A practical guide for small to medium-sized businesses
Data governance provides the foundation for effective data management, comprising of policies, processes, roles, standards, and metrics that ensure data is accurate, consistent, secure, accessible, and compliant.
In simpler terms, data governance is how organisations ensure their data is accurate, consistent, secure, accessible to the right people, and used ethically and legally.
If you work in data, IT, or business leadership, data governance is essential. It transforms raw data into trusted, actionable insights, safely and efficiently.
There are several frameworks available to help organisations structure their data governance efforts. Each has strengths and limitations, and the best approach often combines elements from multiple sources based on your unique business needs.
⚠️ Tip: Always align your governance efforts with business goals, frameworks are simply tools, not the destination.
The key to successful implementation is to embed Data Governance into the existing Policies and Processes, don’t make Data Governance a standalone thing, siloed off somewhere on a shelf, truly successful Data Governance is part of the culture, the businesses DNA.
Data governance should support, not conflict with your company’s existing policies around compliance, HR, finance, and IT.
For example, if your organisation already has a GDPR compliance policy, your data governance efforts should reinforce it by defining how personal data is stored, who can access it, and when it should be deleted. A simple policy like “Customer data must be deleted after two years of inactivity” not only supports GDPR but also keeps your databases lean and relevant. Aligning your governance approach with your business's risk, privacy, and regulatory needs ensures it’s practical and defensible.
The most effective governance happens within the day-to-day processes your team already uses. Rather than creating new systems, embed data checks and ownership into workflows like sales reporting, onboarding, or customer service. For instance, when a new client is added to the CRM, a standardised form can require complete contact details before the record is saved. In inventory management, new products must follow a naming convention and include key fields like SKU, price, and supplier. By weaving governance into existing operations, it becomes second nature to staff, and far more sustainable.
A common gap in small and medium businesses is not knowing who is responsible for fixing data issues. Data governance clarifies this by assigning data owners (typically senior business users) and data stewards (often operational or admin staff) to each data domain. For example, your Marketing Manager might be the owner of customer data, responsible for its accuracy and privacy. Meanwhile, a marketing assistant might be the steward, running weekly checks for duplicates or formatting errors. These roles don’t need to be technical, they just need to be accountable.
Without clear rules, data quickly becomes messy and unreliable. Data standards ensure consistency, regardless of who enters the data or which system it's stored in. Examples include: all dates must follow the UK format (DD/MM/YYYY), email addresses must include “@” and a domain, and product codes must be exactly six characters (e.g., PRD001). By documenting and sharing these rules, your team can input data correctly the first time, reducing the need for cleanup later. You can even bake validation into spreadsheets, forms, or CRM fields to enforce standards automatically.
What gets measured gets managed! Define a few simple metrics to monitor how well your governance is working. For example: “% of CRM records with missing email addresses” “Number of duplicate customer records” “Compliance with GDPR deletion policy” Reviewing these monthly or quarterly can highlight problem areas and show improvement over time. Assign responsibility for tracking each metric, this could be a data steward or analyst, and make results visible to the relevant teams. Even basic dashboards in Excel or Power BI can help spot trends and drive action.
Let’s take a look at a scenario, “FreshBites”, is an online food retailer with about 60 employees. They collect customer data from their website, into a CRM system, on spreadsheets, and via payment processors like Stripe. They use their data for marketing, inventory, finance, and customer service.
Sounds great, right! But they have problems, their website and CRM are two different system causing duplicate customer records, inconsistent product data is causing challenges with stock replenishment, and there’s a significant risk around PCI DSS and GDPR.
Step 1. Assigning Data Owners. FreshBites makes their Marketing Manager data owner for all their customer data. The Operations Lead becomes responsible for product and inventory data. Note, Data Owners are not in the Data Team, they are business users.
Why this matters: Someone is accountable for making sure the data is clean, current, and secure.
Step 2. Create Basic Data Standards. FreshBites define what data is important for their business goals, and decided on how they want to use it:
Why this matters: Everyone inputs and reads data the same way, reducing errors.
Step 3. Implementing Data Cleaning Processes. FreshBites implemented weekly Power Query scripts to remove duplicates and fix inconsistencies in customer records. They now have their SKUs standardised in Excel before uploading to Shopify.
Why this matters: Reliable data equals better decisions and fewer customer service issues.
Step 4. Securing Sensitive Data. Access to financial data is restricted to finance team only, a GDPR policy is documented and stored in SharePoint, and a simple data retention policy says customer data is deleted after 2 years of inactivity.
Why this matters: Avoids data breaches and fines.
Step 5. Using a Central Data Dashboard. They connect Google Analytics, sales data, and stock levels to a Power BI dashboard. Before it’s published, data is checked for completeness.
Why this matters: Trusted, accurate dashboards lead to faster and better decisions.
Data governance is not just for large enterprises, small and medium businesses can benefit immensely from a structured approach to managing their data. By implementing basic governance practices, you can improve data quality, ensure compliance, and enable better decision-making across your organisation.
Remember, the goal is not to create a complex bureaucracy but to establish clear roles, standards, and processes that make data management easier and more effective.
