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Suppose a pharmaceutical company, Company A, is in the process of developing and launching a highly marketed new drug. Most pharma companies usually conduct extensive trials to fully assess the efficacy, safety, and potential side effects of their drug.
Company A did the same. However, it soon emerged during the later stages of development there were tremendous data inconsistencies. Data inconsistencies that delivered varying results related to the supposed side effects of the new drug.
As a consequence, Company A conducts a thorough internal investigation and soon realises that it had not implemented a thorough robust data governance framework. This resulted in different teams working on the drug to function with their own methodologies and analysis that did not align with the other teams. The result? Inconsistent results as well as the lack of any stated process for conflict resolution.
At this juncture, Company A will likely have to conduct a thorough and comprehensive internal audit and assessment to figure out exactly what has been causing the inconsistencies. However, to the reader it should be clear what the main problems are.
The lack of a standardized clinical process, including the data collection, analysis, documentation, and reporting. Roles and responsibilities not being clearly defined across teams leading to unnecessary duplication of efforts. And finally, the absence of any conflict resolution mechanisms, leading to different teams working in their own silos.
In other words, the lack of appropriate data governance.
This anecdote should serve as a reminder of just how important data governance is and how lack of such a framework in place can often snowball into a much bigger problem for organizations. To maintain the accuracy, reliability, and effectiveness of data to maximise its potential to lead to better decision-making, data governance is no longer a choice, but a functional requirement.
At its core, Data Governance (DG) is a set of internal standards and policies that govern how an organization manages the security, integrity, usability, and availability of all data in its data infrastructure. By doing so, an organization can ensure that all such data and the subsequent insights gained from such data are reliable and trustworthy.
More importantly, with the proliferation in data protection and privacy regulations globally, organizations must now approach Data Governance as a critical operational aspect of their overall business decision-making and data processing practices in general.
Aside from regulatory compliance, Data Governance allows organizations to root out any inconsistencies within their data infrastructure that may hinder the integrity of the data. Since various departments within an organization have access to data, their handling, and eventual cataloging may differ, leading to complications in business intelligence (BI) insights, analytics, and other enterprise insights.
Lastly, Data Governance is an essential factor in maintaining data accuracy and consistency by developing standard data definitions and more practical elements, such as standardized data formats across the organization. As a result, any possible data silos can be eliminated, or at the very least, minimized, while harmonizing a centralized data architecture across all business units with access to this data.
There are various other benefits to Data Governance, such as improved data quality, more efficient data management, and better actionable insights for executives, increasing the competitive edge an organization may gain as a result.
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:
Well-designed data governance helps teams avoid errors and inconsistencies in their data that could result in cybersecurity and compliance risks.
It helps companies break free from their data silos and consolidate their data to seamlessly derive better insights and value.
By reducing errors in databases, companies can increase efficiency, and thus, save time, effort, and money which they would otherwise invest in cleaning that data.
With a consolidated and categorized data, security teams can effectively design and apply security rules and policies that prevent the data from being exposed or misused.
Well-managed data governance allows companies to adapt to existing and emerging regulations for seamless compliance as teams can effectively monitor every activity related to their data across the environment.
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 “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 amounts of data swiftly, reduce manual labor and the human error associated with it, set security controls to protect data, and maintain a log and audit reports for compliance. A related article offers more details on the data governance best practices.
Data governance refers to the overall management, control, and protection of an organization's data assets. It involves defining data ownership, data quality standards, access controls, and processes to ensure data is used effectively, securely, and in compliance with regulations.
The three pillars of data governance are:
Examples of data governance include creating data dictionaries, establishing data stewardship roles, implementing data quality checks, defining access controls, and setting up data classification standards.
The four components of data governance are:
Data governance plays a key role in establishing processes and controls to ensure compliance with data protection principles under the GDPA, including lawful processing, data accuracy, purpose limitation, and data subject rights.
The role of data governance is to ensure that data is managed effectively, securely, and in compliance with regulations. It involves defining policies, roles, and procedures for data management, quality, access, and protection.
Data governance focuses on the overall strategy, policies, and accountability for data management. Data management deals with the operational aspects of collecting, storing, processing, and using data while adhering to governance policies.
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