According to IDCs Global Datasphere, 64.2 ZB of data was created in 2020 alone. This number is projected to grow by 23% annually from 2020-2025. Therefore, we need data governance frameworks for efficient data management and control. This will help us extract maximum value out of such high volumes of data.

Such frameworks would be required for data integrity, data protection, and data security. Indeed, according to BDO, the average data breach cost has been estimated to be around USD 3.8 million.

It is no surprise that Mordor Intelligence predicts the data governance market to be valued at USD 5.28 billion by 2026. So, let’s dive a little deeper into data governance.

Table of Contents

  1. What is Data Governance? 
  2. Why does Data Governance Matter in the Modern Data Stack?
  3. What is a Data Governance Policy?
  4. Why Do Companies Need a Data Governance Policy?
  5. Decision Domains of Data Governance Framework
  6. 4 Pillars of Data Governance Framework
  7. Data Governance Best Practices & Tips–How Do You Write a Good Data Governance Policy?
  8. What Are the Limitations in Achieving the Objectives of a Good Data Governance Framework?
  9. Who is Mainly Responsible for Data Governance?
  10. for implementing ETL Data Governance

 What is Data Governance?

There are quite a few definitions of data governance out there. 

According to Otto (2011b), data governance is a framework that defines how data is handled as a company asset.

Similarly, Abraham et al. (2019) state that data governance is the exercise of control and authority over the management of data, while Koltay (2016) defines data governance as the exercise of decision-making and authority that assigns decision rights and accountabilities according to a specific system.

Why does Data Governance Matter in the Modern Data Stack?

Cheong and Change (2007) state that good data governance not only ensures data quality and effective data management, but it also helps companies align data initiatives with the company’s objectives. It induces collaboration from various parts of the organization. This keeps teams in sync, which helps in avoiding inconsistent data across the organization.

Data governance has become even more important with the ever-increasing prevalence of AI and Machine Learning (ML). According to GPAI, bad data governance can be highly detrimental to AI efforts.

For instance, organizations using AI tools for recruitment purposes may find their models producing biased results. With good data governance, the underlying data sets can be properly inspected to remove inherent biases before being fed to an AI model.

What is a Data Governance Policy?

A data governance framework or policy consists of the standards and procedures that help in managing data integrity, availability, security, usability, consistency, and auditability (Al-Badi et al., 2018). 

In contrast, Khatri & Brown (2010) define governance policy as consisting of five decision domains (discussed later) forming a how-to guide for data governance.

Why Do Companies Need a Data Governance Policy?

Janssen et al. (2020) justify the need for a data governance policy by arguing that with the rise of Big Data, organizations are increasingly using Big Data Algorithmic Systems (BDAS) to fuel their AI and ML efforts. They help in different use cases, such as loan grants, school admission decisions, etc.

However, they require data from various resources and in huge volumes. This can lead to compliance and control issues as the data is sourced both internally and externally

Indeed, as per McKinsey, data governance would be a source of core competency against rising regulatory requirements such as GDPR.

Decision Domains of Data Governance Framework

Good data governance frameworks are built on certain factors. But first, it is more important to understand the decision domains of data governance, as mentioned earlier.

Fu et al. (2011) give a comprehensive description of these domains. To begin, the data principle domain stands at the top of the data governance framework. It defines the purpose and goals of data and directs its use to achieve maximum value from an organization’s data assets.

Data quality is a crucial element of any governance framework. In the context of AI and ML, poor data quality can lead to biased predictions, opening doors for bad decision-making. Of course, various domains within data quality also need to be addressed. These include data completeness, data integrity, and data accuracy.

Next comes metadata management. This encompasses a wide array of efforts to simplify data discovery and usage. Essentially, metadata describes other data sources according to a certain category. For instance, physical storage metadata tells users about physical storage sources.

Provenance metadata gives information about the producers, the date of creation of data sources, and their modification details. Domain-specific metadata provides information specific to a business function, such as sales, finance, etc.

Then comes data access. This domain outlines access standards regarding who can access what kind of data and how the access request will be processed. This is essential for regulatory compliance.

Finally, the data lifecycle domain involves the stages of data creation, data processing, data storage, data usage, data archiving, and data destruction. Khatri & Brown (2010) state that data governance should determine how data moves through each stage to minimize storage costs.

4 Pillars of Data Governance Framework

Keeping these domains in mind, there are at least four pillars of a good data governance program - people, processes, contributors, and technology.

People or data stewards are the main drivers of data governance within an organization. These are the ones who identify the data requirements of each team, assess the necessary skill set, and ensure top management’s buy-in.

Processes involve policies and standards for effective data management. This can be in the form of defining goals and KPIs while establishing metrics to measure progress.

Contributors can be any stakeholder, such as IT professionals, analysts, data owners, etc., who serve as guides to ensure that the overall data governance strategy is going in the right direction.

Lastly, technology is concerned with the relevant data governance tools that can provide proper data profiling, data lineage, and automation of data pipelines wherever necessary.

Data Governance Best Practices & Tips–How Do You Write a Good Data Governance Policy?

Effective data governance doesn’t happen overnight. Several best practices need to be followed to ensure successful data governance.

Mckinsey identifies six data governance best practices to drive excellence. The first is to involve the C-level executives, where the Chief Data Officer should take the data governance initiative and highlight its importance and challenges.

A data governance team should then be formed with members from senior management who will direct the overall organization’s data governance efforts and ensure their alignment with business objectives.

This team can then pick subject-matter experts who will act as data stewards for the day-to-day implementation of data standards. They will define the data elements and data issues that need attention to ensure that data governance processes are followed.

Data stewardship also involves defining metrics for measuring goals. It also means getting constant support from top management by emphasizing how an inefficient data governance structure leads to revenue loss.

One way to do this is to tie goals to existing projects that can benefit from high-quality data. For instance, good data governance will be essential if an organization plans to upgrade its ERP systems. This is because an effective ERP system relies heavily on data quality.

Furthermore, organizations should start small and avoid doing everything at once by identifying critical data assets of specific business units for testing.

The criticality of data assets can be determined against various dimensions. For instance, sensitive data in certain business domains should be addressed first.

Organizations need to assess the level of data complexity and regulatory requirements to determine the optimal policies. The higher the regulations around data security and the more complex the enterprise data, the more comprehensive a data governance policy.

The goal is to strike the right balance between value creation and risk mitigation. However, these two goals conflict with each other. Higher value creation means wider data access, while risk mitigation implies more centralized control of the master data, thus limiting the use of data.

Nevertheless, policy design should be an iterative process. No organization may get it right the first time. This is also true as data standards and privacy laws keep evolving. With different types of data being generated daily, governance frameworks should be adaptable.

Lastly, data stewards need to develop a clear vision to ensure active participation from different stakeholders of the organization.

What Are the Limitations in Achieving the Objectives of a Good Data Governance Framework?

Alhassan et al. (2019) identify six critical success factors for a foolproof governance framework. Still, as one would expect, it takes work to get all of them right.

Consider employee competency. A governance strategy will be as good as the people who make it. An incompetent workforce would not only hinder the expansion of the governance program but also introduce inefficiencies in data workflows.

But that's not all. Just like every governance strategy, clarity of processes and procedures is pertinent. However, organizations can easily miss this out and cause a lot of frustration among teams.

Further, it is not just that you keep investing in the latest tools. Rather, it is more about investing in the right tools. IT systems that give value for money are to be prioritized, and vendor lock-ins should be avoided. But sadly, in a rush to get the best, organizations end up with the wrong solutions.

Next comes the ease with which data policies can be followed. In an attempt to protect data privacy, policies become highly cumbersome and create friction even in the simplest of tasks.

In addition, a lack of involvement from top management can blur out the roles and responsibilities of those accountable for enacting governance.

Sometimes, organizations make the mistake of keeping the governance team aloof. However, this practice makes governance just a word in the books and is met with much less enthusiasm.

Finally, data can easily be taken for granted. After all, it is available in such large volumes that its significance gets lost somewhere down the line.

Who is Mainly Responsible for Data Governance?

At the outset, it might seem that data governance falls under the job description of a chief data officer. But perhaps this is a myopic view.

Just like the governance of a country has many stakeholders, data governance can involve interests that vary as much as the world population.

The 2020 GPAI Report explains this with perfection. Of course, the question starts with why governance is needed, with the obvious answer of achieving data quality and regulatory compliance. But who makes these regulations?

And from here, policymakers jump into the picture. Their objective is to regulate the data market and avoid exploitation. They do it while mandating organizations to train their workforce in the art of data science. Data governance is a natural consequence of dealing with such policies and awareness programs.

At the private level, organizations need to maintain their social responsibility by protecting the privacy of their customers. They also develop data models that help manage customers more effectively. Also, as organizations become more inclusive, it's not just the governance team who has a say in devising policies. Employees from different departments chime in as well.

It can also be argued that even the general public is involved. A more inclusive society would have interested members lobbying policymakers for more stringent laws around data security.

Pressures from the international community can also shape and mold data governance structures. In this age of globalization, we see organizations constantly expanding their scale beyond national borders and meeting global data standards while appealing to cross-border customers.

Institutions such as the UN also affect governance policies by setting goals for a fairer society to ensure a level playing field for all stakeholders. for implementing ETL Data Governance

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