In today’s business climate, data is the primary driver of digital transformation.
Businesses need readily available, reliable, and relevant data to make timely business decisions.
Every arm of modern businesses, be it sales and marketing, supply chain management, or customer relationship management, depends on actionable and accurate data to operate effectively.
Without data-driven analytics, it’s nearly impossible to extract value from big data and outperform your competitors.
That’s where data governance comes into the picture — it implements principles and guidelines to manage enterprise data throughout its lifecycle while ensuring quality, consistency, and security.
Data Governance: A Definition
Data governance is the combination of internal standards, policies and technologies that define the management and control of availability, security, privacy, integrity and security of enterprise data.
It includes the totality of processes, data ownership roles, metrics and technology that support the accuracy, consistency and protection of data from its acquisition to use and eventual disposal.
With data governance, you can ensure the data is stored, handled and secured in a way that supports your organization’s goals while maintaining compliance with legal and regulatory requirements.
An effective data governance policy must establish clear data stewardship responsibilities, govern access to various kinds of data, define what kinds of data are under governance and monitor compliance to internal mandates and external standards.
Benefits of Data Governance
You must set extremely clear-cut goals for your data governance policy if you don’t want it to take a backseat when it comes to management and availability of data. Let’s look at some of the benefits of data governance for enterprises that leverage big data. While these benefits might differ slightly for industry verticals, it’ll help you set goals and KPIs for your organization’s data governance policy:
- Empower business decisions with accurate and consistent data: In order to use data to make informed business decisions, it’s important to first instill trust in that data. Ensuring the data is clean, consistent and accurate enables prompt responses to changing market circumstances and helps avoid costly mistakes.
- Enhance compliance with legal and regulatory requirements: Data governance is a major part of compliance. Well-defined processes and workflows can create a culture of risk awareness at the workplace, helping ongoing efforts to comply with data protection standards and regulations like GDPR and HIPAA. Since noncompliance can result in negative PR, fines and penalties, it’s necessary for organizations to not only ensure compliance but also anticipate and adapt quickly to future regulations.
- Secure sensitive data: While modern data storage technologies do a lot of heavy lifting when it comes to the storage, handling, and security of data, it’s equally important to educate your employees on the importance of data security. Holistic data governance covers both the technology and the people side of things to secure sensitive data such as personally identifiable information (PII) and financial data from unauthorized access.
- Improve data sharing and collaboration: Data governance helps implement a democratized single source of truth across the entire organization. Consequently, it becomes easier to share and collaborate on differing sets of data. When the right personnel get access to the right data at the right time, it’s easier for them to excel at their particular roles.
- Generate cost savings: Lack of data governance policies can not only compromise your organization’s ability to react to opportunities in a timely manner but also result in wasted resources. According to Mckinsey’s Global Data Transformation Survey, an average of 30% businesses waste time and resources on non-value-added tasks as a result of poor data quality and availability. A robust data governance framework can minimize instances of data duplication, errors, and omissions, resulting in efficient resource utilization.
- Build trust with transparency and reliability: An actionable data governance policy with regular audits can help you emphasize your organization’s trustworthiness. Transparency in compliance audits with internal and external mandates can go a long way towards establishing a positive brand image.
Components of a Data Governance Framework
The efficiency and integrity of your data governance framework will set the tone for all future data management projects to come, like big data analytics, master data management, data warehousing and more. So, let’s take a look at the components of a typical data governance framework.
Organization
Organization includes the people responsible for developing and maintaining the workflows necessary to implement the data governance framework. Their ranks are comprised of data governance officers, data custodians and data stewards.
Policy and Strategy
Data governance strategy is a formal and documented roadmap of the organization’s data governance framework. It’s a set of policies and guidelines defining how to use, store, collect and protect data within the business.
Processes
Data governance requires a set of processes to monitor and manage data at critical points throughout its lifecycle. These processes include data quality management, data security, data lineage, data classification, data exchange, evaluation and more.
Technology
It comprises of the tools and technologies that automate and streamline an organization’s data governance program. Depending on your requirements, your framework could benefit from data dictionaries, data protection tools, quality evaluation tools, data catalogs and more.
Metrics and KPIs
As the name suggests, it’s the set of metrics and KPIs used to benchmark the performance and effectiveness of data governance processes. Prepare a baseline measurement and establish KPIs that are easily quantifiable and measurable over time. Without a baseline, it’ll be difficult benchmark against industry peers and identify areas for potential improvement.
Communication and Training
Communication is essential to ensure all stakeholders and organizational audiences are caught up on the latest data governance policies, processes and best practices. To build workplace awareness, it’s necessary to implement a common data governance taxonomy with clear-cut objectives and targets for the employees.
Proper training is also required to ensure all the stakeholders understand their roles and responsibilities.
Data Protection Standards and Regulations
A typical data governance framework must have built-in safeguards to maintain compliance with legal and regulatory requirements. There are various data standards that provide a data governance model for ensuring strict adherence to data policies. You can also implement data rules to direct your governance processes towards preferred data standards.
Review and Continuous Improvement
And last but not least, leave provision to regularly review the data governance framework. If at any point the policy needs any changes, you should be able to administer them directly and without delay.
Data Governance in the Cloud
While data governance in the cloud shares the same fundamental concerns regarding cloud security, data consistency and availability, it deviates slightly in terms of how it works.
- The cloud has a shared responsibility model when it comes to data security and governance. In simple terms, the cloud service provider (CSP) will insure the cloud infrastructure and network against potential theft and risk exposure. However, the customer must oversee data governance on their cloud, on-premises and hybrid systems.
- Risk exposure of data increases significantly during cloud migration. Data governance policies for the cloud must define data encryption protocols to protect data in transit and at rest.
- Compliance in the cloud extends to both the CSP and the customer. Therefore, the relevant data governance people must confirm beforehand if the cloud vendor complies with internal and external standards and regulations like GDPR, PCI DSS and HIPAA.
Migrating to the cloud is a massive undertaking and it’s important to become familiar with the rudimentary differences before relocating to a new environment.
Next Steps
While cloud services like data lakes and analytics have simplified data management, access and security, it cannot replace a robust data governance program. It’s an ongoing process that provides the foundation for future data-driven analytics and applications and requires continuous monitoring and re-evaluation in light of changing market trends. Torry Harris Integration Solutions offers an integration governance and empowerment framework to enable cohesive and risk-free integration journeys across enterprises globally.
