Module M3 · Data Literacy for Business Teams · 2026
Module 3 · Half day

Data Literacy
for Business Teams

Understanding data without being a data scientist. Identifying key data within your scope. Becoming an active contributor to data quality — and understanding why every employee is an essential link in the data value chain.

Learning Objectives
01Understand what business data is and why it matters
02Identify key data within your business scope and its classification
03Recognize poor quality data and understand its consequences
04Know your role in governance and the best practices to adopt
All Employees Business Managers Designated Data Owners Compliance Teams Sales & Client Relations
Pejman Gohari · CDO · Chief AI Officer · ORBii
Author DUNOD 2020/2022/2024 · Professor IESEG Business School
academy.orbii.tech
ORBii.Academy · M3 · Data Literacy for Business TeamsConfidential · 202601
M3 · Data Literacy for Business Teams · 02
Introduction

Why this module — What "data literacy" truly means

"Poor quality data doesn't come from servers. It comes from us — from a hasty entry, an ignored field, an Excel file emailed rather than entered in the proper system. Data quality starts on the ground, not in the IT department."
— Pejman Gohari · CDO · Chief AI Officer · ORBii

The fundamental misconception

In most organizations, business employees think data is "IT's responsibility." IT thinks data quality is "the business's responsibility." The result: nobody truly owns it.

Data literacy is not about asking every employee to become a data scientist. It is about giving them the 3 fundamental reflexes that make the difference:

1
Recognize data

Understand what the data in your business scope is, how it is used beyond your screen, and why its accuracy matters.

2
Protect data

Know which data is sensitive, how to handle it with care, and what must never be done (copy-paste into an email, send to an external LLM, etc.).

3
Contribute to quality

Know how to flag an anomaly, correct erroneous data, use the proper reference system — rather than "making do" or creating a parallel file.

4 personas — The employees this module must reach

Client Advisor · Retail
Marie, age 34
Retail Banking Division · 8 years tenure
"I enter client data in the CRM but I don't understand why certain fields are mandatory. Sometimes I put 'N/A' to speed things up."
Compliance Officer
Thomas, age 41
Compliance & Risk Division · 5 years tenure
"I produce my regulatory reports from Excel extracts. I don't always know if the data I'm using is current or validated."
Team Manager · Middle Office
Camille, age 38
Operations Division · 12 years tenure
"My team uses 3 different tools for the same client. We often have discrepancies. We don't know which system 'is right'."
Credit Analyst · Corporate
Romain, age 29
Corporate & Investment Banking Division
"I use financial data that I receive by email. If it's wrong, I don't necessarily realize it until after I've submitted my analysis."
ORBii.Academy · M3 · Data Literacy for Business TeamsConfidential · 202602
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