Data from social media, usage analyses, and digital services offer enormous potential for better decisions in product management. But which use cases deliver real added value? And how do you manage to link data, processes, and software in a meaningful way? The it's OWL  product.intelligence  project has developed a practical methodology for precisely this purpose – together with WAGO, DMG Mori, Isringhausen, Diebold Nixdorf, and Schmitz Cargobull.

Many companies are sitting on a mountain of data – and yet the question remains: Which data truly helps them align products precisely with the market or make more informed decisions? The new method from product.intelligence brings structure to this chaos and shows how companies can develop initial approaches to using data in product management, step by step.

The 4-step plan: How the methodology works

1. Understanding what's there: First, the current situation in product management is analyzed. What tasks are involved? What data is already being used? Where is there still untapped potential? We begin by creating an overview of the specific tasks and framework of product management within the company. Interviews with product managers and other relevant individuals are used to record current tasks and identify initial potential for the use of data analytics. A structured guide helps systematically answer key questions such as existing information usage and potential improvements through data analytics.

2. Linking processes and data: The second step involves describing the product management process in detail. This includes recording the tasks in each phase of the process, systematically mapping the required input data and the resulting output data, and identifying relevant IT systems. A joint workshop with all stakeholders serves to create consistent documentation and develop a common understanding of product management processes.

3. Derive use cases: Based on the analysis, concrete use cases emerge that demonstrate where data can create real added value. Each use case is described in detail: goal, data sources, and expected benefits. This documentation forms the basis for subsequent prioritization.

4. Prioritize & get started: The best use cases are evaluated based on strategic benefits and feasibility, among other things – creating a clear roadmap for getting started with data-based product management.

Practical tools: Software and data landscape & use case collection

When companies engage with data-driven product management, the developed method for identifying use cases provides a structured foundation. To support companies in applying the method, additional valuable tools were developed within the project. These tools, such as the software and data landscape and the collection of application areas, support the application of the method in several ways:

Software and data landscape for the efficient recording of the product management process

Capturing the product management process and the data available within a company can be complex. The developed software and data landscape provides guidance here. This overview structures product management data into 10 clusters, comprising 47 different data types. Clustering analysis enables systematic categorization of data. At the same time, typical software categories, such as ERP systems, in which this data is managed are identified.

In addition, a generic product management process was designed that divides the product lifecycle into five phases: market analysis, product strategy & planning, product development support, market launch, and product management & control. This process assigns specific data types and software tools to the respective tasks in each phase. This allows companies to efficiently analyze their own processes and data and identify gaps in documentation (Phase 2).

• The software and data landscape provides a basis for comparison to quickly and comprehensively record your own tasks, data types and IT systems.

Collection of application areas as inspiration for use cases

Additionally, a collection of application areas was created to serve as a source of inspiration for developing our own use cases (Phase 3). These application areas were developed through interviews with 21 product management experts and workshops with five companies. The results demonstrate concrete areas where data analytics can bring benefits.

Each application area is presented in the form of a profile. These profiles contain a description of the application area, suitable data sources, and practical examples. Companies can use this information to find initial approaches for integrating data analytics into their product management.

• The profiles of the application areas show practical examples that help companies derive their own use cases.

Why companies should now engage with data-based product management

With product.intelligence, companies from the it's OWL network receive practical start-up assistance that is directly tailored to the requirements of medium-sized industrial companies.

The method developed in the project makes a decisive contribution to the systematic identification and prioritization of use cases in data-driven product management. It is complemented by tools such as the software and data landscape and the collection of application areas, which provide companies with valuable guidance and inspiration. Together, these approaches form a comprehensive foundation for successfully implementing data-driven strategies and making product management future-proof.

The methodology and tools are available to companies  free of charge on the it's OWL innovation platform  . This means that not only the participating companies, but all it's OWL companies benefit from the insights gained from the product.intelligence project.

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The article  Recognizing the potential of data: Methodology and tools for getting started with data-based product management  first appeared on  it's OWL  .