Operationalizing Data Quality through Data Governance - Data Governance: Creating Value from Information Assets - page 85

business impact. If the organization has no quality culture, the concept of
data quality will be abstract, distracting, and annoying. Simply writing
new information capture procedures and putting them into a handbook
will not be enough to change the culture of the company; it takes training,
collaboration, and a willingness to make fundamental changes. Manuals
will be put on the shelf and ignored without the cultural adoption.
Step 4: Develop and implement necessary industry and enterprise spe-
cific standards. In the pharmaceutical industry, CDISC standards are well
known and accepted both by industry and regulatory bodies. CDISC has
developed clinical trial standards from trial design, data collection, data
processing, data analysis, to data submission. Implementing data stan-
dards in our clinical trial system allows for automation of data processing
and validation, which leads to high efficiency. In addition, an organization
needs to develop its specific standards according to its business needs that
are based on the industry standard. Different organizations will have dif-
ferent specific situations when implementing standards, e.g., implement-
ing CDISC standards end-to-end, including PROTOCOL, CDASH, LAB,
SDTM, ADaM, define.xml, and Controlled Terminology, and also uti-
lizing BRIDG (Biomedical Research Integrated Domain Group) as their
underlying information model. To leverage the standards, we should
build a metadata repository to govern data collection, data processing,
and data submission, and to leverage the usage of different standards
enterprise-wide. Taking a metadata-driven approach is an important way
to apply standards in the drug development process. 1 he metadata pro-
vides a foundation for connecting multiple processes and systems, thereby
allowing the creation of tools that largely automate the drug development
process. The metadata repository acts as the center of the universe to help
organizations proactively manage their data. Figure 4.5 illustrates how
the MDR is a central source where data standards are applied throughout
the clinical trial data life cycle. In this data life cycle, data collection, data
processes, and analysis are all done with guidance from data standards,
such as CDISC CDASH, CDISC LAB, SDTM, ADaM, and define.xml.
We can streamline data processes under data governance, well-designed
data semantics, and appropriately implemented standards.
Step 5: Develop a data quality tool/system. We have already discussed
improving data quality by implementing data standards throughout gov-
ernance, data design, and data processing. In order to increase efficiency
and effectiveness, a data validation tool (DVT) is needed for improving
data quality and ensuring data from multiple sources (both internal and