The Ultimate Guide to Data Governance: Best Practices for Managing Data Intelligence

In today’s data-driven world, companies collect and store vast amounts of data. This data is often used to make important business decisions, perform predictive analysis, improve services, and create insights. However, if this data is not properly managed, it can lead to serious consequences such as data breaches, privacy violations, loss of trust in data, and sub-optimal decision-making.

Evolution of data journey

How did we get to this? We would like to take you back a few decades to where this data journey began. At that time, nobody could imagine where data would evolve to be. Let’s divide these decades into three phases; the first was the era of ‘collection,’ where organizations started creating interfaces to collect data of all sorts. The second phase was ‘centralization & intelligence.’ During this phase, the core focus was bringing data to a centralized location to extract insights and create intelligent reports. Up until this phase, there was a mere concept of compliance, artificial intelligence, data intelligence, and how large organizations could create a data-driven approach in the true sense and exploit it for a predictive mindset. The third phase is now the era we live in: ‘AI & Governance.’ Large volumes of data have been collected to train AI & ML engines to produce miracles in the data world – but this comes with its own challenges like compliance, regulations, data intelligence, data integrity, privacy, etc. Data governance is the other big feature of this era, providing solutions to a data-driven approach. 

What is Data Governance?

Data governance is the process of establishing policies and procedures to manage, protect, and use data effectively. It involves the creation of a framework to ensure data quality, accuracy, and security. It also involves defining roles and responsibilities for data management, setting up processes for data sharing, and establishing data retention policies. A significant part of data governance is making organizations aware of the data with respect to regulations and compliances like GDPR, CCPA, NDMO, etc. 
Common roadblocks in a data governance model

Organization culture & people:

One of the biggest challenges we have experienced in implementing a data governance framework is the cultural change that organizations need to adopt for it – all other challenges are offshoots of this main challenge. People, by their basic nature, don’t like to change, especially once they get comfortable with existing processes or practices. Hence, there needs to be a well-defined strategy in place to address this challenge. To overcome this, the need for change must come from the organization itself, and a formal governance committee must be established to implement and make it acceptable internally.

Automation of processes & policies: 

Another challenge pertains to automating processes and policies around different data sets. When exploring data, users from different departments have to chase respective owners to get the policies and regulations on specific data sets. This is time-consuming and, in some cases, remains open-ended. Governance enables organizations to centralize these policies and procedures so anyone can view and act accordingly. Information sharing is also challenging in terms of security, access and reusability amongst different departments. The concept of data ownership in governance helps to make this easy as well. 

Standardized business glossary:

The gap between technical and business knowledge has always been a problem for organizations. This is because business users exploit the true value of data, but the understanding of data resides with technical teams.  Therefore, bringing business users on board is a challenge to create business glossaries and data dictionaries. Another challenge is associating business glossaries with technical terms. However, once these documents are standardized, a lot of time is saved for business users as the technical data and its usability start making sense to them.

The challenges underscored above highlight the need for a formal data governance structure for organizations. The absence of data governance policies can lead to a lack of data quality. It can also result in limited visibility of the data life cycle, which can lead to a trust deficit. This can be one of the biggest showstoppers in any organization’s data-driven approach. 

Unlock the Value of Data: The Benefits of Robust Data Governance

Data Governance has been a part of organizational management practices for many years. Still, it has become more critical in recent times due to the increasing importance of data for organizations. With the advent of digital technologies and the growing use of data, organizations realize the need to establish a formal approach to managing their data. 

Data Governance provides organizations numerous benefits, including increased efficiency, improved data quality, enhanced data security, and reduced costs. It also helps organizations comply with relevant laws and regulations and to manage risk more effectively. By establishing a formal approach to data management, organizations can ensure that their data is used effectively and efficiently to support business goals.

Having a strong data governance program is crucial for companies looking to use a data-driven approach and having data intelligent employees. In modern times, decisions are made based on data- if data is not trustworthy or lacks the depth of detailed information, decision-making is flawed, and business users are unable to build acumen with respect to data.

Here are some of the key benefits of data governance for organizations:

Alignment between Technical & Business teams:

One of the biggest advantages is eliminating the gap between the technical and business teams, who truly have to be data intelligent. This gap can be eliminated by relating technical terms to business terms commonly used and understood by the business. This enables business users to understand data for their needs. 
Increased Data Quality: By establishing data governance policies and procedures, companies can ensure accuracy and completeness of their data. It allows them to make informed decisions that are based on accurate and up-to-date information. As a result, users’ trust in data increases, and stakeholders start making decisions based on insights generated from data.
 

Better Customer Experience:

Data governance enables companies to use customer data in a way that is respectful of their privacy and preferences. This helps to build trust with customers and improve the overall customer experience.
 

Data Classification:

By classifying data into different data domains, it becomes easier for businesses to distribute data sets and search for relevant assets according to the defined classification. In addition to this, data classification can also be used for assigning accountability and responsibility for data to the relevant teams.
 

Elimination of Siloed data:

Large volumes of data generated by multiple sources are usually stored in siloes. This presents two problems; first, no relevance is created between data coming in from different sources, and second internal teams are often not aware of all of the data that’s available. Data governance involves creating and cataloging the complete life cycle of data. This helps internal teams in identifying the relevance of siloed data. In addition, instead of conventional reporting, new insights can be discovered once business users are aware of the complete data that’s available to them for use.
 

Improved Decision Making:

Data governance helps ensure that data is used in a consistent and standardized way across an organization. This allows for better decision-making and reduces the risk of errors and misunderstandings.
 

Enhanced Security:

Data governance helps companies protect their data from threats such as cyber-attacks, data breaches, and unauthorized access. This helps maintain the privacy and security of data and avoids costly consequences.

Data Governance Will Power the Next Generation of Business Innovation

The future of data governance is bright as organizations continue to recognize the importance of managing their data effectively. With the increasing use of data and the growing importance of data privacy and security, organizations are likely to place even greater emphasis on data governance in the coming years. Data governance helps organizations to establish a data-driven culture by promoting the use of data in decision-making and improving the accuracy and quality of the data. It also supports data intelligence by enabling organizations to extract valuable insights from their data, which can be used to drive business growth, execute various business use cases, and improve customer satisfaction.

Conclusion

Data governance is a key element for companies looking to build a culture based on data-driven decision-making. By establishing policies and procedures to manage and protect data, companies can ensure that their data is accurate, secure, and used in a way that benefits both the business and its customers. In addition to this, implementing a strong data governance program is an investment in the future success of a company’s data-driven efforts. There are numerous reasons why organizations need to adopt a data governance culture, such as:

Ensure that regulations & compliance around data are in place and all users understand that abiding by these is mandatory. Failure to do so can result in heavy fines.

Enable business teams to understand data in order to produce value- to do so, knowledge of technical metadata must be converted into business terminologies.

Ensure that decisions that are being made based on data are trustworthy.

Data sharing is automated, and mechanisms are in place whereby every department can share its data after undergoing the required approval processes. This will save time and ensures that access to data is supervised by the respective owner.

Policies around data are documented so that users are aware of the guidelines for using a specific data set.
Ad-hoc processes are automated, and respective stakeholders are held accountable.

Keeping in view some of these points a comprehensive data governance framework is highly beneficial for organizations and sooner or later will become the foundation for creating a truly data-driven culture.