Building a Robust Data Governance Strategy: Overview and 7 Best Practices

Almost any business rests upon a solid bedrock of a robust data governance strategy as the sheer volume of data poses a difficulty in upholding this foundation. Data duplication, security concerns, insufficient data quality, and lack of clarity are the most common issues that entail large volumes of data. 

A properly planned and executed data governance strategy is paramount for not only solving those problems but also using and taking advantage of the data. These advantages include data-driven decision-making, data integrity, and refined collaborative efforts across units.

Yet, the establishment of data governance requires a structured approach. It is vital to have a robust framework that encompasses individuals, business processes, and technologies in play. This approach reveals practical insights, ensures adherence to regulations, and mitigates potential risks. 

Let’s explore the fundamental value of a data governance strategy, its key components, and essential steps for building an effective master data governance strategy.

What is a data governance strategy?

Data governance centers around everyday tasks that maintain the usability, accessibility, and security of information. An enterprise data governance strategy encompasses the foundational preparatory work that outlines prerequisites for how a business will handle its data. This entails:

1. Delegating responsibility for implementing policies and procedures.
2. Defining and adhering to regulations governing data sharing and processing.
3. Developing protocols for naming and storing data.
4. Establishing metrics to uphold data accuracy and usability.

A data governance strategy establishes a framework that connects and unites individuals, processes, and technology. It designates responsibilities, holding specific individuals accountable for distinct data domains. This strategy shapes the norms, procedures, and organizational structures for data collection and management. Its objective is to preserve the accuracy, cleanliness, and usability of data, ensuring its integrity. This groundwork will guarantee secure storage and access to data.

Why is data governance strategy important?

Apart from the diverse advantages of a master data governance strategy, such as informed decision-making and consistent adherence to security regulations, it also boasts a notably pragmatic aspect: cost savings. 

A survey conducted by McKinsey revealed that roughly 30% of an enterprise's overall time was dedicated to tasks that added no value to the business. The primary culprit behind this inefficiency was the suboptimal quality and accessibility of data.

Implementation of the data governance strategy and tools, standards, and methodologies facilitate more efficient use of data that can eliminate unproductive tasks. Cutting redundant labor will save both time and costs while improving employee productivity. On top of that, businesses will be able to allocate more time and resources for more essential and value-generating tasks, boosting overall performance.

What are the key components of data strategy?

To establish an effective data governance strategy and roadmap, it's essential to integrate its components into a comprehensive framework that aligns with the company’s objectives. By addressing these components, business leaders can guarantee the quality, clarity, and protection of their data assets, cut operational expenditures, and enhance decision-making.

The crucial components of enterprise data governance strategy encompass:

1. Data policies and standards: Clear and concise guidelines that outline how data should be collected, stored, managed, and shared within the organization.

2. Data quality management: A structured approach to maintaining data quality, encompassing procedures to detect, rectify, and prevent data discrepancies or errors.

3. Data security and privacy: Procedures and directives for safeguarding data, shielding sensitive information against unauthorized access, usage, or exposure.

4. Data lifecycle management: A comprehensive framework to oversee the entire data lifecycle, including creation, acquisition, utilization, retention, and disposal.

5. Data stewardship: Designating specific individuals or groups to manage particular data assets, ensuring their integrity and security.

6. Data architecture: An organized method to arrange and manage data assets, comprising data models, integration, and storage strategies.

7. Data governance processes: Formal processes for decision-making, issue resolution, and engagement with stakeholders, fostering productive collaboration and communication throughout the organization.

8. Data governance tools and technologies: Software and technologies supporting data governance efforts, including tools for data quality management, security, and metadata administration systems.

Other noteworthy components of master data governance include data ownership, transparency, and change management. These aspects promote better control over data, compliance, and auditability. 

How data governance supports your data strategy

Data governance strategy plays a pivotal role in a successful data strategy, building a robust and scalable structure for effective data management. As such, it ensures data quality, mitigates risks, optimizes data value and literacy, encourages collaboration, and supports regulatory compliance.

Ensuring data quality and precision

Effective data governance establishes data accuracy, consistency, and reliability. It sets forth directives and protocols for data quality management, incorporating procedures to identify, predict, and prevent data errors or disparities. This element is paramount for making well-informed decisions based on trustworthy information.

Risk mitigation

Data governance aids organizations in minimizing risks related to data breaches and regulatory violations. By enforcing data security and privacy standards, organizations can protect sensitive data from unauthorized access, use, or disclosure. It also institutes processes for risk management and instant incident response, enabling organizations to counter data-related threats effectively. All of these processes will be a part of the data governance strategy document, as one of the final deliveries of the whole discovery.

Optimizing data value

Data governance empowers decision-makers to extract the utmost value from their data assets. By adeptly managing data, businesses can increase operational efficiency, revenue, and customer satisfaction. This encompasses processes for data discovery, integration, and analytics, allowing enterprises to draw valuable insights for further improvements.

Fostering collaboration

By assigning roles and responsibilities for data management, data governance empowers stakeholders to understand their obligations and collaborate to achieve shared objectives. A robust data governance strategy facilitates secure data sharing and more effective cooperation on different levels. 

Backing regulatory adherence

Data governance aids enterprises in satisfying regulatory compliance prerequisites, encompassing security standards like GDPR, HIPAA, and CCPA. By upholding data security and privacy standards, businesses can sidestep costly penalties and safeguard their reputation. It also orchestrates processes for data retention, deletion, and access, guaranteeing alignment with legal and regulatory requirements.

Enhancing data literacy

A data governance strategy establishes a foundation for educating the stakeholders about optimal data management practices. By establishing data governance guidelines and procedures, companies can boost their data literacy, ensuring each employee within the organization understands their responsibilities. This fosters effective and consistent data management throughout the organization, optimizing value while minimizing risks.

What are the most popular data governance frameworks?

Two contrasting philosophies exist for devising data governance frameworks, each presenting distinct advantages and disadvantages depending on an organization's specific goals.

Bottom-up philosophy

The bottom-up strategy for data governance, popularized by the big data movement, starts with raw data. The process begins with the initial ingestion of data, followed by the construction of structures or schemas atop the data post-read. Simultaneously, governance regulations, policies, and quality controls are incorporated into the dataset. The main advantage of this approach lies in its scalability. However, ensuring uniform quality control across a substantial data volume can pose challenges.

The major problem with this approach is its reliance on smaller batches of data. Mid-size and large enterprises usually deal with increasing volumes of data. As these businesses expand and potentially encounter complex data regulations, they naturally transition toward a top-down approach. A fitting data governance strategy example is businesses working in healthcare. They deal with large amounts of private data that are bound to numerous governmental regulations. For that reason, a bottom-up approach can’t be considered optimal for large enterprises.

Top-down philosophy

In the top-down methodology, paramount importance is placed on data modeling and governance, constituting the initial phases in shaping a data governance framework. This process starts with data experts employing defined methodologies and best practices to manipulate data. The core of this approach is a strong emphasis on quality assurance.

Entities such as banks, insurance firms, healthcare establishments, and other heavily regulated institutions are inclined to adopt the top-down strategy for data governance. The rationale behind this is their frequent possession of extensive data volumes and rigorous regulatory obligations. Opting for a top-down approach affords enhanced quality control and better conformity to regulations.

However, there’s no right or wrong approach as each suits the needs of different businesses. Our goal is to select an approach that matches the unique needs and specifications of our clients and create an effective master data governance strategy that generates maximum value.

Foundations of data governance frameworks

Data governance frameworks rest upon four fundamental pillars that guarantee proficient administration and utilization of data across the organization. These pillars uphold the accuracy and security of data, its effective amalgamation, storage, and management tailored to the organization's demands.

1. Data quality

At the heart of any data governance strategy and framework lies data quality. It ensures that data deployed in decision-making processes is precise, uniform, and dependable. Moreover, managing data quality involves instituting policies and protocols for data validation, cleansing, and profiling.

2. Data integration

Data integration entails the amalgamation of data from a variety of sources to provide a cohesive perspective. This pillar guarantees the seamless unification of data from distinct departments, business units, or external collaborators—facilitating analysis and informed decision-making.

3. Data privacy and security

Data privacy and security are pivotal for all types of businesses, especially for those dealing with sensitive data. This pillar encompasses the formulation of policies and procedures to shield sensitive data and conform to data protection guidelines. It encompasses techniques like data encryption, access control, and data anonymization.

4. Data architecture

The fourth pillar, data architecture, pertains to the structure and blueprint of data systems. It involves devising and structuring data systems to align with organizational requisites. This encompasses the architecture design of databases, data warehouses, and data lakes.

How to master data governance strategy in 7 steps

Creating an effective strategy without a clear roadmap can lead to various complications, such as increased costs, misalignment with your enterprise's specific needs, or even complete failure of your data initiatives. To avoid such pitfalls, let's delve into the essential steps required to build a comprehensive and reliable strategy for your business.

1. Identification and prioritization of existing data

The initial phase involves gaining a comprehensive understanding of your available data. This process encompasses several vital activities that should be undertaken:

• Data classification: Categorizing both structured and unstructured data into appropriate groups.
• Data inventory: Documenting all data resources and pertinent metadata. To put it simply, gathering information about the data.
• Data and knowledge curation: Employing data catalogs and metadata management tools to organize and oversee datasets.

2. Selection of metadata storage solution

Sharing and reusing metadata collected by different departments often presents challenges, underscoring the necessity for data governance. The practice of using separate databases to manage metadata, common in many organizations, leads to isolated data with limited accessibility. 

The choice of an efficient storage option, like a data lake or datawarehouse, is crucial. This centralizes all collected metadata across the organization and addresses key metadata management requirements:

• Gathering metadata from different platforms
• Effective reuse of metadata
• Governance and stewardship
• Clear insights into data history

3. Preparation and storage of metadata

This time-intensive step in crafting a data governance strategy involves transforming, correcting, and amalgamating raw metadata datasets into data catalogs. It mainly consists of three pivotal tasks:

• Cleansing and validation: Standardizing data and concealing sensitive information.
• Value and format transformation: Rendering data comprehensible throughout the entire enterprise.
• Data dictionary and business glossary development: Templates to manage the influx of uploaded data assets.

4. Development of data governance model

Appraising your requirements and selecting the model that aligns with your enterprise's nature is a vital stage in strategy formulation. While there may be variations, data governance models generally fall into two categories:

• Centralized: A single entity or group holds authority over data, setting rules and granting access.
• Decentralized: Data authority is distributed among various groups, allowing departments with diverse needs to utilize shared resources efficiently.

5. Defining the implementation process

Data governance only succeeds if people adhere to its protocols. An effective enterprise data governance strategy should encompass practices that seamlessly integrate these protocols into everyday workflows. These may involve implementing robust onboarding, conducting regular policy training sessions, and promoting knowledge sharing.

6. Risk mitigation

Understanding and preparing for potential risks is the optimal strategy to mitigate them. Data invariably faces risks of compromise, whether through external breaches or internal negligence. These risks can be mitigated by:

• Limiting user access: Granting minimal data access required for task execution.
• Secure data storage: Ensuring data is stored in secure locations with adequate cybersecurity measures.

7. Ongoing adaptation of data strategy and governance

Business needs evolve due to market dynamics, and data governance strategies must account for these changes. Enterprises should continuously monitor the efficiency of data governance processes and adjust them according to new policies or industry challenges.

All these steps are covered within the discovery phase, where the Trinetix team aids clients in validating their ideas, assessing product market viability, and preparing for successful implementation.

Creating a data governance strategy is equal to the discovery phase in its traditional sense. You start with a specific need and move to the roadmap or strategy for addressing that need. Proper data governance will play a massive role in an enterprise’s data strategy, which is why it is vital to stick the landing here, at the discovery stage.

Oleksandr Ieremchuk, Senior Data Architect

Build your data strategy through effective governance

​​As the volume of data amassed by businesses expands, the importance of tools for its management and utilization rises correspondingly. That’s where data governance takes center stage. Yet, for it to operate successfully, crafting a well-devised strategy is a must. 

The Trinetix team offers experience in building a data governance strategy and robust big data systems for enterprises across such industries as professional services, logistics, finance and banking, and healthcare.

For instance, our team worked with a Fortune 500 client seeking guidance in modernizing their data strategy to enhance data efficiency and accuracy across 10+ enterprise systems. Our integrated master data management solution eliminated manual processes, streamlined research, and boosted enterprise worklows with 360-degree visibility. With increased efficiency and productivity, the client progressed to the next phase, collaborating with Trinetix to develop an AI-powered version of the solution.

Let’schat and create your data governance strategy with scalability, security, and quality in mind!

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