Operationalizing Data Quality through Data Governance - Data Governance: Creating Value from Information Assets - page 84

Step 2: Following the data governance and data quality roadmap, we
can start from the design, then development and finally implementation.
To efficiently operate a clinical trial system that includes many different
subsystems, tools, and functions, interoperability plays an important role.
Assuming data governance has been developed and implemented, then
quality data designing will be next. Quality by design drives clinical trial
excellence by setting the right data quality systems under appropriate
data governance; an organization can improve the process efficiency and
reduce the cost, then increase the ROI. Quality by design makes for an
integrated framework that effectively drives quality into the design, execu-
tion, analysis, and reporting of clinical trials to ensure protocol and regu-
latory compliance, patient safety, and data integrity. It also can proactively
manage quality, focus on “what matters most,” and detect/respond to
threats to quality. For example, some protocol amendments are avoidable
if the data quality is built into the design. To build quality into proto-
col, we first identify what matters most for the study, such as study risk
factors, operational feasibility, scientific integrity and clarity of endpoints,
and strategic alignment with clinical development plan. Secondly, we will
guide protocol design using known quality criteria, such as a protocol
quality control checklist and a protocol quality assessment tool, then we
will assess protocol quality with clinicians, study teams, and investigative
sites. Designing data is about discovering and completely defining your
application's data characteristics and processes. It is a process of gradual
refinement, from the rough stage of solving questions such as “what data
does your application require?” to the more precise refinement of the
data structures and processes that provide data. A data design defines
data availability, manageability, performance, reliability, scalability, and
security. It includes identifying the data, defining specific data types, stor-
age mechanisms, and ensuring data integrity by using business rules and
other run-time enforcement mechanisms. With a good data design, your
application's data access will be fast, easily maintained, and able to grace-
fully accept future data enhancements.
Step 3: To establish enterprise data quality culture. Data quality can
seem like a daunting task, but it is really all about having the right people,
processes, and technology in place. The quality of data cannot be improved
by only applying technology or only applying the process improvements.
It requires a collaborative effort that arms business process experts with
the right technical tools to make cost-effective decisions about identifying,
reacting to, and anticipating the types of data errors that lead to negative