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Most of the challenges can be overcome by re-discovering and standardizing the current data quality management process. There is a need for a target operating model that consists of discrete functional modules that collaborate through service calls, but would not take month's of personnel hours in establishing the same.
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It is time to Standardize and optimize your Data Quality Management service
Managing business rules to better manage Data Quality
Data Quality Management
When an organization widely explores the benefits of standardizing Data Quality management, they look to find efficiency and scalability in their data quality operations. At the same time, the industry standards including DAMA, EDM and Cobit provide best practices & guidelines to start. Getting a framework that has the dimensions of quality including Completeness, Accuracy and others is not much of a challenge but maturing across Data Quality operations, while making it suistanable is a challenge today.
Most of organizations are mature in their data quality and Governance practices to meet the Enterprise goals and thus, the stakeholder's needs. While the risk management function has an objective of assessing the risks related to data, it's management and it's governance; major challenges are in understanding the blueprint of current processes along with the gaps in Data Management and Governance.
Quoting an example, data quality services would have been initiated and grown in-organically based on the "then needs" of the organization to realize the complete value of data, reduce data issues and meet regulatory needs. But, the recent organizational - internal and external drivers necessitating “management of data as a meaning” along with “simplification of data landscape” and “aligning with Risk management” are pushing the need for maturity in Data Quality management.
Sustaining Data Quality while integrating it into daily data operations in a way that these services are not perceived as an overhead, is one of the major challenges.
Further challenges most organizations are facing today are detailed below -
- Challenges are in the cultural adoption of Data Quality, in a distributed way in the organization. Most of the organizations take a value driven approach to data quality management but fail to understand that the Risk based approach is also required, in interest of Enterprise goals. Currently, Risks related to “data being considered as Enterprise asset” are not identified actively in the Business Units.
- Awareness on data Quality is a major challenge in Enterprises. With awareness comes adoption and enablement of taking it forward in an enterprise.
- Project Governance structure along with well-defined artifacts at every stage, in a change lifecycle, are already defined. This has limited adoption and needs to be enforced through Data Governance, risk management and PMO standardization.
- There is a need for continuous enhancement of services, frameworks, and thresholds to complement the current needs of stakeholders, organizations, challenges.
Most of the challenges can be overcome by re-discovering and standardizing the current data quality management process. Also, with the right assessment plan that is endorsed by the executive leadership on the measurement of the benefits, is much required. There is a need for a target operating model that consists of discrete functional modules that collaborate through service calls, but would not take month's of personnel hours in establishing the same.
In the taxonomy above, there is a sample of the functional patterns that the service operations can leverage. Every function like "Plan Data Quality Strategy" performed in a
service domain say data quality service will be sourced from the taxonomy of functional patterns.
The Data Quality domain must align to the Service Domains and their associated service operations. These would be defined in natural language terms, providing operational features and communication exchanges at a high level. This is with an intent to define clear, unambiguous functional partitions in Data Management.
These boundaries must be used to align and arrange activities into discrete (non-overlapping) functional partitions with clear interfaces that are well suited for service enablement. The model defines the role of the Service Domain in two facades – a type of business function performed ("functional pattern") and a type of object that is acted on. A best practice is to fulfill the asset lifecycle like "Data quality service set up" from start to end.
A data quality service can be well defined with a set of service domains including Service set up, service promotion, service usage, service protection, service monitoring and improvement. Quoting an example from Data Quality service, the service domain "Data Quality service set up" in the below metamodel details the Service operations including functional pattern and Asset.
COBIT provides an industry accepted framework, once implemented, executives can ensure that data governance is aligned effectively with business enterprise goals and better directs the use of data for business advantage. COBIT provides best practices, controls activities and tools for assessment, monitoring and governing data management (IT) activities.
It also helps the organization understand and manage data investments throughout its lifecycle and provides a method to assess whether data management & governance services and new initiatives are meeting business needs and are ensure that they deliver the benefits expected.
The need for Data Governance and inception of Risk management within Data Governance cannot be less stressed for Value realization. Thus, there is also a need for high level mapping of the data quality service domain to COBIT Domain/Processes.
Here is a changing perspective in most organizations, to view Governance from the outlook of Risk. This is primarily due to the fast changing business and regulatory drivers. Along with best practices put forth by the industry groups, COBIT methodology can be leveraged from the perspective of risks associated with data, its management and governance.
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