The global multi-cloud management market is expected to grow at an annual growth rate of 27.5% from 2022 to 2030, pointed a research report by Grand View Research. This underscores the increasing need for robust data governance as 92% of industries adopt cloud solutions. As organizations rely more on data to make informed decisions, innovate, and stay competitive, maintaining the quality, security, and integrity of that data becomes vital. Effective data governance ensures accuracy, consistency, and protection, enabling compliance with regulations, risk mitigation, and the maximization of data value.

The Importance of Data Governance in Multi-Cloud Environments 

As organizations increasingly depend on vast amounts of data to drive decisions, innovate, and maintain a competitive edge, the quality, security, and integrity of that data become critical. Data governance ensures that data is accurate, consistent, and protected, enabling organizations to comply with regulations, reduce risks, and maximize the value of their data assets. The lack of robust data governance can force companies to make decisions based on flawed or insecure data, leading to operational inefficiencies, financial losses, and damage to their reputation.

Effective data governance becomes crucial as organizations increasingly adopt multi-cloud strategies to enhance flexibility and innovation. Proper data governance ensures that businesses maintain data security, integrity and compliance across multiple platforms even as they scale their cloud environments. This is essential for maximizing the benefits of multi-cloud management while mitigating risks associated with data breaches and regulatory non-compliance.

This blog delves into the essential elements of crafting a powerful and cohesive data governance strategy. We’ll cover how to evaluate data governance maturity, articulate a clear vision and objectives, and create a robust framework that nurtures a data-driven culture.

What exactly is data governance and why is it so important?

Data governance is the cornerstone of effective data management, encompassing the creation of policies, procedures, and standards that guide how data is handled within an organization. It involves clearly defining roles and responsibilities, establishing rigorous data quality standards, and ensuring that data is utilized in alignment with the organization’s strategic objectives and core values.

A strong data governance strategy is critical to maintaining the integrity, accuracy, and security of an organization’s data assets. Implementing strong governance practices can help organizations to ensure compliance with regulatory requirements and drive better decision-making, foster trust in data, and ultimately support the achievement of business goals. Data governance requires a system and a strategy. It’s crucial to have a solid framework of the people, processes, and technologies to maintain compliance with regulations, and mitigate risks.  

To establish an effective and future-proof Data Governance Strategy, organizations must:

• Defining a clear set of principles.
• Establishing accountability for decisions.
• Selecting a correct operating model of governance.
• Future-proofing their governance strategies and safeguarding global information to attain exceptional flexibility and control. 

We have listed a few data security frameworks for future-proofing data governance strategies for businesses.

Building a Future-Proof Data Governance Strategy With 5 Steps 

Establishing an effective data governance framework can be a complex undertaking. Organizations often encounter various obstacles along the way. These may include securing support from key decision-makers, addressing reluctance to adopt new processes, and ensuring data integrity throughout the system. However, with a well-planned strategy and unwavering commitment, it’s possible to navigate these hurdles and develop a tailored data governance program that meets your organization’s specific needs and goals. A good governance strategy must mitigate the risk related to poor data quality, minimize the compliance risk, encourage data utilization, and demonstrate success through short-term goals. 

Here are five key steps that companies should follow to build a strong Data Governance Strategy:

1. Eliminate Redundant Data: Redundancy is a major cause of poor data quality and governance  inefficiencies. The best way to govern every bit of data is to ensure it is trustworthy and consistent! The identical copies of transactional data, records, billing, etc., could exist in multiple sources like data lakes, data warehouses, on-premise data marts, and others. These copies increase the chances of data getting misused and make it  prone to cyber crimes. Data virtualization is an approach that prevents the creation of duplicate data, analytical data marts, or additional data repositories allowing virtual data creation on top of existing data repositories. This ensures maintenance of a single authoritative source. 
2. Centralize Data Access Control: Centralized access control allows access to all applications, websites and other computing systems from a single profile anytime, anywhere. It allows the user to login using the same credentials from any location. Formulating a logical data fabric with data virtualization would centralize and simplify the data access control. Data fabric architecture facilitates end-to-end integration of various data pipelines and cloud environments. It helps unify data, embeds data governance, strengthens data security, and facilitates easy access to users and data organizations. 
3. Decoupling Data Security from Data Repository: Modern data security policies are complex and need real-time controls. Hence, organizations need security measures that are technology agnostic. Decoupling the security measures from repositories and defining them across all BI tools and repositories would map the policies across all data sources.
4. Data Separation: When an organization uses a multi-tenant environment, like  multi-cloud, the best way to secure cloud data governance is through data separation. Multi-cloud data can be separated in the application layer, where it resides easily and can be accessed anytime when required. This data rests in an independent layer and can be accessed through any cloud resource. 

A user can access information without breaching the privacy of other tenants. Hence, organizations need to map their data streams and establish buffers to minimize damage and avoid compromise. 

• Governing Unstructured Data With AI: Storing unstructured data in the cloud has made it more vulnerable. Most of this data is redundant, obsolete, and trivial. Organizations should consider these factors before choosing an AI solution:
• A solution has a toolbox full of AI algorithms and can map the solution to its data corpus.
• A solution that uses AI deep learning visuals to track visuals like personally identifiable information (PII) in any location within documents.
• A solution that can scale and efficiently run all of its functionality on a large scale.

Unstructured data can be easily organized and included in data governance programs with AI innovation, artificial intelligence (AI) has led to the powerful data management of unstructured data, which SMEs can accomplish. 

The practices mentioned above are a great way to start data governance in a company. Implementing easy and simple steps of setting accountability will do great work with agreeable objectives.

Final Thoughts: Data Governance as a Strategic Imperative

Data governance must be treated as a key element of a data product to ensure accessibility, compliance, and reliability. The challenges of managing data governance require a clear understanding of responsibilities, decentralized data ownership, and robust metrics for data quality.

Businesses often find it difficult to manage data governance. They must first list all parameters and determine who is in charge of them. Data assets must therefore be decentralized in order to disperse data ownership. Organizations must design ownership & accountability, clear, quantifiable measurements, and data quality statistics in order to reduce the risks associated with data governance.

Techment provides multiple solutions around data touching each facet of a secure cloud perimeter to address issues pertaining to secure cloud management. Our expertise in implementing the right tools and technologies ensures that your data governance framework is robust and efficient. Finally, we foster a data-driven culture through continuous training and open communication, ensuring that your team is well-equipped to maintain and enhance data governance practices. By partnering with us, your organization can achieve robust data governance that drives value and compliance.

To learn more about our cloud data solutions and how we can help you build a strong data governance framework,  connect with us.