The 3 Biggest Data Challenges – and How Companies Can Master Them Sustainably

Data is now considered one of the most important resources for businesses. It is expected to enable better decisions, automate processes, accelerate innovation, and drive new business models. In reality, however, a different picture often emerges: despite significant investments in technologies and digitalization initiatives, many companies only partially succeed in generating measurable business value from their data.

The recently published Global Data Impact Index study clearly shows where the biggest obstacles lie. Enterprise companies were asked which challenges currently most prevent them from achieving their strategic and operational goals. The result makes it clear: the biggest problems are not exclusively in the technology itself. Rather, it is organizational structures, a lack of orientation in the technology market, and lengthy decision-making processes that slow companies down.

In this article, we take a detailed look at the three biggest data challenges – and how companies can overcome them sustainably.

1. Internal Processes and Organization: When Complexity Hinders Innovation

At 33.7 percent, this is the biggest challenge for companies, according to the study. This is hardly surprising. Companies with a long history, in particular, often have complex organizational structures, evolved process landscapes, and isolated business units. What worked for decades is increasingly becoming a problem today.

Many companies still operate in silos. Departments pursue similar goals but work with different systems, data structures, and processes. Information is maintained multiple times, coordination takes a long time, and responsibilities are not clearly defined. This not only complicates daily collaboration but also, in particular, the implementation of new strategies and innovative business models.

This problem is particularly evident in central software and transformation projects. Their success critically depends on all involved business units pulling together. If this alignment is missing, friction, resistance, and delays arise. Projects become unnecessarily complicated, more expensive, or, in the worst case, fail completely.

Furthermore, many companies try to implement new technologies on top of existing inefficient processes. This doesn't solve problems; it merely digitizes them. However, modern data and technology landscapes also require modern organizational and process models.

Companies that are acutely feeling their complexity today can therefore hardly avoid a fundamental transformation. This is not just about new software, but about how teams collaborate, make decisions, and utilize data.

External digitalization partners can play a crucial role here. They bring not only technological expertise but also experience in change management. Especially in large transformation projects, a neutral external perspective helps to identify inefficient structures, define clear responsibilities, and sustainably embed changes within the company.

2. Lack of Knowledge About the Right Systems and Tools

According to the study, the second biggest challenge lies in limited knowledge about suitable systems and technologies. 19.3 percent of companies see this as a central problem. This is hardly surprising: the technology market is evolving faster than ever today. New platforms, AI functionalities, data architectures, and automation possibilities emerge in increasingly shorter innovation cycles. 

For specialist departments and internal IT teams, continuous market observation is usually not part of their daily business. Their focus is understandably on operational activities and ongoing projects. At the same time, however, the demands on modern data and system landscapes are continuously increasing.

Companies therefore often realize too late that existing systems no longer meet their requirements. Processes become less efficient, data quality suffers, and important innovation potential remains untapped. Another challenge arises: many companies know they need to "do something with AI," data, or automation – but find it difficult to define their specific needs. Without clear requirements, however, it becomes almost impossible to select the right technologies.

This is precisely where holistic digitalization partners become particularly valuable. They not only help companies categorize technological trends but also translate specific business requirements into meaningful system landscapes and implementation strategies. An experienced partner knows the software market, understands the differences between various platform approaches, and ideally has a broad partner network. This allows companies to identify suitable options much faster without having to conduct months of market analysis themselves.

At the same time, this expertise ensures that technologies are not viewed in isolation. Successful digitalization doesn't come from individual tools, but from the interplay of data, processes, and organization. Companies should therefore evaluate early on which systems align with their long-term strategic goals – and not wait until existing solutions have already become an obstacle to innovation.

3. Slow Decision-Making Processes Hinder Digitalization

Even when the need has been recognized and suitable technologies identified, for many companies, the real challenge only just begins: internal decision-making. At 18.7 percent, slow decision-making processes are also among the biggest challenges for the surveyed companies.

Especially in large organizations, digital projects often drag on for months or even years. Requirements need to be documented, business cases created, and investments coordinated across multiple hierarchical levels. Conflicting interests, budget issues, and uncertainties lead to decisions being continuously delayed.

And while internal discussions are ongoing, the market continues to evolve: competitors drive innovation, new technologies emerge, and customer requirements change. Companies thus risk losing valuable time.

It becomes even more critical when projects fail due to internal resistance in early phases. A lack of understanding of the strategic value of data and technology often leads to digital initiatives not receiving the necessary priority. Therefore, there needs to be company-wide awareness of the impact data has today on competitiveness, growth, and innovation. 

Equally crucial are well-prepared project pitches. Successful initiatives are characterized by clearly documented and comprehensibly communicated needs, goals, business value, and planned measures. The more transparent a project is structured, the easier it becomes to convince internal stakeholders.

Experienced digitalization partners also provide valuable support here. Thanks to their project experience, they know typical pitfalls, can introduce best practices, and help develop realistic roadmaps and robust business cases.

This not only reduces uncertainties but also increases the likelihood that projects will be approved and implemented successfully more quickly.

Conclusion: Data Value Is Not Created by Technology Alone

The results of the Global Data Impact Index study clearly show: The biggest challenges surrounding data today extend far beyond mere technology. Complex organizational structures, a lack of orientation in the technology market, and slow decision-making processes prevent many companies from unleashing the full potential of their data.

Anyone who wants to sustainably increase their data value must therefore think holistically. Successful digitalization only emerges when data, processes, organization, and technology work together seamlessly. Companies that act early, question their structures, and rely on experienced partners create the foundation not only for managing data but also for strategically leveraging it as a competitive advantage.

This is especially true as AI projects are being driven forward in more and more business areas today and in the future. And this is despite these initiatives currently adding little value: According to analyst firm Gartner®, 50 percent of all GenAI- and 40 percent of all Agentic AI-projects. High-quality data and end-to-end processes are the absolutely necessary foundation for successful AI projects. With a stronger focus on data, data quality, data governance, and data processes, it can be assumed that the ROI of AI projects will increase in the long term. Combined with clear objectives and clean scoping, companies set the course for the successful use of AI in relevant business processes. 

Our experts will be happy to help you master your personal data challenges – feel free to contact us without obligation!