for a new set of challenges. Data quality in an age of privacy concerns usually means that data collected on individual patients is accurate. With the rising use of secondary data that’s often fused or integrated, as well as the fact that secondary data may not be originally generated for the latest research purposes, data quality and transparency become more eminent.

Article 7 of the ICC/ESOMAR Code (ESOMAR 2016) states, in part, that: researchers must ensure that findings and any interpretation of them are clearly and adequately supported by data; researchers must allow clients to arrange for independent checks on the quality of data collection and data preparation; and researchers must provide clients with sufficient technical information about the research to enable them to assess the validity of the results and any conclusions drawn.

When healthcare market research is based on secondary data, or a blend of primary and secondary data, practitioners must consider how data is compatible and transparency can be ensured. To have complete confidence in data quality, a clear view of the quality of the original data sources is required, as the rules under which the data was collected and processed might not meet the requirements to merge with other data.

Please take a look at our latest white paper: "Quality Doesn't Cost – It Pays!". Or, feel free to reach out to me directly at Jessica.Santos@kantarhealth.com to discuss how Kantar Health can help you improve data quality in your organization.

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