Operationalizing Data Quality through Data Governance - Data Governance: Creating Value from Information Assets - page 91

people and monetary) and mindshare. The problem will involve key
stakeholders from across the organization.
Data quality measuring: You cannot improve what you cannot mea-
sure, so we need a means for measuring the data quality. Once the sys-
tems and the data quality rules are identified and the data is characterized,
scoring the data quality needs to be performed. Scoring represents the
state of the data quality for identified data quality rules, and it is a relative
measure of conformance to rules.
Responsibility of data stewards: The data steward is responsible for
tracking and improving data across the company supply chain, ensur-
ing the trustworthiness of business data. This includes monitoring data
quality and fitness for purpose, and demonstrating measurable benefits
of data management to lines of business, business processes, and systems.
Data stewards also participate in data governance activities, serving as the
connectors between data governance and data management communities
within the organization. Without strict controls, an organization has no
idea when or how changes were made, who made them, or why an origi-
nal entry was altered. Thus, corporations run the risk of owning multiple
versions of the same information or building a business model on faulty
data. To avoid errors and confusion, and to ensure corporations create
and adhere to stringent information governance controls, best practices
suggest that leading organizations employ data stewards who determine,
describe, and administer the company's business policies and data defi-
nitions. Data stewards steer their company's information policies and
pilot employees through the deluge of data housed in multiple databases
throughout the corporation.
CONCLUSIONS
In this chapter, we have defined data quality and suggested some data
quality strategies under data governance by implementing data standards.
We also introduced some standard implementation methodologies and
shared some data quality best practices. To achieve high data quality, we
need the right governance, strategy, methodology, technology, and cul-
ture, and we should think globally, act locally, start from small, and scale
up to create values.