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.
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 Gartnerreveals 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:
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.
Organizations looking to improve decision-making and business outcomes should align their business objectives with high-quality data creation and implementation. According to aHarvard Business Reviewreport, 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.
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 byerwin 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.
