Data Quality Management


Is Quality Data a business enabler or a risk enabler?

Most organizations like yours, are mature in their data quality and Governance practices, to meet their Enterprise goals. But, are you able to meet your compliance needs and reduce operational risks at the scale you intend to?

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 & improvement.

























Quoting an example, data quality services would have been initiated and grown in-organically based on the "then needs" of your 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 key to the success of the data office
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 Kick-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 sustainable is a challenge today; We can assist you to get past these challenges.

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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CHALLENGES IN ADOPTION OF DATA QUALITY SERVICES


Further challenges most organizations are facing today are detailed below. Dattamza performs a survey of the challenges every year in the industry.

  • 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 the interest of Enterprise goals. Currently, Data Quality Risks are not identified actively by the Business divisions.

  • Awareness of data Quality is a major challenge for Enterprises. With awareness comes adoption and enablement of trust in the data that is being used.

  • Project Governance structure along with well-defined artifacts at every stage, in a change lifecycle, need to be defined. This has limited adoption across divisions 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.























      HOW CAN WE HELP YOUR FIRM GET PAST THESE 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.

Connect with us today to understand your needs better while either kick-starting or standardizing your data quality management.

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