80% of AI effort goes into data preparation, not model building. Ask any data scientist and they’ll confirm it. The AI itself is the easy part. The hard part is getting your data into a state where AI can actually use it.
Yet most businesses skip this step entirely. They buy an AI tool, point it at their data, and wonder why the results are wrong. The answer is almost always the same: the data wasn’t ready.
Data foundations carry a 25% weight in the AI readiness framework. That’s more than any other dimension. Here’s why, and how to get it right.
What “Bad Data” Actually Costs You
34% of low-maturity organizations cite data quality as their top AI challenge (Gartner June 2025). But “data quality” sounds abstract until you see what it looks like in practice.
A Greek retail company we assessed had customer records in three systems: their POS, their e-commerce platform, and a CRM that two people used. The same customer appeared as three different records with three different email addresses. Their AI-powered recommendation engine was sending conflicting offers to the same person from different channels.
The result? Customer complaints went up 15%. The AI was working exactly as designed. The data just wasn’t telling it the truth.
The 4 Pillars of Data Readiness
Data readiness isn’t one thing. It’s four things that all need to work together.
1. Data Quality
Is your data accurate, complete, and current? Common problems: duplicate records, missing fields, outdated entries that nobody cleans up. A customer database where 20% of phone numbers are wrong isn’t a minor issue. It’s a foundation that will make every AI model built on it 20% less reliable.
Quick test: Pull 100 random records from your main database. Check for completeness, accuracy, and duplicates. If more than 10% have issues, your data quality needs work before AI.
2. Data Accessibility
Can the people (and systems) that need your data actually access it? We see this constantly in Greek businesses: the finance team has their data in SAP, operations has theirs in custom spreadsheets, sales uses a CRM, and nobody can see the full picture. AI needs to connect these dots. If your data is locked in silos, AI can’t reach it.
Quick test: Ask yourself, “How long would it take to generate a report that combines customer, financial, and operational data?” If the answer is more than a day, you have an accessibility problem.
3. Data Governance
Who owns the data? Who decides what gets collected, how it’s stored, and who can access it? Without governance, you get data chaos. People create their own tracking spreadsheets. Departments define the same metric differently. Nobody knows which version of the truth is correct.
With the EU AI Act requiring transparency in AI decision-making, governance isn’t optional anymore. You need to know where your data comes from, how it’s processed, and how AI uses it to make decisions.
Quick test: Can you name the person responsible for data quality in your organization? If not, governance needs attention.
4. Data Pipelines
How does data flow from collection to use? Manual processes (exporting CSVs, copy-pasting between systems, emailing spreadsheets) create errors and delays. AI needs automated, reliable data pipelines that deliver clean data in real time or near-real time.
Quick test: How much of your data movement involves a person manually transferring information between systems? If it’s more than 30%, you need pipeline automation before AI.
The Data Readiness Maturity Scale
Where does your business sit?
Level 1: Spreadsheet chaos. Data lives in Excel files on individual laptops. No central repository. No naming conventions. “Ask Nikos, he has the latest version.”
Level 2: Siloed systems. You have real software (CRM, ERP, accounting tools), but they don’t talk to each other. Reports require manual data gathering from multiple sources.
Level 3: Connected but messy. Systems are integrated, but data quality is inconsistent. Duplicates, missing fields, and conflicting definitions across departments.
Level 4: Governed and clean. Data ownership is defined. Quality is monitored. Pipelines are automated. You can trust your data enough to let AI make decisions with it.
Level 5: AI-ready. Real-time data flows, automated quality checks, full audit trails, GDPR-compliant, and structured for machine learning. This is where AI delivers real value.
Most Greek SMEs are at Level 1 or 2. Getting to Level 4 is the minimum for successful AI deployment. The good news? You don’t need to reach Level 5 overnight. You need a plan to move up one level at a time.
How to Start: The 90-Day Data Readiness Sprint
Days 1-30: Audit. Map every data source in your organization. Who owns it? What format? How current? How does it connect to other sources? Document everything.
Days 31-60: Clean. Focus on your highest-value data first (usually customer and financial data). Remove duplicates, fill gaps, standardize formats, and assign ownership. This alone can improve operational efficiency by 15-20%.
Days 61-90: Connect. Build automated pipelines between your core systems. Even simple integrations (CRM to accounting, POS to inventory) eliminate manual work and create the connected data foundation AI needs.
ESPA funding can subsidize up to 50% of digital transformation costs, including data infrastructure projects. If budget is a concern, explore the funding options available for Greek businesses.
Don’t Build AI on a Broken Foundation
74% of companies haven’t seen real value from their AI investments (BCG 2025). In nearly every case we’ve assessed, the root cause is the same: the data wasn’t ready. Not the model. Not the team. Not the budget. The data.
Fix the foundation first. Then build the AI.
At Proxima, data foundations are the first thing we assess in every AI readiness engagement. We help businesses map their data landscape, clean what matters, and build the pipelines that make AI actually work.
Related guides: How to Set Up a BI Dashboard People Actually Use and How to Train Your Team on AI Tools.
Let’s Talk about getting your data ready for AI.