Importance of Data Governance

data governance

Data is probably an organization’s most valuable asset. Data governance helps make data useful, accessible, and secure. Effective data governance improves data analytics, which improves decision making and operational support. It also helps prevent data inconsistencies or mistakes, which may lead to data integrity concerns, poor decision-making, and organisational challenges.

Data governance is also critical for regulatory compliance, ensuring that businesses meet all legal obligations. This is critical for lowering risks and expenses.

In essence, data governance improves data quality, lowers data administration costs, and increases data access for everyone. Better decisions and business results follow.

Data has two sides. While it may lead to new goods, services, and business models, it can also lead to data breaches, regulatory issues, and expensive customer service mistakes.

Data governance comes into play here. While it may not be the most interesting subject, and may even be seen as a burden to data consumers’ daily operations, data integrity and security are very important.
Most companies have a Chief Data Officer (CDO) charged with balancing value and risk management over corporate data. They provide untapped analytical potential and act as a data sheriff, continuously monitoring for problems of accuracy and trust.

With the implementation of GDPR, the CCPA, and other industry, government, and healthcare compliance requirements, data governance has become a business need. As a result of GDPR law, many businesses now regard data management as being similar to data governance, with duties concentrated upon building controls and audit processes and looking at things from a defensive perspective.

Defensiveness is understandable in light of the financial and reputational risks associated with data leaks and mishandling. There is a danger, though, that being too careful could prohibit businesses from reaping the benefits of data-driven collaboration, especially when it comes to software and product development.
Bureaucracy is a manifestation of data defensiveness. To deal with internal demands, you create roles such as “data steward” and “data custodian.” While they are in the trenches, a “governance council” sits above them issuing orders and establishing operational procedures. Blockades will appear in due time.
Blockages may be disastrous for a company’s operations. As soon as “data breadlines” appear, it’s time to be concerned. Employees in need of critical information are forced to argue their case in front of whomever is in charge. The passage of time is squandered.
This is a disaster in and of itself. However, the ramifications for society’s culture are even worse. People have a built-in ability to find solutions to issues. For software developers, this is especially true. As a result, people begin to devise ways to go around established protocols and store data in isolated “silos.” There is a breakdown in cooperation. It is inevitable that teams will use various versions of the same data source, which introduces inconsistencies.
For data scientists and analysts who want to push the boundaries of what’s possible with data, governance might suggest limits, limitations, and extra bureaucracy. In businesses where regulation, compliance, and privacy and data protection are top priorities, the idea of increased government control is inimical to the objectives of innovators and those who want “data freedom.” Despite its bureaucratic nomenclature, Data Governance has emerged as a critical necessity for any company hoping to get insight and financial value from its data assets, as opposed to the sexy-sounding phrase “Big Data.”

Main Reasons why data governance is failing us:

1. A manual method is impractical in the modern-day.
While we’ve made significant strides in areas like self-service analytics, cloud computing, and data visualisation, we’re still a long way from being ready for governance. Numerous organisations continue to implement data governance through manual, out-of-date, and ad-hoc tools. Teams of data analysts spend days manually reviewing reports, configuring custom rules, and comparing statistics side by side. As the number of data sources increases and technology stacks grow more sophisticated, this method becomes inefficient and unscalable.
Manually mapping upstream and downstream dependencies takes time in certain companies, not to mention the maintenance effort needed to maintain this up to current. Instead, data teams should use ML and automation to minimise human labour. Our advice: let ML handle the hard work while your team focuses on what they do best.
2. While data is pervasive, data governance is not
Everyone wants to utilise data. Across the organisation, teams are gathering and analysing data to improve business choices. Employing data engineers and analysts by the bushel, businesses are building new data assets and pipelines. Previously only available weekly, hourly analytics are now available.
Increasing data innovation speed is crucial to many businesses’ survival. While data infrastructure and business intelligence tools have evolved to enable this innovation, DataOps has lagged, with manual, one-dimensional, and unscalable solutions like data quality alerts and lineage tracking.
Software engineering principles may help DataOps and solutions catch up. Many of the data issues we encounter have been addressed in engineering, security, and other fields. Like observability, every engineering function has a solution for it. The capacity to completely comprehend an organization’s data may transform data governance from a hindrance to a collaborator in innovation.
3. Data privacy and security are of the utmost importance.
Increasing laws and media attention to hacks and breaches are making data privacy and security concerns for everyone. Small and large businesses alike must address these issues via sound data governance.
A cross-functional data committee that sets security and privacy KPIs and holds the whole company responsible for achieving them should be formed by CTOs, CDOs, and VPs.


Automation/machine learning, next-generation DataOps, and data privacy and security are key components of not just innovation, but also the future of data governance. The confluence of these three developments will result in the emergence of data governance 2.0, which will take centre stage for not just CDOs, but whole companies.
If you’re having difficulty with data governance, you’re not alone. While there is much space for improvement, we are eager to watch whether new methods emerge to address this issue. As data leaders, it is our responsibility to emphasise the critical nature of this subject inside our organisations and across the community.
After all, if data governance is a priority, it’s past time we treated it as such.