5 Effective Best Practices for Data Governance Success

By 2022, the total enterprise data volume is estimated to be more than 2.02 petabytes. As a result, businesses that work in highly data-intensive environments need robust data management capabilities to monitor, manage, store, access, secure, and share information in a streamlined and standardized way. You’ll Want Your Business to Have the Best Practices in Data […]

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By 2022, the total enterprise data volume is estimated to be more than 2.02 petabytes. As a result, businesses that work in highly data-intensive environments need robust data management capabilities to monitor, manage, store, access, secure, and share information in a streamlined and standardized way.

You’ll Want Your Business to Have the Best Practices in Data Governance

Consolidating and scaling more data sources and assets, an appropriate data governance architecture can help organizations maximize data value, minimize risks, and eliminate unnecessary operating costs.

A survey report by Gartner reveals that 55% of organizations lack a standardized approach to data governance and identify it as the most significant barrier to achieving data objectives. However, a carefully designed data governance strategy can solve many problems in terms of providing consistency, standardized data, and delivering better business outcomes.

Here are five key best practices for accomplishing data governance success:

• Define the proper process in alignment with the right people

Bridging the gap between the strategic teams and the data processes is critical in developing an inclusive and data-driven organization. To get a handle on data and control it from a centralized location, creating a unified database, along with a master dataset, is key.

This structure can standardize how data is used in various areas across the organization. Defined roles are essential to every data governance program, and it is vital to assign ownership levels. As such, the right people can access the best-suited data as they need it to provide the best insights – yielding the best results.

However, the data governance team should be cross-disciplinary, from data stewards to top-level executives. The team should comprise a cluster of subject matter experts, data security experts, project managers, and data governance visionaries who can deliver frontline and cross-functional experience to the entire organization.

• Build the roadmap that ensures trusted data always

Organizations looking to improve decision-making and business outcomes should align their business objectives with high-quality data creation and implementation. According to a Harvard Business Review report, 47% of data records are created with critical errors that affect work.

Companies often lack the processes for validating data characteristics, which include data accuracy, uniqueness, completeness, relevance, and timeliness. Moreover, organizations should build data quality controls to develop better insights and meet required data standards – tackling and identifying erroneous or inadequate data.

Organizations performing analytics without quality data can lead to inaccurate interpretations and decisions. Apart from data objectives, any additional goals that are unique to the business or necessary to address specific organizational goals should also be considered.

However, ensuring better and cleaner data should be paramount for businesses aiming at digital transformations and business analytics.

• Become consistently compliant with regulatory requirements

Adhering to critical compliance and regulatory mandates like General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA) is crucial for every data governance assessment. It is essential to ensure that organizations are consistently compliant with all levels of regulatory requirements to minimize risks and reduce operational costs.

Compliance ensures that data treatment follows applicable regulatory requirements, whether from the government, accreditation bodies, or the business itself. These regulations are designed to protect data from misuse, loss, and theft.

According to a survey conducted by erwin and UBM (erwin dotcom), 60% of organizations believe that regulatory compliance is the most essential factor in strengthening data governance. Regulations vary widely across geography and industry, complicating management tied to them.

However, organizations should follow best practices to ensure compliance and safeguard themselves from compliance failures that could lead to tarnished brand images and the subsequent downfall of the business.

• Evaluate risks across the board

The need to protect data and reduce risk is an essential factor in driving data governance at many organizations – data security and data privacy being the most visible ones. According to a survey by Gartner, 42% of data and analytics leaders do not assess, measure, or monitor their data and analytics governance.

Data security starts with understanding the risks related to data distributed across sources like data lakes, data warehouses, and individual silos. Also, it is important to protect the data across the organization to control data leaks, which mainly originate from improper data access permissions.

The organizational structure should ensure appropriate access to data while maintaining adequate privacy. Security threats that constantly evolve are unpredictable.

The best way to protect against data loss or theft is to stay updated on security risks and detect and respond to them on time. 

• Bring the right data management platform to the table

Regardless of the industry, unlocking the potential of data is only possible through proper data management. A data management platform is the backbone of any enterprise data strategy; choosing the right data management platform means selecting the firm’s long-term success.

A forward-thinking organization should look into the future, aligning its data governance expectations with its technology stack to implement robust quality controls, risk assessments, and ongoing monitoring and testing mechanisms. It can be done by opting for an Artificial Intelligence (AI)-driven, cloud-based platform that can deliver value, adapt to the data requirements, and evolve with the organizational changes.

A cloud-based data management platform will allow organizations to quickly plug into robust capabilities that are cost-efficient and will avoid the overhead required for on-premise servers. It can also radically simplify complex legacy operations, lowering running costs, improving agility, and gaining breakthrough performance that delivers real business value.

Safeguard the Future With Data Governance

Implementing advanced procedures and appropriate policies is essential for reliable data governance outcomes. Any business needs to unlock the value of data and boost reliable business decisions regardless of size and sector.

A recent study from McKinsey & Company reveals that companies, on average, are investing between 2.5% and 7.5% of their IT spend on data governance. This will enhance an organization’s strategic, operational, and tactical levels and deliver value, scale, and speed to the governance process.

In addition, to achieve futuristic analytics, visualizations, and automation goals, enterprises should strive to improve data quality (pimcore dotcom) and data access. 

Conclusion

Organizations should also realize that an integrated data governance architecture is essential to improve decision-making and ensure successful outcomes. Data should be well-documented and easily accessible; moreover, it should be secure, compliant, and confidential to manage risk and improve business decision-making.

Data governance is not a one-time strategy; it is an ongoing process that includes organizational tasks and responsibilities, regulatory requirements, and industry protocols. Data, one of any organization’s most significant assets, impacts decision-making and risk mitigation, and it should be governed accordingly.

Moreover, top-level executives and management should ensure organization-wide data awareness and quality improvement initiatives. Ultimately, they need to understand that data governance is unique to each organization, and data should be crafted accordingly to meet business demands.

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