Published on: Feb 11, 2021
Why Accountability in Data Privacy is Important
Regulations are making organizations increasingly accountable for the data they process and the trend is unlikely to change. GDPR has not only upped the game when it comes to accountability, making organizations pay (literally) for data breaches or for failing to report them - it also created new roles with responsibilities related to privacy ownership within organizations and builds consciousness into the data categories you collect and process.
In the first two parts of our guide; we covered how to prepare and build your data privacy framework, we looked at policies and procedures, and how important they are in defining and enforcing day-to-day privacy compliance.
In this article, Part 3: Accountability, we will cover the varying levels of accountability and why assigning accountability is instrumental in the ongoing management of your data privacy program.
What Do We Mean by Accountability?
In the context of data privacy, accountability takes care of not only making sure there is someone or a group of people who are responsible for data privacy as part of any processing activity, but also to ensure they have the capability to clearly demonstrate compliance with indisputable evidence, such as:
Documenting privacy policies, procedures, notices, requests, and consents
Adopting an accepted internal transfer mechanism
Maintaining a risk registry and asset inventory
Keeping records of all data processing activities.
General Data Protection Regulation (GDPR), article 5, paragraph 2 defines that: the controller shall be responsible for, and be able to demonstrate compliance with, paragraph 1 ('accountability').
In essence, accountability summarizes all privacy principles described in paragraph 1, ensuring controllers understand their responsibility to enforce those obligations in their data processing practices.
Including accountability, the GDPR sets 7 total principles:
Lawfulness, fairness, and transparency
Purpose limitation
Data minimization
Accuracy
Storage limitation
Integrity and confidentiality (security)
The Problem with “Data Ownership”
While “data ownership” is a familiar term in data governance conversations, it’s a problematic concept when it comes to truly ensuring data quality and accountability. Why? Because, unlike a physical asset, data doesn’t sit quietly under the care of a single person or department. It’s constantly flowing—created in one place, transformed in another, and used across multiple teams for entirely different purposes.
In many organizations, confusion arises over who really “owns” the data. Is it the department where the data originates? The team that manages the database? Or the analytics folks who glean insights from it? This lack of clarity can quickly lead to gaps in responsibility, finger-pointing when issues arise, and, ironically, less accountability rather than more.
Further muddying the waters, the idea of ownership often gets tangled up with stewardship and accountability. For example, traditional definitions of data ownership may sound a lot like stewardship—with expectations attached for making policy decisions, maintaining data quality, and following best practices. However, in practice, no single individual can control every aspect of the data lifecycle as it moves through an organization.
Data’s “wild side” shows up especially in complex environments, where multiple processes and people interact with it, often without knowing the full picture. If we try to force traditional ownership frameworks upon this reality, we risk making leaders accountable for things far beyond their control—and that’s a recipe for frustration and, ultimately, disengagement.
A more practical approach is to recognize that accountability should be tied not to “ownership” of abstract data, but to the concrete processes that create and handle it. Process owners are already responsible for ensuring their operations run smoothly; extending this to data simply means holding process owners and teams accountable for:
The quality and completeness of the data they input and output,
Communicating clearly about data requirements to upstream and downstream partners,
And ensuring that the data their processes produce meets the needs of those who rely on it.
In other words, robust data governance isn’t about chasing a mythical “data owner” across the organization—it’s about embedding accountability where it makes sense: at each step in the data journey, with the people who actually touch and transform the data. This reflects the reality that, just as processes are “owned,” so too should the accountability for the data those processes generate and use. This approach increases clarity, supports continuous data quality, and reduces the risk of missed responsibilities as information flows through your organization.
Why Assigning a Single Data Owner Doesn’t Work
While it’s tempting to appoint one person as the “data owner” across an entire organization, this approach quickly runs into practical roadblocks. Data is rarely created, stored, and used by a single team; instead, it flows through various departments, systems, and business processes—each shaping and consuming information in its own unique way.
Let’s ground this with a simple example: Imagine expecting the head of claims processing to be responsible not just for the claim data produced in their department, but also for how that same data is later stored in a data warehouse, used by analytical teams, or reported across the organization. In reality, they may have zero control over those downstream systems or how other teams use and modify the data. Holding them responsible for outcomes beyond their influence is not just unfair—it’s a recipe for confusion and frustration.
Data ownership, then, is not a one-size-fits-all badge that can be pinned to a single executive. Data, much like a baton in a relay race, passes through many hands. The real leverage comes from making each process owner accountable for the quality and integrity of the data their team generates or transforms. Their responsibilities should cover:
Setting clear quality expectations for incoming data. For example, making sure staff members know exactly how claim information needs to be submitted for efficient processing.
Ensuring that their process outputs meet defined standards. This could involve timely and accurate claim adjudication, reducing costly errors or delays.
Understanding the requirements of those downstream who will use the data. Whether that’s the finance team expecting clean data for reimbursements, or IT needing specific formats for seamless integration with reporting tools.
By distributing accountability this way, you avoid saddling individuals with impossible mandates. Instead, you anchor responsibility where it naturally fits: with those who directly manage the processes that shape your organization’s data ecosystem. This also acknowledges the dynamic, interconnected, and often unpredictable journey data takes as it travels across your business landscape.
In sum, trying to shoehorn “single-point data ownership” into your org chart usually creates more headaches than it solves. Instead, treat your internal supply chain of data the same way you’d approach any business process—with clearly defined responsibilities, aligned to the people and teams who actually have the power to influence outcomes along the way. This brings us to a key tool in managing these responsibilities: Governance, Risk, and Compliance (GRC) solutions, like StandardFusion, are designed specifically to document, monitor, and empower meaningful accountability at every step.
IT vs. Business: The Tug-of-War Over Data Ownership
Despite years of regulatory guidance and best practice recommendations, the question of who truly "owns" organizational data remains a common stumbling block. Historically, many organizations defaulted to placing the responsibility for data with the IT department—after all, IT manages the infrastructure where data resides. But as regulatory frameworks like GDPR have emphasized, effective data governance goes far beyond just servers and systems.
Here's where confusion often arises:
IT's Perspective: Most IT professionals recognize they are custodians of the technology that stores and processes data, but they’ll be quick to clarify that they don't "own" the data itself. Their job is to keep the data secure and systems operational—not to dictate how the business uses information or what data should be collected in the first place.
The Business Side: Ask a business stakeholder who owns the data, and the response is often, "That's IT's job." After all, IT maintains the tools and platforms. Yet, when pressed, business leaders often realize that responsibility for how data is used, shared, and interpreted really falls under their remit. Data is a business asset, and decisions about its use must align with business strategy and compliance requirements.
This disconnect leads to a common organizational paradox: while both teams agree on the importance of data, neither feels fully empowered—or accountable—to govern it comprehensively. In reality, true data accountability requires joint stewardship: IT ensures the technical controls and security are in place, while business leaders define the purpose, usage, and compliance with data policies.
Bridging this gap is central to a mature data privacy program. Clear ownership, transparent roles, and ongoing collaboration between IT and business functions are essential to embed accountability at every level. Only then can organizations confidently demonstrate compliance and earn trust with stakeholders.
The Spectrum of Accountability
Accountability might be understood along a spectrum, ranging from basic accountability requirements required by law (such as under the GDPR, PIPEDA, CCPA) to more granular accountability measures that may not be legally required, but that your organizations may decide to implement because it conveys substantial reputational benefits and increases your competitive advantage.
It is precisely demonstrating accountability and assigning ownership where GRC tools add value to any organization willing to implement a strong privacy program. Leadership is a crucial component of accountability and being transparent from top to bottom, supports the desired data governance posture. Executive buy-in will determine whether your organization decides to make data privacy a priority by going above and beyond the legal requirements.
Rethinking Data Accountability: A Process-Based Approach
So, how should organizations practically assign accountability for data? Rather than focusing on elusive "data owners," a more effective strategy is to consider who owns the business processes that generate—and use—data.
Typically, teams are already responsible for the processes they oversee. By extending this existing responsibility, process owners become naturally accountable for the quality, accuracy, and delivery of the data produced by their teams. This approach makes accountability clear and manageable. Here's how it works in practice:
Define Clear Data Quality Expectations: Make sure those providing data inputs to a process understand what's needed. For example, if your claims team submits information, specify the proper format and completeness required upfront.
Monitor Process Outputs: Hold process owners accountable for the quality of data outputs, just as they're responsible for other results their teams deliver—like accurate, timely processing.
Consider Downstream Needs: Encourage process owners to understand how their data will be used next—by colleagues, data warehouses, or other processes—and meet those requirements.
This method recognizes that data naturally flows and evolves throughout an organization. No single person or department has permanent control; instead, accountability shifts as data moves between processes. It's both more practical and fair, since people aren't held responsible for factors outside their influence.
Ultimately, this process-centered model fosters clarity and shared responsibility along the entire data supply chain—making it much easier to demonstrate compliance, foster trust, and improve the overall effectiveness of your data governance framework.
The Recommended Model for Data Accountability
When building a culture of accountability, clarity is key. Assigning responsibility to the right people ensures that accountability is meaningful—and fair. Rather than looking for elusive “data owners,” it's more practical to assign accountability to process owners within your organization.
Here’s how this works in practice:
Accountability Follows the Process: As data journeys through your internal supply chain—created, transformed, and utilized at different stages—the person responsible for that business process is also responsible for the integrity, quality, and compliance of the data at that specific point.
Defined Areas of Control: This approach keeps accountability within the sphere of influence of those who actually oversee the processes, rather than assigning responsibility to individuals or teams without hands-on control.
Executive Oversight: The executive entrusted with overarching data responsibilities—often a Chief Data Officer (CDO)—should collaborate closely with each process owner. Together, they ensure that, from creation to deletion, data stewardship is followed across all departments and workflows.
By focusing on process-level accountability, you empower teams to take ownership of the data they handle, just as they do with any other output of their roles. This approach creates a logical chain of responsibility and sets the stage for effective, organization-wide accountability.
Managing Accountability and Compliance
A typical GRC tool can be used to document and control privacy documentation. It can also be a foundational tool for a robust risk management program, including the execution of data processing impact analysis (DPIA), data privacy assessment (DPA), third-party management process, and other essential elements of organizational accountability, including:
Leadership and oversight
Transparency
Competence, training, and awareness
Monitoring and verification
Enforcement (of laws, policies, and procedures)
It is essential the Data Protection Officer (DPO) - or however you choose to identify the professional accountable for deploying your privacy principles within your organization has visibility of the entire program. With the appropriate tool, such as a GRC software, privacy professionals can gain invaluable insight into the inner workings of their privacy program to quickly identify issues and find a resolution. Making informed decisions is a core element of a properly managed privacy program and cannot be done without the proper data or insight.
The Key Responsibilities of Process Owners
At the heart of a strong data privacy program lies the crucial role of process owners. But what exactly does that mean in practice? Process owners are responsible for shaping the quality of the data their teams handle at every stage, from intake to delivery, ensuring nothing falls through the cracks.
Key responsibilities include:
Setting Clear Data Requirements: Process owners must clearly communicate to their teams—and to any contributors—what “high quality data” means in the context of their work. For example, specifying what data must be included, the expected formats, and the standards for accuracy ensures that everyone is on the same page from the outset. This reduces the risk of errors and builds efficiency into your processes.
Overseeing Output Quality: There’s no room for mystery when it comes to output. Process owners should put mechanisms in place to assess and document whether the work product meets established privacy and quality standards—before it moves down the line. Whether it’s processing claims, updating databases, or delivering reports, double-checking accuracy and completeness avoids costly rework and fosters trust.
Anticipating Downstream Needs: Accountability doesn’t stop when the task is finished. Process owners must understand how others—colleagues, clients, or external systems—will rely on the data. This means collaborating across departments to make sure data is timely, in the correct format, and ready to be integrated smoothly into operational systems or regulatory reports. In short, it’s about delivering what’s needed, when it’s needed, with zero surprises.
Taken together, these responsibilities empower process owners to safeguard both compliance and the reputation of the organization—making them indispensable champions of privacy, reliability, and smooth operations.
How Do Leading Frameworks Define Data Ownership and Stewardship?
There’s often a tendency to blur the lines between data ownership, accountability, and stewardship, but leading data management frameworks offer some needed clarity.
Data ownership, as defined by organizations like DAMA International, isn’t about individuals holding personal rights to data. Instead, data is considered an organizational asset. The “owner” is typically assigned based on who is responsible for defining, authorizing, and overseeing how data is managed within a specific business area. In practice, this usually means a business unit leader—think department heads or functional managers—who has the authority to make key decisions around data policy, use, and access.
On the other hand, stewardship refers to the ongoing care, quality, and compliance of that data. Data stewards are appointed to implement and enforce the established rules, maintain data integrity, and ensure that corrective actions are taken if issues arise. Essentially, stewards act as hands-on guardians of data, ensuring day-to-day practices align with organizational standards and relevant regulations.
The relationship between these roles can sometimes lead to confusion:
Data owners set the direction and approve key decisions.
Data stewards execute those decisions and maintain oversight on data health.
Frameworks like the DAMA Data Management Body of Knowledge (DMBOK2) acknowledge that these responsibilities may overlap in smaller organizations—or where resources are limited—but the distinction remains crucial for a robust privacy program.
By clearly defining these roles, organizations avoid the pitfalls of ambiguous "ownership" and make it far easier to demonstrate compliance and assign responsibility when needed. This structured approach is foundational to sound data governance and ultimately, greater accountability.
Summary
In the context of data privacy, accountability plays a big role in your privacy program. Processes to ensure policies and procedures are being followed must be implemented, and clearly demonstrating compliance with physical evidence is critical to the strength of your privacy program. While some accountability requirements must be met, there are requirements that exceed what is legally necessary. Satisfying these additional requirements could provide your organization with a competitive advantage and illustrate your company's position and commitment to data privacy and security. Manage your data privacy program and accountability using GRC software to provide management, stakeholders and clients with complete transparency.
