Translating a Vision for Data and AI Into a Commercial Reality

Project snapshot

The amount of data created worldwide exceeds 400 million terabytes a day and IDC estimates that 83% of companies are actively investing in data initiatives. However, few companies understand how to translate their data into commercial value.

GBG, a leading expert in global identity and location tech, recently demonstrated how a data-rich company can convert fragmented data into a strategic product intelligence capability and a valuable asset. By aligning platform modernization with strong data governance principles, a data-driven culture and a single, high-value use case, GBG moved beyond simple internal efficiency gains to make a dramatic positive impact on their customer value proposition. Supported by AWS as a strategic technology enabler, Intellias and GBG built a future-proof data platform, a solid foundation to scale AI products and features.

Key initiatives

Customer Obsession: GBG treated this data initiative not as a technical project, but as a strategic customer value initiative. With a growth mindset, the focus was always on using data to improve interactions. As defined by GBG’s CTO Luuk early in our dialogue:

Customers want more from us, more guidance, more expertise –  it’s about how we show up.

Identifying the Backbone Use Case: Intellias defines the Backbone Use Case as the one most commercially lucrative use case that drives a vision for data. GBG had this vision driven by a holistic global product strategy. Together, we validated the Backbone use case of Automated Product Insights and used it to structure a rapid delivery roadmap of data initiatives.
Breaking silos: a global company with over 1,250 employees, GBG has grown their product portfolio both through internal development and acquisition. With the launch of their new unifying platform, Go, the data platform initiative was conceived as a way to unify these different product teams.
Security by design: GBG’s products securely manage high volumes of sensitive data. Privacy, security, and data governance considerations were paramount and integrated early in the process to support their data-driven ambitions, which helped build trust across the business.
Use-case momentum: Using the Backbone Use Case to deliver regular increments of value back to the business. Sales teams received new insights and dashboards first, building excitement with customers and internal teams alike while generating demand for the new platform.
Ecosystem value: AWS and Intellias provided complementary strengths, including cloud scale, data strategy expertise and custom platform capabilities. This co-innovation allowed GBG to balance deployment speed with system stability and align tightly with business goals.

Business challenge

Executives are leaning heavily on AI in 2026 to drive business forward. Roughly 51% of CXOs anticipate that AI will generate actual revenue growth by the end of the year, and a similar number of CEOs expect it to happen within the next few years. Yet there is a gap between expectations and production. Only half of AI projects produce measurable results. Currently, about 83% of organizations have a few successful pilot projects, but their architecture traps data in silos and limits the project’s scope. Only about 8% have managed to thrive. Data modernization, of course, is not a new concept; the value of data as a commercial asset has been obvious for some time, but it’s only recently that it’s become a top priority for enterprises, especially for software and platform companies that are hungry for competitive advantage in a market under stress from the advent of AI. An AI-ready data engine is the re-requisite to scaling AI products and features, and by the end of the year, 70% of enterprise CEOs want proof of ROI before they approve any new AI spending. They also want to see engineering efforts translate directly into growth and business value.

This makes GBG’s challenge highly relevant. GBG’s customers operate in heavily regulated financial markets that are dealing with several major industry changes simultaneously. To retain their position as a leader in trust and anti-fraud technology, GBG faced intense pressure to turn their massive volume of transaction data from an operational byproduct into a core asset. Operating successfully in these markets requires locking down sensitive personal data as an obligatory requirement, but simply securing it isn’t advantageous. The real advantage is to build secure, compliant, scalable mechanisms that turn raw data into analytics that tell GBG’s customers insights about their businesses that they didn’t already know.

Scaling globally and acquiring other companies come with a lot of technical baggage. GBG’s case highlights the challenges of scaling, having done acquisitions of all sizes, across all regions. This led to the accumulation of many customers and platforms whose data was spread out and managed by different teams. This data fragmentation created several challenges:

Data compatibility gaps: Like trying to push a square peg through a round hole, the data in one system did not fit with the others. The lack of a unified, compatible dataset prevented the company from comparing data across different systems.
Slower time-to-value: For many analyses, the data had to be extracted manually, transformed, and then interpreted. This process delayed decision-making for sales and account teams, restricting the value GBG could create for their customers.
Discovery lag time: Churn risk, reporting anomalies and potentially valuable trends were not delivered in real-time. As a result, opportunities were being missed and our client was not using their product data proactively.
Customer intelligence gaps: The company lacked a consolidated view of multi-account customers, a consistent measure of their customers’ risk tolerances and single-account or product customer views.
Unsuitable foundation for AI: Existing data architecture and governance practices could not support scalable real-time analytics or advanced AI capabilities.

These improvement points needed to be addressed to support the company’s vision of being a proactive partner rather than a vendor. Without a unified data platform, providing real-time, AI-powered analytics through a single portal would remain difficult. The data engineering challenge was a unified, coherent intelligence platform that would seamlessly manage product, customer, and operational data.

Solution

A rapid transformation of this size requires the right tech talent, including high-demand skills in MLOps, AI and Data Engineering. The success of this project came from AWS providing the scalable, cloud-native technology foundation and Intellias delivering the transformation design and execution discipline. A combined team of 30 data product managers, data platform engineers, data scientists, MLOps specialists and GBGs data and product teams developed an ambitious roadmap with monthly releases.

“Intellias prioritizes truly understanding what they’re talking about. They come across as very knowledgeable while staying in tune with what you want to deliver as a business. It’s not ‘we’ll take it away and do it for you’ — it’s ‘we’ll do this together’, said GBG’s CTO Luuk.

GBG already had a deep, long-standing footprint with AWS for their cloud platform and infrastructure needs. Expanding that existing relationship for data AI was both commercially and technically pragmatic. This relationship with AWS:

Significantly minimized architectural disruption and deployment risk.
Provided a familiar service ecosystem for engineers.
Offered commercial economies of scale for additional AWS services.

GBG uses managed services like Amazon SageMaker Unified Studio and Amazon Bedrock. These tools blend structured and unstructured data using a knowledge-based approach to give internal teams natural-language access to highly complex information. The new platform would therefore replace fragmented data systems with a single, scalable environment that could support automated analytics, customer-facing intelligence, and provide a foundation for scaling AI in the cloud.

While AWS provided the platform, Intellias helped translate GBG’s vision into a blueprint and supplied a highly skilled team to build it. Intellias used proprietary AI Ready Data Engine methodology in a rigorous AI and data maturity assessment as a starting point. Everything was audited from GBG’s data strategy and governance frameworks to the customer’s AI engineering and MLOps capabilities and then combined with diagnostics such as the Data Strategy Diagnostic to build a de-risked data roadmap and backlog, ready for execution. The solution included the following:

Backbone use case: The key data use case to support the work was automated customer recommendations. It served as a guide to value and was released back to business stakeholders and sales teams while building a data platform on AWS.
Central AWS platform: The unified platform included SageMaker Unified Studio for model training and monitoring, Glue and EMR for data processing, and Quick Suite for creating dashboards.
Unified Cloud Data Architecture: Separate identity platforms were integrated into a consolidated AWS data layer. While the platforms previously had independent architectures and data schemas, the unified architecture of the modern data platform on AWS ensured compatibility across different global regions.
MLOps enhancement: Model training, versioning, monitoring, and retraining were formalized using AWS SageMaker in a controlled MLOps lifecycle.
Responsible AI: In addition to formalizing MLOps, the new AWS enterprise data platform aligned AI governance with the EU AI Act. It also ensured compliance with older frameworks like GDPR to allow the processing of large volumes of sensitive PII data.

High value use cases

By focusing on customer impact, GBG has deployed several use cases that prove the platform’s worth in a live environment:

Recommendation-led product adoption: Instead of relying on generic product messaging, GBG uses data-driven recommendations from the product suite to drive customer outcomes. They can show customers the exact performance improvements they will get by adopting new identity onboarding products or configuring the products they already use, such as calculating the expected increase in pass rates from specific populations or geographies. Integrated analytics directly supports product adoption and accelerates sales conversions.
Advanced peer benchmarking: GBG’s products now offer comparative analysis, benchmarking customers against similar organizations across related sectors, geographies and operating contexts. This helps GBG’s customers understand their own competitive advantage.
Algorithmic prospecting and growth targeting: GBG’s sales teams are now using recommendations, peering, and clustering techniques to pinpoint new prospect segments and growth targets. The value this has created for sales and account teams has helped prevent churn, build pipelines, and crucially, create champions who will advocate for the new data platform both internally and externally.
Proactive account intelligence: In a good example of the platform’s monitoring capabilities, the alerting and triggering features identified unusual transaction patterns in a particular customer account. The system caught what turned out to be a penetration test accidentally being run against a live production environment, voiding results from a GBG product set. By proactively reaching out to warn the customer, GBG transformed a churn risk account into a growth opportunity for additional products and services.
Enterprise-wide cross-functional adoption: The platform’s success has broken down data silos. Interest and adoption are expanding beyond technical departments and into finance and go-to-market teams.

Business outcomes

As a result of the AWS-based platform delivered by Intellias, the company was able to turn siloed product and customer data in their product portfolio into a new revenue stream, better customer relationships and a future-proof foundation for AI. This included:

Value-creating, incremental data roadmap: By identifying the Backbone Use Case early on and working backwards from it to deliver regular value back to the business, the team delivered visible wins early, keeping executives engaged, sales teams excited and ultimately leading to a backlog of eager new customers before full rollout.
Unified product ecosystem: Consolidating over 80 products into the GBG Go platform reduced maintenance and established a central pipeline for deploying AI features across the portfolio.
Active intelligence & customer success: GBG’s customers are now served by automated peer benchmarking and actionable recommendations. Analyzing specific data points empowers sales teams to strategically advise customers, boosting satisfaction and actively preventing churn.
Embedded data & new revenue: Our customer was able to leverage the data they held on their customer base to open an entirely new revenue stream, aligned to new product launches.
Proactive governance: Bringing privacy and security teams in during the initial design phase turned usual compliance roadblocks into active champions, allowing the data pipelines to scale confidently.
Future roadmap: The next phase will expand on these capabilities by adding additional AI features, including a conversational AI giving customers the ability to query data directly through the unified portal.

22,000

Account-specific recommendations

614M

Identity journeys tracked

33B

Data points processed

months from program inception to new revenue stream

Technologies: Amazon Quick Suite, Amazon SageMaker Unified Studio, Scikit-learn, PostgreSQL, AWS S3, AWS Lambda, Custom RBAC, audit logging, Airflow, Python