Europe's premier service infrastructure provider, generating €11bn in revenue as of Q4 2023, has revolutionized its data projects with Witboost, slashing time to market from 2 years to 6 months.
With over 50 years of market presence, the company diversified into postal, financial, insurance, and utility services, adapting to evolving consumer needs and technology. With 100,000 employees and four interconnected business lines, efficient data control became imperative for synergy and performance optimization.
Recognizing the potential buried within their data lakes and warehouses, the organization sought an enterprise solution. In response, the organization fully embedded Witboost in 2023, now spearheading the adoption of the data mesh paradigm throughout the entire enterprise.
The Challenge
A large enterprise that is imbued in so many aspects of modern day-to-day life has access to vast amounts of data. This data must be stored and processed but also safeguarded, as much of the data contained in their databases is PII data (Personally Identifiable Information).
The enterprise faced the challenge of efficiently locating data for specific use cases. Data teams often needed to navigate multiple channels, engaging various project managers for access and details. This process was not only time-consuming but also prone to complexities in data interpretation before it could be utilized for Business Intelligence. Amplify this scenario across numerous projects, and the scale of the challenge becomes apparent.
Such a long and arduous process meant a very long time to market which in turn led to data lagging behind the speed that the business required. This predicament swiftly evolved into a pervasive challenge that permeated through various departments and functions, necessitating a comprehensive solution. In an effort to expedite time to market and enhance flexibility in managing data projects, leadership identified impediments in consolidated processes intricately woven into various technologies, particularly grappling with legacy solutions in their database.
Internal attempts to address these challenges proved futile, primarily due to the extensive time frame estimated for the development and implementation of in-house technology solutions, which stood at a staggering minimum of three years. Compounding the urgency for intervention was the profound business cost associated with maintaining siloed data repositories. A recent study conducted by IDC Market Research underscored the dire consequences of erroneous or isolated data, revealing potential annual revenue losses of up to 30% for large enterprises grappling with such issues.
Consequently, central to the client needs was the imperative to provide data consumers with a seamless discovery experience, demolishing siloed barriers and offering a unified view of the expansive data ecosystem. Simultaneously, the enterprise sought to empower data producers by expediting the time-to-market for their projects, thereby enhancing their agility and responsiveness. Furthermore, seamless integration with existing technologies emerged as a critical requirement, ensuring minimal disruption to ongoing operations while facilitating the adoption of novel solutions. Finally, recognizing the indispensable role of governance in maintaining data integrity and compliance, the Data Platform Team was seeking a solution capable of expediting post-runtime governance policy checks. This imperative aimed to streamline the regulatory landscape, ensuring adherence to industry standards and mitigating potential risks associated with data mismanagement.
To further their data driven transformative journey, the company decided to explore the Data Mesh paradigm. This, however, posed intricate challenges, demanding cultural and organizational shifts. The complexity escalated with the absence of specialized tools supporting the connection of siloed data within a data mesh or hybrid architecture founded on the four core principles:
- Domain Oriented Ownership for unifying business domains autonomously
- Self-Service Data Infrastructure as a Platform for automated provisioning
- Federated Computational Governance for enforcing data governance through computational policies
- 'Data as a Product' thinking for standardizing data as a product
The Solution
By implementing Witboost, the enterprise managed to unify all business domains into a single view and accelerate their data practice with pre-defined templates, computational governance capabilities and intuitive UI to manage the entire data lifecycle.
In order to address the need to adopt the Witboost platform while maintaining current day-to-day operation levels, data teams created a set of requirements and standards. These were natively incorporated by Witboost, which adopted the enterprise’s inherent data practices and architecture, with zero lag time to connect with the current tech stack.
It allowed the company to customize the platform to their needs as well as stay on top of upcoming changes and ensure these would reflect their internal compliance standards. In fact, the Governance Team used Witboost’s computational governance capabilities to create computational policies that respected first and foremost their PII data regulations as well as industry standards, throughout the data’s entire lifecycle.
This formed the foundation upon which the data team could now start mapping and creating their data projects. Using Witboost’s native development features, such as the built-in and re-usable templates, self-service capabilities ensured the enterprise could start creating data components immediately, with minimum onboarding and effort required. This resulted in a first use case that was able to be developed under the correct compliance requirements, within just six months.
Finally, Data Consumers were also able to automatically access and discover business-relevant data, which now brought to light available opportunities that lay hidden before.
The Results
The decision to adopt Witboost yielded substantial risk reduction, estimated at over €10 million.
This figure stands in stark contrast to the projected costs and risks associated with pursuing an in-house solution.
By leveraging Witboost, the enterprise not only mitigated potential financial risks but also gained access to a proven, scalable solution tailored to their specific needs, thereby fostering greater efficiency and competitiveness in the market.
With Witboost, our client created a business-driven data discovery ecosystem that crawls enterprise data and retrieves it as a data product, ready to be used for insights generation.
Data Consumers, the Governance Team, and the Platform Team can now find out what data they have available (SLAs, SLOs, and data quality) and how to access it with minimal delays.
This has saved tens of hours for a single employee. Zooming out, this led to hundreds of hours saved on data projects across the organization.
With Witboost, a once siloed and fragmented data ecosystem became interconnected within a single, standardized platform. It sped up the customer’s ability to create a data mesh solution from what would have been a minimum of 2 years to just 6 months. This led to the validation of the Data Mesh paradigm and its implementation across the entire organization.
Any data practitioner within the enterprise can now:
- Discover data and collect user feedback on it
- Create computational governance policies that act both at deploy and run time
- Collect feedback in a unified location for better auditing and monitoring
Specifically, each team can function in a more streamlined and efficient manner, with both data production and people onboarding costs being significantly reduced:
- Data Producers are more productive thanks to Templates that help save time and skill required to build data components
- The Data Platform team can now perform automatic policy checks
- Data Consumers can easily discover, trust, and access data projects thanks Business Driven Business Discovery
As the customer continues towards making Witboost ubiquitous in its data ecosystem, they anticipate exploiting the platform to achieve significant advancements in several other key areas, such as finding new revenue streams and improving acquisition, retention and enrichment.
While the platform aids in reducing operational and overhead costs and will support in saving on audit fees, the company has already defined a yearly roadmap with the intent to broaden Witboost’s adoption in other data practices that involve laboratory environments, DWH, and machine learning algorithms.
We’re excited to follow their accelerated journey with Witboost!
Discover how Witboost can transform your organization's data lifecycle management and enable business-driven data discovery
