The Pragmatist’s Guide to Data Management Frameworks

Data professionals are an increasingly diverse bunch. But one tie that binds our professional cadre is pragmatism. Data processes that lack practicality will, by design, struggle to function with the efficiency, effectiveness, and ability modern organizations increasingly rely upon.

Pragmatism keeps us focused on outcomes that directly support business goals. It helps us balance the positive tension between resource optimization and the need for data teams and infrastructure to remain flexible and adaptable while we curate the data lifecycle. 

It’s the reason we approach hard problems, difficult conversations with stakeholders, and the growing ethical considerations of data management. Of all the isms, pragmatism ensures we’re not just solving problems—our solution is as practical as it is potent.

This is why it’s imperative to know how a pragmatic approach to formalizing a robust data management framework naturally benefits data stewards, data consumers, and data assets alike. 

What is a data management framework?

Data management frameworks are structured models that guide organizations in effectively managing their data by providing the following structure:

• Outline policies and principles by defining an organization’s fundamental guidelines and procedures for data handling
• Set standards by determining the criteria for data processes and formats
• Provide best practices by prescribing effective methods and techniques for managing data
• Include both tools and methodologies to provide systematic approaches and instruments for all stages of an organization’s data lifecycle

In general, data professionals employ various data management frameworks to serve as a blueprint for planning, executing, and monitoring data management strategies. These frameworks align these activities with business goals, making them mission-critical for organizations that need to manage their data effectively (i.e., all of them). 

As such, these management frameworks are typically established during the planning and design phases of data projects or system implementations. When put in place before data collection begins, before data consumers put that data to work, proper data governance can occur. Preemptively established, these management frameworks also contribute to the overall quality of data, data security, and compliance.

How the importance of data management frameworks differs between industries

Based on pragmatism, the key considerations of a data management framework may not vary much between industries. The specifics, however, certainly do. 

Organizations in all industries benefit, more or less, from effective data management. Major factors such as regulatory requirements (as mentioned earlier), operational needs, and data sensitivity can vary greatly between industries—even potentially affecting the value of the data management framework itself. 

Regulatory requirements

The need for data compliance is an excellent example of a factor that impacts every organization using data, but some certainly more than others. The healthcare and financial services industries especially rely on robust data management frameworks to enable stringent data protection and privacy regulations. 

For instance, healthcare organizations must ensure the confidentiality, integrity, and availability of protected health information (PHI). In the banking and financial services industry (BFSI), data management frameworks must contend with highly regulated financial laws, in addition to a multitude of data privacy protection regulations. 

Operational needs

Some industries simply consume more data than others, employing data management frameworks to tame the torrents of data that day-to-day operations and strategic decision-making rely on.

Retail and ecommerce stand out here. These sectors rely heavily on big data analytics, machine learning (ML), and data modeling to manage inventory and supply chains at scale. They comb through mountains of customer info for insights, and deliver highly personalized marketing.

Data sensitivity

Yet other industries trust their data management frameworks to ensure they can deal with highly sensitive data. This places a comparatively higher priority on mitigating data breaches, attempts at fraud, identity theft, and maintaining consumer trust. 

The government and public sectors handle information that requires exceptionally stringent data management to protect citizens' information and national security matters. Alternatively, the telecommunications industry processes massive amounts of data traffic—a capability that wouldn’t be possible without a pragmatic framework maintaining operational efficiency and service quality.

Key considerations for implementing a pragmatic data management framework

Fortunately, ensuring data management frameworks align with enterprise data and IT infrastructures requires consideration, not complexity. 

In fact, the following nine considerations can help optimize implementation in most use cases. 

1. Ensure your alignment with business goals

Take the pragmatic first step by prioritizing outcomes. Take time to highlight how your framework should serve relevant and specific business outcomes. Common outcomes include improved or more data-driven decision-making and increased operational efficiency or customer satisfaction.

Facilitate goal-driven strategies by discussing the data management framework with all relevant stakeholders. Listen to what they say. It’s important to tailor data management practices to the unique needs of the business because a one-size-fits-all approach rarely fits an organization’s needs in the long term.

2. Plan for scalability and flexibility

Data management frameworks must be adaptable. During the implementation process, stress the importance of the data governance framework, which needs to scale and adapt if business needs or technologies evolve (because, friend, they always do).

Leverage use cases here, too—real-world scenarios that help you demonstrate the benefits delivered when frameworks can scale and adapt to businesses of different sizes and industries. 

3. Verify framework integration with existing systems

Follow practical integration strategies, describing how new data management practices will integrate with existing IT infrastructure and systems. 

Additionally, recommend tools and technologies that promote seamless integration.

4. Lay the foundation for data quality and governance

A data management framework should help form the bedrock of treating data as a product within your organization. Emphasize optimal data strategy and ways for ensuring data quality and accuracy throughout the data lifecycle.

Encourage everyone within the organization to share the responsibility of maintaining high data quality. Moreover, outline robust data governance practices within your framework. Balance the pragmatic governance practices needed to enable secure, compliant, ethical data usage with reasonable administrative overhead.

5. Formalize “data quality as culture”

As your framework helps grade the foundation of how your organization treats its data, look for ways it can also help promote data literacy and fluency among all employees. 

Ask yourself: How can a data management framework promote data democratization? What can be done to foster data transparency, usability, and accessibility? Would incentives and rewards help establish and reinforce good habits? 

And what’s it going to take to get leadership on board? Because a pragmatist knows they absolutely need to be. 

6. Be realistic about resilient risk management

Identify potential risks as often as possible and establish plans to mitigate them. Discuss this part of the process as well. Work with key players in the organization to get a 360-degree assessment of potential risks to the data management process. 

The pragmatic data management framework should also provide insights into integrating compliance with all relevant protection regulations. Including this in the framework helps set the expectation that integration with various regulatory bodies—regional, national, and global—will be a natural part of the data management process. 

7. Encourage cost and resource efficiency

While working through a data management framework, hunt for ways to get more done using fewer resources. Be a loud voice in the room for cost-effective and efficient data management practices to maximize the ROI of all data management activities. 

Plan how to optimize resources over time while maintaining the fidelity of data quality and security.  

8. Bake in methods for measurement and continuous improvement

Define clear metrics and KPIs to measure the ongoing effectiveness of your data management practices. 

Frameworks should promote an iterative improvement approach to data management. The ongoing learning and adaptation required help organizations more easily adapt to regulatory and technology changes while also supporting valuable feedback loops.

9. Outline actionable and specific guidance

Finally, the pragmatic data management framework will provide clear, crisp, actionable steps to guide its own implementation. 

To this end, checklists, templates, and workflows that assist during the implementation process can also prove invaluable.

Managing common framework implementation challenges

Regardless of industry, data teams face common challenges when implementing a data management framework. 

Handling each involves tackling unique issues, ranging from the strategic to the cultural. But their collective commonality makes them easier to anticipate and account for.

Needs and gaps

• One reality of the business world is that there is often a delta between the priorities of data teams and other business units. This is usually the case when data initiatives proposed via a pragmatic data management framework are received as low-priority, not deserving of support and funding, or even irrelevant. 
• Unfortunately, this can’t be left as an agreement to disagree. Without clear business goal alignment, even the most passionate data management efforts can grow listless—still focused on achievements, yes. But with little or no impact on delivering business value. 

Human psychology 

• Problematic deltas between teams and departments can also be products of change resistance within organizational culture. Pushback regarding the adoption of new tools and processes can strike data professionals as superfluous. But messing with a collective’s comfort zones can quickly snowball, resulting in implementation delays and, potentially, entire initiatives being derailed. 
• As much as possible, work to align the data management framework with the organization’s culture as-is. Sometimes, the best conflicts are avoided completely. Alternately (or additionally) include actionables within the framework to gradually align the cultural norms with ideal data management practices. 
• Highlighting early successes during the framework’s implementation can also be useful, as can appointing enthusiastic and like-minded data champions throughout the organization—partners who can help advocate for new tools and processes.

Operational rigidity

• Apart from culture, some organizations simply have rigid operational structures that aren’t naturally inclined to adopt changes the data management framework will necessitate. Operational rigidity, in this sense, can stonewall the adoption of data best practices or make it difficult (if not impossible) to foster the concept of data as a product organization-wide. 
• Data management frameworks that take the pragmatic approach will endeavor to find flexibility and stakeholder engagement. These facets alone can begin dissolving counter-productively rigid processes and points of view. What’s more, planning to break the implementation process down into phases can make changes and challenges to established practices more digestible. 
• Beginning this phase-based approach with pilot projects in less rigid areas of the organization can also prove helpful. In addition to provoking less initial resistance, successful pilots can demonstrate the benefits of the new data management practices the framework entails. You can also leverage these successes as case studies to advocate for broader organizational support. 

Operationalizing governance

• Effective data governance relies on employees having a crystal-clear understanding of who can (and/or should) take which action, upon which data, using which methods in which situations. An ongoing lack of this clarity can produce conflicts over data ownership, data accessibility issues, operational inefficiencies, and decision-making processes.
• Gaining and maintaining this clarity, on the other hand, requires active and ongoing cross-departmental agreement and cooperation. This can be difficult to achieve. But the pragmatic data professional should consider it essential.

Technical technicalities

• Legacy systems can be inflexible, especially when compared to contemporary solutions and technology. Don’t be surprised to find a legacy system does not support your designs for a modern data management framework. 
• Tackling legacy issues is often complicated because they require time, money, and talent to resolve. Cost-benefit analysis can at least put the investment needed to correct these issues into perspective, as potential ROI and other benefits serve as a holistic counterfactual.

Productizing data 

• With inaccuracies, incompleteness, and inconsistencies, data quality issues must be continuously rooted out and corrected. Much like tackling legacy technology, data quality bug hunts can also be resource-intensive. For this reason, and the mission-critical need to maintain optimal data quality, data professionals must be sure that these corrections don’t detract from more strategic data activities. 
• Balancing operational efficiency with data quality can be tricky. As part of your data management framework, identify strategic places to foster data quality. Make sure you parse critical data from all data to focus quality efforts on the former. 
• Additionally, assess the effects data quality issues can have in different areas of your organization. This will help you prioritize efforts where risks to business outcomes are highest. 

Resource constraints

• Another business truism is that budgets, time, and skilled personnel will always be limited. Resource deficits can restrict the totality of data management initiatives scoped within a framework.
• For pragmatists, it can be difficult to embrace these constraints as constructive. But they are further reasons for strategically identifying critical points of data quality and risk-based thinking. Inevitably, certain data functions will need to be prioritized over others. A data management framework should help guide these difficult conversions toward the best possible outcomes.

Security and privacy concerns

• Data accessibility and usability are crucial for democratizing data and treating it as a valuable product. However, balancing the needs of data consumers with regulations and compliance can be a complicated dance. In organizations with sensitive data, an overemphasis on security can hamper consumers' ability to access and use the data they need. 
• Role-based access control (RBAC), when implemented as part of the framework, makes sense for many larger organizations. Limiting who can access what data goes a long way toward ensuring sensitive data remains protected. Moreover, systematically classifying data according to sensitivity levels can make it easier to balance security with accessibility.
• For organizations where RBAC may be unnecessary or prohibitive (e.g., smaller teams or organizations, highly collaborative or research-driven work environments), attribute-based access control (ABAC) or discretionary access control (DAC) models may prove more pragmatic.

Meaningful measurement 

• Finally, developing metrics to assess the effectiveness of data management practices (and their impact on business objectives the framework is aligned to) can get complicated. But sans meaning metrics, it becomes difficult for data professionals to justify continued investments in data management. 
• A management framework’s alignment with clearly defined business objectives sets a helpful foundation for measurement and performance evaluation. To this end, KPI-related discussions should help ensure performance metrics are relevant to said business outcomes. Balanced scorecards (BSC) also help ensure that chosen KPIs are strategically comprehensive—consisting of a holistic mix of financial, customer, growth, and internal process metrics.
• Professionals should also ensure multiple data quality dimensions will be measured and that automated data quality tools continuously monitor and report on these metrics, providing ongoing insights into data management performance.

Practicing preemptive pragmatism

As we conclude our journey through the pragmatic approaches to data management frameworks, it’s clear that the challenges and opportunities detailed above require strategic consideration and (ideally) preemptive action. Data contracts can serve as a key tool in this regard—supporting robust data management frameworks before implementation even begins.

Data contracts establish explicit guidelines and expectations for data handling, usage, and security. For data professionals seeking to be pragmatic, they offer a structured and clear framework to ensure compliance, improve collaboration, and maintain high data integrity and privacy standards. They also serve as binding agreements that outline the responsibilities of all parties involved and provide a solid foundation for governance and operational consistency across various data initiatives.

So, whether you’re looking to refine your current framework or build a new one from the ground up, Gable’s approach to data contract drafting and enforcement should be considered a prerequisite to data management success.

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