Why data quality is your best performance driver

Raw data is no longer enough

In the era of Data-driven decisions, businesses collect thousands or even millions of information points every day. Customer data, products, stocks, sales, digital interactions... Sources are multiplying.

But this abundance does not guarantee relevance. According to a Gartner study, more than 80% of data projects fail or do not meet their goals due to poor data quality.

Before investing in solutions ofartificial intelligence, of Business intelligence Or of prediction, it is essential to lay a solid foundation: data reliable, Coherent and operated intelligently.

What is quality data?

One quality data meets five fundamental criteria:

• Complete : it does not have any missing essential fields (e.g. email, product code, price).
• Exact : it accurately reflects reality at a given moment.
• Up to date : it is updated regularly, without obsolescence.
• Coherent : it is aligned with all systems (ERP, CRM, dashboards).
• Relevant : it is useful in the business context (marketing, logistics, finance...).

These pillars guarantee the reliability of analyses, the relevance of automations, and confidence in your internal tools.

The consequences of poor data quality

One Uncontrolled data has repercussions at all levels of the business. Among the most frequent impacts:

• Reporting errors (e.g. overvalued sales, incorrect margins)
• Ineffective marketing campaigns (sending to wrong segments or wrong addresses)
• Decisions based on flawed interpretations
• Repeated tasks or manual corrections, synonyms for waste of time
• Loss of credibility internal dashboards

Concrete example : an e-commerce company that does not make its product sheets reliable risks having inadequate recommendations, stock errors or avoidable customer returns. The result: lower conversion, higher costs.

Key steps to improve the quality of your data

A good quality approach is based on three operational pillars:

1. Audit of existing data

Start with a full mapping of your databases:

• Which fields are most often incomplete?
• Where are the duplicates located?
• Are your repositories shared across departments?
• Are your formats consistent (e.g. dates, country codes, currencies)?

The objective: prioritize critical areas and assess the “data maturity” of your organization.

2. Cleaning and standardization

Once the audit has been completed, it is time for action. This involves:

• The removal or merging of duplicates
• The correction of outliers
• THEharmonization of formats
• THEdata enrichment (e.g.: geolocation, INSEE codes, coordinates)

These tasks can be automated using dedicated tools or assigned to experts in Data management.

3. Governance and ongoing monitoring

Set up rules of data governance makes it possible to anchor good practices over time:

• Clear definition of roles (data owner, data steward...)
• Validation of business rules (mandatory values, thresholds, standards)
• Establishment of indicators of data quality (completeness rate, error rate)
• Integration of automatic controls and alerts

Reliable data then becomes a Active living, reassessed regularly.

Data quality and AI: a virtuous circle

The solutions of AI-assisted data quality make it possible today to process large volumes in record time:

• Automatic detection of inconsistencies
• Suggested corrections via learning models
• Enrichment of data by crossing with external sources (API, Open Data...)

But these tools don't replace humans: they Complete to structure solid, scalable and understandable bases for all.

Conclusion: clean data is a performance accelerator

Do you want to manage your activity in real time, automate your reporting or deploy predictive models? Start with the basics: make your data reliable.

It is a discreet but decisive investment that determines the success of all your future projects.