HEMA accelerates their data governance journey with Amazon DataZone

This post is cowritten by Tommaso Paracciani and Oghosa Omorisiagbon from HEMA.

Data has become an invaluable asset for businesses, offering critical insights to drive strategic decision-making and operational optimization. However, many companies today still struggle to effectively harness and use their data due to challenges such as data silos, lack of discoverability, poor data quality, and a lack of data literacy and analytical capabilities to quickly access and use data across the organization. To address these growing data management challenges, AWS customers are using Amazon DataZone, a data management service that makes it fast and effortless to catalog, discover, share, and govern data stored across AWS, on-premises, and third-party sources.

HEMA is a household Dutch retail brand name since 1926, providing daily convenience products using unique design. HEMA’s more than 17,000 employees bring exclusive, sustainably designed products in more than 750 stores in the Netherlands but also in Belgium, Luxembourg, France, Germany, and Austria, with webstores available in all these countries. HEMA built its first ecommerce system on AWS in 2018 and 5 years later, its developers have the freedom to innovate and build software fast with their choice of tools in the AWS Cloud. Today, this is powering every part of the organization, from the customer-favorite online cake customization feature to democratizing data to drive business insight.

This post describes how HEMA used Amazon DataZone to build their data mesh and enable streamlined data access across multiple business areas. It explains HEMA’s unique journey of deploying Amazon DataZone, the key challenges they overcame, and the transformative benefits they have realized since deployment in May 2024. From establishing an enterprise-wide data inventory and improving data discoverability, to enabling decentralized data sharing and governance, Amazon DataZone has been a game changer for HEMA.

Data landscape at HEMA

After moving its entire data platform from on premises to the AWS Cloud, the wave of change presented a unique opportunity for the HEMA Data & Cloud function to invest and commit in building a data mesh.

HEMA has a bespoke enterprise architecture, built around the concept of services. These services are individual software functionalities that fulfill a specific purpose within the company. Each service is hosted in a dedicated AWS account and is built and maintained by a product owner and a development team, as illustrated in the following figure.

HEMA runs over 400 services, and 20 of them run extract, transform, and load (ETL) pipelines with dedicated data resources, which produce and consume data assets shared across the data mesh.

Data management in a data mesh

Weeks after launch, HEMA’s data platform wasn’t where the company wanted it to be. Building an agile organization that runs on reliable and streamlined processes was the primary goal. Initially, the data inventories of different services were siloed within isolated environments, making data discovery and sharing across services manual and time-consuming for all teams involved.

Implementing robust data governance is challenging. In a data mesh architecture, this complexity is amplified by the organization’s decentralized nature. In this context, HEMA concluded that data governance was no longer a nice-to-have, but had become a foundational piece required to build a healthy data organization.

Why HEMA selected Amazon DataZone

By exploring the preview, HEMA saw how Amazon DataZone covered all the critical pillars of data management in a single solution. It was clear how Amazon DataZone would bring benefit to both the technical teams as well as the business end-users. The technical organization could take advantage of a robust programmatic solution to manage the availability, accessibility, and quality of the data assets that make the enterprise data catalog. The business end-users were given a tool to discover data assets produced within the mesh and seamlessly self-serve on their data sharing needs.

Features such as AI-generated metadata were key to providing end-users with reliable and use case-driven explanations of what a certain data product could provide and solve, while the subscription feature allowed them to start using a certain data asset within their own environment in a matter of seconds, as opposed to the existing lengthy and human-driven process.

These reasons, as well as the self-service capabilities, resulted in HEMA’s decision to adopt and roll out Amazon DataZone at the enterprise level.

Solution overview

The HEMA data landscape is multifaceted, with various teams across the organization using a range of technologies and systems, including Databricks. To effectively govern this complex data environment, HEMA has adopted a data mesh architecture on AWS. This architecture maintains a central intelligence platform (CIP) that enables the activities of both data producers and data consumers by providing the necessary platform and infrastructure. The overall structure can be represented in the following figure.

Each service uses two AWS accounts, one for pre-production and one for production. This separation means changes can be tested thoroughly before being deployed to live operations.

Amazon DataZone is the central piece in this architecture. It helps HEMA centralize all data assets across disparate data stacks into a single catalog. It plays a pivotal role in bridging the gap and integrating different systems, such as Databricks and native AWS services. The integration of Databricks Delta tables into Amazon DataZone is done using the AWS Glue Data Catalog. Delta tables’ technical metadata is stored in the Data Catalog, which is a native source for creating assets in the Amazon DataZone business catalog. Access control is enforced using AWS Lake Formation, which manages fine-grained access control and data sharing on data lake data. The following figure illustrates the data mesh architecture.

The Amazon DataZone implementation follows the same approach as individual services: HEMA maintains two distinct domain data catalogs: preprod-hema-data-catalog and prod-hema-data-catalog. These catalogs serve as the backbone for data sharing across pre-production and production accounts, allowing flexible access to data assets based on the environment’s needs.

The prod-hema-data-catalog is the production-grade catalog that supports data sharing across production services and, in some cases, pre-production services. This catalog only facilitates the production of data assets from production services (disallows publishing of assets belonging to pre-production services) and allows pre-production services to access production-grade data. The following diagram illustrates the architecture of both accounts.

To establish isolation between services in the data mesh, a project is dedicated to a unique service account. The environment profiles and environments are configured to be explicitly used only by the service. This Amazon DataZone configuration is managed centrally by the core team using AWS CloudFormation. After projects are created and configured by the central team, project teams have access to self-service capabilities to create their own environments according to their needs.

The following diagram illustrates the full workflow for onboarding HEMA service teams in Amazon DataZone.

The workflow includes the following steps:

1. A service team (either a data producer or a data consumer) initiates a request to the core data platform team to enable data sharing for their service accounts. This request is typically made when a service team has a use case where they need to either publish data to the catalog (for other teams to consume) or access data that another team has published.
2. After the request is received, the core data platform team assesses the requirements and initiates the creation of projects and environments in Amazon DataZone. This is done using AWS CloudFormation and a continuous integration and delivery (CI/CD) pipeline. The core data platform team makes sure that the appropriate AWS account (pre-production or production) is linked to the environment within the project in the respective catalogs.
3. After the projects and environments are set up, service teams can use Amazon DataZone features to produce and consume data assets:
   1. Producers (for example, Service A) can publish their data assets to the Data Catalog and approve or reject subscription requests.
   2. Consumers (for example, Service B) can search and access these published data assets using the Amazon DataZone catalog and request data access through subscription requests.

In a decentralized data mesh environment, there is a risk of service teams creating resources in service accounts they are not authorized to manage, which may lead to governance issues and data mismanagement. To address this challenge, HEMA followed two principles:

• Amazon DataZone project structure – Each project contains resources that are solely managed by the service team (project members) responsible for it. Each service team’s project provides a clear boundary for the resources they manage.
• Environment isolation – The core teams enforce governance policies in the Amazon DataZone configuration, allowing teams to only deploy resources within their own environments.

Adoption plan: Strategy

In HEMA’s data mesh, the catalog must be built in collaboration with all the services that produce data, so the key for the central data governance team was ideating an adoption plan that would add value to these teams, rather than disrupting the delivery of their projects. With that in mind, HEMA’s adoption strategy was designed on three core principles:

• Launch it – Do not wait until you can ship to production a full-scale service that covers every single feature available. Instead, define an MVP that solves the most critical need for the business and make it available for the business as soon as you can.
• Prove value – HEMA’s data team ran several internal seminars, and dedicated presentations with each of the involved teams to showcase how Amazon DataZone would simplify their data sharing needs. Do not tell them they must invest time to learn and start using a new service, but rather let them get drawn in by the new advantages of the new functionality and stimulate self-adoption.
• Be there – This connects with what HEMA as a company stands for. Be close to the teams when they need support during the adoption stage, like HEMA is close to their customers whenever they need a new product for their lives. Create space for Q&A and develop a collaborative experience for everyone in their adoption curve.

Adoption plan: Action points

While deploying the adoption plan for a decentralized data marketplace using Amazon DataZone, HEMA followed a “start small, fine-tune, and iterate” approach. In practice, this meant that the Data & Cloud team started working with one business unit, expanding then to several business units, while focusing on one single feature: data asset subscription. To increase interest and adoption, this process was introduced for the core data assets that were more used in the company.

After this part of the process was well understood and embraced by everyone, the next step was to start supporting the data pipeline adaptation work needed for each business unit.

Finally, when all teams were onboarded and familiar with the subscription feature, HEMA moved to introduce the business units to the second critical feature: data publishing. In summary, HEMA released new features and allowed the domains to pick up the implementation at their preferred pace before moving onto the next one.

When adoption was at a point where all core data assets were being consumed through the Amazon DataZone catalog, the Lake Formation resource links used previously to share data across accounts were decommissioned, and at the same time the Data & Cloud team interrupted their duty to share data between business units, stimulating the peer-to-peer data sharing practice, where teams can directly talk to each other without having to involve a third party.

Results

The popularity of Amazon DataZone across the enterprise ramped up quickly, and all the involved business units started using the service daily to self-serve their needs. The existence of a central data catalog enabled teams to seamlessly search, discover, share, and subscribe to data assets produced within the business. Only a few months after launching the service, HEMA observed stunning statistics:

• Over 200 data assets published to the catalog
• Over 180 active subscriptions
• Over 100 active users monthly
• Over 20 business units (services) onboarded
• Data sharing average turnaround time cut from 4 working days to few seconds, without the support of any other team

Additionally, they saw massive benefits that can’t be represented by statistics. Above all, the ability to autonomously discover data produced by other teams is enabling a series of new use cases for the business, which weren’t even visible to them earlier due to the lack of awareness and visibility on what others were producing. For example, the data science team quickly developed a new predictive model for sales by reusing data already available in Amazon DataZone, instead of rebuilding it from scratch. This is resulting in an energized data organization, which can collaborate and contribute to shaping the future of HEMA’s data operations.

Conclusion

At HEMA, Amazon DataZone made data governance a reality, and so the company wants to implement new features in close collaboration with AWS, while continuing to work on the rollout of items that are already in HEMA’s roadmap. The team is continuously developing the service, launching a series of new features that will continue to improve the data operations:

• Data quality scores – This feature helps data producers monitor and optimize their data assets, while consumers can see upfront the nuances of a certain asset that they might be using or are looking to use within their ETL pipelines
• Data lineage – This feature allows consumers and the central governance team to trace data sources, transformation stages, and observe cross-organizational usage of data assets
• Fine-grained access control – This feature enables producers to be in full control of what they share with other units, making sure that only the relevant pieces of a data asset are shared with the consuming teams

The long-term vision of HEMA is clear: Amazon DataZone is set to become the central solution for data sharing and data cataloging across the enterprise. Although as of today, Amazon DataZone is focused on supporting the teams running ETL pipelines, the goal is to extend the service to all the business teams that work with data, with the ultimate goal of streamlining their daily operations. Data is one of the most valuable resources a company has, and HEMA is determined to democratize its role by building an efficient data organization, who relies on the most advanced data governance solution on the market.

About the authors

Luis Campos is the Data & AI Governance GTM Lead for the EMEA market at AWS where he helps customers with their data strategies starting with strong data governance and uses his expertise in end-to-end data & analytics management. Luis is also a public speaking coach, based in the Netherlands, and has two boys with 18 years apart, which has taught him to see problems from both ends of a spectrum.

Vincent Gromakowski is a Principal Analytics Solutions Architect at AWS where he enjoys solving customers’ data challenges. He uses his strong expertise on analytics, distributed systems and resource orchestration platform to be a trusted technical advisor for AWS customers.

Tommaso is the Head of Data & Cloud Platforms at HEMA. He joined the business with the goal of modernising the Data Organization by building cloud-based Data Platform – hosted in AWS – which would power a Data Mesh architecture. With a strong passion for both technical and organizational challenges, Tommaso leads the Solution Architecture efforts as well as all core Data Management and Data Governance initiatives, for which he is also a passionate public speaker. Outside the office, Tommaso is a full-time dad with a passion for traveling and sports.

Oghosa Omorisiagbon is a Senior Data Engineer at HEMA. He focuses on leveraging AWS-native tools to optimise data pipelines, modernise HEMA’s data infrastructure and introduce reliable and scalable end-to-end data architecture solutions. Outside of work, he enjoys traveling, playing video games and outdoor activities.