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How to get the most out of your data treasure

In our era of digitalization, data is a valuable resource for companies in all sectors. Most people are aware of this in theory, but not everyone derives concrete benefits from it.  

Yet the ability to use data effectively is crucial to a company's success. At AdEx Partners, we regularly find that much of this potential value remains untapped. Not due to a lack of data or technology, but rather due to an ineffective approach to its use. Our observations also show that digitalization initiatives are often launched as isolated projects that fail to reach their full potential due to a lack of a comprehensive strategy and integration into the overall business. A coherent target picture that includes the entire architecture, data platforms, infrastructure technologies and the project portfolio harbors enormous efficiency potential that often remains untapped. 

A coherent target picture for data utilization must include the entire architecture, data platforms, infrastructure technologies and the project portfolio.

Florian Thieg
Associate Partner,
AdEx Partners

Florian Thieg
Associate Partner, AdEx Partners

Identified obstacles

The challenges on the way to fully exploiting this potential are complex. Use cases that are developed from the bottom up or in a decentralized manner without taking existing organizational silos into account tend to be too ambitious. Another obstacle is the "120% approach", which attempts to tackle everything at once and centrally, which regularly leads to excessive demands and suboptimal results. Another critical factor is the lack of dedicated topic or governance owners who take responsibility for specific areas and actively drive them forward. In addition, the lack of a link between user needs leads to isolated pilot projects with a diminishing marginal benefit curve over time - whether in the areas of robotics, mining or automation. 


Strategies for sustainable growth

To overcome these challenges, we at AdEx Partners propose a dual strategy that targets both top-line and bottom-line growth: 

Top-line growth:  The key is to understand data as an integral part of services or products. The focus is always on the added value for the customer. Through value testing with clear objectives and measurable results, we can secure long-term attention and budget. An agile approach is essential in order to be able to react flexibly to changes in regulatory requirements and market conditions without sacrificing efficiency. 

Bottom-line support:  From an internal perspective, data should be managed as an independent product. This includes the introduction of data ownership and stewardship. It is crucial to consider data as an integral part of services or products, for example to improve controllability or for simulations. The role of data must be considered in the design of every process. It is important to merge the value chain of data and processes - from analysis with mining to business process management (BPM) and automation, e.g. with software robotics. 

Practical example: Customer data base of a bank

Banks generate an extensive database from customer interactions, transaction histories and market data. In order to use this data successfully and effectively, various analyses could be carried out in order to develop new products and services on this basis. There can be many reasons why this is not possible: Data is scattered across different departments, there is no uniform view of customer information or existing digital initiatives are not sufficiently coordinated. 

Dual solution approach

One approach to countering this could be twofold: improving the customer offering on the one hand (top-line growth) and optimizing internal processes on the other (bottom-line growth). 

To better integrate data into customer products, the bank could develop a new app that offers customers personalized financial advice based on their transaction behavior and financial goals. By using big data and AI, this app can provide individualized savings and investment recommendations tailored to the specific needs of each customer. On the one hand, this potentially increases customer satisfaction and loyalty and, on the other, can create a new source of income for the bank by recommending tailored financial products. 

In terms of data management as a standalone product, the bank could introduce a centralized data management system that brings together all customer data from different departments. This enables a unified view of customers and improves decision-making. The introduction of data governance and stewardship principles ensures that data quality and security remain at a high level. In addition, processes such as credit checks and risk analyses could be automated and thus made more efficient through the use of robotics and AI. 

As a result, by implementing a dual strategy, a bank can not only improve its services through data-driven personalization, but also optimize its internal processes. This holds considerable potential for cost savings while simultaneously increasing customer satisfaction. It could also increase cross-selling rates and improve operational efficiency. This practical example shows how the strategic use of data can not only help to solve existing problems, but also open up new growth opportunities for traditional financial institutions. 


Data is the new gold of the business world, but its potential is often left untapped. Success in the digital landscape is not determined by the collection of data alone, but rather by its intelligent use. A coherent vision that encompasses all aspects of data architecture and integration is crucial to establishing a sustainable digital transformation.  

Companies that optimize their data strategy not only open up new opportunities for growth and innovation, but also strengthen their competitive position. This means that the effective handling of data is not an optional luxury, but a fundamental necessity for any forward-looking company.