Solving the Disconnect Between Data Strategy and Data Operations: The Dependencies between Data Privacy and Data Governance
Your data walk should match your data talk - Debbie Reynolds “The Data Diva”
Data is organizations' lifeblood, and businesses handle more than ever before. However, many organizations struggle to bridge the gap between their data strategy and data operations, leading to inefficiencies, risks, and missed opportunities. This disconnect can be particularly problematic regarding ensuring compliance with data privacy laws and effective data governance. To succeed, organizations must ensure that their data strategy and operations are aligned, particularly in data privacy and its impact on data governance.
This article will explore the reasons behind the disconnect between data strategy and operations, provide examples of how this can manifest in an organization, and suggest ways to solve these challenges, particularly through data privacy and governance lenses. Ultimately, your data strategy talk must align with your data operations walk to build a strong, compliant, and efficient data environment.
What is the Disconnect Between Data Strategy and Data Operations?
The disconnect between data strategy and data operations typically arises when an organization's data strategy—its high-level plans for managing and using data—does not translate into effective day-to-day operations. This is often a result of poor communication, unclear roles and responsibilities, or the lack of proper tools and infrastructure. Data strategy is the blueprint for how an organization will manage, protect, and leverage its data, whereas data operations are the real-world practices of collecting, processing, and maintaining data.
While a company may have an ambitious and well-thought-out data strategy, it can lead to significant problems if it is not effectively operationalized. These problems include the inability to comply with data privacy frameworks or regulations, lack of clear data ownership and stewardship, inefficient data usage, and increased risk of data breaches. Even the most robust data strategy will fail if the necessary operational components do not support it.
Common Disconnects Between Data Strategy and Data Operations
Lack of Integration Between Strategy and Operations: Often, data strategies are developed in isolation by higher-level management without consulting those who manage data operations. This leads to strategies that sound good on paper but are difficult or impossible to implement.
Misaligned Objectives: The objectives set at the strategic level may not match the day-to-day needs and capacities of the data operations teams. For example, a strategy might emphasize rapid data collection to fuel AI initiatives, but operational teams might not scale their data governance and privacy compliance efforts fast enough to make this data strategy a reality.
Outdated Processes, Technology, and Talent: Even a sound strategy will fall flat if the processes, technology, and talent supporting data operations are outdated or insufficient.
Three Examples of the Disconnect and How to Solve It for Data Privacy and Data Governance
1. The Disconnect: Data Collection Without a Clear Purpose
One of the most common challenges in data privacy is the over-collection of data without a clear understanding of its purpose. While the data strategy may emphasize gathering as much data as possible for analytics and decision-making, the operational teams may not ensure that all data collected is necessary and relevant or that uses comply with data privacy regulations.
The Solution: Purpose-Driven Data Collection
To solve this, organizations should ensure that every data collected has a specific, well-documented purpose. In the context of data privacy, this is not just a best practice but also a regulatory requirement in many jurisdictions. For instance, the European Union's General Data Protection Regulation (GDPR) and other regulations in various jurisdictions mandate that personal data be collected only for specific legal purposes. By ensuring that data operations are aligned with this requirement from the outset, organizations can avoid the common pitfall of collecting vast amounts of data without any clear purpose, leading to unnecessary and avoidable regulatory risks.
Data collection teams should work closely with privacy and compliance teams to create data flow maps and documentation, ensuring that data is being collected and processed according to its intended purpose. By aligning the strategic goal of data collection with the operational need for privacy compliance, organizations can better protect themselves against data privacy violations.
2. The Disconnect: Mismanagement of Data Deletion Responsibilities
Another frequent issue arises around data retention and deletion. A strategy may outline the need to delete certain data after a set retention period, but operational teams often struggle to implement these processes effectively. This could be due to unclear ownership of the deletion process or a lack of automated systems to manage retention actions.
The Solution: Clear Ownership of Data Deletion Responsibilities
Organizations must assign clear ownership of the data deletion process to address this challenge. This ownership must span multiple dimensions: legal, operational, and technical. Legal teams should define the retention schedules based on regulatory requirements. Operations teams must implement and monitor these schedules, while technical teams should ensure the necessary tools and automation are in place to support regular deletion.
Automated data retention and deletion tools can significantly reduce the burden on data operations teams and ensure that data is deleted promptly and compliantly. A regular review process should also ensure that these systems work as intended, and adjustments should be made as regulations and business needs evolve.
3. The Disconnect: Inconsistent Data Governance Across Teams
A data strategy may emphasize the importance of strong data governance—ensuring that data is accurate, consistent, and secure. However, when data governance is not consistently applied across different teams, it can lead to data silos, massive data duplication, and data inaccuracies. This is particularly problematic in large organizations where different teams may have different approaches to data governance, and a multitude of data uses across different teams.
The Solution: Support Unified Data Governance Practices
To solve this issue, organizations should support or establish unified data governance practices that apply to all teams across the organization. This can be achieved by developing a centralized data governance framework, which outlines the rules, policies, and procedures for managing data across its lifecycle. This framework should be supported by training and tools to help operational teams consistently apply these rules as they change and evolve.
In addition, organizations should create a data governance committee comprising representatives from different departments. This committee can help ensure that governance policies are followed and continuously improved based on operational feedback. By taking a collaborative approach to data governance, organizations can ensure that their strategic data quality and security goals are met in practice.
Your Data Strategy Talk and Your Data Operations Walk Should Align
Organizations must align their strategic goals with their day-to-day data activities to close the gap between data strategy and operations. This alignment requires ongoing communication, cross-departmental collaboration, and the right tools and technologies.
Frequent Communication - Regular meetings between data strategy and operations teams can help identify and resolve potential disconnects early on. These teams should discuss the feasibility of strategy objectives and any operational challenges that need to be addressed.
Cross-Departmental Collaboration - Data governance and privacy efforts should not be siloed. Instead, they must collaborate between IT, compliance, legal, and operations teams to ensure everyone is aligned on goals, roles, and responsibilities.
Appropriate Tools, Techniques, and Automation: The right techniques and technology can significantly ease the operational burden of data strategy implementation. If available, automated data collection, processing, retention, and deletion tools can ensure that operations run smoothly and comply with privacy regulations.
The disconnect between data strategy and data operations is a major and ongoing challenge for organizations seeking to maintain compliance with rapidly expanding data privacy regulations and ensure effective data governance. However, businesses can close this gap by adopting purpose-driven data collection practices, assigning clear ownership of data deletion, creating unified data governance frameworks, and creating a more efficient, compliant, and data-driven organization. Ultimately, the success of a data strategy depends on its alignment with the day-to-day operations that bring it to life. By ensuring that your data strategy talk matches your data operations walk organizations can proceed gracefully with data privacy and governance challenges and make Data Privacy and Business Advantage.