… and all those who may be called upon to work with the data professions.

As a CIO, I often have the opportunity to discuss their company’s data skills needs with senior executives. One question frequently comes up: “What’s the difference between a Data Engineer, a Data Analyst and a Data Scientist?” This confusion is understandable, given how similar these professions may appear from the outside. In this article, I’d like to shed some light on the subject.

Understanding the data value chain

To understand these different roles, let’s imagine data as a raw material that needs to be extracted, refined and then transformed into finished products. In this analogy :

• The Data Engineer is the mining operator who extracts and transports the raw material
• The Data Analyst is the chemist who analyzes the composition of this material.
• The Data Scientist is the engineer who creates new products from this material.

The Data Engineer: the data architect

Imagine you’re building a house. Before you can live comfortably in it, you need solid foundations, electricity, plumbing… The Data Engineer plays this fundamental role for your data.

Main responsibilities:

• Set up “pipes” to collect data from different sources
• Ensure that data is clean, reliable and easily accessible
• Build and maintain storage and processing infrastructures

To return to our house analogy, if your analysts and data scientists can’t find the data they need, or if that data is of poor quality, it’s like having a beautiful house but no running water or electricity.

The Data Analyst: the expert who makes sense of data

The Data Analyst is like a detective investigating your data to answer concrete business questions: “Why did our sales drop last month?”, “What are our most profitable products?”, “In which regions should we concentrate our efforts?”

His day-to-day role :

• Analyze data to identify trends and opportunities
• Create dashboards to track important KPIs
• Produce actionable reports and recommendations

The analyst is often the first data profile a company recruits, because he or she brings immediate value by transforming data into concrete business insights.

The Data Scientist: the expert in predictive models

While the Data Analyst looks at the past and present, the Data Scientist projects into the future. Their role is to create mathematical models to predict behavior or automate decisions.

Typical tasks:

• Predicting the risk of customer churn
• Optimize prices in real time
• Detecting fraud
• Recommend customized products

The Data Scientist combines statistical, programming and business skills to create solutions based on artificial intelligence and machine learning.

Complementary roles

The Chief Data Officer (CDO)

It is the strategist who defines the company’s data vision. He or she ensures that data initiatives are aligned with business objectives, and manages governance and compliance aspects.

The Data Product Manager

He bridges the gap between technical teams and business needs. He defines priorities, ensures that data projects create value, and manages their deployment to users.

The Data Architect

He designs the overall technical vision: how the different systems should interact, which standards to use, how to ensure scalability of solutions.

The reality on the ground: blurred boundaries

While this presentation may seem well-ordered, the reality is often more nuanced:

In small structures

The same person can wear many hats. It’s not uncommon to see a “Data Scientist” who also does data engineering and analysis, or a “Data Analyst” who also develops simple predictive models.

In large organizations

Roles can be highly specialized, with Data Engineers, for example, focusing solely on a specific type of data or technology.

Practical tips for building your data team

Where to start?

1. Start with a Data Analyst if you need to better understand your existing data
2. Hire a Data Engineer if you have data quality or access problems
3. Add a data scientist when you’re ready to move on to predictive use cases

Points of attention

• Your organization’s data maturity must guide your choices
• Technical skills aren’t everything: look for profiles who understand your business challenges.
• Encourage versatility in small teams
• Invest in the necessary tools and infrastructure

Conclusion

The data professions form an ecosystem in which each role brings its own specific value. While the boundaries between these roles can be blurred in practice, understanding their differences helps to better structure teams and data projects.

The key is not to focus on titles, but on the real needs of your organization. A small number of versatile profiles can be more effective than a large team of specialists, depending on your context.