Discover how embedding governance into data platforms can help enterprises meet growing compliance demands and ensure continuous data compliance at scale.
Data compliance demands have become a never-ending game of chess. Releasing a certain data initiative or data product for the organization to use, means a move that can have a knock-on effect, the consequences of which will be felt 3 moves later.
These consequences result in significant fines and worse, a loss of trust in data. This is just a single chess match we're talking about.
But what happens when enterprises have to take on the role of a chess grandmaster, moving from table to table, playing chess with multiple great players? These players are the organizational, industry, and government regulations, each aiming to push the grandmaster into a mistake.
Adding insult to injury, the classic series of matches is in fact a gauntlet in which new players (read new regulations) join endlessly.
Enterprises need to keep up with increasing regulatory demands, ensuring data compliance at a scalable pace. This can only be achieved with a proactive approach to compliance, including data regulations, data auditing, and data risk management (among others), making governance non-bypassable, ideally as code embedded into the data pipelines.
In this article we discuss how enterprise organizations can scale their operations to ensure that their data is continuously compliant and that all of their business and data domains are ready for any new regulation that may come.
The solution is as easy as it's complicated. It requires compliance platformization.
As soon as a compliance project ends, the next one picks up. What's more, they are never done in a linear manner, often forcing the governance team to work on multiple compliance issues in parallel.
Data regulations like the AI Act, or DORA, are constantly being updated, while new ones are always on the horizon. Organizations face a double whammy of:
Ask your governance colleagues and they will probably confirm that there are plenty of data regulations to keep up with even though few seem to be released between them.
The challenge lies with scale, because while data initatives grow exponentially, governance teams do not, leading to massive data compliance risks that will be discovered at the next internal audit (if the organizations is lucky), or realize this when the fine is received.
Organizations in banking, insurance, telecommunications, and utilities (just to name a few) face a tidal wave of deluge of requirements for data collection, storage, reporting, etc. Managing compliance for companies in these industries is exponentially more difficult.
“The standard in terms of keeping pace with compliance and regulation are going down, not up throughout the industry. There is a need to regulate data at a minimal cost given how costly compliance has become.” - said a Senior Director from a large British Enterprise during one of our events
The above situation exarbecates the challenges faced by governance teams, especially in highly regulated industries. Keeping pace with compliance has become a race for these teams, with lowering standards often being the quickest solution to keep their heads above water.
Pushed by the need to keep up with the speed of business, the reverse cannot happen. Compliance checks cannot hinder data initiatives going to market, as they have challenges of their own. With the only way being forward, governance teams need to be creative and practical to solve their issues.
The transformation programs that require governance input regardless of their nature simply drown the team in constant checks. Capacity is over the limit, and each regulatory transformation project's duration is months, if not years, because there are no shortcuts to compliance, except lowering the bar.
But if the governance checks are just that, checks, shouldn't governance ownership at least be shared?
Ask multiple data engineers the above and the responses are very likely to be mixed. Some believe the governance team has this role. They wouldn't be wrong.
Others believe that it should lie with the owners of the data initiative. That includes us as well.
In the end, if you create a data initiative or data product, you should be responsible for everything that it contains, and that includes proper compliance. Governance teams do the checks, but only ex-post. If that sounds a bit off to you, it's because it is.
Ownership brings accountability so key people within cross-functional teams can be empowered to be the point person, no matter what happens. In case of a security breach, they would be the ones that would create the report on the matter and find the proper solutions to make sure that the same risk is completely eliminated.
The above applies for in-house systems, but what about third party vendors? In this case enterprises should ensure that they retain ownership and control over their data. This again created accountability which can reduce risk.
Another argument for ownership is autonomy. Every member of the organization that works with the data sets, or even sees them should have autonomy in handling their respective task.
But going back, why shouldn't the data producers have ownership of data governance as well? It might be a different skillset, but handling compliance from inception would greatly reduce the compounding knock-on effect of ex-post checks that falls on governance teams.
This not only bring a certain structure, but also multiple points in which the chance of compliance breaches decreases significantly. That structure should be imposed at an organizational level, as compliance is embedded into the data governance framework chosen by the organization.
Let's take a look at a few best practices for compliance, why they don't work after a certain point, and explore whether they can be scaled at an enterprise level.
Needless to say, the processing requirement to ensure compliance should be absolutely clear. The data governance team should define these requirements based on the data regulations, while each data producer needs to follow these recommendations to the letter.
This should be a non-negotiable which should win buy-in from the entire organization.
At scale, however, these requirements become far too many to check, which is why shifting them left, into the development lifecycle would be a great advantage to the entire enterprise.
Data is present in a wide array of formats. But it should be consistent across all of them. Enterprises should define data standards to ensure consistency, reliability, and interoperability across complex systems.
Organization face a risk of flawed and unreliable data, missing opportunities for insights as well as hefty fines.
Standardization would also bring about a gains in desiloing all the data within an organization.
Legacy, third-party platforms, and their external partners all bring their diverse formats with them. Standardized data ensures seamless integration and communication between them.
Again, when facing the sheer scale required to standardize across the entire organization with many business units, the best practice quickly becomes untenable as even with guidelines in place, several key people would need to check that these standardized formats are applied across all the silos.
These should follow a specific set of rules or policies that won't allow any data to be used when it is not compliant with a specific policy. More on that later.
Again the team count is a bottleneck, simply running out of capacity to check compliance and reject non-compliant data initiatives. The business moves a lot faster and data producers cannot wait for the check to be done, especially if the initiative is detrimental, needed for a vital business decision.
The lowering of standards which we've mentioned before, leads to data entering production and results ultimately in a faulty data initiative.
The second part of these reviews are corrections. While they are applied by the data engineers after the governance team has made its review, it still a crucial step in the process, since it delays the specific step in the pipeline.
Even after that, it needs an additional check by the governance team, pushing the time to market further back. While this would be the correct process that would maximize compliance, its often abandoned in favor of speed, especially when data needs to be delivered at scale.
This is perhaps the most impacted process which a small governance team cannot hope to cover in an enterprise context.
As many huts, as many customs. Your probably know the saying. Now just replace "huts", with teams/business units/departments/branches/subsidiares and "customs" with data practices.
This divergence in data practices across so many teams leads to a massive disparity in choosing the technologies that teams are working with. It's essentially the lack of standardization takes a step further into technology decisions.
By the time the issue occurs or the fine is issued and the governance team can take a look at what went wrong, they will find a deluge of technologies used that weren't even on the list of approved technologies for proper data governance.
With so many challenges in the way, the only viable solution for enterprises is to embed their policy checks into a platform. This policy-as-code approach automates the entire process for organizations, guaranteeing compliance at every step of the way.
For that to happen, multiple things need to be true.
1. A data governance framework that is applicable across the entire enterprise
2. A decentralized approach that is the core of the platform
3. A balance between autonomy and governance
Let's take a look at what the equirements of such a platform would be.
Governance policies are non-negotiable. They should be universally applicable across each team, department, and business unit.
While choosing a framework, creating the processes, and writing and sharing the policies and guidelines is fairly straightforward, ensuring that they are respected is another thing entirely.
Governance shouldn't be remembered, it should be part of the process from the very beginning when data initiatives are being developed. That is why enterprises should adopt a data governance framework that embraces this approach, such as the Governance Shift Left framework.
Our framework proposes that policies are directly embedded as code into the data and code lifecycle, ultimately speeding up the entire process, while at the same time guaranteeing that each data initiative or data product is fully compliant with governance policies.
This is most easily done using a data governance platform, that can impose quality gates.
Decentralization enables speed and grants autonomy across teams. This, in turn, results in the required speed that enterprises require. Decentralizaiton should be controlled, however, and the afore mentioned standards should be applied.
Coupled with the computational policies and the capability to check data before entering production would result in a streamlined and scalabe process that could easily be replicated for any new data initiative, or even entire business units.
This naturally brings the need of a platform, where all of this could be implemented. Such a platform would guarantee data governance with the afore mentioned checks. Beyond that, it would protect each user from the misconduct of others, and any mistake or attempt to circumvent approved standards would result in a check failure, forcing engineers to respect the approved standards.
The platform's adoption across the entire organization would also result in the reduction of technology lock-in, ultimately removing the silos that have formed over the years.
A platform that can enable decentralization while at the same time impose data initiatives which have compliance by design, would also lead to a certain degree of autonomy. The enterprise needs to balance this with federated governance.
Autonomy would empower teams to innovate, make the more flexible and agile, all within a platform that enables bootstrap, testing, and deployment.
On the other side, embedding the Governance Shift Left framework into the platform would enable:
The universal rules of computational policies, architectural blueprints, and standardized metadata bring in a system that makes compliance a priority eliminating the bottleneck of governance teams' capacity, while at the same time ensuring that no published data poses a compliance risk which could result in fines.
Navigating the labyrinth of compliance regulations—from GDPR and CCPA to HIPAA and the AI Act—is a monumental challenge for any enterprise. As we’ve seen, traditional, reactive approaches to governance are no longer sufficient.
They are manual, error-prone, and implemented too late in the lifecycle to prevent risk. To thrive in this complex landscape, organizations need to move beyond checklists and build compliance into the very fabric of their data ecosystem.
This requires a paradigm shift from a reactive posture to a proactive, automated framework.
Witboost is the strategic platform designed to power this transformation. It isn't just another tool; it is a comprehensive solution that operationalizes compliance by design, ensuring your organization can innovate at speed without sacrificing control.
Witboost guarantees compliance at scale by fundamentally changing how governance is implemented. Instead of treating compliance as a downstream checkpoint, Witboost embeds it directly into the development lifecycle through its "Governance Shift Left" philosophy.
This is achieved through a suite of powerful, integrated capabilities:
Policy-as-code: Witboost transforms abstract policy documents into executable computational policies
These automated rules are managed as code, versioned in a central policy register, and enforced automatically within CI/CD pipelines. This makes compliance non-bypassable and ensures consistency across all data products and teams.
Automated quality gates: The platform establishes computational quality gates that act as automated checkpoints for data quality, security, and regulatory adherence.
Before any data product can be deployed, it must pass these gates, which automatically validate it against all relevant policies. This proactive enforcement prevents non-compliant data from ever entering your production environmen
Data contracts for upstream quality: Witboost uses data contracts to ensure quality and compliance at the source.
These contracts establish clear expectations for data structure, ownership, and validation rules at every integration point, guaranteeing that data entering your ecosystem is already trustworthy.
Real-time monitoring and back-testing: Witboost provides a real-time, global view of your organization's compliance posture. Before rolling out a new policy, you can back-test it against your entire ecosystem to identify and remediate potential conflicts, preventing disruption and ensuring smooth adaptation to new regulations.
By adopting Witboost, organizations are not just meeting today's regulatory demands—they are building a future-proof foundation for whatever comes next.
The platform's technology-agnostic and modular architecture allows you to standardize and streamline data practices without being locked into a specific architecture or toolset.
This unique approach delivers critical business outcomes:
Reduced risk and cost: By catching issues before they happen, Witboost dramatically lowers the risk of regulatory fines and costly data remediation efforts.
Enhanced agility and innovation: With automated guardrails in place, decentralized teams are empowered to innovate freely and securely, accelerating time-to-market. for new data initiatives.
Enterprise-wide trust: When every data product is certified as compliant and high-quality by default, it fosters a culture of trust. Business users can confidently discover and leverage data from the Witboost marketplace, knowing it is reliable, secure, and ready to drive value.
Don't leave your data compliance to chance. Transform it from a complex burden into a strategic enabler that fuels growth and innovation.
Tackling tech stack complexity and achieving data interoperability in enterprises with strategies for seamless data integration and governance.
Explore four data contract implementation patterns for complex environments, emphasizing proactive measures to prevent data issues and maximize...
Data Mesh is completely changing the perspective on how we look at data inside a company. Read what is Data Mesh and how it works.