Insights from the video interview between André Vogt, spokesperson for the Executive Board, and Division Head Wilhem Hardering.
AI, hybrid platforms, and new data sources are changing the requirements for data and analytics. In this video interview, André Vogt, Spokesman for the Executive Board, and Wilhelm Hardering, Head of the Data & Analytics Division at ISR, discuss why modern analytics landscapes must do more today than just traditional data storage and reporting.
Data is now generated everywhere: in ERP systems, cloud platforms, documents, emails, line-of-business applications, IoT systems, and AI-powered processes. At the same time, expectations from business units are rising. Information needs to be available in real time, systems need to work together intelligently, and new technologies such as AI should not just be tested, but should deliver tangible benefits in day-to-day business operations.
But this is precisely where the real challenge of modern analytics landscapes lies: Success is not determined by the volume of data alone—but by the ability to intelligently orchestrate data across systems, platforms, business units, and operational processes.
In a video interview from March 2026, André Vogt and Wilhelm Hardering shed light on this very development and explain why data and analytics must be approached in a more strategic, interconnected, and business-oriented way today.
Analytics has always been more than just reporting
The analytics market has always been somewhat different from traditional IT environments. While ERP or CRM projects often focused on specific business processes, the analytics field has, from the very beginning, been more concerned with data, technologies, architectures, and the question of how information can be turned into real value.
At the same time, however, analytics has always been closely linked to the business units. After all, it is not the technology that ultimately determines a project’s success, but rather the quality of the insights companies can derive from their data.
It is precisely this idea that shapes modern analytics projects today more than ever. At their core, the questions have hardly changed:
What information do business units really need? How can data be meaningfully integrated? Which insights create real added value?
The technological reality, on the other hand, has changed.
From Storage Constraints to Hybrid Data Ecosystems
In the past, data projects were often constrained by technical limitations. Storing large amounts of data—or even analyzing it efficiently—was often a challenge. Storage space, computing capacity, and infrastructure imposed clear limitations.
Today, the situation is completely different. Cloud technologies, scalable platforms, and modern data architectures have eliminated many of these restrictions. Companies can process enormous amounts of data and connect a wide variety of systems with one another.
But as these new possibilities emerge, so does the complexity.
Today's modern analytics landscape rarely consists of a single platform. Instead, hybrid environments are emerging that combine SAP systems, Microsoft technologies, Databricks, Snowflake, data lakehouses, and specialized cloud services. Many companies rely on multiple platforms in parallel because different requirements call for different technologies.
The real challenge, therefore, is no longer to collect data centrally, but to orchestrate it in a way that makes sense from a business, technological, and organizational perspective.
Why Modern Data Architectures Need to Be Completely Rethought
Today, the trend is clearly moving away from monolithic systems toward flexible, networked architectures. Companies are increasingly trying to use data where it is generated, rather than constantly copying it between different platforms.
The goal of modern analytics architectures is therefore:
as few physical data layers as possible, as much virtualization as possible, and access layers that are as intelligent as possible across different systems.
Cloud migrations, in particular, highlight just how important this approach has become. Many companies are currently reevaluating their entire data strategy. Large organizations in particular—those dealing with data volumes in the terabyte range—must carefully consider how to effectively organize data management, performance, and governance.
This is no longer just about technology decisions. Today, companies must also take the following into account:
- existing skills within the company,
- Governance requirements,
- Licensing models,
- Ability to integrate,
- Scalability
- and future AI applications.
Analytics is thus increasingly becoming a strategic architectural discipline.
Multi-platform instead of a single solution
The idea that a single platform can meet all of a company's needs is now largely a thing of the past.
For example, many companies today combine:
- SAP-based analytics solutions,
- Microsoft technologies such as Fabric or Power BI,
- Databricks for AI and data science applications,
- Cloud platforms from AWS, Google, or Microsoft
- as well as specialized data lakehouse approaches.
As a result, the market is increasingly moving toward hybrid multi-platform strategies.
Large technology providers, in particular, are driving this trend forward at a rapid pace. Hyperscalers are increasingly seeking to build complete cloud ecosystems and integrate as many enterprise applications as possible into their respective platforms.
For companies, however, this does not necessarily mean committing entirely to a single provider. Rather, cross-technology strategies are becoming increasingly important—strategies that do not focus on the platform itself, but rather on the question:
Which combination delivers the greatest value to the customer?
End-to-end is becoming a key success factor
As modern data landscapes become increasingly complex, the requirements for consulting and implementation partners are also changing.
In the past, the focus was often on traditional IT outsourcing—which was frequently driven by cost considerations and primarily focused on infrastructure. Today, companies expect much more.
We are looking for partners who can support the entire lifecycle of modern analytics landscapes:
—from strategy through architecture and implementation to operations and continuous improvement.
After all, modern analytics projects don't end with the go-live.
It is precisely during day-to-day operations that we see just how important stable processes, governance, and technical expertise have become. Companies need partners who not only operate infrastructure but also understand the technical context:
Are key metrics accurate? Is data quality up to standard? Do process chains function reliably? Can changes be implemented automatically?
This shifts the focus from pure system operations to intelligent application operations for business-critical analytics environments.
Why Automation Is Fundamentally Changing Analytics Projects
At the same time, the way analytics solutions are developed is also changing.
In the past, many models were built and maintained manually. Large projects required enormous human resources to develop data models, ETL processes, or reports.
Today, this approach is hardly scalable anymore.
The number of data sources, platforms, and requirements is growing so rapidly that companies are increasingly turning to standardization and automation. As a result, modern analytics landscapes are becoming increasingly industrialized.
Automated modeling, standardized development methods, and reusable artifacts ensure that projects can be implemented more quickly while also being operated more reliably.
This changes not only the technology, but also the speed at which companies can implement innovations.
AI is transforming the entire world of analytics
Perhaps the biggest change, however, is currently being driven by artificial intelligence.
This is no longer just about individual pilot projects or chatbots. AI is transforming the entire way companies work with data when data quality, governance, context, and operations are taken into account.
What is particularly exciting here is the increasing convergence of structured and unstructured information.
Today, companies are beginning to combine traditional ERP data with:
- Documents,
- Contracts,
- Emails,
- Drawings,
- Knowledge Bases
- and combine it with semantic information.
This opens up entirely new possibilities for applications.
For example, AI systems can combine historical purchasing data with contract terms and communication histories to prepare for negotiations or support decision-making processes.
The world of analytics is thus increasingly evolving into an intelligent knowledge and decision-making platform that goes far beyond traditional reporting.
Why Data Quality Is Suddenly Becoming Business-Critical
As AI becomes more widespread, the importance of data quality and governance is increasing dramatically.
This is because AI systems only work reliably if the underlying data is consistent, traceable, and trustworthy.
That is why companies today are focusing more than ever on:
- semantic data models,
- Governance structures,
- Trusted Scores,
- Data products,
- Quality Control Mechanisms
- and standardized metadata.
- This opens up entirely new possibilities for applications.
Data quality thus becomes a decisive competitive factor.
After all, the future doesn't belong to the companies with the most data—
—but to those that can make their data usable in an intelligent, secure, traceable, and consistent manner.
The world of analytics is thus increasingly evolving into an intelligent knowledge and decision-making platform that goes far beyond traditional reporting.
Stability and Innovation at the Same Time
The coming years will be shaped above all by a new duality.
On the one hand, companies must operate stable, high-performance, and secure analytics environments. At the same time, business units expect ever-faster innovation, new AI capabilities, and modern data-driven applications.
That is why analytics projects today must deliver both:
stability AND innovation.
That is precisely the new reality of modern data and analytics strategies.
Companies that master this balance will, in the future, be able to not only collect data but also use it to create actionable, reliable, and useful decision-making frameworks—and thus genuine competitive advantages.
Watch the full interview now
In just under 30 minutes, discover how documents can be turned into real decisions in seconds.
WE'D BE HAPPY TO ADVISE YOU
Wilhelm Hardering
Senior Executive Manager
