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
Further challenges most organizations are facing today are detailed below. Soft 2001 Inc performs a survey of the challenges every year in the industry.
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.
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.
