Six Critical Components of Successful Data Governance

What is Data Governance?

For large organizations or companies that are into data management, data governance possesses the key to success in managing, controlling, or governing the data that has been owned or collected. It is fundamental for any organization or companies. Your business benefits from consistent, common processes and responsibilities if your data governance strategy works according to plan. Your business drivers will highlight what data needs to be carefully controlled in your data governance strategy, as the results shall follow the expected benefits of this effort. This strategy shall be the basis of your data governance framework or program.

Data governance defines a set of principles to ensure data quality in a company. It describes the processes, roles, policies or responsibilities, and metrics collectively to ensure accountability and ownership of data assets across the enterprise. 

Data governance is the key to achieve organizational goals, especially on the enterprise level. It defines who can take what action, upon what data, in what situations, using what methods. To sum up, what data governance is, is about standards, policies, and how it can be reusable based on its models. Its overall scope covers the system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models, which describe who can take what actions with what information and when, under what circumstances, using what methods. 

In a given example, consider those entities that are into medical insurance or health-related organizations or companies. Data privacy is very crucial and has to comply with regulatory compliance such as HIPAA or GDPR. If your business driver for your data governance strategy ensures privacy, patient data will need to be managed securely as it flows through your business. Retention requirements (e.g. history of who changed what information and when) will be defined to ensure compliance with relevant government requirements.

Critical Components of Successful Data Governance

Good data and analytics governance enable faster, smarter decisions. Data and analytics leaders, including Chief Data Officers (CDO), must ensure their data and analytics assets are well-governed to enable business strategy and enterprise priorities. A well processed data governance provides necessary insights to make optimal business decisions. With all of these, important factors serve as the key components that are critical for data governance success. 

In this blog, we’ll cover the five critical components of successful data governance.

Data Architecture

Data architecture acts as the heart and soul of data governance. If the architectural design flaws, data quality would end up poor or corrupted in nature. Whatever has been digested and processed, the result cannot be reliable and can affect insights analysed by the enterprise and affect business goals.

According to The Open Group Architecture Framework (TOGAF), Data Architecture describes the structure of an organization’s logical and physical data assets and data management resources. It is an offshoot of enterprise architecture that comprises the models, policies, rules, and standards that govern the collection, storage, arrangement, integration, and use of data in organizations. An organisation’s data architecture is the purview of data architects.

Data Architecture unravels the mystery between a series of components covered by data governance. It systematically connects all the components involved as data is being taken care of within an organization or enterprise entity. It simply explains where data exists and how it travels throughout the organization (either private or egress and ingress data) and its systems. It highlights changes and transformations made as data moves from one system to the next. 

These data inventory and data flow diagrams provide the information and tools the Data Governance Team (DGT) needs to make decisions about data policies and standards properly. In fact, in many instances business stakeholders say they’d like to understand better the data landscape and how it moves across the organization. DGT’s role in educating the organization on this information and overlaying it with architectural policies and standards helps to ensure data accuracy and integrity throughout its life cycle.

Quality of Data

Your data quality reflects on how it is being collected, planned, analyzed, and processed. According to Gartner, Forty-two percent of data and analytics leaders do not assess, measure or monitor their data and analytics governance based on a Gartner survey. Whereas those who said they measured their governance activity mainly focused on achieving compliance-oriented goals.

Data governance motivates the organization or company to enable faster and smarter decisions with the right data and analytics. As data governance is fast improving nowadays, organizations are starting to look into this as it’s a good start to begin focusing on data and analytics to help improve the quality of their data as it grows. It provides better information to drive better information behaviors through their policies. These policies help maximize the investment that organizations have in data and analytics and content either it’s multimedia, business emails, etc. However, governance practices continue to be data-oriented rather than business-oriented. 

As data governance oversees the quality of the data, the DGT shall identify when data is corrupt, stale, or inaccurate. Old or stale data can be archived or purged if no longer needed. Quality of the data is not the only one that has to be maintained but shall also consider the cost, and should consider to clean one’s closet and avoid any mess with your targeted data. Your Data Governance Team has to be able to set rules and processes easily. Your trusted data shall represent as the pillars for data driven organizations that make decisions based on information from many different sources. Dataversity report said that 58 percent of those organizations who participated in their survey said that understanding source data quality was one of the most serious bottlenecks in their organization’s data value chain. It’s worth noting that based on their survey, automating and matching business terms with data assets and documenting lineage down to the column level are critical steps to optimizing data quality.

Data Management

This is where you have to ask these important questions, what data to manage and where it shall reside? Does it need to store on-prem or would it be valuable and valid to have data reside on a third-party such as public cloud.

Data management is essentially the execution of the data governance strategy. It sets the responsibility for implementing the standards and policies that the data governance strategy or framework has been inculcated. It covers the common tasks such as

• Creating Role-Based Access rules (RBAC) which sets the level of access for data

• Implementing database rules in alignment to the data governance policy

• Establish and maintain  the data security ] to comply with what the DGT of CDOs has identified for the data owned by the organization

• Taking the appropriate measures to minimize risk associated with storing sensitive data

• Creating a system for master data management, which is a single view of data across the enterprise.

Data management is key to performing this sort of data inventory: Having a strategy and methods for accessing, integrating, storing, transferring, and preparing data for analytics. According to Forrester Research, effective data governance grows out of data management maturity.

Data Software Tools

Data governance covers the data lifecycle management processes. It aims to ensure the availability, usability and integrity of the data. While these are maintained, the DGT and the CDOs must constantly monitor and analyse the organisation’s data. It has to be taken care of properly and be kept safely and securely. This cannot be achieved without the proper software tools relied on as the solutions available to be used. It can be relying on third-party services, especially when it is stored in the cloud or on-prem.

These solutions help organizations maintain a consistent set of policies, processes, and owners around their data assets enabling them to monitor, manage, and control data movement effectively. These products help users establish guidelines, rules, and accountability measures to ensure data quality standards are met. Data governance tools will often provide recommendations as well, to increase efficiency and streamline processes.

Security

In our previous post, among the countless number of malware threats affecting businesses, ransomware is the biggest offender, costing organizations over $7.5 billion in 2019 alone. Imagine how drastic security breaches could deteriorate your organizational plan to build an enterprise to proliferate.

Data governance is vital as the CDOs or DGT has to analyze thoroughly and cover those confidential data. If data security is systematically architectured, it is traceable same similar to successful data management. It can determine where your data comes from, where it is, who has access to it, how it’s used, and how to delete it. 

Data governance defines your organizational data management rules and procedures, preventing potential leaks of sensitive business information or customer data so data doesn’t fall into the wrong hands. As data grows, it can be a pure challenge. 

For legacy platforms and for bigger organizations with rich data, legacy platforms tend to create siloed information that is harder to determine where it came from. Those silos are often exported, mostly to your database, and duplicated to combine data with other siloed data, making it even harder to know where all the data went. 

Compliance

Data governance and compliance works hand in hand. In the Dataversity report, 48% of companies ranked regulatory compliance as their primary driver for data governance. Without proper data governance, how can you be confident your organization is adhering to regulations?

Data matures quickly, especially when this pandemic has begun; people rely so much on social media and other means over the internet to avoid contact with other people. Data grows so much and that means data compliance has to be addressed priorly and well taken cared of by such organizations or companies holding your sensitive information and data. Organizations have to be compliant with what regulations in their government they belong to. Under the European Union, you have GDPR (General Data Protection Regulation), while in the US you have the well known PCI DSS (Payment Card Industry Data Security Standard), HIPAA (Health Insurance Portability and Accountability Act), and SOX ACT or Sarbanes–Oxley Act of 2002 (also known as the Public Company Accounting Reform and Investor Protection Act). 

Compliance is very critical to successful governance as this strategy has to be in place prior to harvesting and maturing the data within the organization. Compliance directs the organization to get started under the regulations and compliance within the government, which your data governance framework has covered. These regulations require organizations to be able to trace their data from source upto its obsolescence, identify who has access to it, and know how and where it is used. Data governance sets rules and procedures around ownership and accessibility of data.

Your data governance framework ensures that your data is fit for its purpose. By aligning your organization’s people, processes, and technology around a central data strategy, you can begin to leverage your data to benefit larger business goals. In terms of compliance, having clear control processes over your data aligns with pre-set business rules. This is especially important in highly regulated industries such as finance and insurance. Data governance means ensuring you have processes in place to control your data and assure that all regulations are met in all your organization’s data practices. Effective compliance can only come with a holistic and complete approach to your data governance strategy. How can you expect to be 100% certain you are adhering to regulations without complete control over your data and how it is collected and stored. Without it, sensitive information can get into the wrong hands or be improperly expunged, leading to governmental or regulatory financial penalties, lawsuits, and even jail time. Snowflake offers features that can set controls on data ownership and access, enabling the implementation of rules and procedures for data governance. These include Dynamic Data Masking and Secure Views. 

The three critical aspects of building an effective data governance strategy are the people, processes, and technology. With an effective strategy, not only can you ensure that your organization remains compliant, but you can also add value to your overall business strategy.

Conclusion

Data governance is not a fixed and constant flow. It is a convention and practice that has to be dynamically a work in progress. Data governance relies on how the data matures within the organization or company, especially being used in the enterprise level. It is crucial to organizations that value its data or data serves as the primary driver of the organization’s interest to stabilize its financial performance.

Determining these 6 critical components is a must and has to delegate the right people to manage and secure the interest of the organization and company. When you’re ready to tackle security, read this post to learn how ClusterControl can help you secure your open-source databases.

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