Client Overview
A major international retailer with over 180 000 employees operating across multiple markets. The organization manages a highly complex data landscape with numerous systems, diverse data sources, and distributed analytics teams. As part of its data transformation journey, the client aimed to streamline how employees discover, evaluate, and access data.
Challenge
The client faced a common challenge seen in large enterprises: employees did not know where to find data or how to request access to it.
Data was scattered across systems, inconsistent in documentation and quality, and the process of obtaining access was:
- Not standardized
- Not traceable
- Dependent on manual communication (primarily email)
- Slow and difficult to automate
- A burden for data owners and supporting teams
This led to:
- Long request lead times
- Repetitive, unnecessary back-and-forth communication
- Lack of clarity on which datasets fit a specific use case
- The risk of using outdated, incomplete, or incorrect data
- Inefficiencies that impacted both business and data teams
The client needed a One-Stop Shop for data: a unified experience where users could search for data, understand whether it fits their purpose, review a sample, initiate onboarding of missing sources, and request access, all in one place.
Solution
The project was delivered jointly with a consulting partner responsible for the business process definition, interviews, and user journey design.
Murdio was fully responsible for the technical implementation of this use case in Collibra. Murdio’s implementation included:
1. Custom Workflow Development in Collibra
Murdio team designed and built four key workflows covering all stages of the data shopping process:
Data Discovery Workflow
A business user describes their use case and requests help identifying suitable datasets. The request is routed to a dedicated data consulting team who reviews available data and recommends the right assets. The requester reviews the suggested datasets and confirms whether they fit.
Data Provisioning Workflow
If the required data is not yet available or is being procured from external sources, this workflow guides the responsible team through the onboarding process: describing new dataset, documenting it in the database, publishing in Collibra and ensuring it becomes discoverable and usable across the organization.
Data Sampling Workflow
Once the dataset is identified, the user can request a sample to validate whether the data meets their needs. Murdio enabled Collibra’s native sampling feature on top of masked (non-sensitive) data. The user receives a sample, reviews it, and decides whether to proceed. Sampling access expires automatically, ensuring data governance compliance.
Data Accessing Workflow (in progress)
The final step: the user requests actual access to the dataset.
2. Collibra Configuration & Metamodel Design
Murdio prepared a tailored Collibra configuration:
- Metamodel adjustments
- Custom views and diagrams
- Role and responsibility assignment
This ensured a consistent, intuitive experience for business users and data owners.
3. Implementation of Collibra Data Sampling
This included:
- Integrating Collibra with masked data sources
- Enabling sampling capabilities
- Configuring connections
- Solving technical challenges related to environment security and data masking
- Implementing workflow to allow users request access to masked sample data
Team Composition & Roles
Murdio team consisted of 2 experts:
- Workflow Developer & Solution Architect: Responsible for workflow design and implementation, metamodel configuration, and Collibra solution architecture.
- Integration Engineer: Responsible for technical configuration, data integrations, and enabling Collibra sampling.
The small, specialized team delivered a complete end-to-end technical solution. They are part of a bigger Colibra technical implementation team delivering work for this particular client. You can read the full case study here.
Outcome
The client now has a unified, automated process that supports the full lifecycle of working with data, from initial discovery to actual access.
Key benefits:
- One centralized process for discovering, provisioning, sampling, and accessing data.
- Full transparency – each request automatically becomes a Collibra asset, enabling reporting.
- Significantly reduced lead times by removing manual communication and streamlining approvals.
- Better data quality decisions – users validate datasets early through sampling.
- Reduced operational workload for data owners and governance teams.
- A foundation for a future Data Marketplace – the data shopping framework provides the building blocks for scalable, self-service data access.
