When you hear the word ‘regulations’ how does it make you feel? Most businesses understand the need for regulations but sometimes, understanding them can be a challenge. When it comes to implementing them, that challenge becomes a burden, with regulations being seen by some as a constraint on daily operations, profitability and growth.

This effect is only felt more keenly as technology is woven deeper and deeper into the fabric of our lives, with geographical boundaries blurring and the influence of A.I. promising to take the amount of human data to unthinkable heights very rapidly.

It can be easy to feel overwhelmed.

The 2023 KPMG Chief Risk Officer Survey reveals that the primary hurdles for organisations in the coming 2-5 years will be changes in regulations, compliance matters, and cyber security threats. 

Regulatory compliance is crucial for operational efficiency and organisational success, ensuring you operate within legal boundaries, protecting your business from potential fines, reputational damage, and operational disruptions. However, maintaining compliance should not stifle innovation or agility. By embracing the philosophy of “as much as necessary, as little as possible,” it is possible to strike a balance between compliance and operational efficiency. This way your business can meet regulatory obligations without being overburdened with unnecessary processes and controls.

This month we want to help ease the regulatory burden by exploring strategies for smart and standardised implementation and compliance. If you read our article in March introducing a five-step strategy for improved security and compliance, this month takes a deeper dive into step 4: Smart and Standardised Implementation of Regulations.

As Much as Necessary, as Little as Possible

The phrase “as much as necessary, as little as possible” in the context of Governance, Risk Management, and Compliance (GRC) means implementing enough measures to effectively manage risks and ensure compliance, but not so much that it becomes overly restrictive or burdensome.

It’s a tried and test principle widely used in business. In product development it’s known as ‘minimum viable product’ (MVP), aiming to create the product your customer needs, without overcomplicating it, thereby increasing manufacturing costs and reducing useability.

How to Fulfil Regulations in a Smart and Standardised Way

To fulfil regulations effectively while maintaining operational agility, organisations must adopt a smart and standardised approach that integrates several key elements. These elements include data governance, data access, data control, and data quality.

Data Governance

Data governance involves establishing policies, procedures, and standards for managing data throughout its lifecycle. It ensures data integrity, security, and quality, which are essential for compliance. Effective data governance requires:

  • Clear Roles and Responsibilities: Assigning specific roles to individuals or teams to manage and oversee data governance activities.
  • Data Stewardship Programs: Implementing programs where designated data stewards ensure data is handled according to governance policies.
  • Use of GRC Software: Leveraging Governance, Risk, and Compliance (GRC) tools to automate and streamline governance processes.

For another perspective, this Forbes article explores the impact of rapid transformation on Data Governance, and exposes the problems caused by *shoving more and more stuff up in the attic* (to paraphrase the author) as our data warehouses expand.

“Much like there are stringent protocols now in place for handling radioactive materials, I believe the same must happen with data.” Says the author, Vince Berk, Chief Strategist at Quantum Xchange.

Data Access

Controlled data access is crucial for protecting sensitive information and ensuring compliance with privacy regulations. This involves:

  • Identity Access Management (IAM) Systems: Implementing IAM systems to control who has access to data and under what conditions.
  • User Permissions: Setting and managing user permissions to ensure that only authorised personnel can access specific data sets.
  • Privileged Access Management (PAM): Using privileged access management tools to oversee and secure the access of users with elevated permissions.

Gartner Insights have warned that Privileged Access Management should be an absolute priority for any organisation, as “it plays a key role in enabling zero trust and defense-in-depth strategies that extend beyond mere compliance requirements.”

Data Control

Data control encompasses the mechanisms and processes used to manage data usage within an organisation. Key aspects include:

  • Data Management Platforms: Using platforms that provide comprehensive control over data storage, processing, and sharing.
  • Monitoring and Auditing: Continuously monitoring data activities and conducting regular audits to ensure compliance with established policies.
  • GRC Tools: Employing GRC tools to facilitate data control and compliance tracking.

Data Quality

There are many good business arguments for maintaining high quality data. A Forrester survey of top customer intelligence professionals found that they consider the ability to integrate data and manage data quality to be the top two factors that hold back customer intelligence.

But in the world of GRC, poor quality data can get you into serious trouble. Maintaining data quality involves:

  • Data Cleansing and Validation: Regularly cleaning and validating data to remove errors and inconsistencies.
  • AI-Driven Tools: Using artificial intelligence to enhance data quality through automated data management and anomaly detection.
  • Continuous Monitoring: Implementing processes to continuously monitor data quality and address issues promptly.

The Data Quality Coalition

Maintaining the highest quality data is going to take a collaborative effort across various functions within your organisation. To expand on the attic analogy, you don’t want everyone up there together throwing stuff around, or the ceiling will collapse. So, you’re going to need to organise into cross-function alliances that will help to ensure data management practices are consistent and aligned with regulatory requirements.

We have borrowed the terminology ‘data quality coalition’ from the healthcare sector, where data quality can mean the difference between life and death. A data quality coalition brings together all stakeholders to develop and implement a well-defined data quality management strategy. Here are some of the key components of an effective coalition:

Cross-Functional Collaboration

Cross-functional collaboration between IT, legal, compliance, and business units is essential for maintaining high data quality. Each department brings unique perspectives and expertise, ensuring comprehensive data management practices.

Data Stewardship Programs

Data stewardship programs play a critical role in promoting data quality. Data stewards are responsible for maintaining the accuracy, integrity, and security of data within their domains. By assigning data stewards across different functions, you can ensure consistent data management practices.

If you want to learn more, this Dataversity article describes how “Freddie Mac developed the Data Stewardship framework “Ready, Set, Go,” to leverage data for competitive advantage and enhance business value.”

Training and Awareness Initiatives

Training and awareness initiatives educate employees about the importance of data quality and their roles in maintaining it. Not only that, but regular training sessions and awareness campaigns help embed a culture of data excellence within your organisation.

Master Data Management

Master Data Management (MDM) is crucial for maintaining high-quality data. It is a comprehensive approach to managing critical data across the organisation, using technology, tools, and processes to create a unified master data service that consolidates key enterprise data assets such as customer information, product details, and location data. This is especially the case when it comes to using artificial intelligence. Accurate and consistent master data is essential for AI applications, which rely on high-quality information to deliver reliable insights and automation.

Data as the Foundation for Regulatory Compliance

In the same way that road traffic rules are not just there to restrict and inconvenience drivers, data governance policies are not intended purely to slow your organisation down. They can also act as a guide on how data should be handled within an organisation to ensure its proper use and security. The cleaner your data, the smoother that journey will be as, even minor errors can lead to non-compliance and operational risks.

It can also be a massive time-sink. According to Deloitte, “an estimated average of 4 hours weekly (per employee in the IT or data team) is lost to resolve issues related to data preparation for analysis.”

Striving for 100% data accuracy will not only save all those wasted hours, but it will also set your organisation up to support both regulatory requirements and business operations in the most efficient ways possible. Just like in finance and manufacturing, you can’t fulfil all your regulatory requirements if you don’t have your data management under control.