You Don't Need a Data Lake. You Need a Data Strategy. A Guide for Mid-Market Companies.

The Problem With Enterprise Data Advice for Mid-Market Companies

If you're running a $20M-$200M business and you search for "data strategy" advice, you'll find recommendations for data lakes, data mesh architectures, real-time streaming pipelines, and AI-ready data platforms. All of this advice is written for companies with dedicated data engineering teams, data governance functions, and 7-figure analytics budgets.

For most mid-market companies, it's completely the wrong conversation.

Start With the Questions, Not the Tools

Before discussing Snowflake vs. Databricks, before debating Power BI vs. Tableau, answer these three questions:

**1. What decisions are your leaders currently making without data?** Not theoretically — specifically. "I don't know our best customer segment by lifetime value" or "I can't tell which product line is actually profitable after overheads."

**2. Where does your data currently live, and how clean is it?** Honest answer. Not "we have a CRM and an ERP" but "our CRM has 4 years of contact records that are 60% duplicated and our ERP has products mapped to the wrong cost centres."

**3. Who will use the outputs?** If the answer is "the analyst who builds the dashboard will also be the only person who reads it," you don't have a data strategy, you have a reporting automation project. Real data strategy enables self-service.

A Right-Sized Architecture for Mid-Market

For most companies in the $20M-$200M range, the right stack looks something like this:

• **Source systems**: CRM, ERP, e-commerce, marketing platform — whatever you already have

• **Data warehouse**: Snowflake Starter Edition or BigQuery (both allow you to start at <$100/month and scale)

• **Transformation**: dbt Core (free, open-source) for modelling clean, trustworthy datasets

• **BI layer**: Power BI (excellent if you're Microsoft-heavy) or Tableau (richer visualisation)

• **Orchestration**: Airflow or Prefect for managing pipelines

Total implementation cost at this scale: $80K-$200K. Monthly running cost: $500-$3,000 depending on data volumes.

The Governance Piece Nobody Talks About

Technical architecture is 40% of the problem. Governance is the other 60% — and it's where most implementations fall apart.

Governance at the mid-market level doesn't mean a 50-page data policy. It means:

• **Agreed metric definitions**: What does "revenue" mean? When is an order "complete"? Who owns these definitions?

• **A data dictionary**: A simple Notion or Confluence page that explains what each key field in your warehouse means and where it comes from.

• **One trusted person responsible**: Not a committee. One named person who owns data quality and is accountable when numbers don't match.

Where to Start

Pick one high-value, high-pain reporting problem. Build a solution for it. Show the business the value. Then expand.

The biggest mistake mid-market companies make is trying to build the whole data platform before proving value. Start narrow, go deep, deliver a win — then scale from there.

*Ready to build a data strategy that actually fits your business? [Book a discovery call](/contact) with the Zenarix Data team.*