Steps for implementing a non-invasive data governance program

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In the digital world, there is a massive amount of data generated every moment. Organizations have a large bulk of data generation and it quickly becomes tedious to maintain it. Data governance is defined as the authorized management of data and its performance. Organizations need to ensure that the exercise of data governance is non-invasive and transparent so it does not seem forceful.

The approach of non-invasive governance was initiated by Robert Seiner in 1997 to provide valuable content for people working in information and data management or related fields. Big data analytic is all in agitation. Often the situation arises that the big data goes ungoverned which can put organizations at major risk.

In response, non-invasive data governance reflects on formal authority, manage data accountability and organizational asset. An organizations continues to govern data informally, but it can cost more money and provide less security. Formalizing the governed data by putting structure around emphasizes that data governance is a technical solution.

In data governance practices, data stewardship is the management of data resources by formalizing accountability. Mete data is the management of data which is recorded in IT tools that improve business understanding of technical and data related processes.

Data governance programs are often invasive in the existing work culture of an organization. It looks after a practical and pragmatic approach that starts as key points, and based on your selling and understanding, broadens the data stewardship programs.

1. Some basic key steps to data governance implementation

Make it a non-invasive approach by doing the following:

  • Identify data stewards and get engagement according to current roles and responsibilities.
  • Become a data steward for accountable management of enterprise data.
  • Data stewards are not given additional work responsibilities but are ensured to work consistently by complete involvement in data management.
  • Data administrators are provided with more tools and processes according to the number of projects and area of expertise.

Data governance is defined as the basic guidelines for an organization to successfully implement it by assessing best practices.

A gap risk assessment process is required to identify the differences between the current practices and best practices of data governance. The report generated after the completion of this process identifies the risks associated with the deliverance of a data governance action plan.

For secured data governance, there is a need for a senior manager who understands and sponsors the activities of the program. They should obtain an immense level of support and gain trust in the practical and pragmatic issue solutions that are well defined.

Data governance is also regarded as a continuous process and not a temporary one-time program. There must be a group of resources dedicated to the development, sustainability and execution of the data governance program continuously.

The measurement of success should be defined upon the goals, scope, and expectations of the dedicated teams. Data governance should be enlightened as an authority, discipline and changed the behavior of the data management.

An action plan should be developed using best practices:

  • Define the industry standard practices.
  • Assess current practices.
  • Focus on strength of environment.
  • Identify the weakness and opportunities for improvement.
  • Conduct a risk gap assessment.
  • Design an action plan.

2. Enforcing the data governance policy is not easy.

It is based on the two key directives:

  • It needs a data governance organization for maintaining the accountability and authority of the business unit representations in order to make responsible decisions for the improved data quality, data usefulness and data value.
  • Data governance teams should be established as separate project teams to keep a sharp eye on the process and act as a data watchdog to guard enterprise data for better data management practices.

The ultimate purpose behind data governance policy is to design and manage the data generation, transformation and usage by the subsidiaries and affiliates. The rule of the policy is to manage the corporate asset by defined standards and procedures for the data governance.

In the case of policy violations, it is considered as a serious breach of trust that can result in disciplinary action which includes termination of employment or contract with federal laws enforcement. This is done for achieving the scope of standards to corporate responsibility of data governance by every individual.

Operational traits of data stewards:

  • The ability to get others to see the vision and align all data related activities.
  • A desire to search for ways to improve data management constantly.
  • The ability to motivate the organization for the data integration that is interested.
  • The ability to set an example of data related behavior for everyday department looks after.
  • The ability to develop to achieve common goals for a specific subject matter and draw attention towards own strengths and resources.
  • The ability to demonstrate diplomatic behavior, as conflicts is an inevitable part of teamwork.

When implementing the data governance program, internal conflict can be a common problem. A data definition conflict is a blockage for business or technical opinion about the specific data to support business activities. A data production conflict begins on the production of specific data used to support business activities. A data usage conflict is about the usage of specific data of business opinion.

To the above summary, we can design a few action plans and also the marketing plans for a successful data governance program. Let us have a look at some action plans that can be taken for implementation of data governance program.

  • Define the data governance with its best practices and drivers needed for governance and stewardship.
  • Assess the existing versus best practices accomplishment.
  • Track the strengths, opportunities for improved communication gaps and association of risks with gaps developed in data governance action plan.
  • Define the key concepts and keys for specific goals, objectives, measures and communications o frame and sell the data governance program.
  • Include business roles and responsibilities with time commitments and workflow integration to identify the coordination.
  • Use specific tools for a metadata outsourcing plan to manage.
  • Work on a communications and resource plan to develop data governance roll out.
  • Identify the actual data stewards.
  • Get attached to meaningful data integration using pro-active and reactive data governance processes.
  • Engage data domain stewards to turn metadata production.
  • Embrace the new or changed requirements.
  • Evaluate the data governance with the monitor and usage report.

There should also be an action plan for marketing the data governance program which can be defined in a process of steps.

  • Focus on initial support for the selection of projects for Data Governance program.
  • Define use cases to identify ways that are used to support the project.
  • Train the project teams on the use of data governance programs.
  • Introduce tools on expectations, skills and tasks among the project teams.
  • Have project teams report on the measurement of results of data governance program.
  • Use emails, newsletters, memos and announcements to promote measurements.
  • Establish a hot-line or support-line on-call service.
  • Deliver online portal access and support.
  • Impart individual or classroom training.
  • Attract the potential data by providing brown bag launches for data governance users.

We can conclude from the above summary that the data governance plays a crucial role in an organization to improve data quality and provide best big data services. It is necessary to keep any solutions practical and pragmatic while considering the key concepts suggestions. Always stay non-invasive for the data governance approach and emphasize the use of metadata to support the program.

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