Whether you’re migrating, upgrading, or deploying a brand-new solution, there’s always one thing that’s top-of-mind when implementing technology platforms for your customers: data.
Data is the fuel that powers all modern business technology, and how that data is managed, organized, and protected plays a huge part in how well that infrastructure performs.
During an implementation, your team is likely to extract large volumes of customer data from legacy systems, clean it, sort it, migrate it to the new platform, and secure it. The way you handle this complex task lays the foundation for your solution going forward—so getting data governance right from day one is critical in making sure your clients’ data is usable, secure, and compliant.
Let’s take a look at some key data governance practices, why they matter, and how you can action them during your next implementation to deliver the best possible data management and governance for your customers.
Data quality management
Everyone in tech has heard the saying ‘garbage in, garbage out.’ It may sound old hat at this point, but it’s never been more relevant. We generate and analyze more data than ever, but if it’s not in a useful state, your clients won’t be able to squeeze any value from it.
For data to be valuable, it needs to be accurate, complete, consistent, and reliable throughout its lifecycle. Poor quality data can lead to flawed analytics results, skewed AI outputs, loss of customer trust due to errors in billing or reporting, and operational inefficiencies arising from issues like failed integrations and costly rework.
Best practices for data quality management
Data profiling and validation
Data profiling helps analyze datasets to understand their structure, quality, and content. Data profiling tools scan data and report on completeness, uniqueness, and other patterns to help you identify gaps in your overall data hygiene.
Data validation, on the other hand, checks data against predefined rules or standards to make sure it’s accurate, consistent, and reliable. Together, these two practices maintain high-quality data that will set your clients up for success in analysis, reporting, and decision-making.
Using tools like Great Expectations, Talend, or Python, you can assess your clients’ data for missing values, outliers, and formatting errors. These kinds of checks can help you find things like duplicate leads or invalid phone numbers, saving you a lot of time by fixing or deleting poor-quality data before you port it to the new system. Another best practice is setting validation rules (for example, “Email fields must match regex pattern”) to catch any dicey data before it enters the new system.
Master Data Management (MDM)
Master Data Management (MDM) is a method used to create a single, accurate, and consistent view of critical business data—like customers, products, and suppliers—across an organization. MDM eliminates duplicates, enforces data governance, and ensures reliability for better decision-making, operational efficiency, and compliance.
MDM platforms like Informatica MDM and Profisee centralize, cleanse, and synchronize master data across systems to ensure consistency which is especially useful if your clients have a varied and complex tech stack. They use data governance, matching, and merging to eliminate duplicates, enforce standards, and provide a “single source of truth.”
These tools also allow you to define golden records to serve as the authoritative “source of truth” for key entities (e.g., “Customer_ID” or “Product_ID”), so there’s no confusion or duplication around important pieces of data and no reporting mismatches.
Data security and access control
Data security encompasses a huge range of practices and processes that protect data from breaches, loss, or corruption. Core methods include encryption, regular backups, and monitoring.
Access control is central to this, ensuring that only authorized users can view or modify data. What data users can access is typically tied to their roles, predefined permissions, and authentication status, helping shield sensitive information from view and making sure your client complies with privacy regulations.
Failure to enforce these safeguards can have serious consequences, ranging from regulatory fines and reputation damage to AI-based issues, like having sensitive data leak into LLM training sets.
Here are a few things you can do to protect data from unauthorized access, breaches, or leaks during implementation and beyond.
Best practices for data security and access management
Role-Based Access Control (RBAC)
Role-Based Access Control is a security model that grants system access based on user roles. Permissions are assigned to roles, not individuals, ensuring users only access necessary data—HR staff, for example, shouldn’t be able to access financial data. This least-privilege principle minimizes risks and makes user management more straightforward.
The first step is to define roles that reflect your client’s workforce (like “Admin” or “Analyst”) in whichever IAM system they’re using. Then, define what data each role can and can’t access using the least privilege approach; if a user doesn’t need the data to do their job, they shouldn’t have access to it.
Encryption and masking
Encryption and masking are two methods you can use to hide sensitive client data from prying eyes.
Encryption converts data into a coded formats using algorithms and keys to keep it confidential during storage or transmission. Masking, meanwhile, hides sensitive fields to limit exposure while keeping data usable. Both protect privacy but serve different purposes—encryption secures data, and masking restricts visibility.
Despite its usefulness, encryption remains underused in many organizations, with one study finding that a lack of encryption was the top cause of data loss for a third of businesses.
Best practice advises encrypting data at rest (AES-256) and in transit (TLS 1.3), and masking sensitive fields in development environments (replacing real bank card numbers with “XXX-XXXX-XXXX-1234”, for instance). Tools like AWS KMS, Vault by HashiCorp, and Delphix can be useful for masking.
Audit logging
Audit logging tracks system activity. It records detailed, timestamped activities (like accessing or modifying data) within a system to track user actions and detect anomalies. Having eyes on all activity relating to client data means you can foster accountability, conduct forensic investigations when needed, and help clients meet compliance requirements.
With an immutable trail of who did what, when, and how, you can help clients protect their data and build confidence that they can get to the bottom of any issues that might crop up.
Start by logging all data access/modifications (e.g., “User X queried Customer table at 3:00 PM”) and using SIEM tools (like Splunk or Datadog) for real-time monitoring.
AI and ML governance
AI and machine learning can give your clients a real competitive edge, but like any cutting-edge technology, it needs to be used responsibly. Proper governance can make sure your customers’ AI models are fair, explainable, and compliant with any relevant regulations.
AI/ML governance promotes the ethical and transparent use of AI/ML models by aligning them with data governance principles. It helps mitigate risks like bias, model drift, and misuse while providing accountability, data quality, and regulatory adherence—all of which are critical for creating AI tools that are trustworthy and auditable, as well as beneficial.
Best practices for AI and ML governance
Bias and fairness checks
Bias and fairness checks ensure AI models make equitable decisions, preventing discrimination. They uphold ethical standards and regulatory compliance by identifying and mitigating skewed outcomes in data or algorithms.
Tools like Fairlearn and AIF360 can help detect bias, such as gender skew in hiring data. You can also reduce the risk of bias by testing models on diverse datasets before deployment.
Model validation and monitoring
Model validation and monitoring helps AI systems perform accurately, fairly, and reliably over time. These processes detect drift, biases, and degradation, so you can maintain compliance and effectiveness during real-world use.
By continuously monitoring your models, you can track accuracy, precision, and recall over time. Set up thresholds that will trigger the retraining of models if reached—for instance, “Alert if F1-score drops by 10%.” Tools like MLflow, Weights & Biases, and Amazon SageMaker Model Monitor can be useful here.
Ethical AI frameworks
You can help your clients integrate fairness, transparency, and accountability into their data governance by putting an ethical AI framework in place—one that sets clear policies, audits algorithms, and ensures compliance with ethical standards and regulations.
Principles like the OECD AI Principles or the EU AI Act are good places to start.
Metadata and lineage tracking
Best practices for metadata and lineage tracking
Data lineage documentation
Data lineage documentation traces data’s journey from source to destination, meaning clients can better manage transparency, compliance, and quality control.
To implement data lineage documentation:
- Inventory data sources and systems
- Map data flows and transformations (ETL, APIs, etc.)
- Automate lineage capture using tools like Collibra or Informatica
- Tag metadata with indicators like ownership and sensitivity levels
- Integrate with governance tools for audits and impact analysis
Metadata repositories
Need somewhere to store and manage all that metadata you’re now tracking? You need a metadata repository. These hubs store metadata centrally, enabling data discovery, compliance, and governance of data assets across your clients’ organizations.
To implement a metadata repository:
- Choose a tool (Collibra, Alation, or Informatica, for example)
- Define metadata standards (e.g., schema, lineage, business terms)
- Ingest metadata from databases, ETL, and BI tools
- Govern access with roles and workflows
- Integrate with data catalogs and lineage tools
Compliance and regulatory alignment
Best practices for compliance and regulatory alignment
Data residency and sovereignty
We may live in an interconnected digital world, but the location of data still matters. That’s where data residency and sovereignty come in.
Data residency defines the physical location where data is stored, while data sovereignty ensures data complies with local laws—together, these dual principles help you enforce compliance with local laws, protect privacy, prevent unauthorized access, and maintain trust by keeping data secure within regulated jurisdictions.
As a growing priority for businesses, data sovereignty is now considered a crucial aspect of data management strategy by 51% of UK organizations. A good place to start is by using cloud regions that comply with local laws—for example, storing your EU data in a geographically appropriate region like AWS Frankfurt. You can also use data localization tools like Google Cloud Data Loss Prevention to identify, classify, and protect any sensitive data that’s covered by regional regulations.
Privacy by Design
Implementing Privacy by Design (PbD) means taking a proactive approach to data governance that embeds privacy protections into systems and processes by default, not as an afterthought to be tacked on at the last stage. Putting data privacy first during any task that involves data handling means nailing data minimization, user consent, and security from the outset.
Be sure to anonymize data during testing, enable clear consent and opt-out options, and encourage clients to carry out Privacy Impact Assessments (PIAs) for new projects. For more PbD best practices, refer to GDPR’s 7 PbD principles or ISO 27001 for guidance.
Vendor risk management
Third-party data breaches are on the rise, with 61% of companies reporting experiencing one in 2023, up 49% year-over-year. That’s why it’s so important to make vendor risk management part of all clients’ data governance strategy.
Before you give any third-party provider access to your clients’ data, check that they comply with data governance policies by assessing their security, privacy controls, and regulatory adherence to mitigate risks like breaches or compliance violations.
Audit third-party tools for SOC 2 and ISO 27001 certifications, and encourage clients to include data clauses in contracts, such as stating that the vendor must delete any data held upon request.
Data expertise
One of the most effective ways to deliver top-flight data governance is to have strong data expertise on your team. Professionals who know how to achieve quality, compliance, and security while helping clients make the most of their data are invaluable during any deployment.
Without sufficient data handling expertise in your corner, you could put your clients at risk of poor data integrity, breaches, and regulatory penalties.
Best practices for hiring data experts
Competition for data engineering talent is on the rise, making finding the right candidate with the right experience a real challenge. But with Revolent’s pioneering Data Engineering Talent Program, you can get access to certified, deployment-ready Data Engineers the easy way.
Through our talent development program, we recruit experienced candidates with the specific industry experience you need. Then, we train them up, equipping them with skills, certifications, consultancy skills, and hands-on experience tailored to your business needs. When they’re ready, they join your teams, where they start delivering for your customers immediately. While they get to work, we continue to invest in their development, building specializations and keeping skills sharp.
