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A prerequisite to become truly data-driven

Data Management

Unravel the inherent complexity of your data estate. Understand your data to get more value out of it. 

Data Management is a key enabler for a data-driven organization

Over the last decade, many companies have invested heavily in data platforms, BI tools, ML models, and AI, and in the workforce to build and use these technologies. However, many of these companies are failing to reach the objectives they set out with these investments and are struggling to become the data-driven organizations they’d like to be.  

In our conversations with these companies, one recurring theme has emerged: Data Management. Organizations struggle with many of the following questions: Can we trust our data? What is the source of truth for our data? How are metrics calculated? Why do our pipelines or dashboards keep breaking down? 

When done right, Data Management is the key enabler to becoming data-driven. It will help you 

  • Grasp the semantics, meaning, and interpretation of your data. This includes understanding the context, relationships, and implications of the data, as well as the specific definitions and interpretations of the data elements. The semantics of data is important for ensuring that data is accurately understood and used effectively in various applications and analyses 
  • Unravel the complexity of your data landscape. Working with data is inherently complex. Many applications, processes, and actors are involved, and a legacy is typically built over time. Grasping and understanding this complexity enables you to make informed decisions about your own data and how to manage it.
We truly believe that Data Management is not just a technology problem; buying a tool will not solve it. An approach that addresses people, processes, and technology is required to be successful.


Our Data Management Principles

Data Management used to be a bottleneck, a process that happened in meeting rooms far away from the reality data practitioners experience. We believe in a different approach, applying these principles when implementing Data Management:  

Data management implementation is always use-case driven. This provides early value and educates the stakeholders. 

It comprises people, processes, and technology. Without addressing all of these components, your Data Management effort is likely to fail.

Data Management is pragmatic. It requires a collaborative approach between the business, IT, and data stakeholders. Processes mature as the adoption grows.

It relies on automation and usage of metadata, leveraging the team's technical expertise and bridging the gap between code and governance. 

Data Management should be considered a first-class citizen, get the executive sponsorship and leadership it deserves, and introduce new roles and responsibilities.

It benefits from a Data-as-a-Product mindset. Embracing product thinking requires a change in behavior and ways of working.  

Data Management relies on a definition of gone. There's always an exit plan to make your organization and people ready.

What it takes to translate your strategy into business solutions

Your processes with regards to AI solutions are as important as building them and getting it into production. Ideation workshops, AI literacy trainings, product ownership and management of enterprise-scale projects it's all important. To do this you need the right analytical capabilities, workflows and be ready to scale. All this, with the right foundation in place: A modern data stack and data warehouse.

  • Develop business capabilities to discover and prioritize AI use cases
  • Maninging the AI products and leveling up AI-literacy
  • Developing the right analytics capabilities
  • Setting up the foundations with a modern data stack & data warehouse
The core components of Data Management

Our Data Management Services

Based on years of hands-on experience, we offer you numerous solutions to implement Data Management in your organization.

Maturity Assessment

Xebia has a proven Data Management maturity assessment approach, building on market standards such as DAMA and incorporating the requirements from your organization. We offer you the possibility to:

  • Assess the full organization's Data Management maturity 
  • Review a specific topic, e.g., assess your data lineage capabilities or your data ownership setup  
  • Check regulatory compliance on topics such as GDPR  

Data Management Strategy

We will help you define your Data Management strategy, articulating the main objectives, milestones, and required investments in people, processes, and technology. Some of the results will be:

  • A concrete and pragmatic Data Management strategy ready for execution.  
  • A prioritized roadmap with clear links to the gaps in your current state and how to fill them.  
  • A process to inspire and educate stakeholders to increase understanding and buy-in.  

Data Management MVP

Xebia will kickstart your journey towards Data Management maturity. In this phase, we will work together with different departments in your organization to execute Data Management for several use cases. This process includes:

  • Identifying and selecting use cases that are of strategic importance to your organization.
  • Implementing frameworks and practices within those use cases and show the value of Data Management.    
  • Building bridges between business, data, and IT teams through a collaborative approach. 

Scale Out Implementation

With the first use cases delivered and an increase in understanding and buy-in for Data Management, it’s time for the next phase: organization-wide embedding and mature growth.

  • Identifying and select the use cases that are of strategic importance for your organization.
  • Implement frameworks and practices within those use cases and show the value of Data Management.
  • Building bridges between business, data and IT teams through a collaborative approach.
Customer story

FedEx GDX Consolidates its Global Data Teams to Optimize Digital Practices

With Xebia's assistance, the organization's Global Digital Experience team revamps its data practices to facilitate cross-functional collaboration, boost efficiency, and optimize information utilization.

Customer story

Xebia's Collaboration with RTS for Data-Driven Excellence

Xebia and RTS join forces to enhance media impact through an ethical data strategy, unlocking potential and fostering collaboration.

Customer story

Leveraging Data, AI, and MLOps to Make Work as Easy as Possible for Farmers Across Europe

Together with Xebia Data, agricultural distributor Kramp defines its 3-year data strategy, and kicks it off with a brand-new MLOps platform and first AI use case, resulting in an estimated cost saving of 8 million EURO in the first year.

Customer story

What's the "Secret Ingredient" Making Paula's Choice Customers Glow? Data!

Leading direct-to-consumer skincare brand implemented a data strategy with modern architecture, data platform, and predictive algorithms to personalize and add radiance to its offers.

Customer story

Data-Driven Trading Enables Van Caem Klerks Group To Act Faster and Buy and Sell Quicker

Van Caem Klerks needed to update business processes to become a data-driven company. Through high-level data strategy & execution, they managed to gain speed, productivity, sales, and the ability to make better data-driven decisions.

Customer story

Randstad Building a First-Class Data Science and Data Engineering Organization

Leading staffing company professionalizes its data science and engineering department by improving processes and building internal capabilities

Customer story

Transforming Company Culture Allows Transavia Airlines to ‘Take-off’

Low-cost Dutch airlines optimize internal operations to improve customer experience