Data Products
Together, we will turn your data into a competitive advantage
From by-product to core asset
Data is not merely a byproduct of business activities, but rather a key asset that can give companies a decisive competitive advantage. Therefore, it is becoming increasingly important to apply the concept of product thinking to data and offer data products. Data products offer the opportunity to optimally utilize data internally, generate added value for customers through data-based services, or monetize it as a standalone product.
What are data products?
Data products are structured, reusable, and well-documented datasets or data-driven services that can be used for analysis, decision-making, and process optimization. They offer companies diverse opportunities to create added value, whether through internal process optimization, improved customer offerings, or new revenue streams.
Examples of data products and data-based services for customers in the automotive and mobility sector
In-Car App & Service Purchases
Personalized suggestions for digital services in the vehicle are made possible by analyzing user and driving profiles – a trend that is becoming increasingly established in the wake of software-defined vehicles. Data products that evaluate the behavior of drivers with similar usage patterns support the targeted recommendation of additional functions, enhanced assistance systems, or subscription-based services. Customers benefit, for example, from newly developed additional features from the manufacturer or in-car purchases – such as toll services, navigation extras, or comfort packages. For companies in the automotive sector, this opens up new digital business models and revenue streams.
Weather and traffic data APIs for navigation systems
By integrating real-time weather and traffic data, drivers receive situation-dependent route recommendations that take current road and environmental conditions into account. Based on data on precipitation, ice, construction sites, traffic jams, or road closures, journeys can be dynamically adjusted, delays minimized, and safety risks reduced. In combination with adaptive navigation systems, these data products enable not only personalized but also more efficient route guidance – a crucial component for future-oriented mobility solutions and optimized fleet management in both private and commercial applications.
Driver assistance analysis for insurance companies
Data products that, with customer consent, combine driving behavior such as acceleration, braking, and cornering with contextual factors like time of day, weather, and road profile, and take mileage into account, create new opportunities. This allows for personalized insurance models with risk-based pricing to be offered in cooperation with insurance companies. Drivers benefit from individually tailored insurance coverage and premiums, while companies unlock new data-driven revenue streams.
Vehicle telemetry data for fleet management
A data-driven service that provides real-time telemetry data (e.g., speed, fuel consumption, engine status) to companies with vehicle fleets. By continuously collecting parameters such as speed, fuel consumption, engine condition, and idling times, deployments can be planned more efficiently and downtime minimized. Data products that combine this telemetry data with information on routes, traffic, and maintenance intervals enable predictive fleet management – with benefits such as optimized operations, reduced costs, and increased sustainability in fleet management.
Examples of data products for increasing internal efficiency
Data products for optimized sourcing
Time-to-market is a crucial factor for manufacturing companies to succeed in today's market. However, shorter development times often mean specification changes right up until series production, which in turn leads to significant complexity in supplier management. Data products that intelligently connect engineering, logistics, and purchasing information support the departments involved in optimizing supplier management and ensure cost efficiency.
Business partner risk scoring model
Reliable business partners are essential for stable supply chains and dependable order processing. However, unexpected payment defaults or financial difficulties on the partner's side can have significant consequences. Data products that combine internal information such as payment history and outstanding receivables from financial systems with external data such as credit reports, market analyses, and economic information enable a data-driven assessment of default risk. This allows risks to be identified early, relevant stakeholders to be automatically informed, and alternative business partners to be identified if necessary.
Demand Forecasting Dashboard
Accurate sales forecasting is the foundation for efficient sales, production, and inventory planning. However, fluctuating demand, seasonal effects, and external influences complicate planning reliability. Data products that combine past sales figures from ERP systems, seasonal patterns from historical sales data, and external market data such as industry forecasts, economic indicators, or macroeconomic trends enable well-founded demand forecasts. This allows companies to react more quickly to market changes, avoid over- or underproduction, and deploy their resources more effectively.
Customer 360° Dashboard
A holistic view of the customer is crucial for a successful sales strategy. However, different touchpoints, changing contacts, and individual requirements lead to fragmented information in sales. Data products that intelligently link customer master data from CRM systems, sales activities from communication and planning tools, revenue trends from ERP systems, and marketing and service data from campaign and support platforms create a centralized view of the customer. This enables sales staff to act more effectively, develop customer relationships strategically, and better leverage potential.
How do companies create successful data products?
Data products can vary greatly from company to company. Successfully designing good data products is achieved using these fundamental principles:
- Customer benefits: Real added value is created when it is ensured that data products meet the specific needs of users and use cases.
- Domain orientation: Data products typically contain processed information from a wide variety of data sources and business domains. A well-considered domain architecture and demarcation are essential for successful design.
- Defined interfaces: Only with well-designed interfaces is diverse usability guaranteed, e.g., in the form of APIs for integration into software systems or through provision via data catalogs and marketplaces for integration into data platforms.
- Data security & data governance: Data is a valuable asset and must be protected accordingly. Necessary access rights and data protection mechanisms, as well as compliance requirements, must be considered.
- Loose coupling and resilience: Data products are standalone products that are, however, integrated with upstream systems. Loose coupling and resilience to incoming interfaces are essential for long-term design.
- Cross-functional product teams: Close collaboration in product teams, consisting of business and IT including data science experts, enables product orientation, ensures the necessary expertise and also defines clear responsibilities.
Suitable data architecture as a basis
A high-performance data architecture is essential for efficiently developing and operating data products. While service-oriented architectural principles with loose coupling have been used for many years in the design of operational software systems, the central, monolithic data warehouse is still a common architectural paradigm when building data platforms. However, classic warehouse architectures have their limitations.
Modern approaches like the data lakehouse enable the seamless integration of diverse data sources, allowing for the efficient processing of both structured and unstructured data. The data lakehouse combines the flexibility of a data lake with the structure and query capabilities of a data warehouse. This allows companies to connect and store large volumes of raw data from upstream systems via data pipelines. Data lakehouses facilitate the creation of domain-oriented data products and offer a wide range of options for making this data available for further use.
While data lakehouses, despite their flexibility, are based on a central platform approach, data meshes represent a further evolutionary stage in data architecture, enabling complete domain orientation through decentralization. This allows, for example, large companies to independently build their own domain data platforms within their respective business domains and connect them via central data mesh mechanisms.
Modern data platforms offer several advantages:
- High design flexibility
- Diverse data integrations
- Numerous output options
- AI integration
- Scalability
- Data governance
