Success Story

International Brewing Company Drives Business Value with High Quality Data

International Brewery

Industry

Food & Beverage

Challenge

Create a single data quality dashboard for distributed teams across the globe

Goal

Enterprise data quality management & data enablement

Solution

Enterprise data quality platform for monitoring, profiling, and cleansing data

Key results

  • Eliminated a backlog of over 600 change requests for the legacy data quality dashboard.
  • Obtained reliable visibility into data quality for the whole enterprise and every operating country.
  • 65+ projects for which Ataccama ONE has provided support.
  • Widespread adoption with a total of 170 active users in 78 countries.
  • Reduced the duplication of customer data by 60% and material data by 40%, and increased data completeness and accuracy.

Data stack

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The situation

Our client is one of the biggest brewers in the world. Emphasizing a commitment to social betterment, the company pursues a strategic approach to growth and sustainability, aiming to harmonize short-term results with long-term viability while maximizing business value.

In pursuit of their 2025 objective to enhance data accessibility and quality, the company initiated a comprehensive initiative to enable self-service and automated data quality processes across all organizational regions and operational hubs.

The challenge

Maintaining easy-to-use, highly adaptable rules and empowering self-service data quality

The main obstacle for the data quality team was the home-built data quality dashboard that no longer provided trustworthy numbers.

The existing dashboard had a number of architectural issues that didn’t support the brewer's vision for self-service data quality:

  • Rules were hard to maintain and were not source-agnostic.
  • It lacked the flexibility to modify data quality controls tailored to the requirements of specific regions or teams.
  • It lacked capabilities for self-service rule creation and deployment.

In time 600 change requests accumulated with no clear way to accommodate them. As a result, stakeholders from business departments across the world lost trust in the numbers.

Because the client has a large federated system landscape spread across more than 70 countries, the organization needed to find a data quality solution that would integrate with all sources and operating systems. This included over 35 SAP ECCs and other systems like JD Edwards, MS Dynamics Navision, and Azure.

Business objective

The renowned brewer's ambition is to be connected by data and become the best-connected brewery is the next step in their transformation. This step will enable employees and stakeholders to get data-driven key insights at every step of the production process. This journey is fundamental to their business model and revolves around high-quality, live data.

A big part of this goal was to decentralize data quality management while using a central solution. This means enabling local teams to use Ataccama ONE for their projects when needed with minimal assistance.

Selection criteria

Finding the best solution for a federated system

After a competitive POC with two other shortlisted vendors, Ataccama ONE was chosen as the preferred solution to start building the data quality ecosystem around.

Ataccama was ultimately selected for tackling a vast majority of the client's requirements:

  • A future-proof tech stack
  • Provide live data quality monitoring
  • Promote self-driving data quality at a local level to support the implementation of new business processes
  • Integrate with the federated systems of tools
  • Include straightforward, user-friendly rule creation and maintenance processes
  • Run an intuitive and easy-to-use interface

Ataccama plays a critical role in our journey. It allows us to build more mature data models and prevent bad data quality from slipping into our systems.”

The solution

Data quality platform serves multiple use cases in a complex ecosystem

The data-driven beer production process involves many teams and functions, each having a need for high-quality data for their projects. That is why Ataccama ONE is used for many purposes across the world.

Here is an overview of each use case where Ataccama is directly involved.

Deploying reusable and governed rules

After deploying Ataccama ONE, the data quality team parsed through, improved, and redeployed close to 400 data quality checks to cover all data quality dimensions. Now they can create rules once and then deploy them to various catalogs or projects. 

Ataccama also provides a governance framework that perfectly fits into the organization’s federated system. It allows a central data quality team to have a clear overview of what is happening in the rule library as well as a hierarchical change approval workflow. By outlining and defining responsibilities and processes, local teams can follow a clear flow and benefit from self-service data quality.

Trusted data quality monitoring & reporting 

With Ataccama ONE, the client implemented systematic data quality monitoring and a new dashboard with trusted data quality information for each local country branch. In order to update the dashboard daily, the Ataccama engine continuously pumps information to the Azure data lake to be picked up and then shown in a PowerBI dashboard. 

As a result, the data quality team managed to easily solve 600 requests that accumulated in their change management systems and implement a single source of truth to understand the global state of data quality and drill down to each individual country. 

Self-service data quality for local initiatives and projects

Because of the size of the organization and the federated system that’s implemented, operating companies have the liberty to implement relevant projects and initiatives at a local level. 

Local teams can take advantage of the easy-to-use interface of the Ataccama ONE platform to check and improve data quality for local and global initiatives: 

  • Transformation of the supply chain in Europe to make it more efficient and green
  • Reducing the number of apps and ERPs
  • Various data migration projects
  • Consolidating all financial business processing into one system

Ataccama enables us to distribute data quality workloads to local teams. Everyone can do data quality on their own.”

Profiling for advanced analytics

One important aspect of being a data-driven company for the client is advanced data analytics. To make this possible, data is ingested from ERPs into a data lake. The analytics teams use Ataccama ONE to profile data in a self-service manner to assess its suitability for their needs.

Results

Scalable and trustworthy data quality results

With the help of Ataccama, a small team comprised of one architect and four developers managed to take on multiple data quality challenges and add value to key areas of the organization:

  • Eliminated a backlog of over 600 change requests and any risk of future roadblocks.
  • Obtained reliable visibility into data quality for the whole enterprise and every operating country
  • Sunset of all redundant data quality tools and replaced them with Ataccama ONE.
  • Proven reliability from the Ataccama solution with a clear roadmap that the data quality team can rely on.
  • 65+ projects for which Ataccama ONE has provided support.
  • Widespread adoption with a total of 170 active users in 78 countries.
  • Reduced the duplication of customer data by 60% and material data by 40%, and increased data completeness and accuracy.

The future

More data quality automation for improved data ingestion and self-driving DQ

The client wants to implement more advanced data projects that rely on high-quality data. Ataccama will play a key role moving forward in achieving the following goals:

  • Monitor all critical data entities and bring them to 99% data quality.
  • Introduce robust governance and monitoring for datasets used in analytical use cases.
  • Integrate Ataccama data cleansing algorithms to ensure that data arrives in the correct format to Azure and can be consumed by the advanced analytics team.
  • Transition from working only with ERP data to also using and monitoring consumer data.

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