Data is a means by which an organization knows itself – it is a meta-asset that describes other assets. As such, it provides the foundation for organizational insight. Within and between organizations, data and information are essential to conducting business. Most operational business transactions involve the exchange of information. Most information is exchanged electronically, creating a data trail. This data trail can serve purposes in addition to marking the exchanges that have taken place. It can provide information about how an organization functions. Because of the important role that data plays in any organization, it needs to be managed with care.

When it comes to managing data, every company has its own set of challenges. By understanding these challenges, we can develop and implement tailored data governance strategies that maintain data integrity, ensure compliance and optimize data utilization. Effective data governance requires more than just the right tools; it also demands a comprehensive process. This involves not only how data should be initially handled, classified and continuously reclassified and categorized, but also who will be responsible for these tasks. While tools play a crucial role, the overall process and the people involved—though they may vary by company or industry—are critical for a successful data governance program. This article explores how different types of companies approach data governance, highlighting their specific needs and the strategies that can address them.

Also Read: Data Governance with Real-Time monitoring

Legacy Companies

Legacy companies are defined as companies that have been around for quite some time and have legacy on-premises systems, which can lead to various challenges. For example, a large retailer might have separate systems for online and in-store sales. In one system, sales income is labelled as “revenue,” while in the other, it’s called “sales.” This Inconsistent terminology complicates analytics and undermines data governance.

Challenges:

• Data Disorganization: Legacy systems often have different standards for data labeling and management, causing inconsistent data across the organization.
• Lack of Centralized Data Dictionary: Without a unified reference guide, it becomes difficult to standardize data terms and categories, complicating data integration and analysis.
• Migration Delay: The fear of replicating past issues in a cloud environment can delay efforts to modernize data management practices.

Strategies:

• Establishing a Central Data Dictionary: Develop a standardized guide to unify data terminology across all systems.
• Planning Cloud Migration Carefully: Implement a phased approach to cloud adoption, ensuring data governance practices are solidified before full migration.
• Investing in Modern Data Management Tools: Utilize tools that can bridge gaps between old and new systems to facilitate smoother transitions and better data integration.

Cloud-Native/Digital-Only Companies

Cloud-native companies, often referred to as digital-only are those that have always stored all of their data in the cloud. These companies, typically younger and without any on-premises systems, don’t face the challenges of migrating data to the cloud. However, they still encounter issues with data management due to using multiple cloud services and different storage solutions. This can create various forms of data siloing. For instance, establishing a centralized data dictionary is already a complex task and managing one that spans multiple cloud environments adds an additional layer of difficulty.

Challenges:

• Managing Multiple Cloud Environments: Different cloud services and storage solutions can create complexity in maintaining a centralized data dictionary and consistent governance practices.
• Ensuring Consistent Processes: Variations in data management tools and practices across different cloud environments can hinder effective governance.

Strategies:

• Creating a Unified Data Dictionary: Develop a comprehensive guide that spans all cloud environments and ensures consistency in data definitions and usage.
• Standardizing Data Management Practices: Implement uniform processes for data enrichment and governance across all cloud platforms to maintain consistency.
• Leveraging Cloud Governance Tools: Use cloud-specific tools that support centralized management and monitoring of data governance practices.

Retail Companies

Retail companies are an interesting category, as not only do they often ingest quite a bit of data from their own stores, but they also tend to ingest and utilize quite a bit of third-party data. In retail, data governance extends beyond simple classification to include its intended use. While classifying data helps in managing access and treatment, it’s crucial to consider how data will be used. For example, email collected for receipts cannot be used for marketing without explicit consent. This may require complex processes beyond role-based access controls, particularly when employees handle multiple roles.

Challenges:

• Integrating Third-Party Data: Effective governance requires managing and standardizing both internal and external data sources.
• Ensuring Appropriate Data Use: Data collected for specific purposes, such as receipts, should not be repurposed without proper consent, complicating governance practices.

Strategies:

• Developing a Comprehensive Data Governance Framework: Create processes for integrating third-party data and ensuring it adheres to the same governance standards as internal data.
• Establishing Use Case Policies: Define clear guidelines for how data can be used based on its intended purpose and obtain necessary consents for repurposing data.
• Implementing Robust Access Controls: Ensure that data access is tightly controlled based on user roles and data use cases to prevent misuse.

Highly Regulated Companies

Highly regulated companies represent the sector of companies that deal with extremely sensitive data —data that often carries additional compliance requirements beyond the usual handling of sensitive information such as finance, pharmaceuticals and healthcare.They have to juggle not only basic data governance best practices but also the additional regulations related to the data they collect and deal in and they face regular audits to make sure that they are aboveboard and compliant.

Challenges:

• Compliance and Audit Readiness: These companies must adhere to rigorous data handling regulations and are subject to frequent audits.
• Limited Flexibility with New Tools: Regulatory constraints may limit their ability to experiment with new data management tools.

Strategies:

• Implement Advanced Compliance Systems: Develop sophisticated systems for data classification and handling that meet regulatory requirements.
• Dedicate Resources to Data Governance: Invest in specialized personnel and tools to ensure data governance processes are robust and compliant.
• Adopt Risk Mitigation Strategies: Carefully evaluate new tools and technologies to ensure they meet all regulatory standards before adoption.

Small Companies

Small companies, with fewer than 1,000 employees, often have a more straightforward data landscape due to their smaller size and fewer data handlers. Smaller companies often have small data-analytics teams, which means that there are fewer people who actually need to touch data. This means that there is less risk over-all. 

Challenges:

• Limited Data Complexity: Smaller data sets and fewer employees can simplify governance but may also mean fewer resources for implementing sophisticated practices.

Strategies:

• Streamlining Data Access Controls: Implement simple and effective access controls based on the limited number of employees.
• Tracking Data Usage: Maintain clear records of data origins and usage to ensure effective governance with minimal complexity.
• Focusing on Essential Data Governance Practices: Prioritize basic data governance practices that provide the most benefit with the least overhead.

Large Companies

Large companies, with over 1,000 employees, not only generate a great deal of data themselves but also often deal with a lot of third-party data. This results in immense difficulty in wrapping their arms around it all, they are overwhelmed by the data and often struggle to govern even a portion of it. As such, only some data gets enriched and curated, which means that only some of their data is able to be used to drive insights.

Challenges:

• Data Overwhelm: Managing and governing large volumes of data can be overwhelming, leading to incomplete or inconsistent data management practices.
• Complex Access Control: With many employees needing access to data, managing who can access what data becomes complex.

Strategies:

• Prioritizing Key Data: Focus on governing critical data categories and known data pipelines to manage resources effectively.
• Use Role-Based Access Control (RBAC) and Automated Permissions: Implement RBAC to assign access levels based on job roles and use automated tools to regularly review and adjust permissions, ensuring appropriate access without manual intervention.
• Integrating Data from Acquisitions: Establish processes for integrating and governing data from acquired companies to ensure consistency across the organization.

Effective data governance requires a tailored approach based on the unique needs and challenges of each company. By understanding these needs and implementing targeted strategies, organizations can ensure their data is managed efficiently, securely and in compliance with relevant regulations.

References

• Data Management Body  of Knowledge, DAMA International Technics Publications, Basking Ridge, New Jersey.
• The KPI Institute and Aurel Brudan, ed. The Governance, Compliance and Risk KPI Dictionary: 130+ Key Performance 
• Indicator Definitions. CreateSpace Independent Publishing Platform, 2015. Print.Evren Eryurek, Uri Gilad, Valliappa Lakshmanan, Anita Kibunguchy, Jessi AshdownData Governance: The Definitive Guide. People, Process and Tools to Operationalize Data Trustworthiness. March 2021.
• Anderson, Dean and Anderson, Linda Ackerson. Beyond Change Management. Pfeiffer, 2012.