Delineate’s Data Governance Framework

Introduction

Data governance is the practice of managing, securing, and empowering the use of data across an enterprise. Effective data governance is a strategic necessity for any organization aiming to transform data into actionable insights. Without a structured approach to data governance, organizations risk dealing with inconsistent and inaccurate data, security issues like data breaches, and non-compliance with data-specific state and federal regulations. A thoughtfully designed data governance framework, however, helps organizations transform their data into a strategic asset that drives innovation and growth.

Delineate’s Data Governance Framework is a holistic approach that is built around four key components: (1) Data Strategy and Ownership, (2) Data Accessibility, (3) Data Lifecycle Management and (4) Data Privacy and Regulatory Compliance. This article explains each of these elements and how they are leveraged to help organizations achieve their full data potential.

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1. Data Strategy and Ownership

A robust data governance framework needs two important components at its core: Data Strategy and Ownership - i.e. crafting a clear blueprint for how data should be harnessed within your organization and defining roles and responsibilities towards these goals. This component should not be treated as an afterthought but as a foundational step that guides the organization’s data journey. Delineate addresses Data Strategy and Ownership in two key ways:

1. Data Strategy: Delineate works closely with key stakeholders to craft a master blueprint for your organization’s data efforts. This comprehensive plan outlines everything from identifying areas of immediate return on investment (ROI) to establishing data principles for the organization. The goal is to ensure that all data initiatives are purpose-driven and tightly aligned with your organization’s long-term vision and to outline a clear path forward.
2. Data Stewardship: Data Stewardship is what ensures that the data strategy is implemented effectively, focusing on identifying and training key personnel to be data stewards. Data stewards are responsible for the day-to-day management of data. Their responsibilities include monitoring data quality, maintaining data integrity, and enforcing data standards and policies. When data stewards are equipped and empowered to be diligent custodians of data, they ensure that it remains a valuable resource that drives tangible business impact.

2. Data Accessibility

Data Accessibility is focused on ensuring that the right people have access to the right data at the right time. It is crucial to fostering a culture of data and insights-driven decision-making across your organization. Data accessibility involves data lineage, maintaining a robust data catalog and data democratization:

1. Data Lineage: By maintaining clear data lineage, your organization can easily identify where data came from, how it was processed and by whom, and where it was consumed. This traceability helps not only with troubleshooting but also in ensuring that all data operations are compliant with your organization’s data governance policies.
2. Data Catalog: A data catalog is a centralized repository of all data assets. A well-maintained data catalog often contains additional information such as its format, sources, any known anomalies or inaccuracies, and guidelines for usage. This reduces the time spent in searching for data assets and learning how to use them properly. Additionally, data catalogs offer foundational support for data lineage and data democratization efforts.
3. Data Democratization: Delineate strongly advocates for ensuring that relevant data is available to everyone within your organization, without compromising on privacy, security, or governance. One powerful way to do this is to implement intuitive, self-service analytics tools across the organization. These platforms empower users from across the organization to explore data and discover insights without needing the help of data specialists to access data.

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3. Data Lifecycle Management

Intentionally managing data throughout its lifecycle is essential to maintaining its relevance and quality in the long-term. Data Lifecycle Management integrates with other data governance components by creating processes that guide data from three essential phases: ingestion, translation, and eventual disposal. Key practices within this component include data management, data quality, data archival and disposition, and data policies and procedures:

1. Data Management: Delineate translates the data strategy into distinct systems and processes with clear ownership across the data lifecycle. This includes, for example, protocols for dealing with duplicate records or processes for translating ingested data into a standard format. Data management also involves establishing protocols for integrating data between different systems within the organization.
2. Data Quality: By establishing rigorous standards for data quality, your organization can ensure that all stakeholders can trust the integrity and accuracy of your organization’s data and data products. This involves regular audits, validation, monitoring, and cleansing of data. This also involves setting stringent data standards and working with the relevant stakeholders and data stewards to enforce them across the organization.
3. Data Archiving and Disposition: Organizations must consider what to do with data that reaches the end of its useful life span. Data that is not in active use but may have long-term value (for example, in a predictive model) is archived to a secure storage location. This often involves moving data from storage designed for quick access (i.e. ‘hot’ storage) to storage solutions that are more cost-effective for infrequently accessed data (i.e. ‘cold’ storage). Some types of data must be archived for compliance or regulatory reasons, while some data can or must be disposed of at some point, such as personally identifiable information (PII). Organizations that deal with such types of data must have defined procedures and assigned personnel to execute data disposal when necessary.
4. Data Policies and Procedures: Supporting all practices within the Data Lifecycle Management framework requires a well-thought-out set of activities that integrate into the framework, followed by policies and procedures. Data policies are the guidelines that set expectations for how everyone across the organization contributes to and manages data, while data procedures are the specific steps that must be followed to comply with these policies. Data policies and procedures also ensure continuity and longevity of effective data governance in your organization by providing a clear reference point.

4. Data Privacy and Regulatory Compliance

Data is under greater regulatory scrutiny now than ever before. Organizations have a responsibility to protect data from unauthorized access and ensure that all data activities, from how data is sourced to its eventual disposal, are in compliance with relevant regulations. Delineate outworks this component with four practices:

1. Data Classification: This is the process of categorizing data based on its sensitivity, criticality to business operations and strategic value, and the level of security required. Data such as  PII or financial records are highly sensitive and must be treated differently from, for example, general information found in the public domain (i.e., US Census data, publicly released data, etc.). Classifying highly-sensitive data enables organizations to apply different policies and procedures, limit access and ensure proper archival and disposal of necessary data assets.
2. Access Controls: Implementing robust access controls minimizes the risk of both external data breaches and unauthorized internal access. For example, HIPAA mandates strict access controls on healthcare data to limit who can view sensitive patient information. Access controls also protect data integrity by limiting who can modify or transform the data, while ensuring that data is freely accessible to be viewed and consumed by those who need it.
3. Data Encryption and Anonymization: Encryption protects data both in transit and at rest by converting it into a format that can only be decoded with the correct decryption key. This protects the data from unauthorized viewing, even in the event of a breach. Anonymization removes or obfuscates personal identifiers from data making it impossible to correlate data points with specific, identifiable individuals. Some data protection regulations do not allow the processing of PII without clear and informed consent.
4. Data Compliance: The regulatory environment for data protection is constantly shifting as new laws and regulations are being introduced and existing regulations are being interpreted in the courts. It is important for organizations to stay informed about legal requirements in all the jurisdictions they operate in and ensure that they remain compliant. Data compliance includes setting up procedures to respond swiftly to any compliance-related incidents.

Conclusion

Effective data governance is a crucial part of unlocking your organization’s full data potential. Delineate’s holistic approach ensures that every facet of your data, from strategy to ownership to accessibility and compliance, is effectively managed and optimized. Our Data Governance Framework has empowered organizations across sectors to make data- and insights-driven decisions with confidence and precision. If you are ready to transform your data into actionable insights and drive your business forward, contact Nathan Reed, our Directory of Delivery, to learn more. Email him at nreed@delineateconsulting.com.

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