Let's face it "Data Governance" is the core of good data management.

The aim of Data Governance is the management of information as a business asset, with the focus on securing a high level of data confidence through a dedicated focus on organisation, processes, responsibility and ownership.

Good data governance is proactive rather than reactive and should be implemented throughout the entire business to ensure data management is front of mind and consistent throughout.

Data management may be divided into several components; data architecture management, data development, data operations management, data security management, reference and master data management, document and content management, metadata management, data quality management, and business intelligence. 

Data architecture management

Involves defining and maintaining the blueprint for managing data collections.  Data architecture is composed of models, policies, rules or standards that govern which data is collected, and how it is stored, arranged, integrated and used in the data systems within an organisation.

Businesses that have Technology and Transformation teams who are responsible for defining the target state, aligning during development of solutions and then following up to ensure enhancements/changes are done in the spirit of the original blueprint will drive the technical landscape which will be needed to control multiple forms of data within the organization.

Data development

Comprises Analysis, design, implementation, testing, deployment and maintenance.

Data operations management

Involves the provision of operational and technical support from data acquisition, recovery, retention and purging;

(a)       Retention:

As part of data retention, robust protocols are implemented. Various pieces of legislation governing superannuation funds require trustees to retain certain documents and records for a certain length of time. The data retention policy outlines the legislative requirements and the procedures that have been developed to meet the legislative requirements.

(b)       Disposal:

           (i)         Disposal of electronic material

Electronic media which has previous stored Protected or Restricted information must be securely erased, preventing forensic retrieval of that information. Alternately, the media may be physically destroyed to a level preventing information retrieval.

 Recipients of Protected or Restricted information outside of the business must be able to demonstrate that the information has been appropriately erased or destroyed.

         (ii)        Destruction of Protected and Restricted Material

Public materials may be placed in general recycling or rubbish bins.

Protected and Restricted materials on paper must be disposed of in on premise locked secure destruction bins.

Data Security Management

Data security management ensures privacy, confidentiality and appropriate access.

A business that embraces this is committed to the application of Information Security to protect its people, information and assets. Businesses will also abide by their obligations to statutory and legal regulations and compliance that are applicable to them.

Data Governance and Information Handling policies are developed to provide a framework for identifying and protecting data and information that is critical to the business.  Proper handling of information is required to comply with legal and regulatory obligations. The data classification principles in this area are designed to assist employees and contractors to classify data for the purposes of determining its level of sensitivity and criticality.  It is envisioned that the sensitivity and criticality ratings will inform physical and digital security measures and business processes relating to data and information.

Reference and master data management

Reference and master data provide the contextual capabilities for transactional data.  Managing reference and master data enables organizations to understand operational data and analyse disparately collected data effectively. It Involves managing authoritative information about standards and the business of the enterprise.

Data warehousing and business intelligence management

Business Intelligence (BI) comprises the strategies and technologies used by enterprises for the data analysis of business information.  It Enables reporting and analytics.

Document management and content management

Relates to managing data and information assets held both inside computer systems and physical manifestations. Documents are usually considered structured data types while content may be images, audio, video and considered unstructured. Both forms need to be effectively managed.

Document management systems are used to track, manage and store artefacts. You will find more on this topic by searching for “Document Management Systems” (DMS).

While an enterprise content management system (ECM) is a system that can be used to create and modify digital content. For more on this topic do an internet search for “Content Management Systems”  (ECM).

Metadata management

Metadata management is defined as the end-to-end process and governance framework for creating, controlling, enhancing, attributing, defining and managing a metadata schema, model or other structured aggregation system, either independently or within a repository and the associated supporting processes.

Data quality management

Data quality refers to the condition of a set of values of qualitative or quantitative variables. There are many definitions of data quality, but data is generally considered high quality if it is "fit for purpose in operations, decision making and planning. It Involves defining, monitoring and improving data quality.

(a)       Data Quality

   There are three aspects to managing data quality:

• working to maximise the currency and quality of the data;
• ensuring the data are used appropriately given their quality; and
• reporting on data quality.

Establishing policies, procedures and processes in each of these components is a considerable effort and needs to be approached in a pragmatic fashion otherwise maturity of practice will suffer. Involving the right people early will help greatly as a control group (as per my previous post describing an essential group of stakeholders that can drive the decision making) or "Data Governance Board".

This brings me to the typical Data roles and responsibilities. The "Data Governance Board" is normally comprised of a Data Governance Lead, Data Governance Sponsor, and Executives or leadership representatives. The Data Governance Lead, with Data Architect (if one exists) and Data Analysts (or business/risk analysts) provide a Data Governance Support function for the governance organisation. Another key element is the Data Governance Organization or Community, which is a virtual community made up of Data Owners and Data Stewards formally recognising any existing data management responsibilities within applicable business areas with clear staffing numbers and roles appropriated to contribute to the overall Data Governance and Management effort. 

Regular workshops and meetings of the control group  or "Data Governance board" is essential to drive outcomes and for decision making on all major Data topics so it cannot be under estimated. The top down approach works to cement practices and uplift capabilities over time so buy in by the most senior leaders is critical to maintain focus and relevance. All efforts should be under pinned by risk, privacy, security and compliance rationale as they are the corner stones of good business practices. Driving ongoing Data compliance and awareness training gives staff the knowledge and understanding to comply with the policies, procedures and standards relating to Data Management.

• Helping staff understand the benefits of archiving wanted information and dispose/delete the unwanted information carefully.
• Explaining types of issues that occur and what is required to identify (and fix) them
• Explaining the role of all the players in the Data Management framework (Enterprise Data Management, Data Owners, Data Stewards, Data Quality Administrator)
• Explaining how actions and decisions in the business are linked to the data coming and going out of the organization
• Helping them to define 'good' and 'bad' for their own data, based on how the business uses the information.

And finally I recommend that any data classification framework implement be reviewed annually by the relevant Senior Executive stakeholders of the entire business to keep it current and relevant in future.

Be sure to read my next post on this topic where I talk in more detail around the components of Data Governance and Management giving you some practical examples to make all this more relatable. I would love to hear from you if you have any questions and/or suggestions. Thank you kindly for reading my post.