Data Quality: The Practical Guide to Reliable Data in 2025

Why Data Quality is Critical in 2025

In 2025, 92% of companies consider data quality a strategic priority, up from 75% five years ago. And for good reason: poor quality data costs companies an average of $12.9 million per year (Gartner).

At Datakhi, we have developed strong expertise in data quality governance. Our pragmatic approach enables companies of all sizes to structure their Data Quality approach, from initial audit to implementing sustainable governance. We support both SMBs starting their data journey and large groups looking to industrialize their quality processes.

What is Data Quality?

Data Quality measures data's ability to fulfill its function in a given context. Data is not "good" or "bad" in absolute terms: it is either suitable or not for its use. This is why quality must always be evaluated against specific business needs.

Concrete Example

An email address with a typo:

• For marketing analysis: acceptable (the domain remains usable)
• For sending emails: critical (the email won't be delivered)

The 6 Dimensions of Data Quality

Each dimension represents a different aspect of quality. Understanding these dimensions helps precisely identify where to act and how to measure improvements.

1. Accuracy

Accuracy measures whether data corresponds to the reality it's supposed to represent. It's often the most visible dimension because accuracy errors have a direct impact on decisions.

Example problem: In your CRM, 15% of customer postal addresses are incorrect. Result: your marketing mailings come back as undeliverable, you waste budget and damage your reputation with postal services.

Datakhi solution: We implement an address validation process via the National Address Database API during input, and a batch cleaning process to correct existing data. We also define rules for automatic detection of inconsistencies (postal code vs city).

2. Completeness

Completeness evaluates whether all necessary data is present. Empty or missing fields can block entire processes or skew statistical analyses.

Example problem: 40% of your product sheets have no description or photo. Consequence: these products are invisible on your e-commerce site (poorly referenced) and generate fewer sales.

Datakhi solution: We create a completeness tracking dashboard by product category, with automatic alerts for catalog managers. We define mandatory fields by product type and implement validation workflows before publication.

3. Consistency

Consistency verifies that the same data is identical across different systems. To achieve this, you need to define a data repository (Master Data) that centralizes your reference data, and identify your Golden Data: the unique, validated and certified version that is authoritative throughout the company.

Example problem: The customer "Dupont SAS" exists in your CRM, ERP and marketing tool with different addresses and spellings. Nobody knows which version is correct, invoices go to the wrong address.

Datakhi solution: We implement a customer repository (MDM) that centralizes your Golden Data and automatically feeds all your systems. A validated change propagates everywhere, with reconciliation checks in case of discrepancy.

4. Timeliness

Timeliness measures whether data is recent enough for its intended use. Accurate but outdated data can be as problematic as incorrect data.

Example problem: Your inventory report is updated once a week. In the meantime, you sell products that are no longer available, generating order cancellations and unhappy customers.

Datakhi solution: We analyze the necessary frequency by data type and implement appropriate refresh pipelines. For inventory, we implement real-time or near real-time updates with Microsoft Fabric Real-Time Intelligence.

5. Uniqueness

Uniqueness ensures that the same entity is not recorded multiple times. Duplicates skew statistics and generate embarrassing multiple communications.

Example problem: A customer receives the same promotional email 3 times because they exist in triplicate in your database (with variations: "John Smith", "J. SMITH", "john.smith@email.com"). They unsubscribe, annoyed.

Datakhi solution: We implement a deduplication process based on business matching rules (phonetic comparison, address normalization). We also implement input controls to detect potential duplicates before creation.

6. Validity

Validity verifies that data respects defined formats and business rules. Invalid data can block automated processes or generate silent errors.

Example problem: Your "date of birth" field accepts any value. Some customers have a date of birth on February 30th or in the year 3025. Your age segmentation is skewed and your targeted campaigns miss their mark.

Datakhi solution: We define business validation rules (format, acceptable value ranges) and implement them at two levels: front-end to guide the user, and back-end to guarantee integrity. Existing invalid data is flagged for manual correction.

Data Quality Best Practices by Company Size

Not all companies have the same resources or needs. A 10-person startup cannot implement the same governance as a Fortune 500 company. Here are our tailored recommendations.

For Small Structures (fewer than 50 people)

When starting out, the key is to lay the right foundations without drowning. You probably don't have the means to have a dedicated Data Owner, and that's normal.

Where to Start?

Start by identifying your 3 to 5 most critical data for your business: typically customer data, product/service data, and financial data. Focus your efforts on these only.

Hold each business lead accountable for their data quality. The financial controller is responsible for financial data, the sales manager for customer data, the product manager for the catalog. The person who uses the data daily is best positioned to ensure its quality.

Implement simple but systematic controls: format validation at input in your forms, duplicate checking with Power BI and automation flows, or even setting up a database. No need for expensive tools to start.

What Datakhi Can Do for You

We offer flash audits to quickly identify your main quality problems and give you a prioritized action plan. We can also help you set up simple automated controls in your existing tools (Power BI, Excel, CRM).

For Medium-Sized Companies (50 to 500 people)

At this size, data starts flowing between multiple teams and systems. Governance becomes necessary to avoid chaos.

Structuring Governance

It's time to appoint formal Data Owners for each data domain (customers, products, HR, finance...). These business-side people are responsible for defining what "correct" data means and validating quality rules.

Create a Data Steward (even part-time): this is the person who operates daily quality controls, manages anomalies and liaises between IT and business.

Formalize your business rules in a shared document (data dictionary) and set up quality KPIs tracked monthly.

What Datakhi Can Do for You

We support the implementation of your governance framework: role definition, data dictionary creation, automated control implementation and monitoring dashboards. We also train your teams in best practices.

For Large Companies (more than 500 people)

At scale, data quality must be industrialized with formalized processes and dedicated tools.

Recommended Organization

Set up a dedicated Data Quality team reporting to the data department or CDO. This team defines standards, tools controls and supports business.

Each business department must have an identified Data Owner accountable for measurable quality objectives.

Invest in professional Data Quality tools (Informatica, Talend, or native Microsoft Fabric/Purview capabilities) to automate controls at scale.

What Datakhi Can Do for You

We work on complete Data Quality transformation programs: maturity audit, organizational target definition, tool selection and implementation, change management. We can also provide Data Quality consultants on assignment.

Data Quality Documentation: The Fundamentals

Good governance relies on clear, maintained documentation. Without documentation, rules stay in people's heads and disappear with turnover. Here are the essential documents to implement.

The Data Dictionary

This is the reference document that describes each piece of data in your information system. For each data item, it specifies:

• Technical and business name: "customer_id" = "Unique customer identifier"
• Functional description: What this data is for, how to interpret it
• Format and type: 50-character text, integer, date in DD/MM/YYYY format
• Possible values: Closed list or acceptable value ranges
• Source of truth: Which system is authoritative in case of conflict
• Data Owner: Who is responsible for this data

The Data Catalog

Broader than the dictionary, the catalog lists all datasets available in the company. It allows users to discover what data exists and where to find it. A good catalog includes:

• List of available tables, files and APIs
• Data lineage (where it comes from, where it goes)
• Technical and functional metadata
• Access rights and sensitivity classifications

Tools like Microsoft Purview or Atlan automate catalog creation and maintenance.

Data Quality Rules

This document formalizes the controls applied to data. For each rule, we specify:

• What is verified: "The postal code must match the city"
• The dimension concerned: Consistency, validity, etc.
• The alert threshold: At what percentage of errors we act
• The corrective action: Who does what in case of anomaly

Data Lineage

Data Lineage traces a data item's complete journey: where it comes from, what transformations it undergoes, and where it's used. It's essential for quality because it allows tracing back to the source in case of problems and evaluating the impact of a change.

• Data origin (source system, file, API)
• Applied transformations (calculations, aggregations, joins)
• Final destinations (reports, dashboards, applications)
• Dependencies between data

Data Contracts

A more recent concept, Data Contracts formalize the commitment between a data producer and its consumers. They define:

• The expected schema (columns, types, constraints)
• The guaranteed quality level (freshness, minimum completeness)
• The SLA (availability, response time)
• Each party's responsibilities

This approach is particularly useful in Data Mesh architectures where multiple teams produce and consume data.

Data Quality Testing: Technical and Functional

Too often neglected, tests are the key to lasting quality. Like application code, data must be systematically tested. We distinguish two types of tests.

Technical Tests

These tests verify the structural integrity of data, regardless of their business meaning. They are generally automated in data pipelines.

Schema Tests

Verify that data structure is compliant: expected columns are present, data types are correct, uniqueness and foreign key constraints are respected.

Volume Tests

Detect volume anomalies: a table that no longer receives data, an abnormally small or large file, a row count that drops suddenly.

Freshness Tests

Verify that data is being updated: the last modification date doesn't exceed a defined threshold, the pipeline executed on time.

Recommended Tools

Great Expectations (open source, Python) and Soda (SaaS) are the references for implementing these tests. In Microsoft Fabric, native Data Observability capabilities also allow monitoring these aspects.

Functional Tests

These tests verify that data makes business sense and respects rules defined by Data Owners. They require domain knowledge.

Business Value Tests

Verify that values are consistent with business context: a customer age is between 18 and 120 years, a selling price is higher than the purchase price, a delivery date is after the order date.

Cross-System Consistency Tests

Compare data between different sources to detect discrepancies: physical stock matches computer stock, CRM revenue matches accounting revenue.

Regulatory Compliance Tests

Verify compliance with legal obligations: personal data is properly pseudonymized, retention periods are respected, consent is tracked.

Integrating Tests into Your Pipelines

Tests must run automatically with each data load. In case of failure, the pipeline must:

1. Alert those responsible (email, Slack, Teams)
2. Block propagation of corrupted data (or quarantine it)
3. Log the anomaly for later analysis

This "shift left" approach enables detecting problems early, before they impact end users.

Implementing a DQM Approach

Phase 1: Diagnosis

Before acting, you must understand. This audit phase provides a clear view of the current situation.

1. Identify business-critical data
2. Measure current quality on each dimension
3. Identify root causes of problems (processes, tools, training)
4. Estimate the financial impact of quality problems

Phase 2: Remediation

Once the diagnosis is made, we take action to correct existing problems.

1. Clean existing data (standardization, enrichment)
2. Deduplicate databases with business-validated matching rules
3. Correct inconsistencies between systems
4. Document corrections to prevent regressions

Phase 3: Prevention

Remediation isn't enough: without prevention, problems will return. This phase implements lasting quality.

1. Implement automatic controls in pipelines
2. Train teams in input and management best practices
3. Set up ongoing quality KPI tracking
4. Establish regular quality reviews with Data Owners

The ROI of Data Quality

Investing in data quality generates measurable and rapid ROI. Companies that structure their quality approach see on average:

• -40% time spent correcting errors and reconciling data
• +25% user confidence in reports and analyses
• -60% errors in automated business processes
• +15% marketing campaign effectiveness through better targeting

Beyond the numbers, good data quality improves decision-making at all levels of the company and strengthens confidence in the data strategy.

Conclusion

Data Quality is no longer optional in 2025. With the explosion of data volumes and AI adoption, data quality becomes the foundation of any data-driven strategy. AI trained on poor quality data will produce mediocre, even dangerous results.

Whatever your company size, progress is possible: the important thing is to start with realistic goals and progressively build your maturity.

Need a quality diagnosis of your data? Contact our Data Quality team for support tailored to your context and budget.