Knowledge Center

Data Quality & Governance

Implementing a Data Quality and Governance Program

To ensure high-quality master data, you need a well-planned and implemented Data Quality and Governance Program.

Data quality is all about how well data serves the purposes it is required for in a given context.  Data quality has various dimensions – accuracy, completeness, update status, relevance, consistency across data sources, reliability, appropriate presentation, and accessibility.

Data governance is about how decisions are made on data-related matters and who has what authority to make those decisions.  Data governance incorporates: organizational bodies, rules (policies, standards, guidelines and business rules), decision rights (including guidance on “deciding how to decide”), accountabilities and enforcement

High-quality data can provide an organization with valuable information and insights. But obtaining and sustaining high quality data is a greater challenge than most people think!

Data quality starts with the organization having the right Master Data Management (MDM) process and systems for providing users with data which is complete, accurate, consistent, available, time-stamped, and based on industry standards.  

Improving the data quality will ultimately result in reduced costs, improved efficiency, better insights, and enables collaboration across industry trading channels.

A well-planned Data Quality and Governance Program will:

  • Enable better decision-making
  • Reduce operational friction
  • Protect the needs of data stakeholders
  • Train management and staff to adopt common approaches to data issues
  • Build standard, repeatable processes
  • Reduce costs and increase effectiveness through coordination of efforts
  • Promote transparency on processes.

 

How can you achieve Data Quality and Governance?

Build a clear understanding of what you want and frame achievable goals around this. Then secure management approval for the steps needed to reach those goals and for allocating accountabilities to the people who will be involved.

Ensure that you spend time properly identifying data custodians and their value contribution to solving issues that will, or might, arise along the way.  

It’s also important to ensure that management endorses and enforce such a program.  Otherwise, systems and process will simply erode over time.

Here are 10 key dimensions in a Data Quality and Governance Program:

  1. Integrity: Program participants need to practice integrity in their dealings with each other; and that includes being truthful and forthcoming on the drivers, constraints, options and impacts related to all data-related decisions.
     
  2. Transparency:  It needs to be clear to all participants, and to the program auditors, how and when data-related decisions and controls will be made and implemented.
     
  3. Auditability: Data-related decisions, processes and controls in Data Governance need to be capable of an audit. That means each element being accompanied by documents that demonstrate compliance and support the other requirements of a future auditing process.
     
  4. Accountability: Data Governance needs to define accountabilities in any cross-functional data-related decision making and work processes.
     
  5. Stewardship: Data Governance also needs to define accountabilities for stewardship of data – that includes the responsibilities of individual program participants and of relevant groups.
     
  6. Checks-and-Balances: The definition of accountabilities needs to create checks-and-balances between business and technology teams as well as among those who gather information, manage it, use it and/or have responsibility for standards and compliance.  
     
  7. Standardization: The program needs to introduce and support business and industry standards.
     
  8. Data Validation: It also needs to build and maintain data validation rules that support the requirements of the organization.
     
  9. Change Management: The program needs to enable effective change in the use of reference data values, and in the structuring of master data and metadata, in response to or anticipation of new organizational demands.
     
  10. Documentation: This need to obvious but still needs direct attention so that each system and the process is, indeed, clearly documented and subject to the right level of record keeping thereafter.

​What would otherwise be simple management tasks – sales analysis, promotional activity, changes in ranges or prices, and so on – become major challenges that consume enormous amounts of staff resources, time, and cost. 

 

Need Help?

We have a range of tools and fact sheets to help you plan and implement a data quality and governance prigram which is right for your own type of business.

Master Data Questionnaire (Download Here)

Data Quality Survey (Download Here)

Innovit Trading Partner Analysis Program (Register Here)

 

Need to speak with one of our solution experts?

Innovit has helped many companies across multiple industry segments to take control of their master data.

Please contact us on sales@innovit.com