In recent years, it has been realised that ERP systems and data warehouses are insufficient in themselves to really tackle the problem of inconsistent, inaccurate and unreliable data. In particular there is a growing awareness that the processes that creates and updates corporate data need to be addressed if data anarchy is to stop. This involves understanding, documenting and controlling the business rules that surround the creation of new business classifications (such as a new customer code, a new product line or brand, an updated hierarchy of engineering assets or organizational structure). This is commonly termed Data Governance.

Data Governance is the process of establishing and maintaining cooperation between lines of business and management to establish standards for how common business data and metrics will be defined, propagated, owned and enforced throughout the organization. In brief it is collectively:

- The governing body responsible for data policy relating to how data is defined, owned, stored, reconciled, deployed, and enforced. 

- The process for agreeing data ownership and rights and reconciling conflicts. -

- The embodiment of the organizational culture relating to the management of the decision making process. 

- The function targeted with ensuring data security and integrity across the enterprise.

- The scope of data governance also needs to extend to the semantics used in systems, to ensure consistency in the way that data is handled.

Data governance is an umbrella term that is much broader than master data. It also encompasses such areas as archiving policy, and compliance with data protection laws and security policies as well as the quality and accessibility of data.

Many organizations have attempted to address the governance of business processes and data, but few have genuinely succeeded. Documenting business models in some sort of data dictionary or even on Excel or PowerPoint, is a useful start and can have many benefits but it is, essentially, a passive activity and gets quickly outdated.

Data Governance is closely related to master data management. Managing master data is a business issue and responsibility. No longer should this be delegated to IT as is often now the case. Indeed, to be effective the governance process must be sponsored and led at the board level in the business. Data Governance is central to the business process and so must be managed by business experts who have the understanding of the business goals and strategy. The whole success of being able to understand and consolidate business data is dependent on the business taking an active role here. Choosing how to classify products is a business decision, depending perhaps on the marketing strategy. Clearly not everyone in the business can decide this. There must be a process and business owners who are appointed by the business to take these decisions and resolve conflicts. This in turn needs to be supported by some form of workflow process. Putting in place an effective Data Governance structure is essential to the success of a Business Intelligence initiative.

It’s important to understand from the outset that data governance and the associated master data management initiatives is not a project. The project element is to set up a sustainable and efficient process for data governance going forward.

To be successful with Data Governance the business must be fully committed to a long-term program supporting both the technology and process. Most companies will find if they look closely that they are already spending lots of money fixing problems arising from poor data, inconsistent data and conflicting responsibilities and ownership, so it makes economic sense to establish this as a Business Process.

Establishing effective Data Governance is essentially about introducing changes in the business processes by which businesses manage their key business data. Although to be successful it requires that appropriate supporting technology be put in place, this is not the major challenge. Introducing the framework and Roadmap to compliance is our service.

Interim can help to:

Develop, implement and refine data standards, policies and procedures and Communicate the data strategy across yout business.

Pro-actively monitor and manage data quality across your business and commission and manage any work required to resolve issues.

Provide  input from a data governance perspective to all major programmes and projects to maintain the integrity of the data as the business expands.

Manage known data issues, including collection, logging, resolution and any follow-up activities.

Develop and set out the Data Governance Roadmap, which will determine the current and future states of data governance within your business and, in particular develop internal relationships to achieve understanding and communicating the vision and guiding principles for data governance within you business and its regulatory compliance obligations.

Take ownership and develop the Data Governance Roadmap, including implementation of the various workstreams and modifications to the roadmap as required.

Develop, publish and communicate your company's data standards, data policies and procedures, and manage regular follow-up activities.

Develop and implement your company's Data Governance Strategy.

Work closely with departmental managers and HR&D to ensure that good data governance is embedded in existing staff training where appropriate, and develop and deliver new training courses where required.

Schedule and run regular Data Quality audits.

Analyse the results of the Data Quality audits and put in place any short- and long-term corrective measures required, including the commissioning of IT projects to resolve data issues and the development and implementation of new policies or procedures to prevent issues recurring.

Implementation, publication and maintenance of you company's reference data repository, including product codes and names, SIC codes and other standing data.

Development, population and maintenance of your company's metadata management tool.

Keep abreast of all major programmes across your company in order to ensure that standards, policies and procedures are followed or modified as appropriate and that data divergence is minimised.

Own and maintain your company's Data Issues register; ensure new issues are captured and progressed and that appropriate steps are taken in the management of issues which involve third parties (e.g. other Licensed Providers, Wholesale Providers or regulators)

Publication of a regular Data Management Scorecard to monitor performance against a set of key performance indicators.

Provide support for specific data initiatives  in Customer Experience and Sales and Marketing.

Provide support where specific information about the quality of your company's data is required by the auditor, data protection inspector or regulator, including the provision of robust plans for resolving any outstanding issues.