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 for efficient and control. This will help us extract maximum value out of such high volumes of data.
Such frameworks would be required for USD 3.8 million., , and . Indeed, according to BDO, the average cost has been estimated to be around
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
- What is Data Governance?
- Why does Data Governance Matter in the Modern Data Stack?
- What is a ?
- Why Do Companies Need a ?
- Decision Domains of
- 4 Pillars of
- & Tips–How Do You Write a ?
- What Are the Limitations in Achieving the Objectives of a ?
- Who is Mainly Responsible for Data Governance?
- Intregate.io 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 , while Koltay (2016) defines data governance as the exercise of 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 , 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.not only ensures and effective
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, the underlying can be properly inspected to remove inherent biases before being fed to an AI model.
What is a?
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?
Janssen et al. (2020) justify the need for a , organizations are increasingly using Algorithmic Systems (BDAS) to fuel their AI and ML efforts. They help in different , such as loan grants, school admission decisions, etc.by arguing that with the rise of
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 requirements such as .
Decision Domains of
Fu et al. (2011) give a comprehensive description of these domains. To begin, the data principle domain stands at the top of the. It defines the purpose and goals of data and directs its use to achieve maximum value from an .
is a crucial element of any governance framework. In the context of AI and ML, poor can lead to biased predictions, opening doors for bad . Of course, various domains within also need to be addressed. These include data completeness, , and data accuracy.
Next comes. This encompasses a wide array of efforts to simplify and usage. Essentially, describes other according to a certain category. For instance, physical storage tells users about physical storage sources.
Provenancegives information about the producers, the date of creation of , and their modification details. Domain-specific provides information specific to a business function, such as sales, finance, etc.
Then comes. 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 .
Finally, the data domain involves the stages of data creation, data processing, data storage, , 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
Keeping these domains in mind, there are at least four pillars of a - people, processes, contributors, and technology.
People orare 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 .
Processes involve policies and standards for effective. This can be in the form of defining goals and while establishing to measure progress.
Contributors can be any, such as IT professionals, analysts, , etc., who serve as guides to ensure that the overall is going in the right direction.
Lastly, technology is concerned with the relevant , and of data pipelines wherever necessary.that can provide proper data ,
& Tips–How Do You Write a ?
doesn’t happen overnight. Several best practices need to be followed to ensure .
Ashould then be formed with members from senior management who will direct the overall efforts and ensure their alignment with .
This team can then pickwho will act as for the day-to-day implementation of . They will define the and that need attention to ensure that are followed.
also involves defining for measuring goals. It also means getting constant support from top management by emphasizing how an inefficient leads to revenue loss.
One way to do this is to tie goals to existing projects that can benefit fromdata. For instance, will be essential if an organization plans to upgrade its ERP systems. This is because an effective ERP system relies heavily on .
Furthermore, organizations should start small and avoid doing everything at once by identifying criticalof specific for testing.
The criticality ofcan be determined against various dimensions. For instance, in certain business domains should be addressed first.
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, while risk mitigation implies more centralized control of the , thus limiting the .
Nevertheless, policy design should be an iterative process. No organization may get it right the first time. This is also true asand keep evolving. With different being generated daily, governance frameworks should be adaptable.
Lastly,need to develop a clear vision to ensure active participation from different of the organization.
What Are the Limitations in Achieving the Objectives of a?
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.
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, 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. But perhaps this is a myopic view.
Just like the governance of a country has many, 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 achievingand . 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 developthat 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.
Pressures from the international community can also shape and mold. In this age of globalization, we see organizations constantly expanding their scale beyond national borders and meeting global 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.
Intregate.io for implementing ETL Data Governance
With all the discussion regarding the significance of data governance, it must now be clear why your organization needs a .
Integrate.io is a state-of-the-arttool that optimizes data pipelines and ensures your data warehouse does not become a data jungle vulnerable to .
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