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.
Watch our webinars and read about Data Management in these resources
Our Data Management Principles
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
Our Data Management Services
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.
Upskilling
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.
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.
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.
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.
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.
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
Transforming Company Culture Allows Transavia Airlines to ‘Take-off’
Low-cost Dutch airlines optimize internal operations to improve customer experience