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Companies are churning out data more rapidly than they were a decade ago. As data is being produced and processed across a dynamic environment, consisting of on-premise infrastructure and multi-cloud applications, ensuring data ownership, security, quality, consistency, integrity, and interoperability has become paramount for companies to further their business. This is what Data Governance is all about.
Seamless data governance is further required to protect the data against a growing number of cyberthreats and to ensure compliance with existing or emerging global data regulations, such as GDPR, CCPA, CPRA, HIPAA, and PIPL. These regulations are established to govern how businesses or entities should treat the personal data of individuals (either consumers, employees, patients, etc.) and give individuals control over the collection and processing of their data.
The most pressing concern while establishing data governance is the number of data silos within the organization. Data silos lead to a lack of visibility into critical data metrics because of disparate, disorganized, and uncataloged data. Apart from that, there are many hurdles that companies have to overcome while implementing data governance, such as:
Apart from data protection and compliance, there are many other reasons why organizations must strive for thoroughly planned and well-executed data governance:
Building robust data governance means having a solid team of capable hands building and supporting the system. While a data governance team may have many people populating it, the core job roles remain the same in every organization.
Data owners are the team members who are mainly responsible for the use and processing of the data and its protection as a business asset. They are also to ensure the quality of data.
Apart from the key players mentioned above, a data governance team may also involve data strategists, quality analysts, finance executives, engineers, data architects, and board members.
The Chief Data Officer (CDO) is responsible for formulating, implementing, and overseeing the overarching data governance strategy. CDOs usually have a high-level authority on the implementation and performance of DG. Apart from that, they also help with the funding and staffing of the data governance team and advising on related matters.
In some organizations, CDOs also play the role of DG managers who head the team, monitor metrics, and manage a few other responsibilities.
Data stewards are also sometimes called data champions as they are responsible for enforcing data governance strategy and ensuring that the end-users comply with it. Data stewards are often subject matter experts in particular domains or attributes. They train new data owners and work together with existing owners to ensure effective governance.
The role of a DG committee is to formulate governance policies, associated rules, and standards. They often work together with the CDO to decide and establish the core aspects that make up a data governance framework. DC committees are also responsible for handling disputes between teams and other escalated problems.
There may be many models and processes that affect the success of data governance. However, the following are the core models without which effective governance may not be possible.
Data quality is the core pillar of every governance program in any organization. Poor data quality leads to ineffective decision-making, and thus, undesirable business outcomes. Therefore, the need to have consistent, accurate and high-quality data is paramount to the success of organizations.
Data stewards have an important role in a data governance program as they are responsible for navigating collaboration between stakeholders and other members. Moreover, data stewards are the individuals who ensure the protection, use, and quality of data.
The efficacy of a governance program also heavily depends on its security and compliance. For that reason, data must be labeled according to its sensitivity level, so relevant security measures are implemented. More importantly, data protection is associated with core governance efforts under global regulations, such as the European Union’s General Data Protection Regulation (GDPR) and California Privacy Rights Act (CPRA).
Data tends to evolve, change, and increase over time. Data should be monitored and tracked efficiently to ensure governance throughout the lifecycle of data. This can be done by implementing intelligent data scanning and mapping.
A data governance framework outlines the guidelines on how governance is established across the board, why it is needed, what it will govern, and who will govern. In other words, a governance framework answers the following:
This defines the list of people involved in the governance program and their respective responsibilities. As mentioned previously, a governance team may comprise a governance committee, CDO, data stewards, board members, and other personnel.
It outlines the type of data that falls under the governance program. It defines the type of data that needs to be regulated, filtered, and protected. All in all, it is the data around which the entire data governance policy needs to be established.
One of the most important aspects of a governance program is to have proper and complete control of your data. Therefore, the “where” in a governance framework defines where the critical data resides in your systems.
The when in a governance framework helps teams in various ways. For starters, it allows companies to ensure compliance with regulations like PCI DSS and HIPAA by defining the retention period for the data. Secondly, it also helps the team control how frequently they should conduct audits.
The “why” is the mission and vision of a data governance program, explaining why the organization needs to have data governance and what it expects to achieve with its implementation. Everyone in the team must understand the “why” to be on the same page.
Perhaps the most pressing challenge is when the team doesn’t receive buy-in from leaders, board members, advisors, or the C-suite to implement the data governance program. Nikola Askham, a renowned Data Governance Coach, pointed out in one of her blogs, “It can be a real struggle to get your data governance initiative approved in the first place.”
Following are some of the best data governance practices that allow companies to overcome the challenges that were mentioned earlier:
Automation is key to increasing the efficacy of a data governance program. Automation enables teams to sift through towering amount of data swiftly, reduce manual labor and the human error associated with it, set security controls to protect data as, and maintain log and audit reports for compliance.