Operationalizing Data Quality through Data Governance - Data Governance: Creating Value from Information Assets - page 88

DVT Rules:
• SAFETY checks:
• Send
• CDASH
• SDTM
• CDISC Lab
• erapeutic standard checks
• Company-speci�c checks
• EFFICACY checks:
• ADaM Checks
• Company-Speci�c Checks

DVT Capability:
• Validate EDC data
• Validate CRO data
• Validate Lab data
• Validate SEND data
• Validate SDTM data
• Validate ADaM data
• Validate De�ne. xml

Raw
Data

Quality
Data

Derived
Data

DVT Users:

• CROs - Contract Research Organizations
• EDCs - Electronic Data Captures
• FSPs - Functional Service Providers
• Central Labs
• Company data managers
• Company statistician and statistical programmers
• Company Global Patient Safety & Risk Management

FIGURE 4.7
Data validation tool's (DVT) basic capabilities, potential users, and some requirements.
CROs, EDCs, FSPs, and Central Labs are external activities.

validations rules. This will include checks from SEND, CDASH, SDTM,
ADaM, and define.xml, plus the company's specific data checks. This DVT
will be able to check data from EDC, CROs, and different central labs. The
possible users can be in-house users, such as data managers, statistical
programmers, statisticians, clinicians, and program/project managers;
in addition, the outsourced partners are possible users. With the effi-
cient DVT tools designed, developed, and implemented, we can achieve
high-quality data goals with the help of data governance and standards.
Next, we would like to summarize some experiences and lessons learned
from quality data management.

Experiences and Lessons Learned

To solve root cause data quality problems: Organizations spend lots of
valuable time cleaning up data quality problems in the data warehouse
and business intelligence environment, when a more proactive strategy
could save time, ensure more reliable information, and ultimately help
business workers make better decisions. You should solve the root cause
data quality problem and focus on being more proactive when designing
or updating data quality management strategies. The decision to design a