About our customer

Stellantis is a leading global automaker and mobility provider that offers clean, connected, affordable, and safe mobility solutions. The firm meets the evolving needs of customers in more than 130 markets, as they embrace electrification, connectivity, autonomous driving, and shared ownership.

Stellantis was founded by visionaries who fused passion and competitive spirit into 14 automotive brands, ranging from luxury, premium, and mainstream passenger vehicles to pickup trucks, SUVs, and commercial vehicles. In 2023, the manufacturer’s portfolio includes Abarth, Alfa Romeo, Chrysler, Citroën, Dodge, DS Automobiles, Fiat, Jeep®, Lancia, Maserati, Opel, Peugeot, Ram, Vauxhall, Free2move, and Leasys.

Context

Stellantis forms a unique but dynamic and diverse ecosystem, being the result of the merger of various brands from different countries and data cultures. As a consequence, the firm handles plethoric volumes of data coming from various locations, plants, buildings, vehicles, supply chains, partners, customers, and so.

Yet, Stellantis seeks to leverage this diversity and take this opportunity to better innovate and gain a competitive advantage in the fast-changing automotive and mobility industry. Indeed, the firm’s main objective is to grow from a pure automotive company to a tech firm.

Challenge

Becoming a tech firm can only be achieved by getting a clear and complete understanding of all the data assets at hand, ie. with effective data governance. In fact, data governance became essential to support Stellantis’ strategic goals:

Becoming agile and competitive: help the entire organization to better know their data and its quality to generate new ideas and use cases to explore.

Being global: use data to break silos, sharing common definitions for all business users, regardless of the market or brand.

Democratize data: know where the data is, and how data assets are linked to each other.

Satisfy customers: protect the data that customers are willing to share with the best security standards to maintain their trust and design the best services.

To achieve effective data governance, Stellantis needed tools.

In fact, Stellantis was targeting to converge all their data assets into three main data platforms: Snowflake as their data lake, databricks as their high computational database to process data, and Palantir for end users to exploit the firm’s data. In parallel, they were using an internal “Wikipedia” for data called WikiData that would be supported by the data catalog, and act as a business glossary.

But in reality, lots of other platforms were still being used throughout the organization (Oracle, Greenplum, Google Analytics, etc. ). Thus, a data catalog naturally became a must-have, ie. the necessary tool to connect to all these data platforms and get the best out of them before the targeted convergence.

Solution

The firm chose Zeenea’s data catalog to meet this challenge. In fact, Zeenea’s solution was fulfilling all the specific requirements defined by Stellantis:

Pure and simple: Zeenea Explorer is a user-friendly and easy-to-use web app for all business/end users in the organization;

Non-intrusive deployment: so are Zeenea’s agents that are deployed at and by the firm;

Connectivity: Zeenea offers dozens of native connectors to a wide range of data sources and tools;

Full Cloud: so is Zeenea Data Discovery Platform;

Metadata only: Zeenea only centralizes and synchronizes metadata, not data;

Human and dedicated vendor: Zeenea’s Customer Success team works closely to meet the specific needs of Stellantis’ data teams and provide the best support possible on a daily basis.

Moreover, Zeenea is not just a data catalog at Stellantis. It also motivates business users to spend time with the solution as it provides additional value to them. In fact, the Data Discovery Platform adds value in terms of: 

Security & privacy: the identification of data confidentiality. With Zeenea, business users can easily mark confidential and personal data to activate additional security measures.

Use Case Portfolio: a unique and global repository of all use cases mapped to a dataset. While navigating into the company’s WikiData, business users can leverage synergies with existing use cases to avoid duplicated work.

Data Coverage: the identification of data gaps between the regions and brands. It enables a global vision of their KPIs.

Transparency & compliance: end users know where the data is, its quality, and how it can be used, ensuring full data compliance (purpose vs. legal basis).

Automation: automation of various tasks based on the data catalog. For example, access all data collected from a specific customer.