Reply Helps Showpad Switch to Data Mesh Architecture on AWS

Executive Summary

Belgium-based Showpad provides a sales enablement platform that uses technologies like digital deal rooms and video messaging to enable sales reps to share personalized content and deliver the best buyer experiences. On top of that, Showpad gives sales managers the insights to bring their teams to the next level and accelerate sales cycles. To support its strategy of becoming a data-centric company and improve customer experience, Showpad is adopting AWS technologies and innovative approaches such as Data Mesh and serverless.

Showpad Chooses Reply to Accelerate its Journey to Data-centric Company

Belgium-based Showpad helps make the sales process more efficient and improves how salespeople engage with prospects by automating content management—how, and what, material is shared with clients. It already had a sophisticated data strategy built on Amazon Web Services (AWS). But it wanted to increase its ability to innovate, to truly democratize access to data across the organization, reduce management and access overheads, and bring down costs. It wanted its data strategy to provide better personalization for clients and give its internal teams better access to data and better insights. Democratization of access to data and analytics is critical to the success of the company and its clients. Showpad uses AWS technologies and innovative approaches such as a serverless Data Mesh architecture to accelerate its journey to being a truly data-centric company.

Its existing data system worked well but was challenging to manage. One of the main risks was that it created a single point of failure for the business. Showpad sometimes encountered availability or performance issues when developers or data scientists made heavy use of the system, which affected business users. Furthermore, the system was not optimized to deal with unstructured data—a key part of Showpad’s future ambitions for its core product line. It wants to provide analytics and predictive insights for all content uploaded by customers—including slides, presentations, and PDF documents. It wants data use to become a central part of thinking across the company—not just something to justify decisions that have already been made.

Showpad knew it would need a partner to help accelerate the transition to the Data Mesh architecture. AWS Premier Partner Reply had the niche expertise required, could support the broader end-to-end cloud needs that Showpad might have in the future, and was a good cultural match. Showpad was impressed by Reply’s ability to collaborate well with a variety of internal customers and respond quickly to queries.


This is a big cultural change, but it’s the best solution for organizations that need to seriously scale access to data.”

Jeroen Minnaert
Head of Data, Showpad

Data Mesh Improves Data Quality and Maturity

The first strategic and architectural decision for Showpad was the move to a Data Mesh architecture to provide much better data access and analysis across the business. This means giving individual business teams responsibility for their own data, so instead of a central data team having to deal with conflicting demands from different departments, workloads and ownership are spread more evenly across the business. Additionally, teams can search from a data catalog and easily request access to data sources that they need. “This is a big cultural change, but it’s the best solution for organizations that need to seriously scale access to data,” says Jeroen Minnaert, head of data at Showpad. Although businesses of 100–200 people can work with a central data team, it becomes more challenging as demand grows.

By democratizing data skills—both responsibility for keeping data clean and usable, and distributing skills to make the best use of that data—a business can accelerate its data maturity. Pragmatically shifting responsibilities from a central team to teams that own and understand the data resulted in a more streamlined approach and better-quality data. This prevents bottlenecks that might develop when several business units try to schedule work only through a central data unit. Different teams across the business can then analyse data in parallel, helping everyone to work more efficiently and still use a central data team if required.

Data Mesh to Bolster Security, Access and Privacy

The move to a Data Mesh architecture is expected to achieve a 50 percent reduction in data infrastructure costs. The migration also improved data privacy and security. For example, in order to retrieve insights on Showpad employees, Human Resources (HR) used to download data and perform the analysis on local machines. Now, HR can work in an isolated cloud environment where access to employee information is strictly monitored. Showpad designed-in security from the start of the project, allowing users to access only the data they need to do their jobs. “We see this security as an enabler, not a hindrance, to people’s work,” says Minnaert. “Security and access work to curate the data—so people can more quickly get what they need—but also stop them accessing what they do not need.” Staff access is assigned according to roles, but the data itself is also ranked by levels of sensitivity.

Moving to a Data Mesh has also simplified the management of security. Showpad uses AWS Lake Formation to provide centralized granular access control.

Moving to Machine Learning, Intelligent Recommendations

Showpad wanted to build a recommendation engine for its customers that would use machine learning (ML) to suggest to salespeople which pieces of content to share with their customers. As the activation pattern of the recommender system is expected to be very irregular, it was important to build this on a serverless solution to control costs—something that was not possible using its previous systems. Showpad uses Amazon SageMaker to build, train, and deploy machine learning models. It uses tailored models that can deploy instantly on AWS Lambda, ensuring algorithms only run when needed by customers, removing unnecessary overhead and reducing operating costs.

Showpad also wants to further consolidate its reporting and ingest more unstructured, client-created content into its analysis data set. Extracting more understanding from this content will allow systems to provide more accurate advice on the sales process to its customers.

Systems that can better understand content will also help salespeople building their presentations—they can search into the content and the intelligence of the system will support the search: for instance, they can find a “timeline slide” by its appearance, even though the word “timeline” is never explicitly mentioned in it.

But just as important as adding new features and making more use of artificial intelligence (AI), are the strategic benefits of moving to a Data Mesh. Showpad is confident that empowering individual departments and giving them control over their data and how it is analysed will accelerate its journey to being a truly data-centric business. Using serverless solutions so systems can scale fast—without incurring additional cost or infrastructure overhead—is crucial to supporting this strategy and is possible thanks to AWS and Reply.


About Showpad

Belgium and US-based Showpad helps to automate the sales process by reducing administration around content and client contacts. Its sales-enablement software aims to increase sales, improve content use, and make it easier to onboard new sales staff.

AWS Services Used


  • Moved to a data mesh structure, giving power and control to individual departments
  • Improves data/information quality, access control and application security
  • Decentralises the management burden of maintaining datasets
  • Builds systems that can more easily integrate unstructured data in the future

About the AWS Partner Reply

Reply is a data specialist that turns human-centered data products into effective business tools. It builds big data platforms and implements machine learning (ML) and artificial (AI) models in a way that is repeatable, efficient, scalable, simple, and yet secure.

Published February 2023