Data Governance

Posted on by Gary Ackerman

a post for students in my database course

Ever wondered what keeps all that digital information flowing smoothly, securely, and ethically in the vast world of data? It’s not magic, it’s data governance. Think of data governance as the traffic laws, road signs, and even the emergency services of the data highway. Without them, it would be pure chaos, leading to accidents (data breaches), wrong turns (inaccurate analysis), and stalled vehicles (unusable data).

As you embark on your journey into data analytics, understanding data governance isn’t just about memorizing rules; it’s about building trust, ensuring accuracy, and protecting individuals in an increasingly data-driven world. This comprehensive guide will break down the essential components of data governance, drawing on what you need to know for a successful career in data, even as you’re just getting started.

The Foundation: Understanding Data Security

At its heart, data governance is deeply intertwined with data security. Why? Because data is the most crucial asset for any data analyst or company. If data isn’t secure, its integrity – meaning its validity and accuracy – is compromised, making it unreliable and potentially useless. Data security helps protect your company’s valuable information from competitors and, crucially, meets legal requirements, especially when dealing with protected data. While you might not be the one implementing security protocols (that’s often for cybersecurity specialists), it’s your job to understand and follow them.

Here are the common types of data security you’ll encounter:

  • Access requirements—These are the most fundamental level of data security, limiting who can get their hands on specific data. Companies typically grant access in two main ways:
    • Role-based access—This grants access based on a person’s job title or role within the company. For example, all data analysts might have access to a general company-wide dataset, regardless of their specific department.
    • User group-based access—This focuses on specific teams or departments. For instance, only members of the marketing department would have access to marketing data. Often, companies use a combination of these methods. It’s also vital to remember that Data Use Agreements (legal contracts) can limit who can see data, and sharing it outside these agreements requires explicit release approval.
    • Security requirements—Beyond who can access data, there are methods to make data unusable if it falls into the wrong hands:
      • Data encryption—This uses algorithms to scramble data, making it impossible to read without a “key” to decrypt it. This means even if a bad actor gets the data, they can’t understand it. Laws regarding encryption can vary widely by country.
      • Data transmission—Data is vulnerable when it’s moved from one place to another. Using unsecured public Wi-Fi to download sensitive company data to a personal laptop is a big no-no, as it risks theft, alteration, or corruption of the data. Always use approved, secure connections provided by your company.
      • De-identification/masking of data—This involves removing any personal or sensitive information from data so it can be safely reported without compromising individual privacy. For example, a bank might remove customer names from a report about last year’s financial performance to protect privacy while still sharing insights.

The Blueprint: Knowing Use Requirements Through Data Use Agreements

A Data Use Agreement is a crucial legal document, often encountered as a “Terms of Use” policy when you install software. It precisely outlines what data can be collected, how it can be used, how it will be handled, when it will be deleted, and if it can be retained. As a data analyst, you won’t write these agreements, but you must know the rules set within your company’s Data Use Agreement. Deviating from it can have serious legal consequences for both you and your company. Always verify data handling practices yourself, rather than just trusting someone else’s word.

Key sections of a Data Use Agreement include:

  • Acceptable use policy—This part specifies how data can and cannot be used, along with the penalties for breaking these rules.
  • Data processing—This section details how data will be treated during various steps, like transfers, to protect the security and rights of individuals. It can cover training, data protection officers, and impact assessments. A significant element within this section is data breaches.
    • Data Breaches: A data breach occurs when data security is compromised, giving unauthorized access to data. If you suspect a breach, the most critical steps are to report it immediately and notify all impacted parties. Staying silent is the worst possible response.
  • Data deletion—This section outlines how and when data will be deleted. Data can be deleted for legal reasons (e.g., consent withdrawn, illegal collection, legal obligations, data from minors) or practical ones (e.g., data no longer needed, saving storage costs, system efficiency). It’s generally a best practice to delete data once it has served its intended purpose and is no longer required.
  • Data retention—This specifies how long data will be kept and how it will be stored, including security protocols for stored data. It might include a “data retention date” after which the data will be automatically deleted.

Protecting Sensitive Information: Understanding Data Classifications

Beyond general security, certain types of data are legally protected due to their sensitive nature. Handling these classifications incorrectly can lead to severe trouble.

  • Personally identifiable information (PII)—This is any data that can be used to identify a specific person. This includes obvious identifiers like names, physical addresses, email addresses, phone numbers, and Social Security numbers. However, it also extends to seemingly less obvious information like IP addresses, login IDs, social media IDs/posts, dates of birth, digital images, geolocation, biometric data, and behavioral data. If data can even theoretically identify someone, it’s PII and must be kept secure and never reported without de-identification.
  • Personal health information (PHI)—This refers to identifiable information related to a person’s past, present, or future health. It encompasses all medical records, including dental. PHI is similar to PII but has its own specific laws, like the Health Insurance Portability and Accountability Act (HIPAA) in the US. Always check the legal requirements for handling health-related information in your region.
  • Payment card industry (PCI)—This classification focuses on financial information, particularly credit and debit card details. It ensures compliance with the Payment Card Industry Security Standards Council. While often considered a type of Personally Identifiable Financial Information (PIFI), it has its own distinct regulations.

Connecting the Dots: Handling Entity Relationship Requirements

Entity relationship requirements are rules that define how different pieces of data – tables, models, or data objects (called “entities”) – can relate to one another. These relationships are crucial for structuring databases effectively.

  • Record link restrictions—These rules prohibit linking certain pieces of data, even if they pertain to the same individual. This is critical to prevent individual data points from becoming protected (e.g., PII) or dangerous when combined. For instance, a company might intentionally keep separate pieces of information about a person to prevent identity theft, even if each piece is safe on its own.
  • Data constraints—These are rules designed to protect data integrity by controlling what kind of data can be entered into a database. They specify acceptable data types, formats, conditions, and even how/when data is entered. Data constraints ensure that only high-quality data enters the system from the start.
  • Cardinality—This describes the type of relationship between two entities (e.g., tables). Common types include:
    • One-to-one (1:1)—Each record in one table links to exactly one record in another table.
    • One-to-many (1:M)—One record in the first table can link to multiple records in the second table, but each record in the second table links to only one in the first.
    • Many-to-many (M:M)—Multiple records in the first table can link to multiple records in the second table, and vice-versa. This can get complex, so restrictions often focus on managing the number of relationships each row can have.

The Output: Data Quality and Management in Practice

While data governance sets the overarching policies, data quality and management are about the practical execution of these policies to ensure data is accurate and usable. Good governance leads to good quality.

  • Quality control—This involves testing data to ensure its integrity. You should perform quality control checks whenever there’s a major change to the data. This includes:
    • Data acquisition—When you receive new data, check for inherent biases in collection and the data’s current state.
    • Data transformation—After changing data from one form to another (e.g., normalizing, reformatting), check for accuracy.
    • Data manipulation—After changing the shape of data (e.g., breaking variables down, combining them), ensure no errors were introduced.
    • Final product—A crucial last check before any report or dashboard goes live to prevent errors from reaching stakeholders. Key data quality dimensions include consistency (data is uniform), accuracy (data is correct, often cross-referenced), and completeness (no missing values or required variables). Data integrity encompasses all these, plus security, focusing on the overall process of maintaining high-quality data.
  • Master data management (MDM)—This is a high-level approach to creating a centralized data system that serves as a “golden record” or a “single source of truth”. It consolidates, cleans, standardizes, and updates all critical data in one place, leading to higher data quality, integrity, and faster access.
    • When to use MDM—It’s especially useful during mergers and acquisitions (combining disparate datasets), for policy compliance (e.g., with PII, PHI, PCI regulations), and for streamlining data access (making data easier to get for analysis).
    • Processes of MDM—Key steps include consolidation (combining data from multiple sources into one record, which then updates all original sources), standardization (making data uniform across field names, units, formats), and creating a data dictionary (a document defining every variable, its usage, and relationships). A data dictionary is vital for documentation, especially when multiple people use a database or it’s handed off.

Why This Matters for You

For community college students aspiring to be data analysts, understanding data governance is paramount. It’s not just a theoretical concept; it’s about the practical, legal, and ethical responsibilities you’ll hold. Bad data, mishandled data, or unsecured data can have severe consequences, from misleading business decisions to legal penalties. By mastering these concepts, you’ll not only be better prepared for certifications like CompTIA Data+ but also become a more responsible, trustworthy, and effective data professional in any field you choose.

So, as you continue your studies, remember: data governance is your shield, your map, and your compass on the data highway. Drive safely, analyze wisely, and always prioritize data integrity and privacy.