Why 80% of data projects fail (and how do you succeed with yours?)

The pitfalls to avoid and the keys to turning your data into success

AI and data are now at the heart of business strategies. Everyone wants to use their data, automate tasks, and make smarter decisions. However, a striking reality persists: the majority of data projects do not reach their goals.

According to Gartner, 85% of data projects are doomed to failure due to a lack of a clear strategy and effective governance. For its part, the Standish Group, in his Chaos Report, believes that only 29% of IT projects succeed, all methodologies combined.

Why such a failure when investments in AI and data are exploding? And above all, How do you avoid being part of the 85%?

The mistakes that kill a data project before it even starts

1. The lack of a clear vision

Many companies embark on AI projects without a specific objective. “We need AI” or “We need to use our data” are not strategies, but trends that are followed without careful thought.

The problem? Without a clear business purpose, the project lacks direction and risks never being adopted or not generating the expected return on investment.

How to avoid it?

• Define a measurable goal before thinking about technology.
• Examples: reduce churn by 15%, automate report production, optimize inventory to save €100,000 per year.
• A data project must be driven by a business need, not by a technological trend.

2. Unusable or poor quality data

One of the biggest pitfalls is wanting to build an ambitious project on shaky foundations.

If data is incomplete, duplicated, or poorly structured, AI won't be able to produce reliable analytics. Even worse, it risks generating biased or erroneous recommendations.

How to avoid it?

• Spend time cleaning and structuring data before any analysis.
• Set up an automated data pipeline to ensure consistent quality.
• A good data project always starts with a clean and well-organized database.

3. A lack of support from business teams

A data project should not only be an IT department project. If it does not meet the expectations of business teams (sales, finance, supply chain...), it will remain unused.

The problem? A powerful dashboard but left out because it was considered too complex or unsuited to the needs of the field.

How to avoid it?

• Involve users from the start to understand their real needs.
• Test quickly with a prototype and adjust with their feedback.
• Train teams to adopt the tool and integrate it into their daily work.

A successful data project is above all an adopted project.

4. Poor integration into existing tools and processes

A powerful prediction algorithm is useless if it has to be used manually in an Excel file.

The problem? If the tool is not integrated with business tools, it will not be used.

How to avoid it?

• Integrate the results directly into existing tools (CRM, ERP, Metabase, Power BI...).
• Automate the generation of insights to avoid manual manipulations.

A good data project must be integrated naturally into the daily life of teams.

5. A lack of follow-up and adjustments

Launching a data project is one thing. Making it evolve to remain relevant is another.

The problem? Many businesses deploy a project and then let it run unattended. Result:

• Models that lose precision.
• Insights that are no longer relevant.
• An adoption that crumbles over time.

How to avoid it?

• Monitor project efficiency indicators (utilization rate, business impact, model accuracy).
• Continuously improve thanks to user feedback.
• Set up a dedicated team to ensure the evolution of the project.

A data project is never static, it must evolve with the business.

How to ensure the success of your data project?

Now that the errors have been identified, here is a five-step road map:

1. Define a clear and measurable goal.
   A project must respond to a business problem, with precise indicators.
2. Clean and structure the data beforehand.
   AI does not work miracles: quality data is essential.
3. Involve business teams from the start.
   If they don't see the value of the project, they won't use it.
4. Integrate the solution with existing tools.
   A good data project must be fluid and easily accessible.
5. Ensure regular monitoring and improvement.
   A successful data project is a living project.

Conclusion

If you want your data project to be part of 15% who succeed, the main thing is to adopt a pragmatic approach, focused on business impact and real use.