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Core Data Governance Principles, Processes & Strategies

Do a quick search on “data governance” and more than 1.3 billion results will be returned within a few milliseconds: there is even a Data Governance Institute. Repeat the search in combination with “communications surveillance” and the result set looks entirely different. In a simple phrase – there’s not much there – yet it’s at this intersection of data governance strategy and communications surveillance where things get interesting. The modern business landscape is full of potholes, speedbumps, and landmines given the challenges of corralling a remote or hybrid workforce. Here, we present data governance principles, processes, and strategies to avoid these pitfalls.

The pace of change presents one of the biggest challenges of the modern workplace today. Technology is advancing so quickly that employees are struggling to keep up. The result? Frustration and anxiety both the staff and their employers—who fear being left behind—both feel. Unlike the pace of change and advances in personally accessible technology such as mobile phones where “FOMO” (Fear of Missing Out) is a major purchase driver, and a quick-buy decision puts you ahead of the pack; the fear employers feel about being left behind is on a whole different level.

Employers must keep up with and maintain compliance with, “post-financial crisis regulatory reform (including the Basel Committee on Banking Supervision’s standard number 239 (BCBS239) and the European Union’s (EU’s) Solvency II Directive) … the European Central Bank’s Data Quality Dashboard, the California Consumer Privacy Act (CCPA) and, most notably, the EU’s General Data Protection Regulation (GDPR),” among other regulations. Non-trivial, for sure.

On top of all that, employers are faced with extreme security concerns, high complexity, a lack of clarity, limited to no control over their emplolyees’ behaviors, and a shortage of critical resources. Each of these challenges on their own is enough to cripple an organization.

Add cybersecurity threats and global compliance regulations that are changing constantly and now that fear becomes highly amplified. Shifting goal posts for data governance affect everyone – especially compliance officers – who are often grossly under-resourced and under-valued for the enormity of their task at hand.

They need our help.

In 2023, data breaches worldwide have increased nearly three-fold in Q2 compared to Q1. Translated to a metric we can all understand, an average of 855 accounts are leaked every minute. The total number of people impacted is 111 million in Q2-2023 alone. Most accounts are being compromised in the US and Russia. Data breaches are not only traumatic for those involved with serious reputational risk for the companies entrusted with protecting the data, but remediation is very costly: today, the average data breach costs $4.45 million to correct.

Core Data Governance Principles

  1. Data quality: This pillar focuses on maintaining data accuracy, completeness, consistency, and reliability. It involves defining data standards, implementing data validation processes, and monitoring data quality continuously. High-quality data is essential for informed decision-making and ensuring trust in the data. All of us are no doubt familiar with the adage about data quality, “garbage in, garbage out.”
  2. Data stewardship: In implementation, individuals or teams responsible for overseeing and managing specific data domains are designated / assigned. Stewards are accountable for data’s lifecycle, including data acquisition, transformation, storage, and dissemination. They enforce data policies, resolve data-related issues, and bridge the gap between data producers and consumers.
  3. Data protection and compliance: Protecting sensitive data and adhering to legal and regulatory requirements is crucial. This pillar involves defining data classification, access controls, encryption, and audit trails. Compliance with laws like GDPR or HIPAA ensures data privacy, security, and the avoidance of legal penalties which have proven to be very steep, especially around eCommunications. One of the biggest trends is enforceable data governance where static policies become active measures in office software.
  4. Data management: Effective data management involves organizing data assets, defining data governance roles, and establishing data catalogs and metadata repositories. It also includes data integration, data modeling, and data archiving to streamline data usage across the organization.

Enterprise Data Governance

In a word, it’s all about integrity. Large organizations need advanced enterprise data governance schemas in place as part of their societal contract for permission to operate. Consider this – would you share your social security, birth date, home address, and account coordinates with a bank that you didn’t trust to keep your personal identifiable information (PII) safe? Of course not. If something shady and less-than-transparent leaked into the news about your bank, it’s likely that you would immediately transfer funds to protect your assets.

The absence of trust, which stems from the absence of a data governance strategy, puts a bank or any organization on the path to credibility problems. Enterprise Data Governance is a systematic approach to managing an organization’s data assets effectively, ensuring data is trustworthy, secure, and aligned with business objectives. Successful deployment of enterprise data governance involves several best practices:

  1. Secure executive support and ownership
  2. Develop a clear & comprehensive data governance framework
  3. Conduct a data inventory and classify content for importance & sensitivity
  4. Implement data quality management processes
  5. Develop robust data security and compliance controls
  6. Define data lifecycle management policies for data retention and archiving
  7. Provide employees with data training and create a culture of compliance
  8. Invest in data governance technology
  9. Implement continuous monitoring and improvement processes
  10. Increase communication and enable change management

Data Governance Process

Data governance is a multifaceted process critical to the effective management of an organization’s data assets. It involves several key components, each playing a crucial role in establishing a robust data governance framework. As with every framework, the process begins by establishing roles and responsibilities. This step involves identifying individuals or teams within the organization who will take ownership of data-related management tasks. Assigning roles such as data stewards, data owners, and data custodians is vital to ensure that there is accountability for all aspects of data management. For example, data stewards are responsible for overseeing specific data domains whereas data owners take ownership of data assets and make decisions about their use and protection.

Next, identifying policies, standards, and procedures is integral to data governance: these may include national or global compliance regulations such as those upheld by the SEC, FINRA, GDPR, Dodd-Frank Act, and so on. Policies define overarching principles that guide data management, while standards establish specific criteria and rules for data quality, security, and usage. Procedures outline the step-by-step processes for implementing these policies and standards. This helps ensure that data is handled consistently across the organization, reducing the risk of errors and data misuse.

Data governance involves ensuring optimal data architecture and analysis capabilities. This encompasses designing a data architecture that supports the organization’s data needs, including data storage, integration, and access. Additionally, it involves providing the necessary tools and capabilities for data analysis, ensuring that stakeholders can derive meaningful insights from the data. Ensuring security and quality management is paramount to data governance.

Security measures include implementing access controls, encryption, and authentication to protect sensitive data. Quality management involves continuous monitoring and improvement of data quality through data validation, cleansing, and enrichment processes. These measures safeguard data integrity and mitigate the risks associated with data breaches or poor data quality. When executed effectively, data governance ensures that data becomes and remains a valuable strategic asset for an organization.

Communication Surveillance in Data Governance

In the landscape of modern data governance, communication surveillance has emerged as a crucial concept. It entails the monitoring, analysis, and management of digital communications within organizations, encompassing emails, instant messages, voice calls, and other forms of digital interaction. While not a new concept, its prominence and significance have grown in recent years due to the evolving nature of technology and the heightened concerns around data privacy and security.

The primary role of communication surveillance within data governance is multifaceted and pivotal. It includes data protection to safeguard sensitive information. This is typically done by actively monitoring electronic communications to identify potential data breaches or unauthorized access to confidential data. Compliance, of course, is well understood, although it took over $1 billion in fines to be levied by the SEC on the world’s biggest banks for the eComms challenges of WhatsApp and other unapproved channels was taken seriously.

Risk mitigation is also well understood as the protective measure to secure data assets from insider threats, cyberattacks, or employee misconduct. However, successfully implementing it in the arena of dComms is another matter. Communication surveillance also lends itself to operational efficiencies relieving bottlenecks and enabling secure data sharing when implemented correctly. As part of operational efficiency, data minimization is a viable strategy: collect only the data that is needed and don’t store it if you don’t have to.

Anonymization and encryption are now ubiquitous in eComms solutions. In contrast, defining clear, ethical guidelines for the use of communication surveillance data has proven to be problematic. Employers enact policies and enable company devices with monitoring solutions, but employees often use their personal phones to transact business.

Data Governance Solutions

Automated processes enable data governance strategies with the potential for success. Through a combination of surveillance, eDiscovery, supervision, and establishing MNPI barriers (controls) around who has access to which data, companies can get some peace of mind and overcome their fears. The outcomes are confidence, clarity and control, predictable spending, and rapid deployment that does not interfere with ongoing daily operations. Our solution enables seamless integration with any data format – including challenging eComms data types.

Data governance begins – and ends – with one thing: Integrity. If you are concerned about the integrity of your data, speak with one of our compliance professionals.


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