"How do we effectively create value from our data?" Many organisations ask themselves these and similar questions when dealing with their data. While the fundamental importance of data is undisputed, it is often unclear how the growing volume of data, metadata and data sources can be effectively and repeatably transformed into added business value.  

Last year, Gartner published a Surveyin which 56 % of the data analytics leaders surveyed from 500 companies worldwide stated that their teams were unable to create effective value for their organisation. In times of increased efficiency pressure, such statements are particularly painful. The wave of enthusiasm that enthusiastically celebrated data as "the new oil" has now subsided. It has therefore become all the more important to demonstrate the added value of data and the solutions based on it. It is therefore important to document the impact of data-driven improvements on business decisions, cost effectiveness, earnings potential, customer satisfaction and general company-wide productivity.  

In addition, we repeatedly encounter challenges in practice that make data value creation more difficult. One approach that is dedicated to securing the added value of data is the Data Product Management.  

We have identified three common challenges in data value creation and explain below how data product management can provide efficient solutions in each case:  

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Challenges in data value creation 

Challenge 1: Lack of user-orientation 

No use, no benefit. In order to utilise data effectively, a user group must first be defined and the data prepared accordingly. In concrete terms, this means preparing data in such a way that it corresponds to the intended purpose of the user. This applies to all data products, from raw and metadata to processed data and more complex products such as code assets, decision-support dashboards or AI and machine learning models. 

Data value creation therefore begins with the best possible understanding of the needs of a user group and the delineation of homogeneous and stable needs. However, too little space is often given to understanding requirements through user research with direct user involvement. As a result, although data is maintained, processed and provided, it fails to fulfil user needs. In extreme cases, the users are even unknown.  

If the requirements are not or only partially fulfilled, users often fall back on manual or outdated processes despite the new data product. Consistent data product management avoids these pitfalls by focussing on added value and user orientation.  

Challenge 2: Lack of scalability along the life cycle 

Data value creation is a complex process that requires company-wide accepted and tried-and-tested workflows. Such processes must be scalable across a large number of possible use cases. Unfortunately, this scalability is often not given in practice.  

This is because many use cases often do not make it from project to product. Although wish lists and requirements can be formulated, the lack of user-orientation and missing validation steps to ensure added value often result in expensive mistakes during product development. As a result, solutions are developed with a high investment of resources, which then come to nothing before going live. 

In addition, further development and maintenance are neglected, especially in the later life cycle phases. This is particularly annoying if an existing "productive" data product is to serve as the basis for further data products/applications. This also applies to current approaches in the field of generative AI, for example. Here, for example, Retrieval Augmented Generation (RAG) opens up new possibilities in knowledge management. However, the best search function will not help if the underlying documents and data are not sufficiently maintained and are therefore out of date.  

For these processes to function independently of the requirements of a specific use case, clearly defined standards, roles and process steps must be practised within the organisation. Only if this is guaranteed can an organisation benefit from important economies of scale and learning curve effects. Data product management along the life cycle phases provides the basis for this. 

Challenge 3: Lack of reusability 

As already explained in the second challenge, data products can also build on each other. The utility value of the underlying data products multiplies when previously developed data solutions are reused and combined across departmental and team boundaries.  

However, ensuring this reusability is the next challenge that many organisations are not up to. One reason for this is that it requires data (analytics) managers to change their perspective: away from thinking in terms of individual use cases and towards systemic thinking in terms of product portfolios. This change in perspective is important because it is the only way to ensure effective management of dependencies and synergies between data solutions - the only way to create valuable, interoperable and scalable data solutions. 

One major obstacle is the creation of transparency. Many organisations have built up data catalogues over the last few years. In reality, however, these catalogues struggle with various problems, such as high maintenance costs or incomplete entries. From a portfolio perspective, prioritisation is therefore also necessary in order to create a data map that focuses on the relevant data products. 

Conclusion 

The aspects described so far are not new in the world of data, but have been the focus of various data management initiatives for many years. However, these challenges have not yet been adequately addressed. Even today, data value chains are still not fully traceable, metadata is not available or maintained and centralised solutions are not user-oriented enough.  

Data Product Management opens up a promising perspective for organisations to tackle the aforementioned challenges and significantly improve their own data value creation. Data product management involves the consistent understanding and management of data/data solutions as products. It thus represents a departure from the concept of data assets that are hoarded (and not shared!) like a treasure. This is also clear from our definition of a data product: The user-centred integration of data and information technology to support tasks in such a way that real added value is created.