For organizations of every type, data modernization is key to staying relevant and competitive. At the same time, for data modernization efforts to succeed, effective enterprise data governance is critical. Data governance influences data modernization and is in turn influenced by it, so embarking on data modernization initiatives with an eye on effective governance is crucial to operational success and stability. 

A Word About Data Modernization and Governance

Data modernization is all about updating and improving an organization’s data practices, tools, and infrastructure to meet the changing needs of data-dependent operations and analytics. Its purpose is to ensure that data is secure, accessible, and effectively used for business processes. Making the transition to a modern data platform architecture breaks down data silos and improves compatibility across systems while improving operational performance and making scalability easier. 

Governance plays a crucial role in data modernization efforts by providing a framework to manage and ensure the security, integrity, availability, and compliance of an organization’s data assets. 

Why Legacy Data Handling Methods Just Don’t Cut It

Legacy methods of handling data have poor data governance, which inhibits their usefulness and scalability while also exposing data systems to risks. While data center modernization has improved the situation in certain organizations, many organizations still make use of outdated methods of data management. Here are some reasons why these methods are problematic. 

  • Data silos: Legacy methods of data management compartmentalize information across different systems and hinder a holistic view of data. 
  • Compatibility issues: In the legacy approach, different systems and formats across teams make data integration complex and time-consuming. 
  • Data errors: In legacy methods, manual data entry and outdated systems lead to inaccuracies and inconsistencies. 
  • Performance bottlenecks: Legacy methods of data handling struggle to handle increasing data volumes and complexity. 
  • Slow response times: Legacy data methods are slow and cause delays in accessing information. 
  • Data-loss risk: Outdated legacy systems are susceptible to cyber threats and data loss due to system failures or human errors. 

Data Governance: What Do the Numbers Say? 

Data governance is all about making data within an organization accurate, relevant, and usable. In this regard, a concerning statistic is that 27% of the data in the world’s largest corporations is erroneous. This data costs businesses 12% of their revenue annually. Further, between 60% and 73% of organizational data is never used for any strategic purpose, while executives spend 70% of their time looking for data and only 30% evaluating it. 

Data governance is critical to fostering business collaboration. In this regard, 77% of businesses would stop partnering with a company that lost or misused their data. In other words, a company with inadequate data governance will face disastrous consequences. 

The increasing need for data governance is underscored by the fact that the data governance market is growing at a CAGR of 20.83% annually and is expected to reach $ 5.28 billion by 2026. 

 All said and done, following a robust data governance process is both beneficial and non-negotiable for organizations keen to work on a modern data platform architecture. 

Data Governance: Loaded with Benefits for Data Modernization 

Data governance is the bedrock upon which successful data modernization is built. Consequently, it offers multiple benefits for data modernization initiatives. 

  1. Improved data quality and consistency: Data governance ensures consistent data definitions, formats, and metadata. Further, governance efforts identify and correct inaccurate, incomplete, or duplicate data. At the same time, rules and checks ensure data integrity. 
  2. Enhanced data accessibility and usability: Data governance catalogs data, thus creating a centralized repository of data assets with clear metadata. Further, data governance efforts empower users to access and analyze data independently by facilitating easy location and understanding of relevant data. 
  3. Mitigated risks and better compliance: Data governance protects sensitive data through access controls and encryption while also ensuring adherence to regulations and maintaining a clear record of data changes and access. 
  4. Accelerated decision making: Data governance provides reliable, accurate, and consistent data for analysis and reduces the time it takes to derive actionable insights. 
  5. Optimized resource allocation: Data governance helps focus on high-value data assets while eliminating redundant data and processes. Overall, it streamlines data management operations. 
  6. Stronger data-driven culture: Data governance assigns accountability for data quality and integrity while improving data understanding and encouraging data sharing and organization-wide collaboration. 

Data Governance During Key Modernization Events: Data Integration & Migration 

Data governance is a cornerstone of successful data modernization, particularly during data integration and migration. These phases are critical for transforming raw data into actionable insights. Effective governance during these phases ensures data quality, consistency, and security throughout the process. By meticulously planning and executing data integration and migration projects, organizations can mitigate risks, optimize data value, and drive better decision-making.  

Effective governance during data integration ensures standardization of formats for data elements and accurate metadata and master data management. It helps to maintain data integrity by tracking data lineage. It also sets in places data retention policies. 

Data governance is an indispensable component of any data migration project. Effective governance identifies and corrects data errors and inconsistencies before migration. It ensures adherence to regulatory requirements and industry standards. Further, it clearly defines responsibilities for data management. Finally, an effective data governance mechanism verifies data accuracy and completeness after migration. 

What Modern Data Platform Architecture Ensures Optimal Governance? 

Since legacy data-handling methods are weak on the governance front, which modern data platform architecture ensures optimal data governance? 

A data platform architecture capable of ensuring meticulous data governance has certain hallmarks. Firstly, it contains a metadata repository that captures data lineage, quality metrics, and usage statistics. It enables data discovery, impact analysis, and compliance reporting. 

Secondly, it provides a self-service platform for users to find, understand, and consume data and incorporates data quality metrics, usage analytics, and access controls. 

The architecture implements data profiling, cleansing, and validation processes and monitors data quality continuously. 

Next, it enforces robust access controls, encryption, and data masking and adheres to industry regulations. It also implements data-loss prevention methods. Further, it tracks data lineage transformations from source to consumption. 

Finally, such an architecture automates governance policies and rules and integrates with CI/CD pipelines for continuous governance. 

Leverage These Data Governance Best Practices During Data Modernization 

Data governance needs to be front and center when embarking on a modernization initiative. Here are some best practices to follow: 

  • Comprehensively identify and map all data sources, formats, and locations. 
  • Evaluate data accuracy, completeness, consistency, and timeliness. 
  • Categorize data based on sensitivity, value, and compliance requirements. 
  • Define data definitions, formats, and naming conventions. 
  • Establish rules for data access, usage, sharing, and retention. 
  • Set metrics and guidelines for data accuracy and consistency. 
  • Clearly define who is responsible for each data asset. 
  • Encourage data ownership and accountability throughout the organization. 
  • Implement security measures to protect data from unauthorized access, use, disclosure, disruption, modification, or destruction. 
  • Ensure adherence to regulations and privacy laws like GDPR and CCPA. 
  • Assess vulnerabilities and implement necessary controls. 
  • Establish clear processes for data creation, storage, use, and disposal. 
  • Determine appropriate data retention periods. 
  • Implement secure methods for data destruction. 

Final Word: Ready to Take Your Data Modernization Initiatives to the Next Level? 

Adopt a modern data platform architecture to offer your organization a much-needed competitive edge whilst ensuring operational excellence. 

Aspire’s data experts from around the world can help you modernize your data infrastructure while ensuring optimal data governance through the process. 

Here are some of the services relevant to data modernization that we offer: 

  • Big data integration 
  • Data migration 
  • Cloud-based storage and processing 
  • Data lake implementation 
  • Enterprise data warehouse implementation 
  • Data governance and quality automation 
  • Advanced analytics and business intelligence tools setup 
  • Data virtualization 

If your goal is to modernize your legacy data infrastructure whilst ensuring governance, we have you covered. 

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