The Importance of Data Governance7 min read

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In today’s fast-paced and intensely dynamic business world, data governance is a must. Now that companies have the capacity to gather vast volumes of complex internal and external data, they must establish a discipline to optimize value, mitigate risks, and minimize costs.

Data Governance

Data governance is a series of procedures, roles, rules, principles, and indicators that ensure an organization’s successful and productive use of information to achieve its goals. It defines the procedures and obligations that ensure the consistency and protection of data used by a company or organization. Data governance determines who should take what actions, with what data, under what contexts, and with what methods.

A well-crafted data governance policy is important for any enterprise that deals with big data, and it can clarify how the company profits from reliable, structured processes and responsibilities. Business drivers show what data in the data governance plan needs to be closely managed and the gains anticipated from this initiative. This approach would act as the base for the data governance system.

For example, if protecting the integrity of healthcare-related data is a business driver for your data governance strategy, patient data must be handled safely as it flows through the enterprise. To maintain compliance with applicable government regulations, such as the GDPR, retention requirements (e.g., a history of who updated what information and when) would be specified.

Data governance guarantees that data functions are specifically established, and that responsibility and accountability are decided upon in the organization. A well-planned data governance structure contains roles and responsibilities at the strategic, tactical, and organizational levels.

What Data Governance is Not!

Data governance is often confused with other terminology and principles that are closely related, such as data management and master data management.

1. Data Governance is Not the Same as Data Management

Data management is the management of an organization’s entire data lifecycle needs. Data governance is the building block of data management, connecting nine other disciplines such as data consistency, reference and master data management, data protection, database operations, metadata management, and data warehousing.

1. Data Governance is Not Master Data Management

Master data management (MDM) focuses on defining and maximizing the consistency of an organization’s core entities. It means that you have the most up-to-date and reliable knowledge of key entities such as consumers, retailers, medical professionals, and so on. Since such entities are exchanged in the enterprise, master data management is concerned with reconciling fragmented perceptions of such entities into a common view—a practice that goes beyond data governance.

However, without proper governance, there can be no effective MDM. A data governance software, for example, would define master data models (what is the definition of a client, a product, and so on), outline data retention policy, and define roles and duties for data authoring, curation, and access.

1. Data Governance is Not Data Stewardship

Data governance means that the required persons are assigned data roles. Data stewardship refers to the tasks needed to ensure that data is reliable, under management, and convenient for the relevant parties to find and process. Data governance is mainly concerned with strategy, functions, organization, and regulations, while data stewardship is concerned with implementation and operationalization.

Data stewards look after data assets, ensuring that the real data is in accordance with the data protection strategy, connected to other data assets, and under supervision in terms of data quality, enforcement, or protection.

Benefits of Data Governance

An effective data governance policy offers numerous advantages to a company, including:

• A common understanding of data — Data governance offers a coherent view of, and standard language for, data, while individual business entities maintain sufficient versatility.
• Improved data integrity — Data governance develops a strategy to ensure data precision, completeness, and continuity.
• A 360-degree view of each client and other business organisations — Data governance provides a mechanism for an entity to rely on a “single version of the facts” across important business entities and provide an acceptable level of continuity between departments and business operations.
• Data map — Data governance enables an advanced understanding of the position of all data connected to key organisations and is required for data incorporation. Data governance, like a GPS that can represent a physical landscape and help people find their way in unfamiliar landscapes, makes data assets usable and easier to connect with business outcomes.
• Consistent compliance — Data governance acts as a forum for following the criteria of government legislation such as the EU General Data Protection Regulation (GDPR), the US HIPAA (Health Care Portability and Transparency Act), and business guidelines such as PCI DSS (Payment Card Industry Data Security Standards).
• Improved data management — Data governance applies a human aspect to an increasingly digital, data-driven environment. It develops data management codes of ethics and best practices, ensuring that issues and requirements outside conventional data and technology fields — such as legal, security, and enforcement — are handled regularly.

Cloud Data Governance

If more companies and organisations recognise the benefits of shifting any or all of their data storage and processes to cloud integration techniques and iPaaS, the need for efficient data governance grows at a rapid pace.

Moving to the cloud entails delegating certain functions, such as infrastructure management, application creation, security, and so on, to third parties. Cloud storage often requires the virtualization of technical infrastructure and may raise data sovereignty concerns, such as laws requiring data to be processed in a particular location or region.

Furthermore, cloud-first policies typically promote decentralisation, encouraging lines of business or workgroups to individually carry out their own system, which could result in unregulated data sprawl.

That is where government fits in. To begin, a strategic data governance strategy is essential for moving information to the cloud. Whether an enterprise transitions to a hybrid or fully cloud data platform, the data transformation process will reap all of the advantages of a robust data governance approach, and the migration itself will be more effective and safer.

Moving data processes to the cloud often add a layer of complexity in terms of confidentiality and control. Although a truly on-premises data approach also necessitates a solid data governance policy, stakeholders understand the relevance of data governance as data is flowing across the cloud.

Data Governance Tools

To identify the best data governance solution for the enterprise, look for open source, flexible solutions that can be easily and cost-effectively implemented into the organization’s current environment.

Furthermore, a cloud-based platform would help you to easily attach to robust capabilities that are both cost-effective and simple to use. Cloud-based systems also eliminate the need for on-premises servers.

When comparing and choosing data governance solutions, choose tools that can assist you in realising the market advantages outlined in your data governance strategy.

These tools can be useful to you:

• Capture and comprehend the data with tools and capabilities for exploration, profiling, and benchmarking. For example, the right tools will identify and warn on a piece of personal data, such as a social security number, in a new data set.
• Improve the accuracy of your data with Validation, data cleansing, and data enrichment.
• Manage the data with metadata-driven ETL and ELT, as well as data aggregation software, so that data pipelines can be tracked and traced from start to finish.
• Use tools that actively review and manage the data and gain control of it.
• Document the data so that it can be enhanced with metadata to improve relevance, searchability, usability, linkability, and compliance.

With self-service tools, allow the people who know the data the most to contribute to data stewardship activities.

Platingnum understands data governance and offers useful, cloud-based solutions that can assist any size enterprise in transitioning from ungoverned data to active data governance. Platingnum’s data quality, data and metadata management, and data stewardship solutions are comprehensive and straightforward to use, helping you to solve your data governance needs easily and efficiently.

Data Governance is Not Optional

Organizations now provide vast volumes of data on their consumers, employers, vendors, staff, workers, and other stakeholders. A company would be more competitive if this intelligence is used correctly to help identify the competition and its target audience. The same data governance would ensure that your organization’s data is trusted, well-documented, and easy to locate and view, as well as safe, legal, and confidential.

Ascertain that your organization is well-positioned to optimize data governance investments while minimizing the likelihood of data breaches. When you’re ready to get started, take a look at our data governance solutions.

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