Program of Primetime for Big Data 2025

Data is now as important as finance, people or brand. But how can we ensure that it adds value rather than just costing money? CIOs who view data purely as an IT issue are missing out on opportunities, whereas those who understand its strategic value are changing the rules of the game.

This is a case study of our three-year collaboration with a major player in the e-commerce market. During this time, we sought to transform data overload into information that helps to make better decisions. Starting as a Power BI specialist, I gradually took on the role of product owner, becoming responsible for the direction of data products and cross-team collaboration. In this lecture, I will demonstrate why obtaining quality data is often just the beginning, highlight the pitfalls of interpreting it, and explain how AI can help clean up dirty data. We will also focus on what it actually means to 'have a product' in the field of reporting, how to align the goals of different teams, and the advantages of finishing one thing over starting five. Using specific examples, I will demonstrate how we gradually built reliable reporting, improved the NPS mechanism and identified new use cases that added measurable value to the company.

The term 'big data architecture' tends to conjure up images of something large-scale, complex and expensive, whether on-premises or in the cloud. But does it have to be that way? No! In this talk, we will demonstrate how we supplemented a powerful analytical and visualisation platform with a specialised data layer. This approach saved on computing resource costs, increased user response times and freed up the analytical team.

We will describe what a simple architecture that supports the reliable processing of large volumes of data (terabytes) and is flexible and easy to integrate can look like. We will also demonstrate how it is used in practice.

It creates a springboard for the rapid implementation of AI use cases, which often depend on data.

Microsoft Fabric offers a vision of a unified data platform capable of handling everything from data integration to visualisation. However, many companies encounter problems during migration, not with the technology itself, but with their expectations for the project. This session will openly address the most common mistakes made when migrating to Fabric, from underestimating the required architecture and capacity planning, to believing that the new system will replace the existing solution on a one-to-one basis. It will demonstrate how poorly set expectations can impede progress, demoralise the team and cast doubt on the platform's overall benefits. The goal is to demonstrate how to realistically adopt Fabric with clearly defined roles, priorities and expectations on both the IT and business sides.

We started with an Excel prototype of Data Boutique. Today, we are developing a modern platform for data governance that supports data products and AI. Pavlína will explain how she developed a strategy and gained company-wide support for her idea. Štěpán will use specific examples to demonstrate what the implementation process involved, from tool selection and user training to content management. Together, we will look at five expectations that clashed with reality, and the practical lessons you can learn from them.

Join us for an in-depth seminar exploring a cutting-edge solution that transcends the capabilities of traditional Customer Data Platforms (CDPs). This session will delve into how unify and harmonize data from your sales, service and external apps, enabling businesses to create actionable, unified customer profiles. Learn how to leverage advanced features such as identity resolution, granular segmentation, and AI-driven insights to drive personalization and engagement across your call centre, sales agents and marketing. Moreover, explore the reporting on your strategic KPIs, that is built on your data and paid channels.

In a world where decisions often have to be made in seconds, the right data and signals from the network or smart devices (IoT) must reach those who can act on them immediately. This talk will show how T‑Mobile’s systems work to connect 5G and IoT data in real time from devices all the way to the cloud. These systems make it possible to detect anomalies, predict issues, and automatically trigger processes that respond to them for example, in machine maintenance or supply chain and logistics management.
You’ll learn how such systems are built how data streams are processed, data quality and security ensured, and how the impact on business operations is monitored, such as shorter repair times, reduced downtime, and improved delivery accuracy. This isn’t about “AI magic” but about practical, proven approaches that deliver real‑world results.

Artificial intelligence is changing the way companies work with data. While it opens up new opportunities, it also raises fundamental questions about responsibility, regulation and security. How can data governance be implemented to ensure that innovations are effective, secure, and compliant with legal regulations? This lecture will explore the specific challenges that companies face when implementing AI tools, from risk management to practical process setup for data protection and the responsible use of artificial intelligence.