Nonexistent or bad quality data management can lead to catastrophic consequences ranging from damaging business decisions to probable data violations and costly compliance infringements. To prevent such issues, there is a need for deployment of a solid data governance strategy. For the success of this strategy, there is a need for attaining a great data maturity level. Therefore, organizations have to adhere to the data governance maturity level.

There are different types of data governance maturity models. EWSolutions is one of the best data governance services that have been helping clients to plan and implement data governance initiatives. An immature organization has piles of unorganized data, which ignores this data and misses the opportunity to drive growth. On the other hand, mature organizations are familiar with the significance of data and consider it as an asset that needs proper governing and managing.

The data governance maturity model comprises methodologies and tools used to communicate and implement the data governance initiative at an enterprise level. In a mature company, all processes to access, manage, and innovate using the data assets are clearly defined.

Effective data governance maturity models

All businesses differ, so you need to choose one that suits your organization. With a high-level data maturity model, your business will experience tangible results. While choosing a maturity model there are several factors to consider including –

  • Primary business drivers
  • Existing data management
  • Governance framework
  • Niche you operate
  • Budget to implement

Adopt a data maturity model that aligns with your data governance structure and journey that adheres to the same methodology.

Progressive maturity model

Companies can track their data governess progress at different levels –

  1. Unawareness – The processes are volatile and usually chaotic.
  2. Awareness – Existing data practices documented and inventoried.
  3. Defined – The policies and rules are defined and responsibilities assigned.
  4. Implemented – Data Governance project is enforced, training conducted, and data is compiled & measured.
  5. Optimized – Optimization of rules and policies to remove redundancies. Data is tagged for better visibility to users.

IBM maturity model

The model is designed to determine progress across eleven crucial data governance elements including –

  1. Data awareness & organizational structure
  2. Data quality management
  3. Data stewardship
  4. Data policy
  5. IT security& privacy
  6. Data lifecycle management
  7. Data classification
  8. Data compliance
  9. Data architecture
  10. Data audits
  11. Value creation

Gartner maturity model

It enables organizations to attain 5 main goals –

  1. Data integration across the organization
  2. Content amalgamation
  3. Unhindered information channels
  4. Integration of master data domain
  5. Metadata management

Data is crucial to driving growth in this digital environment. It not only supports crucial business decision-making but is also possible for working collaboratively which helps in better innovation and production.

If data is not governed intelligently, it is not viable to attain these benefits. A suitable data governance maturity model will help to measure data maturity level at every phase in the data journey. This helps to stay focused and progress forward and reach the highest data proficiency level.