Navigating Regulatory Compliance With Data Governance Strategies

Posted on November 5, 2024 by Kopius

Nowadays, data is everything. It fuels your decisions, drives business growth, and improves customer relationships. Data governance and regulatory compliance are heavily intertwined aspects of managing and securing your organization’s data. A strong data governance policy sets the standard for how you collect, store, process, access, and use data throughout its life cycle.

Without a proper governance strategy, it becomes increasingly difficult to maintain compliance when handling and processing sensitive data, such as financial, personal, or health records. Failure to comply with these regulations can result in significant financial and reputational losses for your business. Understanding data governance and compliance is key to implementing robust policies and practices.

Understanding Data Governance and Regulatory Compliance

The terms “data governance” and “regulatory compliance” are often used interchangeably, but they differ. Before you can implement effective data governance, it’s important to know the definitions, objectives, and importance of each term.

What Is Data Governance?

Data governance refers to the processes, guidelines, and rules that outline how an organization manages its resources, including data. These guidelines exist to make sure data is accessible, accurate, consistent, and secure. The key components of data governance typically include:

Ensuring regulatory compliance.
Maintaining data quality.
Outlining roles and responsibilities.
Monitoring the use of resources.
Facilitating data integration and interoperability.
Scaling based on demand.
Securing sensitive data against unauthorized access and breaches.
Improving cost-effectiveness.

Data governance is essential for protecting and maintaining crucial data and confirming that it aligns with business objectives. Data governance also plays a pivotal role in helping organizations meet regulatory compliance requirements for data management, privacy, and security. As regulations continue to evolve, so does the need to meet them. Data governance supports organizations in this regard by establishing and enforcing policies for responsible data use.

What Is Regulatory Compliance?

Regulatory compliance refers to the regulations, laws, and standards that an organization must meet within its industry. Compliance standards vary by state and industry, but their primary purpose is to ensure organizations securely handle personal and sensitive data. Data protection and privacy laws are essential aspects of regulatory compliance. For instance, health care organizations are required to meet industry-specific regulations like the Health Insurance Portability and Accountability Act to protect patient privacy.

The Fair Credit Reporting Act outlines protection measures for sensitive personal information regarding consumer credit report records. The Family Educational Rights and Privacy Act is another example of a data governance policy that protects access to students’ educational data. Compliance is essential for organizations because it enables them to build trust with their customers, improve their reputation, and avoid legal risks.

Data Governance vs. Compliance

Data governance refers to how organizations use, manage, and control their data internally, while regulatory compliance is about how they adhere to external regulations. Data governance guides decision-makers to be proactive, while compliance is often reactive.

Can an organization be compliant without data governance? The answer is yes. It’s possible for your organization to have data governance standards in place without being fully compliant if your policies do not meet industry or external regulations. Alternatively, your organization may be compliant by meeting the minimum regulatory standards without establishing an effective data governance framework.

While one is possible without the other, both data governance and compliance are crucial for a cohesive data management strategy. Governance builds the framework within which compliance operates to keep your business efficient. These two closely related aspects help your organization achieve business objectives, identify opportunities for strategic data utilization, and improve legal integrity.

The Role of Data Governance in Ensuring Compliance

Now that you know the distinctions between data governance and compliance, it’s time to examine the integral role of data governance in adhering to policy, regulatory, and legal requirements.

Data governance significantly supports compliance efforts by ensuring the enforcement of data procedures and their alignment with regulatory requirements. Additionally, having strong data governance standards in place can help organizations achieve data compliance by:

Simplifying the interpretation of compliance laws and regulations.
Proactively addressing compliance needs.
Establishing data stewards to create data governance consistency.
Identifying data governance risks and areas of noncompliance.
Reducing the complexity required to adhere to regulatory standards.
Maintaining well-documented data processes to facilitate streamlined audits.
Continuously monitoring data quality management practices.
Establishing the traceability of data processes.

Similarly, poor data quality can lead to compliance issues, which can result in fines, penalties, and legal complications. As a result, data governance procedures are necessary to verify that data is ethically and securely aligned with industry regulations. Safeguarding your organizational data’s integrity with data governance policies can also enhance your ability to demonstrate compliance with external standards — a benefit to all stakeholders.

Challenges in Meeting Regulatory Compliance

What stands in the way of compliance? In the digital age, organizations in all industries face obstacles due to ever-changing regulatory landscapes. Here are some of the most common challenges in working toward compliance:

1. Evolving Regulations

Laws and regulations constantly change, making it challenging for organizations to keep up. As lawmakers develop new policies for protecting consumer data, organizations must frequently update to meet diverse compliance demands. Following the continuous growth of data governance regulations can put additional strain on compliance teams as they strive to safeguard data integrity.

2. Gaps and Overlaps

Alongside rapidly evolving laws is the challenge of balancing internal policies with external regulations. As new regulations arise to meet data privacy and security concerns, organizations must address existing gaps and overlaps to create consistency.

3. Monitoring Needs

Tracking data flow and usage is a key part of data governance. However, organizations that fail to properly monitor and audit data practices may struggle to adhere to compliance regulations. Some organizations may lack the staff or resources needed for continuous monitoring.

4. Vast Amounts of Data

It’s no secret that businesses are collecting, using, and storing more data than ever. Maintaining compliance becomes even more complex as more and more data flows in. Without proper data storage, managing these large volumes of data can be difficult.

5. Vulnerability of Legacy Systems

Relying on outdated technology to maintain compliance is nearly impossible due to the lack of security upgrades and other modern compliance essentials. Organizations that still use legacy systems will find it increasingly complex to meet today’s strict regulations.

6. Risk of Data Breaches

Data breaches increased by 20% in 2023, along with significant spikes in ransomware attacks and theft of personal data. However, as companies put more and more of their data into computerized systems, the risk of data breaches grows without proper configuration and security measures.

7. Lack of Expertise

As a result of increased data security concerns, there is a growing need for skilled personnel who can navigate the legal aspects of data compliance. Staff training is also required to keep employees up to date on changing regulations to ensure ongoing compliance.

8. Cost Concerns

Maintaining compliance can be costly, especially when factoring in hiring skilled personnel, training internal compliance staff, and upgrading technology. Maintaining ongoing compliance in an evolving landscape of regulations can lead to increased operating costs as continuous audits and assessments are needed.

Benefits of Implementing Data Governance Strategies for Compliance

Implementing a robust data governance framework is essential to creating a culture of data compliance. Here are some advantages you can expect with data governance policies:

1. Minimized Legal Risks

Data governance procedures can help your organization identify and manage potential compliance risks. Adhering to data regulations can protect your organization from legal consequences, such as fines and penalties. Without an organized framework for every team member to follow, it can be challenging to know whether you’re meeting regulatory requirements.

Data governance allows you to meet standards that dictate how data should be managed and protected. Similarly, data governance guidelines can simplify compliance reporting and audits, which can also reduce the risk of fines and legal issues.

2. Enhanced Security

Robust data security measures can benefit businesses across all industries. Establishing data governance strategies can protect sensitive data from breaches and cyber threats. Data governance also prevents the unauthorized use or misuse of data, which is particularly important in the health care and finance industries. In today’s landscape of increasing cybersecurity hacks and threats, data governance allows for a proactive approach to organizational security.

3. Improved Decision-Making

Data governance is a powerful tool that decision-makers across your organization can utilize to drive your business forward. Data governance strategies can help your teams make well-informed decisions by gathering key insights on how data is being accessed, handled, and secured.

4. Increased Data Accessibility and Quality

Effective data governance strategies help your teams properly manage your data, meaning it will be organized and cataloged effectively. As a result, users can find the data they need when they need it and expect it to be accurate, up to date, and complete. Additionally, you and your teams won’t have to rely on poor-quality data to make important decisions.

Adhering to data regulations can lead to minimal errors and allow employees to quickly and easily access the information they need to do their jobs. Organizations that have multiple business partners or units can feel confident in data sharing, knowing their data is consistent and well-controlled.

5. Improved Compliance

Though the existence of data governance strategies does not make an organization inherently more compliant, it creates an environment that prioritizes compliance. Establishing data governance strategies demonstrates that organizations take data privacy seriously and will continue to update policies as needed to align with relevant regulations. Companies that use data governance procedures may also be more likely to meet regulations that govern the use and protection of data because they’re well-informed of the potential risks of noncompliance.

6. Strengthened Reputation

Transparency is key when it comes to building and maintaining customer relationships. Organizations that adhere to data regulations and strive to keep consumer data safe may enhance their reputation among stakeholders, customers, partners, and employees. They are more likely to foster trust among clients and consumers who want to know that their data is being handled responsibly.

7. Facilitate Room for Innovation

When it comes to data, organizations have to think three steps ahead. Data governance strategies ensure your data is well-managed and maintained, creating an environment conducive to business innovation. Employees can access high-quality data faster, enabling more time for innovative solutions and new ideas. What’s more, a robust data governance framework signifies to stakeholders that future innovation efforts are built on secure, dependable data governance practices.

8. Identify New Revenue Opportunities

Taking a proactive approach to data security with data governance allows you to identify potential risks and gaps in your current workflow. However, it can also help you identify opportunities for revenue growth.

Effective data governance means you can more easily view customer trends and market insights that enable you to develop new products and services to meet current demands. Data governance procedures turn your data into a strategic asset, allowing you to take advantage of opportunities to improve sales and customer satisfaction.

Implementing a Data Governance Framework for Compliance

Every organization has unique needs for meeting compliance regulations by state or industry. However, there are some practical steps you can follow for effective data governance implementation:

Conduct an assessment: The first step is to identify your organization’s data needs. What are the current noncompliance risks you’re facing? Identify and catalog all data assets and determine how they should be handled moving forward.
Choose a solution: If your organization has vast amounts of data or significant security issues, it’s time to choose a data storage solution or data security compliance service to help you address your data needs.
Establish a team: Create a data governance team or committee within your organization to help facilitate cross-department collaboration and oversee continuous auditing. This cross-functional team should include compliance, business, legal, and IT team members who routinely develop, improve, and enforce data governance policies.
Train and educate: Once you’ve developed and documented your data governance policies, it’s critical to make sure all employees understand their role in maintaining data integrity. Provide training on the importance of data governance and compliance to raise awareness of all new and existing policies.
Continuous auditing and improvement: As with any company-wide adjustments, it’s important to regularly review and update your data governance framework to align with current regulations and arising cybersecurity risks.

JumpStart Your Data Governance and Compliance

Data governance is nonnegotiable, especially when it comes to regulatory compliance. However, aligning data governance with compliance requires careful balance. At Kopius, we offer data security compliance services to help businesses meet their industry standards.

Our experts will manage your data collection and establish an infrastructure that makes compliance fulfillment more achievable. As a reliable data security compliance company, our top goal is to mitigate data security breaches without restricting your business growth. Contact us today to see how we can help you meet your data security obligations and learn about our JumpStart program.

JumpStart Your Data Governance and Compliance

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Data Mesh Architecture in Cloud-Based Data Warehouses

Posted on June 6, 2022 by Kopius

Data is the new black gold in business. In this post, we explore how shifts in technology, organization processes, and people are critical to achieving the vision for a data-driven company that deploys data mesh architecture in cloud-based warehouses like Snowflake and Azure Synapse.

The true value of data comes from the insights gained from data that is often siloed and spans across structured, semi-structured, and unstructured storage formats in terabytes and petabytes. Data mining helps companies to gather reliable information, make informed decisions, improve churn rate and increase revenue.

Every company could benefit from a data-first strategy, but without effective data architecture in place, companies fail to achieve data-first status.

For example, a company’s Sales & Marketing team needs data to optimize cross-sell and up-sell channels, while its product teams want cross-domain data exchange for analytics purposes. The entire organization wishes there was a better way to source and manage the data for its needs like real-time streaming and near-real-time analytics. To address the data needs of the various teams, the company needs a paradigm shift to fast adoption of Data Mesh Architecture, which should be scalable & elastic.

Data Mesh architecture is a shift both in technology as well as in organization, processes, and people.

Before we dive into Data Mesh Architecture, let’s understand its 4 core principles:

Domain-oriented decentralized data ownership and architecture
Data as a product
Self-serve data infrastructure as a platform
Federated computational governance

Big data is about Volume, Velocity, Variety & Veracity. The first principle of Data mesh is founded on decentralization and distribution of responsibility to the SME\Domain Experts who own the big data framework.

This diagram articulates the 4 core principles of Data Mesh and the distribution of responsibility at a high level.

Azure: Each team is responsible for its own domain, and data is decentralized and shared with other domains for data exchange and data as a product.

Snowflake: Each team is responsible for its own domain, and data is decentralized and shared with other domains for data exchange and data as a product.

Each Domain data is decentralized in its own data warehouse cloud. This model applies to all data warehouse clouds, such as Snowflake, Azure Synapse, and AWS Redshift.

A cloud data warehouse is built on top of a multi-cloud infrastructure like AWS, Azure, and Google Cloud Platform (GCP), which allows compute and storage to scale independently. These data warehouse products are fully managed and provide a single platform for data warehousing, data lakes, data science team and to provide data sharing for external consumers.

As shown below, data storage is backed by cloud storage from AWS S3, Azure Blob, and Google, which makes Snowflake highly scalable and reliable. Snowflake is unique in its architecture and data sharing capabilities. Like Synapse, Snowflake is elastic and can scale up or down as the need arises.

From legacy monolithic data architecture to more scalable & elastic data modeling, organizations can connect decentralized enriched and curated data to make an informed decision across departments. With Data Mesh implementation on Snowflake, Azure Synapse, AWS Redshift, etc., organizations can strike the right balance between allowing domain owners to easily define and apply their own fine-grained policies and having centrally managed governance processes.

JumpStart Data Success

Innovating technology is crucial, or your business will be left behind. Our expertise in technology and business helps our clients deliver tangible outcomes and accelerate growth. At Kopius, we’ve designed a program to JumpStart your customer, technology, and data success.

Kopius has an expert emerging tech team. We bring this expertise to your JumpStart program and help uncover innovative ideas and technologies supporting your business goals. We bring fresh perspectives while focusing on your current operations to ensure the greatest success.

Partner with Kopius and JumpStart your future success.

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How to Develop a Data Retention Policy

Posted on October 22, 2021 by Kopius

by Steven Fiore

We help organizations implement a unified data governance solution that helps them manage and govern their on-premises, multi-cloud, and SaaS data. The data governance solution will always include a data retention policy.

When planning a data retention policy, you must be relentless in asking the right questions that will guide your team toward actionable and measurable results. By approaching data retention policies as part of the unified data governance effort, you can easily create a holistic, up-to-date approach to data retention and disposal.

Steps to Creating an Effective Data Retention Policy

Ideally, any group that creates, uses, or disposes of data in any way will be involved in data planning. Field workers collecting data, back-office workers processing it, IT staff responsible for transmitting and destroying it, Legal, HR, Public Relations, Security (cyber and physical) and anyone in between that has a stake in the data should be involved in planning data retention and disposal.

Data Inventory

The first step is to understand what data you have today. Thanks to decades of organizational silos, many organizations don’t understand all the data they have amassed. Conducting a data inventory or unified data discovery is a critical first step.

Review Data Retention Regulations

Next, you need to understand the requirements of the applicable regulation or regulations in your industry and geographical region so that your data planning and retention policy addresses compliance requirements. No matter your organization’s values, compliance is required and needs to be understood.

Recognize Your Data Risks

Then, businesses should identify where data retention may be costing the business or introducing risk. Understanding the risk and inefficiencies in current data processes may help identify what should be retained and for how long, and how to dispose of the data when the retention expires.

If the goal is to increase revenue or contribute to social goals, then you must understand which data affords that possibility, and how much data you need to make the analysis worthwhile. Machine Learning requires massive amounts of data over extended periods of time to increase the accuracy of the learning, so if machine learning and artificial intelligence outcomes are key to your revenue opportunity, you will require more data than you would need to use traditional Business Intelligence for dashboards and decision making.

What Types of Data Should be Included in the Data Retention Policy?

The types of data included in the data retention policy will depend on the goals of the business. Businesses need to be thoughtful about what data they don’t need to include in their policies. Retaining and managing unneeded data costs organizations time and money – so identifying the data that can be disposed of is important and too often overlooked.

Businesses should consider which innovation technologies are included in their digital roadmap. If machine learning, artificial intelligence, robotic process automation, and/or intelligent process automation are in your technology roadmap, you will want a strategy for data retention and disposal that will feed the learning models when you are ready to build them. Machine learning could influence data retention policies, Internet of Things can impact what data is included since it tends to create enormous amounts of data. Robotic or Intelligent Process Automation is another example where understanding which data is most essential to highly repeatable processes could dictate what data is held and for how long.

One final note is considering non-traditional data sources and if they should be included. Do voice mails or meeting recordings need to be included? What about pictures that may be stored along with documents? Security camera footage? IoT or server logs? Metadata? Audit trails? The list goes on, and the earlier these types of data are considered, the easier they will be to manage.

Common Data Retention Strategy Pitfalls

The paradox is that the two biggest mistakes organizations make when building a data retention policy are either not taking enough time to plan or taking too much time to plan. Spending too much time planning can lead to analysis paralysis letting a data catastrophe occur before a solution can be implemented. One way to mitigate this risk is to take an iterative approach so you can learn from small issues before they become big ones.

A typical misstep by organizations when building a data retention policy is that they don’t understand their objectives from the onset. Organizations need to start by clearly stating the goals of their data policy, and then build a policy that supports those goals. We talked about the link between company goals and data policies here.

One other major pitfall organizations fall into when building a data retention policy is that they don’t understand their data, where it lives, and how its interrelated. Keeping data unnecessarily is as bad as disposing of data you need – and in highly silo-ed organizations, data interdependencies might not surface until needed data is suddenly missing or data that should have been disposed of surfaces in a legal discovery. This is partially mitigated by bringing the right people to the planning process so that you can understand the full picture of data implications in your organization.

JumpStart Data Retention Policy Solutions

At Kopius, we’ve designed a program to JumpStart your customer, technology, and data success. Tailored to your needs, our user-centric approach, tech smarts, and collaboration with your stakeholders equip teams with the skills and mindset needed to:

Identify unmet customer, employee, or business needs
Align on priorities
Rapidly prototype solutions
And, fast-forward success

Gather your best and brightest business-minded individuals and join our experts for a hands-on workshop that encourages innovation and drives new ideas.

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The Right Data Retention Policy for Your Organization

Posted on October 12, 2021 by Kopius

by Steven Fiore

Every business needs a strategy to manage its data, and that strategy should include a plan for data retention. Before setting a data retention policy, it’s important to understand the purpose of the policy and how it can contribute to organizational goals.

There are four values that drive most businesses to do anything:

To make money and increase revenue
To save money by decreasing costs
Because they must comply with regulations
Because they want to use the business as a platform for social good

While each of these values will be represented in any organization, some investigation will usually reveal that one or two of these values outshine the rest. Which values are most important will vary from one organization to another.

Organizations need to start by clearly stating the goals of their data policy, and then build a policy that supports those goals. We help companies unearth business drivers so data policies can contribute to the company values and goals rather than compete with them.

In this post, we explore best practices in establishing and maintaining a data retention policy through the lens of these business drivers.

What are the goals of your data retention policy?

Value: Make Money

Companies that rely on advertising revenue like Google and Facebook want to keep as much data as necessary to maximize revenue opportunities.

Companies that mine their data can spot trends in their data that inform product enhancements, improve customer experience (driving brand loyalty), and reveal revenue opportunities that would have otherwise been hidden.

In both cases, the data retention policy should focus on what data can contribute to revenue, and how much of it is needed. Balancing aggregate data versus more granular data is the key so you retain enough data to achieve your objectives without retaining unneeded data that adds cost, complexity, and security or privacy risks.

Value: Save Money

Many businesses focus on the bottom line and prioritize efficiency to avoid wasting time, money, and energy.

Businesses that want to save money can use data retention to make the organization more efficient. While data storage is inexpensive, it isn’t free – and access can be more expensive than storage. So, for an organization that wants its data policies to help save money, the policy might focus on retaining only the data that is necessary to avoid extra storage and management overhead.

Further, retaining more data than you need to can be a legal liability. Having a data retention and disposal policy can reduce legal expenses in the event of a legal discovery process.

There’s also an efficiency cost to data – the more data you have, the slower the process will be to search and use that data. So, data retention policies can and should be part of a data governance strategy aimed at making the data that is retained as efficient to manage and use as possible.

Value: Comply with Regulations

Many industries have their own regulations while some regulations cross industries. Businesses that must have a data retention policy may need it to comply with laws that govern data retention such as the Sarbanes Oxley Act, the Health Insurance Portability and Accountability Act (HIPAA), or IRS 1075. Even US-based companies may be subject to international legislation such as the European General Data Protection Regulation (GDPR), and companies that have customers in California need to understand how the California Consumer Privacy Act (CCPA) can impact data retention. Government agencies in the US are also bound by the Freedom of Information Act and some states have “Sunshine” laws that go even further.

Businesses that are motivated to comply with regulations will need their data retention policy to reflect federal, state, and local requirements, and will need to document compliance with those requirements.

Value: Business as a Platform for Social Good

Whether an organization was established as an activist brand or has been drawn to social responsibility as investor demand has risen social responsibility, many companies are finding ways to use data to understand their social and environmental impact. This impact is often also reported on through Environmental Social Governance (ESG) reporting, Carbon Disclosure Projects, and reporting structures like GRESB (Global Real Estate Sustainability Benchmark).

In these cases, organizations that use their business as a platform for social good, may identify key metrics such as energy consumption or hiring data that can be used to inform reports on social responsibility.

In closing

By understanding your organization’s values and priorities, you can ensure that its policies support those values. Every company has data to collect, manage, and dispose of, so it’s critical to have a roadmap for how to address data requirements today and into the future. This framework is a starting point to that effort because there’s nothing worse than going through the effort to implement a complex policy, only to discover that it moves the business further from its goals.

JumpStart Successful Data Retention

At Kopius, we’ve designed a program to JumpStart your customer, technology, and data success.

Tailored to your needs, our user-centric approach, tech smarts, and collaboration with your stakeholders equip teams with the skills and mindset needed to:

Identify unmet customer, employee, or business needs
Align on priorities
Rapidly prototype solutions
And, fast-forward success

Gather your best and brightest business-minded individuals and join our experts for a hands-on workshop that encourages innovation and drives new ideas.

Additional resources: