A comprehensive guide to data governance

Organizations operating in the cloud typically handle substantial volumes of data. To harness the full potential of that data while also mitigating risk, businesses must develop a comprehensive data governance strategy. Effective data governance ensures data quality, security and compliance with regulations, enabling organizations to make informed decisions and maintain customer trust.

Data governance is a set of policies that affect the availability, usability, integrity and security of the data held by a business. This means developing internal standards for how data is collected, stored, processed and disposed of. Ideally, this is an end-to-end policy, which determines the best practices for all of the data an organization manages, from initial collection to final disposal.

Though data governance and data management are closely related concepts, they are not the same. Broadly speaking, data governance is the set of policies developed to handle an organization’s data. Data management is the implementation of that plan in a practical sense.

It goes without saying that good data management requires a foundation of comprehensive data governance. The governance model an organization adopts, acts as the basis to inform policy applications all the way down the line for the life of a project. It should be established in advance and communicated to teams, like blueprints before construction. A good data governance model can help reduce the cost of cloud services, improve data security and prevent bottlenecks in how large data sets are managed.

There are several goals that a good data governance plan sets out to achieve. That said, each business is likely to have a different set of priorities driving their data governance. 

• Enhanced data quality: Good data governance ensures that data quality and accuracy are maintained at all times by enforcing standards for shared data and checking for conformance and completion.

• Optimized costs: When operating at scale, using outdated or unnecessary data can be costly. Effective data governance ensures efficient use of data and optimizes costs. 

• Expanded data accessibility for stakeholders: Ensuring data is centrally located, reliable and securely accessible helps teams efficiently find and use their data so that they can maximize productivity.

• Enhanced business decision-making driven by improved data quality: The more complete and reliable data is, the clearer the picture for stakeholders, which drives more informed decision making.

• Reduced potential risks: Securing personal information is a critical responsibility for businesses and a well-defined approach to data governance can help limit risks.

• Defined internal guidelines for data utilization: A solid data governance plan can ensure that use cases complement and stay in alignment with each other. 

• Enforced compliance standards: A formal data governance plan can help coordinate data collection, storage and management across teams and enforces best practices of the organization and government regulations.

• Simplified data management and efficient operations: The structure a governance plan offers can streamline operations and help speed up and optimize inputs, queries, analysis and outputs of the data being managed.

• Competitive edge, boosted revenue and higher profits: With quality data powering strategies and streamlining processes, companies can improve and innovate faster, reduce costs and expedite growth, improving competitiveness and the bottom line.

Data governance strategies revolve around a few core concepts, such as data gathering, secure data storage and effective decommissioning of obsolete information. Each concept includes steps that should be considered when crafting an effective data governance plan.

Data gathering:

• Data discovery and lineage

• Modeling and design

• Data warehousing and business intelligence

• Metadata management

Secure data storage: 

• Data mapping, classification and cataloging

• Data storage and operations

• Data integration and interoperability

• Sharing and collaboration

Decommissioning of obsolete information:

• Data security and compliance

• Quality assurance

• Secure removal, overwriting and deletion of sensitive information

Data governance plans take careful planning and execution by an experienced team. To build an effective data governance strategy, it can be helpful to implement a data governance framework. Teams should also define clear goals, confirm key stakeholders and accountable parties, establish a communication plan and classify data as it is received. A clear plan, supplemented by ongoing communication and training, can help teams deliver effective governance while continuing to evolve the strategy as needed.

A data governance framework is a structured approach to managing, organizing, securing and using data within an organization. It consists of a set of guidelines, policies, procedures and roles designed to ensure that data is handled appropriately throughout its lifecycle. Each development framework should cover the four pillars of good data governance:

• Data quality: The reliability and accuracy of your organization’s data. 

• Data stewardship: The management and oversight of data within an organization. 

• Data protection: The security of your organization’s data. 

• Data management: The process of organizing and using data in a structured and efficient manner.

Common practices enterprises use in establishing good data governance policies start in the pre-planning stage, run through the management and application stage and end with ongoing areas of improvement. 

• Defining goals and scope. Define the goals of the project and the scope that operations are expected to cover. 

• Establishing frameworks. Establish frameworks collaboratively with the main stakeholders in the project.

• Data inventory and classification. As data starts to come in, inventory and classify it as it’s added to the database.Incorporate automated classification of new inputs from the start.

• Data quality management. Quality management can include verification and screening out of fictitious, unreliable information before it’s uploaded to the data warehouse.

• Data security and privacy. Use the best and most up-to-date security tools the cloud provider offers to protect data. 

• Data stewardship. Everyone with access to a data store has a responsibility to act as a steward for what’s contained within it, but it’s sometimes expedient to designate a specific person on the team as the point of contact for proper access and data management protocols.

• Lifecycle management. Each stage in the data lifecycle is important to an enterprise that takes data governance seriously, and each deserves its own best practices developed from the start.

• Communication and training. It’s important to establish clear lines of communication up front. Project managers and planning staff should lay out lines of communication, both vertical and horizontal, from the early stages of planning, and frequent reviews should be conducted to keep the lines clear.

• Continuous improvement. Regular meetings provide an opportunity to upgrade and improve processes as they go along. Members of the team should be invited to participate in quality assurance plans. 

Data governance involves many moving parts and a significant amount of planning and commitment from various stakeholders. There are constraints and challenges that might inhibit a data governance plan, including:

• Consideration of resource constraints

• Data fragmentation in secure and relevant silos

• Demonstrating business value to stakeholders

• Securing adequate resources and skills

• Governance of data in the cloud environment

• Facilitating self-service analytics

• Management of big data and the challenges of large data blocks 

• Handling expectations and internal transitions among team members

Effective data governance is a foundational part of successful cloud data management. By implementing a comprehensive data governance strategy, organizations can ensure the availability, usability, integrity and security of their data assets. With clear policies and guidelines in place, businesses can enhance data quality, optimize costs, improve accessibility, drive better decision-making, mitigate risks and ensure adherence to governance standards. 

Sophisticated data governance in a complex cloud environment can seem like a significant undertaking, but understanding the key elements of a data governance framework and implementing best practices will ensure that your organization builds an effective data governance strategy that streamlines governance and simplifies data management. Ultimately, it will allow teams to unlock the full potential of data, driving growth, innovation and profitability.