How to turn data into a competitive advantage

Data products are structured, reusable, and well-documented data sets or data-based services that can be used for analysis, decision-making, and process optimization. They offer companies a wide range of opportunities to create added value, whether through internal process optimization, improved customer offerings, or new sources of revenue. A data product could be, for example, a constantly updated “delivery time forecast service” that can be integrated into operational systems via an API. Data products thus offer the opportunity to make optimal use of data internally, generate added value for customers through data-based services, or monetize them as standalone products.

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Appropriate data architecture as a basis
Modern data architectures such as the data lakehouse or data mesh form the basis for successful data products. A lakehouse combines the openness of a data lake with the structure of a data warehouse. This enables companies to efficiently integrate and store raw data from different sources and use it for analysis or AI. For large organizations, a data mesh offers additional value: responsibility for data is shifted to the specialist domains. Each unit can develop data products independently, while central governance and uniform standards ensure exchange. This creates scalable, flexible data platforms with clear responsibilities. The lakehouse, as a platform for all types of data and analytics, represents a technological approach. Data mesh, on the other hand, is an organizational approach in which teams build and operate their data products independently.

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High added value of modern architectures
Modern data platforms offer numerous advantages: flexible design options, diverse data integration and extraction options, seamless AI integration, high scalability, and effective data governance. A data lakehouse, for example, supports the optional processing of batch and real-time data. Data meshes, on the other hand, enable the decentralized design of data products by individual teams. Modern data architectures also support various data integration and export options. These include connecting cloud and on-premises data sources, APIs, streaming data, and exporting to business intelligence and analysis tools. A key advantage is AI integration, which enables the direct application of machine learning frameworks to data stored in the lakehouse. Another added value is high scalability. For example, the infrastructure in the cloud can be expanded horizontally to ensure high performance even with rapidly growing data volumes and user numbers. Data governance combines central policy management in the lakehouse with decentralized responsibility in the data mesh. In addition, access controls, data catalogs, and versioning ensure transparency and security.

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Data products & data-based services
Data products offer a wide range of opportunities for a variety of industries. They open up new digital business models and revenue streams for companies. In the automotive and mobility sector, companies and customers are already benefiting from data products and data-based services, such as in-car apps and service offerings. The analysis of user and driving profiles enables personalized recommendations for digital services in the vehicle. This trend is becoming increasingly established in the wake of software-defined vehicles. Weather and traffic data APIs for navigation systems integrate real-time information to create situation-specific route recommendations that take current road and environmental conditions into account. Data products that combine driving behavior with contextual factors and take mileage into account open up new opportunities for insurance companies to offer risk-based pricing and thus personalized insurance models for consumers. Vehicle telemetry data can be used to provide a service that enables real-time information such as speed, fuel consumption, engine status, or idle times for fleet management. Continuous recording of these parameters allows for more efficient planning of operations and minimizes downtime.

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Data products for increasing efficiency
Data products can also contribute to increasing efficiency within a company, e.g., by linking engineering, logistics, and purchasing information. This service supports the optimization of supplier management and ensures cost efficiency. Data products that combine internal information (such as payment behavior) with external data (credit reports, market analyses, etc.) enable a data-based assessment of default risk. This allows risks to be identified at an early stage, relevant stakeholders to be informed, and alternative business partners to be identified. Another application is demand forecasting dashboards, which link sales figures from ERP systems, historical sales data, and external market data (such as industry forecasts and economic data) to enable informed demand forecasts. This allows companies to respond more quickly to market changes, avoid over- or underproduction, and use their resources in a more targeted manner. In sales, a 360-degree dashboard provides a holistic view of the customer by linking customer master data from the CRM system, sales information and sales trends from the ERP system, and marketing and service data. This enables sales staff to act on a more informed basis, develop customer relationships in a targeted manner, and make better use of potential.

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Tips & tricks

How to design successful data products

Data products can vary greatly within companies. These basic principles will help you design successful data products:

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• Customer benefit: Real added value is created when data products are tailored to the specific needs of users and their respective use cases.
• Domain orientation: Data products typically contain processed information from different data sources and specialist domains. A well-considered domain architecture and delimitation is therefore essential.
• Defined interfaces: Only well-designed interfaces can guarantee versatility, for example in the form of APIs for integration into software systems or through provision via data catalogs and marketplaces for integration on data platforms.
• Data security and data governance: Data is a valuable asset and must be protected accordingly. Necessary access rights, data protection mechanisms, and compliance requirements must therefore be taken into account.
• Loose coupling and resilience: Data products are standalone products that are integrated with upstream systems. Loose coupling and resilience to incoming interfaces are essential for a long-lasting design.
• Cross-functional product teams: Close collaboration in product teams consisting of business, IT, and data science experts enables a product-oriented way of working, provides the necessary expertise, and also defines clear responsibilities.