Data quality & data cleaning (B2B)

In a world where decisions are increasingly based on data, the quality of that data is crucial. Incomplete, duplicate, or outdated information can lead to wrong conclusions, missed opportunities, and loss of revenue. For medium and large B2B companies (SME) that want to integrate data and AI into their organization, getting data in order is not a side issue but a strategic necessity.

Data cleaning – also known as data cleansing or data scrubbing – is the process of checking, cleaning, and enriching data to deliver maximum value.

What is data quality and why is it important?

Data quality refers to the accuracy, completeness, consistency, and relevance of data. High-quality data enables reliable analyses and better decision-making.

Key criteria for data quality

Accuracy: The data is factually correct.
Completeness: All required information is present.
Consistency: No contradictions between data sources.
Timeliness: Data is up to date.

Example: A B2B software provider that sends email campaigns based on outdated customer data sees open rates drop by 40%.

The role of data cleaning in commercial growth

Data cleaning is essential for improving data quality and prevents poor data from slowing down or damaging commercial processes.

Benefits for B2B organizations

Better targeting in marketing campaigns
More efficient sales processes through up-to-date customer information
Higher conversion rates through more relevant communication
Fewer operational errors and misunderstandings

According to Experian, 91% of companies estimate that poor data quality has a direct impact on their revenue.

Would you like to improve your data quality and maximize value from AI and automation? Schedule a consultation with Sales Improvement Group.

Step-by-step plan for data cleaning

Data audit: Analyze current data for errors, duplicates, and gaps.
Set rules: Define quality criteria and validation rules.
Clean: Remove or correct incorrect and duplicate data.
Enrich: Add missing information using internal or external sources.
Validate: Check whether data meets quality standards.
Monitor: Set up processes for periodic quality control.

Step	Goal	Result
Data audit	Gain insight into current state	Overview of improvement areas
Cleaning	Remove errors and duplicates	Consistent dataset
Enrichment	Add valuable information	Complete and usable data

Technologies and tools for data quality

Use cases

Use case 1: IBM – cleaning global CRM data

IBM discovered that 30% of their CRM data was outdated or incomplete. By launching a global data cleaning project, where AI tools removed duplicates and verified contact details, data quality improved by 85%. This resulted in a 16% higher conversion rate in their B2B campaigns.

Source: IBM Data Quality Solutions

Use case 2: Adobe – enriching the marketing database

Adobe integrated external data sources with its marketing CRM to fill in missing information about leads. Thanks to this enrichment, campaigns could be better personalized, leading to 25% higher open and click rates in email campaigns.

Source: Adobe Customer Data Solutions

Use case 3: Siemens – standardizing production data

Siemens had data coming from different factories worldwide, with varying formats and standards. By combining a data cleaning program with standardization protocols, Siemens was able to analyze and forecast faster, increasing production efficiency by 18%.

Source: Siemens Industrial Data

Checklist: does your data score high on quality?

Are contact details up to date and correct?
Are there no duplicate records in your CRM?
Is customer information completely filled in?
Is data regularly checked and updated?
Are all data sources consistent with each other?

4 or more “yes” answers? Then you’re on the right track. Fewer? Time for action.

Frequently Asked Questions

What is the difference between data quality and data cleaning?: Data quality is the state of the data; data cleaning is the process to improve that quality.
What does data quality mean?: The extent to which data is accurate, complete, current, and consistent.
What does data cleaning mean?: Cleaning data by correcting errors, removing duplicates, and filling in gaps.
What is the difference between data maintenance and data cleansing?: Data maintenance is continuous management; data cleansing is a targeted action to improve data.

Contact us

Ready to make your data clean, complete, and reliable – and lay the foundation for better decisions? Contact Sales Improvement Group.

By: Aynsley Romijnsen