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The sheer volume and complexity of data being generated today is simply staggering! In just two years, from 2016 to 2018, enough data was generated globally to account for 90% of the total data in existence. According to McKinsey, it has been doubling every two years ever since.
However, one might wonder, is all this data simply generated with no organization and management? That would be like having cars to move about without road directions, traffic signals, speed limits, or overall, a central traffic management system. Imagine the chaos.
When it comes to data, data governance is the singular discipline responsible for ensuring the smooth functioning of the data strategy employed by an organization.
Without it, a business is almost guaranteed to struggle in the modern digital world.
Data governance is the formalized agreement within an organization that defines how to handle data throughout its lifecycle by documenting how to store, organize, use, update, and dispose of data. It involves specifying the technologies to use, the processes to deploy, and identifying stakeholders along with assigning them their responsibilities.
It is important to delineate between data governance and data management here for clarity. Data governance creates the policies and frameworks that establish and dictate all data management methodologies, responsibilities, and processes. Take Data Integration, for example. Data governance policies and procedures dictate how data integration processes are carried out. Governance also ensures that data integration meets regulatory requirements and aligns with the organization's objectives.
Think of data governance as a federal legislative body – a country’s parliament or congress. In this analogy, the police would be data management that enforces the rules laid out by this body.
Pro tip: Inefficient data management is costing you money. Get in touch to know how data governance streamlines processes, eliminates data silos, and saves you time and resources.
At the highest level, data governance constitutes five core components;
Together, these pillars help you extract value for your business from otherwise unsubstantial data. The sum of the whole really is greater than the sum of the individual parts in this case.
Tapering quarterly sales figures is actionable data and could lead a business development manager to push their team by increasing their sales targets. However, consumer feedback data on the product they are selling gathered from the marketing team could identify that the product is losing its appeal in its consumer group.
The insights created by the synergy between the two data sets hold huge value, revealing that it’s more on the marketing team to try and revive the appeal of the product than the sales team to simply sell. Now, this wouldn’t be possible without having some of the pillars of data governance in place like data quality, data stewardship, and data management.
Without updated data, this valuable insight wouldn’t have been extractable, or at least accurate. Additionally, without data management and stewardship, the right data wouldn’t have been shared with the right teams.
Data Stewardship is a practice that ensures an organization’s data is being managed and maintained in accordance with the policies set in the data governance framework, with the responsible parties being data stewards. This helps establish quality and readiness of organizational data.
Data quality is judged based on the accuracy of data, data being routinely updated, and the completeness of data being maintained. It is the culmination of these three factors that ensure good hygiene and a robust data governance policy.
Another subset of data governance is data security, also known as data security governance. It is the process of using strictly defined policies and protocols to protect an organization's data from internal and external threats – for example, preventing deletion of data and unauthorized access to sensitive data.
Different systems, internal or external, intake data in different forms, formats, protocols, and encryptions. For data to be consumable by these systems, it must be standardized and compliant. This is particularly important when it comes to financial data and interfacing with regulatory body systems. Data protection and privacy regulations, like the General Data Protection Regulation (GDPR), are crucial for safeguarding individual privacy in today's data-driven world. These regulations establish rules for how companies collect, use, and share personal information, ensuring individuals have control over their data and are protected from misuse. Without having this incorporated into an organization’s data governance policy, irregularity of compliance could arise.
Data management is essentially the end-to-end process of generating, storing, organizing, maintaining, and using corporate data as it is defined in the data governance policies, rules, procedures, and frameworks.
Developing and executing a tailored and comprehensive data governance framework can turn your organization into a well-oiled machine operating at peak performance – where the right data is always maintained in a way to enable timely and accurate decision making.
Business owners can leverage data governance for multiple benefits:
A highly engineered data governance architecture can ensure that metrics from different data sets of different business operations are always being measured to continuously optimize resource utilization and thus increase the return on the finance employed.
A major benefit that comes from maintaining financial data that is compliant with regulatory authorities and independent third-party organizations is drastically reduced risk and increased consumer trust.
An example is following the Payment Card Industry Data Security Standard (PCI DSS) compliance when handling and storing customers’ credit card information. A study by Verizon shows that businesses that are PCI DSS compliant are 50% less likely to be the victim of a cyber-attack.
The risks of not implementing data governance are as extensive and varied as the benefits that come from executing an effective data governance framework. If any of the elements of data governance are lacking, businesses can run into many challenges. To name a few, outdated data, data silos, data breaches, wrong data being served to different data consumers, and legal and financial troubles.
At the very least, it’s important to get the basics right to avoid being at a disadvantage.
In this model, different business units manage their own data and its distribution according to their own standards and objectives with no organization-wide standards or a central authority. This is more suited for micro to small-sized businesses where there aren’t many data generation centers and infrequent data sharing.
Here, a single body has full authority over all matters related to data and is responsible for defining and enforcing data policies for all business units. This is the leading model of data governance.
Medium-sized organizations and upwards all stand to benefit from this model.
In the hybrid model, the data governance policy is defined by a central body. However, that body is not the custodian of the master data, and each business unit independently owns its data. This is mostly used as a transitory model as businesses grow and move from a decentralized data governance model to a centralized one.
However, in certain instances of organizational mergers and acquisitions, organizations may use this data governance model to find a middle ground between centralized and localized control and governance.
A robust data governance structure requires key roles to be present, including a data governance coordinator, a team of data stewards, and a data management committee. This structure typically involves a diverse set of individuals, such as business executives, data management experts, IT personnel, and knowledgeable end users who are well-versed in an organization's data domains.
These data governance champions are important for several reasons – the foremost being carrying out regular, effective communication to keep all stakeholders aware of data governance initiatives, policies, and their roles in data management.
The explosion of Artificial Intelligence (AI) and the mushroom growth of AI tools to facilitate every business function also extends to the fields of data governance and data management. We are already seeing increasing adoption of AI and Machine Learning (ML) automation solutions that are revolutionizing data management.
Ultimately, AI will be guiding and executing end-to-end data governance policies and standards in organizations, minimizing the role of humans and reducing errors significantly.
At Data Pilot, we understand that in today’s digital landscape, effective data governance is not a luxury but a necessity. Our commitment to data governance ensures that our clients can navigate the complexities of data management with ease and confidence.
By leveraging our expertise, organizations can achieve improved decision-making, enhanced business planning, optimized operations, and reduced risks. Our data governance practices ensure that data is not only managed efficiently but also compliant with industry standards and regulations, thereby increasing consumer trust and reducing the likelihood of data breaches.
We pride ourselves on our holistic approach to data governance, which integrates cutting-edge technologies and best practices to create a seamless, efficient, and secure data environment. Our team of experts offers free consultation to collaborates with organizations to design, implement, and maintain data governance frameworks that drive business value and ensure long-term success.
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