Data Governance Tailored for Small Companies

#33 Start smart, stay simple

Hello 😀

I’ve received a message on my Discord community, let’s say from someone called Bob. Bob just joined a medium-sized industrial company with ambitious data goals and moderate resources. He asked me :

Do you have a light approach with the essentials that need to be put in place to establish a small governance framework?

Street art in Aix-les-Bains where I’ve spent a girls' weekend in July

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📢 New Community Project Alert : Data Governance Maturity Assessment !

I'm excited to share something I've been working on : a quick and insightful Data Governance Maturity Assessment that helps you understand where your organization stands when it comes to managing data effectively.

In just 5 minutes, you'll answer 13 questions and get category-wise scores, actionable insights and a personalized benchmark - sent in a few weeks !

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Let’s see how to help Bob 👇

1. Why start now?

2. What’s essential

3. Lay the foundations

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Why start now?

During our conversation, Bob wasn’t sure it was worth it to start now. His company is still quite small and he doesn’t have a lot of resources. It’s a good question… should the topic of governance only be addressed when the first real issues are observed? When data analysts are complaining too much?

🤯 Or worse, when somebody has access to confidential data? When there’s the first data breach?

You’re small, you have other priorities - like making revenues to survive. But once survival is out of the table, you want to build the future of the company. And this means INVESTING.

Data governance is an investment that will help you ensure :

• People know where to find the right data

• Everyone speaks the same “data language”

• You comply with internal and external standards

• Decisions are based on trusted, traceable information

🤯 Warning : not investing will expose you to employee fatigue, meaning that people will be fed up to not be able to do their job because of data chaos. They’ll end up leaving your company and writing a bad comment on Glassdoor.

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What’s essential

You want to focus on what matters and avoid bureaucracy.

1. Define “What is Data Governance” for your company

We’re aiming for a clear link with your business strategy. Think of the “why” - why do you want to govern data? It should be overall :

“The people, rules, and processes that make our data trustworthy and usable.”

Here’s a shortlist of questions to clarify :

• What business problems or inefficiencies are caused by poor data today?
  (E.g. inconsistent reports, manual rework, delays, missed opportunities)

• What does "trusted data" look like for our company, and why does it matter?
  (Think in terms of operational reliability, customer satisfaction, or regulatory needs)

• Which decisions or processes would improve if everyone had access to consistent, high-quality data?
  (Examples: production planning, sales forecasting, better pricing)

• How can data governance support our company’s strategy and growth without adding complexity?
  (Examples: simple naming conventions, standard fields for critical data elements, critical KPIs and calculation logic in a shared glossary)

👉 If you want to go further with more questions for interviews, checkout my ebook.

2. Identify your key data domains

Start with what matters most to the business. Example of domains might include :

• Production data

• Customer & sales data

• Maintenance & equipment logs

You don’t need a data domain map, just identify roughly your critical data elements and associated data sources.

3. Set lightweight policies

Think of policies as common-sense rules to follow, like :

#1 - Customer data entry policy

All new customer records must include a name, email, and assigned region.

#2 - File naming convention policy

Shared reports must follow a consistent naming format:
[Team]_[ReportName]_[YYYYMMDD].xlsx

#3 - Duplicate record prevention

Before adding a new product or customer, check if it already exists in the system.

#4 - Single source of truth policy

Reports used for business decisions must pull data from the official data platform.

#5 - User access policy

Access to sensitive data (e.g. costs, salaries, customer pricing) must be role-based and reviewed monthly.

#6 - Data ownership policy

Every dataset or report must have an assigned business owner and technical contact.

#7 - Retention policy

Temporary data (e.g. test or versioned files) must be archived after 30 days.

They’re short, practical, and designed to support, not slow down, operations.

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Lay the foundations

Now that you have setup the essentials - vision, critical data elements and lightweight policies - you want to make sure that these will be owned, updated and relevant in the long term. This means you'll need a small operational framework :

👥 Assign simple roles

As a small company, you can directly identify data stewards from existing teams, no need to have a split with owners and stewards. To make it even simpler, you can even name them Business Referent or Data Contact Point. This role is here to :

• Data Steward : Understands and defines what the data should mean from a business point of view, implements policies on data, raises any quality issues encountered.

🧿 Form a mini Data Council

Just a small group that meets monthly to :

• Prioritize fixes and improvements

• Approve policies and critical terms / KPIs

• Share what’s working (or not)

It’s cross-functional and practical : think Data + Tech + Business, not theory.

🛠️ Use what you have to make it operational

Start a business glossary for critical KPI. You’ll have stewards adding definitions, calculation rules and quality levels required.

In my Starter kit article I give you an example of first glossary with Notion and a simple process to tackle first quality issues.

Don’t forget that your legacy BI and data platform can :

• Check data quality with existing reports

• Highlight gaps or mismatches

• Track compliance with rules

👉 No need to invest in new tools (yet).

Your first goal should be no more misunderstandings between teams. Just clarity and reliability of indicators.

See you soon,

Charlotte

I'm Charlotte Ledoux, freelance in Data & AI Governance.

You can follow me on Linkedin !