Make the most of your existing data

Over the past few years, companies have invested heavily in digitalization. The result: more data is available than ever before. Globally, the volume of generated and stored data is expected to reach around 528 zettabytes by 2029 (statista.com). Yet many companies in Switzerland still rely on manual analyses and incomplete information when making day-to-day business decisions.

The main reason is that existing data is often inaccessible, inconsistent, and difficult to use across different systems. Around 78% of the data stored by companies is unstructured (wasabi.com). As a result, it remains isolated where it is created – within individual departments or production facilities – without being connected to the next level of the organization’s systems.

The Axians Business & Process Automation Maturity Assessment  helps you determine where you stand today – and where the biggest opportunities for improvement lie.

However, it’s not just about data storage – data quality is what really matters. Data quality is the biggest challenge for Swiss companies looking to use data in a structured way. More than 80% of executives rate it as “very important”, ahead of data security, operations, and funding (swissdataaliance.ch).

At the same time, there is a lack of interoperability: over 75% see technical, semantic, and organizational interoperability as critical. According to a study by Bern University of Applied Sciences commissioned by digitalswitzerland and the Swiss Data Alliance, the biggest single hurdle remains the “definition, enforcement, and compliance with standardized formats”.

There is also a structural pattern: most data initiatives in Switzerland are located in public or highly regulated sectors. Industry and manufacturing are significantly underrepresented, each accounting for only 5% (swissdataaliance.ch). In the private sector, the issue is often not a lack of willingness, but a missing structural foundation: a clear platform strategy, binding standards, and defined governance.

Poor data quality and lack of integration have a direct impact on decision-making, processes, and overall competitiveness. Only 8% of Swiss companies have a consistent data structure – meaning that 92% make strategic and operational decisions based on incomplete foundations (corpin.ch).

And the most underutilized potential lies in exactly the type of data that is critical for AI applications: personal and unstructured data still play a secondary role in Swiss companies (swissdataaliance.ch). The reasons are again a lack of legal frameworks, missing standards, and insufficient infrastructure. As a result, investments in analytics, automation, and AI do not deliver the expected impact.

The solution is therefore not to introduce yet another tool. It lies in the ability to prepare data in a way that makes it usable across systems, reliable in quality, and clearly governed.

In concrete terms, this means:

Ensure data quality

Define clear standards for data collection, maintenance, and description. Without reliable data, there are no reliable decisions.

Enable integration

Make the existing system landscape transparent: which systems are in place? Which interfaces exist? Where are the gaps? Only this level of clarity creates the foundation for a functioning integration architecture.

Establish governance

Clarify data responsibilities, not just from a technical but also from an organizational perspective. Who maintains which data? Who decides on usage and access? Without clear roles, data quality remains a mere intention.

Think of data as products

Data must be visible and accessible to users, available in standardized formats – including metadata that describes what a dataset contains, how up to date it is, and under which conditions it may be used.