End-to-end data journeys: An operationalization approach for a truly data-centric company
Industry: Automotive | Period: > 6 months Months | Team size: 2 consultants
“Data is business” – this is how we counter the widespread assumption among companies that data is “an IT matter”. This also sums up why modern companies strive not only to define a data strategy, but also to operationalize it through consistent processes, roles and tools. Data forms the basis for innovation, efficiency and growth. However, just like disorganized resources, data also remains in a state of unproductivity if it is not collected, processed and made accessible in a structured manner.
Our client – a leading German car manufacturer – was faced with the task of establishing a company-wide and user-centered data management system. The aim was to promote overarching collaboration between data providers and data users, standardize processes and guidelines and at the same time ensure a high level of data availability and quality. On the one hand, this requires processes and methods that allow data requests and offers to come together from a wide variety of departments – a working model that does not exist in a matrix-oriented organization. On the other hand, central changes in mindset and prioritization are necessary. Simply put, data requests from other areas of the company that do not come from the day-to-day business of the data creators are not considered a priori in any resource planning and are therefore quickly deprioritized. This mechanism must be broken.
Together with the customer, we were able to successfully use our “E2E Data Journey” as a template to consolidate grown and unconsolidated data management frameworks in different areas of the company within two months and identify corresponding gaps in responsibilities, processes and tooling. This enabled us to quickly and easily create a concrete implementation plan, which will be piloted and refined in the next step using initial use cases.
Challenges: Various hurdles on the data journey - fragmented data management, lack of collaboration and lack of transparency
- Inconsistency in data management practices:
Different departments manage and use data in their own way. They often even access the same source data, but process it differently. This leads to contradictory processes, redundant structures and ultimately a lack of data quality and consistency. This makes it extremely difficult to gain useful insights from the data, which can significantly impair analysis and decision-making. There is also often a fundamental lack of common understanding about what data exists in the company, how it is available and how it can be used. This goes hand in hand with the need to actively prioritize the implementation of necessary changes in data management. - Lack of collaboration between providers and users:
Teams often worked in isolation from each other, which led to misunderstandings, delays and inefficient data exchange. For example, data consumers sometimes require data in a different form than the data provider has previously made available from their day-to-day business. Instead of working together to prepare the raw data for reuse, data is often prepared in a stand-alone solution. Other teams that subsequently face the same problem are unaware of this solution and build another individual solution. - Lack of transparency and quality:
Without comprehensive data governance, there is no authority that maintains an overview of the data supply, demand, structures and access paths. Due to a lack of consistent standards for metadata and quality checks, trust in the quality and timeliness of the data is limited. - Fragmented data architecture:
Dedicated value creation from data often begins with initial use cases and PoC. This approach is also correct in terms of an iterative implementation strategy. However, if there is no overarching data architecture that clearly regulates the technical stages of data transfer from the source to the target system, the result is an uncontrolled, evolved data landscape. This leads to a wide variety of problems in terms of data availability, quality and security. Specifically, it can also significantly increase setup and operational costs due to inappropriate redundant data storage.
Success Journey: Transparency, user centricity and efficiency along the data journey
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Impact At Launch: Clear progress along the data journey - transparency, collaboration and data quality at Launch
- Comprehensive transparency regarding the fields of action:
The analysis of the status quo and the development of standardized processes led to significantly more clarity about tasks, risks and opportunities in data management. Our framework enabled us to harmonize the complexity of more than 30 processes that describe overlapping, different or even conflicting data management steps with over 250 process steps and 10 roles. - Better collaboration between data providers and data consumers:
Barriers between departments and functions were broken down through a clear division of responsibilities and the introduction of a standardized communication model. A standardized process model creates synergies and offers potential for increasing efficiency. At the same time, it promotes transparency, controllability and an overview of the complex world of organizational data processing. In addition, the clear derivation of data requirements and data supply from strategic business interests ensures clear prioritization and subsequent measurability of data use cases. - Higher data quality and establishment of AI readiness:
Centralized and standardized metadata management practices and a systematic quality approach are introduced as standard. These are necessary to increase trust in the data. The semantic separation of raw data, processed data, metadata and data analytics findings anchored in this facilitates data-centric collaboration in the interpersonal sphere, as well as in the interaction between humans and artificial intelligence.
Conclusion: Data Journey as an enabler for digital solutions and sustainable data management with Ventum Consulting
The consistent implementation of the end-to-end data journey and modern analytics services has enabled our customer – a leading automotive manufacturer – to unite fragmented structures into a holistic digital data management system. Transparency and an overview of data, roles, processes and technologies were created for the first time, resulting in a consistently coordinated and open data ecosystem. Thanks to our methodical, business-driven approach, traditional barriers between data providers and users were broken down and the basis for productive collaboration and successful change management was created. This not only ensured digital readiness for current and future requirements, but also laid the foundation for successfully operationalizing data-driven services, innovative products and sustainable analytics strategies. The key to sustainable success lies in a clear vision, structured implementation and the active involvement of all stakeholders along the entire data journey – from the initial needs assessment through to productive operation.
Take advantage of our consulting expertise in data journeys, analytics and digital strategies to optimally position your company for the digital future and achieve sustainable economic success.
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- Transparency: Clear analysis of the data journey for maximum visibility and well-founded decisions.
- Efficiency: Standardized processes for smooth collaboration and less complexity in data management.
- Tried and tested: 20 years of consulting experience from successful data projects guarantees reliability
- Quality: Higher data quality and AI readiness through centralized, digital solutions.
- Future-proof: Tailored implementation plan for sustainable, data-centric growth and digital strategy.
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