Data Regulations: 5 Steps to Keeping Regulators Happy


With the global data footprint growing by the day and data protection laws putting increasing pressure on organizations, your business needs ensure it is fulfilling it's data security obligations.

But keeping your regulators happy and avoiding hefty penalties isn’t only important from a compliance perspective. It’s also the best way to retain control of all your data processes and prevent lengthy project completion times.

So, how can you build a compliance strategy that achieves all this?

To provide an answer, let’s first look at the specific intent behind today’s data regulations.

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The nature of regulators and their regulations

Data regulations require significant infrastructure and process changes to achieve compliance. Regulators and other data protection agencies are there to incentivize your business to adopt better data procedures that benefit your business in the long run.

The banking industry’s BCBS 239 regulation, for instance, encourages businesses to use as much automation as possible to help:

  • Speed-up processes (by cutting the number of ‘human’ steps required)
  • Reduce human errors that may result in governance issues
  • Deliver on-demand, ad-hoc reporting quickly and accurately, without the need for time-consuming, manual work
  • Cut the need for time-consuming reporting and, in turn, sourcing Excel experts to translate data
  • Make data processes and reporting more trustworthy and repeatable

So, as well as making your data more auditable, the push for more transparent processing also improves speed and efficiency. It’s a win-win situation.

Let’s dig a little bit deeper into the practical steps your business can take today to please your regulators. Here are five ways to keep your data and processes consistent and compliant.   

How CloverDX can help you standardize data reconciliation processes for effortless reporting and compliance

Step 1: Develop a common data vocabulary

Data processing is often split between different teams within your organization. Your sales team, for instance, may manage customer data in a different way to your financial team.  

In cases such as these, it’s crucial you champion data consistency for your own sake, as well as your regulator’s.

This means creating a standardized, common data vocabulary that every employee can access. These definitions and standardizations should extend to writing, tagging and formatting data, in a bid to ensure that there’s a single version of truth, and that each team is working with the same dataset.

The best way to keep this consistent data vocabulary is to create collaborative data models that both development and business teams can use to build data jobs. These models encourage everyone to use a single version of the truth for all datasets.

However, to simplify the data modelling process and reduce development errors or inconsistencies, your organization needs a way to translate your data models into actionable run-time processes.

Step 2: Create accessible data governance policies

Clear documentation ensures everyone in your team follows a set list of best practices. It’s also a valuable way to display regulatory requirements and consolidate your data governance strategy.  

These data governance policies must include:

  • Your essential governance practices, such as how you manage data privacy (e.g. anonymizing sensitive data fields)
  • How your teams plan on maintaining data pipelines
  • How, if necessary, you will map/migrate your data to other systems
  • Who in your organization is responsible for your data processes
  • How different teams within your business communicate and share data with one another (this is essential for retaining “one version of the truth” and avoiding duplication)

By standardizing your practices, responsibilities, and processes, you’ll gain more transparency and consistency throughout your organization.

Step 3: Build effective, automated data models

Next, you’ll need to ensure that your data processes are transparent, repeatable, and error-free. This, of course, means avoiding poor quality, unformatted, and duplicated data.

Automating the process cuts out errors that arise when translating your models into code and guarantees that what’s in your documentation matches what’s in your production systems. Turning data models into a library of reusable rules and transformations will eliminate the need to code complex data pipelines, ensuring there is a direct conversion between model definitions and IT operations.

The main benefit of this approach a simple, repeatable data modelling process that provides a clear audit path from start to finish.

Step 4: Record all data activities

Keeping a Record of Processing Activity (ROPA) is essential under various data regulations, such as the GDPR. This document must be available to your regulators, should they ask for it.

At its core, a ROPA must contain an in-depth list of:

  • Your data processing activities
  • The purpose behind your data processing
  • The data recipients and third parties
  • Data categories and the group of data subjects

Ultimately, this document ensures your processing activities are transparent and watertight. It’s crucial that you have this information to hand for the sake of your regulators, as well as your data subjects.

An automated data transformation process enables a single point of control across your organization. It helps you and regulators get a clear, organization-wide view of all your data process, and will make it much easier to track all required activity, eliminate data siloes, and provide clear data lineage for all pipelines.

Step 5: Take steps to maintain data governance and privacy

Your data governance, data privacy and reporting efforts should be continuous. An evolving part of your organization.

As such, your data teams must keep a close eye on data activities to ensure you treat critical data accordingly.

For example, if a data subject requests that you discard of their personal data, it’s your obligation to do so. Unfortunately, discovering and classifying sensitive information at enterprise scale is a difficult task to do manually, particularly if your teams don’t have the expertise or time.

If you want to ease the process, use technology which scans databases and uses algorithms to determine which contain sensitive information.

This helps you know exactly what data you have and where, as well as making the actionable ‘decision’ process quicker, helping you keep in line with regulators guidelines.

Reinvent your data regulation culture

Keeping your regulators happy requires transparency into your data pipelines and knowledge of how to discover and handle sensitive information. Ultimately, your organization should strive to create a culture that values consistency and compliance, both in your documentation and your practices.

Through education, modelling and stringent data records, your data processes will be easily trackable. And, if you require a quicker, more reliable way to track and repeat your processes, bridging the gap between your data models and your IT operations is a great place to start.

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Posted on September 18, 2019
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