Data Governance, a systematic approach

By Gurumurthy, Vijay, Director IT, Capgemini

What is Data Governance

Data governance is a systematic approach aimed at ensuring that data meets the necessary standards to facilitate business initiatives and operations. It involves overseeing the availability, usability, integrity, and security of data within enterprise systems. As an integral part of a comprehensive data management strategy, data governance is crucial for resolving potential inconsistencies in data across various systems within an organization. Without effective data governance, disparities in data quality and integrity may persist across different facets of the organization.

Why Data Governance is required

Data governance holds significance as it assigns meaning to the data within an organization. It cultivates trust and comprehension in an organization’s data by implementing stewardship and maintaining a robust business glossary, thus accelerating digital transformation across the enterprise. Data governance goes beyond data management it allows data owners to access the right information and extract value from the data. Data governance establishes a collaborative framework, promoting a shared language within teams and across departments. This facilitates communication using uniform terminology and enables the analysis of identical data, ultimately extracting value from the data.

Data governance in the modern enterprise

In the modern enterprise, data is the backbone of decision-making. With an increase in data volume and complexity, ensuring data quality, security, and compliance has become a major challenge. Data governance provides a framework for organizations to manage and protect their data effectively and efficiently. Key considerations involve optimizing data value, mitigating risk, delineating roles and responsibilities for data management and control, fostering a collaborative decision-making approach, and adapting to evolving business requirements.

Data Quality assurance and Compliance

Reliable and cleansed data is essential for accurate analytics and business intelligence. Decision-makers rely on data-driven insights to understand trends and make informed choices, Dependable data ensures that decisions are based on accurate and up-to-date information, business rely on accurate and consistent information. Data quality is essential in maintaining trust and satisfaction, accurate data helps organizations manage risks more effectively and ensures compliance with industry regulations. Maintaining data quality contributes to a positive reputation in the eyes of customers and business stakeholders.

Data compliance ensures adherence and conformity of an organization’s data practices with relevant laws, regulations, and standards. It involves ensuring that the collection, processing, storage, and sharing of data align with legal requirements and industry-specific guidelines. Data compliance is essential for safeguarding individual privacy, maintaining data security, and meeting the expectations set forth by regulatory authorities. Enterprises often implement policies, procedures, and technologies to achieve and demonstrate compliance with applicable data protection and privacy regulations. Top of Form

Data Classification and protection essential for data governance

Data classification is a process of categorizing data assets based on their sensitivity, criticality, and usage, The purpose of data classification is to ensure that data is appropriately managed, protected, and accessed based on its classification level, as defined by the organization’s data governance policies. data classification process usually involves in Identifying data categories, defining data classification levels, labelling and tagging data, Implementing access controls, data handling guidelines, for compliance and regulatory requirements.

Safeguarding data is essential for individuals, businesses, and organizations overall. Beyond fulfilling legal and regulatory obligations, upholding strong data protection practices plays a vital role in establishing trust with customers and stakeholders. With the continuous expansion of digital data, the significance of implementing effective data protection measures is ever-growing to ensure the security of sensitive information.

Who should be part of Data Governance

Crucial to business operations, enterprise data is shared among users throughout the organization, spanning various departments and geographic regions. Multiple enterprise applications require access to this data for their daily operational needs. Enterprise needs data governance council for key stakeholder management who will influence the data governance process across the organisation. The key stakeholders in enterprise includes, data managers, data architects, data governance council and business teams. Roles and responsibility for each of data custodian must be clearly defined for the success of the data governance program.

Data governance framework

A data governance framework creates a single set of rules and processes for collecting, storing managing organization’s critical data assets. Without following framework these assets are at risk of becoming fragmented, imprecise and non-compliant with relevant regulations.

With exponential data growth in the enterprise, data governance framework makes it easier to rationalize and scale core data governance, maintain policy and regulatory compliance, democratize data and support collaboration. There are several industry-leading data governance frameworks that organizations commonly adopt DAMADMBOK , CMMI, ITIL, ODGF,COBIT and DGI. Keeping it simple without too many rules and procedures into a governance framework can be convincing to promote organization-wide adoption and compliance.

Future of Data Governance

Enterprises will probably see a growing need for data officers and data scientists and data specialists to understand and plan the technical landscape of business data. There is already an expanding gap between the need for specialists and the availability of qualified personnel. A few of the exclusive skills that data management will demand include data analytics, security skills, artificial intelligence expertise, and enterprise architecture.

Data Governance will improve customer experience bring in more business and make informed decision for customers, data security, protection and compliance will become core for business in the future. Actionable data will provide insights to leadership team to make clear decisions. As the data increases in the enterprise the need of  data lakes for centralised repository of unstructured data which is the largest portion of data.


Enterprises aspiring to become data-driven, it is essential to evaluate existing data challenges, pinpoint gaps in data utilization, and prioritize focal points, all of which should be integral to a comprehensive data governance initiative. Selecting an appropriate data governance framework is crucial, considering factors such as data privacy, data access, and metadata management within its scope. Once a suitable framework is chosen, enterprises should initiate the execution phase by aligning framework components with their structure and data utilization landscape. To ensure the effectiveness of data governance, establishing interim checkpoints is vital, ensuring that the organization steadily progresses towards its defined governance objectives.

Hot Topics

Related Articles