The Role of Data Governance in an Organization - Data Governance: Creating Value from Information Assets - page 12

propagated, owned, and enforced throughout the organization. Data
governance helps to improve and maintain the quality of data in the orga-
nization. Data quality is continuously measured and monitored and the
results fed back to the data governance process. Effective data governance
leads to formal, standardized data quality processes and clearly defined
quality metrics in place throughout the organization. These processes
govern the performance of daily activities, such as data entry, change
management, improvement, and migration activities. Metrics provide
quality checks to identify any potential problems before they proliferate
through the organization's systems.
Data quality is a prerequisite for master data management (MDM),
which is the management of data that is shared between computerized
systems. Both MDM and data quality are key components of a data gover-
nance initiative. Data governance helps in ensuring the use of timely and
reliable information, improving the quality of business decision making,
and ensuring the consistent use of information.
Poor data quality results in increased costs due to wasted resources. It is
not always easy or tangible to measure the full cost of poor-quality data.
It involves resources spent on correcting errors, lost revenue due to cus-
tomer dissatisfaction, and the costs of poor decision making based on the
flawed data. To ensure data quality, data governance processes need to be
developed. It ensures that data can be trusted and that there is account-
ability for any business impact of poor data quality. Data validation and
utilizing data standards are some ways to improve data quality. Using
data standards can increase process efficiency and effectiveness, saving
resources as well as improving compliance. Data validation improves data
quality and ensuring that data provided meets all specified requirements.
Data validation provides data quality checks based on implemented stan-
dards. It helps in correct data collection, transmission, and data derivation
processes, and can identify data outliers and data errors. As part of a data
governance process, a quality control process for assessing, improving,
monitoring, maintaining, and protecting organizational information is
required, along with metrics to gauge data quality
It is a commonly known fact that “garbage in” leads to “garbage out” in
computerized systems. Sound data collection techniques lay the founda-
tion to data quality efforts. It starts with an inventory of data in exist-
ing systems. An inventory of data fields, their data type, and data format
ensures that the analyses are based on meaningful data fields. Adopting
the practice of naming identical data with identical field names from one