Data Governance Operating Model at an Automobile Club

Data Governance Operating Model at an Automobile Club

Data Governance Operating Model at an Automobile Club

Expert: Michael Scharpf

Industry: Finance & Insurance

Area: Finance & Controlling

Increase the efficiency of your data science projects with the Data Governance Operating Model - your key to optimisation in the data-driven automotive industry.

Our AI and Data Science Case Studies:
Experience from over 1,600 customer projects

Positioning in the data age: the central challenge

In the fast-moving world of the automotive industry, the data & analytics team of a renowned automobile club is facing a crucial challenge: the team wants to establish itself company-wide as a leader in the Positioning the area of Data Science and thus significantly increase the relevance and impact of their projects.

Her vision was, Data-driven decisions become the norm in the company and the value their work brings, Communicate transparently. But how can they successfully drive this change?

Strategic realignment: The path to data-driven excellence

With extensive expertise in data analytics and artificial intelligence, we have developed a strategy for our client that focuses on the Data Governance Operating Model. This model not only strengthens the five dimensions of the data strategy: processes, use case pipe, roles, organisational structure and system landscape - but also helps to ensure that the Data & Analytics team is operationally at the forefront.

The first step was to create a Use Case Library in the Share Point. This library enables a central and structured storage of use cases that can be used by different departments to create synergies and avoid duplication of work.

In order to clarify the structure and responsibilities within the team, we have a detailed Elaboration of necessary data roles in the Data & Analytics Team. Along with this, we have created an organisational structure based on the hub & spoke model, which optimises communication and collaboration between the different departments.

Another key element of our solution was the Elaboration of the core tasks of the Data & Analytics Team and the Clearly defined range of services to the company. This enabled expectations to be communicated clearly and resources to be used efficiently.

Measurable successes: The transformation in facts

Thanks to our detailed approach and expertise, we were able to achieve impressive results. There were Roll-One-Pager created that can not only be used for job advertisements, but can also be used for staff development. These role profiles contribute to the clear Definition of responsibilities and qualifications and support the recruitment process.

The use case library has created unprecedented transparency about ongoing, completed and planned use cases in the company. This facilitates the Prioritisation of projects and ensures a more effective resource management.

Finally, the master strategy slide deck we created allowed for a Effective communication of activities of the Data & Analytics team throughout the company. This has not only raised awareness of the value of the data strategy, but also increased stakeholder buy-in and engagement across the organisation.

Curious now? Let us show you what sets us apart from other companies and how we can help you achieve your goals.

Your expert

Michael Scharpf - Key Account Manager

Michael Scharpf

Key Account Manager | Alexander Thamm GmbH

Interactive manual search

Interactive manual search

Interactive manual search

Expert: Michael Scharpf

Industry: Consumer & Retail

Area: Marketing & Sales

Experience how effortlessly product information can be found with our interactive manual search - precisely, quickly and in any language you want.

OUR AI AND DATA SCIENCE Case studies:
EXPERIENCE FROM OVER 2,000 CUSTOMER PROJECTS

[Challenge]

Our industrial clients faced the complex challenge of providing their customers with immediate answers to their questions about the products. The product information was spread across different sections of the manual, which made finding specific answers time-consuming and tedious. In addition, it was important that the answers provided were reliable and accurate to avoid misunderstandings or misinformation.

Improving the customer experience through responsive, efficient and interactive search was a key objective to increase customer satisfaction and loyalty and strengthen the market position.

[Solution]

To meet this challenge, we have developed an advanced system based on Aleph Alpha's powerful language models. These models are able to answer customers' questions directly by analysing existing documentation and manuals and generating a precise and understandable answer.

This approach ensures that no misinformation (so-called "hallucinations") is included in the answers, as the information is derived exclusively from the existing documents. Our system is designed to automatically interpret and respond to these types of questions, which allows for a significant increase in efficiency and customer-friendliness.

Furthermore, our system is multilingual, which makes it suitable for markets in different countries and regions and thus strengthens the international competitiveness of our customers.

[Result]

Our solution has led to a significant speed-up in answering customer questions. The system is now able to provide immediate and accurate answers, which has significantly increased customer satisfaction.

In addition, the ability to indicate the sources of the responses has increased customers' confidence in the information provided. This feature has proven particularly valuable in helping customers gain a deeper understanding of the products and take full advantage of their benefits.

Finally, the multilingual compatibility of our system has enabled our clients to enter new markets and expand their business internationally.

Curious now? Let us show you what sets us apart from other companies and how we can help you achieve your goals.

Michael Scharpf - Key Account Manager

Your expert

Michael Scharpf | Sr. Principal Key Account Manager | Alexander Thamm GmbH

Data Mesh Concept for an Industrial Company

Data Mesh Concept for an Industrial Company

Data Mesh Concept for an Industrial Company

Expert: Michael Scharpf

Industry: Consumer & Retail

Area: Marketing & Sales

Discover how we helped a leading industrial company revolutionise its data architecture and use valuable IoT data to make strategic business decisions with the innovative Data Mesh concept.

OUR AI AND DATA SCIENCE Case studies:
EXPERIENCE FROM OVER 2,000 CUSTOMER PROJECTS

[Challenge]

Our client, an international manufacturer of chainsaws, forestry and gardening equipment, faced a significant challenge. The company wanted to overhaul its analytics architecture to fully utilise the valuable data from digital twins and IoT devices. This is a key aspect in the modern data-driven economy, as such information provides valuable insights into product performance and customer usage. It was important to the client to have a pragmatic and goal-oriented approach that covered all aspects of a modern architecture. The challenge was not to let the intended data lake become a data swamp - a common problem where data becomes disorganised and inaccessible.

[Solution]

As a solution provider for data analytics and artificial intelligence, we rose to the challenge with a concrete plan. We started with requirements gathering and conducted a comprehensive preliminary study to understand the client's specific needs. From these insights, we developed a data mesh concept. A data mesh shifts the scaling of data architecture from centralised teams to domain-oriented teams, providing a scalable solution for big data. This concept also included data governance and permission control, two critical factors to maintain data quality while ensuring secure access to the data. We then moved on to the implementation phase and started building the individual domain instances. We successively implemented the defined use cases to demonstrate the performance of our solution.

[Result]

The result was compelling. The Data Mesh approach recognises that only Data Lakes have the scalability to meet today's analytics needs, and our client now has a data management framework for their first IoT use case. Our 'bottom-up' ownership structure under clear data governance rules enabled the company to fully realise the value of its data. We also provided a roadmap for further implementation, including the definition of further pilot use cases. Thus, our client was able to further develop its data-driven strategy, relying on our expertise in the data mesh concept.

This project highlights our expertise in Data Science and Artificial Intelligence and shows how we can help businesses realise their data-driven ambitions. Our comprehensive view of business issues and understanding of our clients' challenges enables us to provide tailored solutions that have been proven in practice. If you're looking for an experienced partner to help your data analytics and AI projects

Curious now? Let us show you what sets us apart from other companies and how we can help you achieve your goals.

Michael Scharpf - Key Account Manager

Your expert

Michael Scharpf | Sr. Principal Key Account Manager | Alexander Thamm GmbH

Retail sales forecast at a food retailer

Retail sales forecast at a food retailer

Retail sales forecast at a food retailer

An automated solution for predicting the sales of any product is being developed and transferred to pilot operation.

Transparent white-box approach for the customer

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Creation of independence from external provider through in-house solution

High cost savings

Challenge

An internationally active food retailer wants to automate the sales forecasts of its products and introduce an automatically controlled value chain. The software used so far is to be replaced by the development of a solution that can be adopted and further developed internally.

Solution

Forecast models are developed and optimised specifically for certain requirements (e.g. introduction of new products). The procedures are then integrated into an automated process for expansion to any markets and product groups.

Result

The forecasting quality can be increased compared to the software used so far. The process is automated and can be controlled and further improved by the customer.

Are you interested in your own use cases?

Challenge

An automotive company would like to visualise various market-specific data in order to create a Competitive analysis for the US market.

Solution

There will be a interactive and Flexible application, including of different maps with two different views implemented.

Result

Relevant markets are identifies, analyses and visualises. The dealer or the respective sales department have the possibility to compare the direct competition with their own product and to visualise the relevant data.

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Demand forecasting of spare parts through machine learning

Demand forecasting of spare parts through machine learning

Demand forecasting of spare parts through machine learning

Expert: Michael Scharpf

Industry: Consumer & Retail

Area: Procurement & Supply Chain

Optimise your inventory through informed and accurate demand forecasts with the power of Machine Learning.

Our AI and Data Science Case Studies:
Experience from over 1,600 customer projects

Strategic planning against unforeseeable fluctuations in demand

In the past, a renowned distributor of construction equipment spare parts faced a significant business challenge: it sought to accurately forecast the quantities of demand for its products in the following months at different locations. The goal was to use this forecast to improve its Stocking the warehouse optimally and according to demand and thus achieve the highest possible efficiency.

Integration of machine learning for optimised demand forecasting

Our experienced team of data analytics and AI specialists has taken on this challenge with a particular focus on demand forecasting. Based on a variety of in-house data, such as historical demand quantities, detailed product master data and master data on sales locations, we have developed an deep data analysis carried out.

We also integrated external data sources, such as relevant weather and economic data, to better understand the context and potential external drivers. By combining these extensive data sources relevant predictive indicators were identified. The application of a machine learning algorithm enabled us to forecast spare parts demand at all locations for the next 12 months with a precision that previously seemed unattainable.

    Quantifiable economic added value

    Thanks to our solution, the retailer was able to realise considerable added business value. The increased predictive accuracy of our demand forecasting solution enabled the retailer to increase its Storage strategies more efficient to manage.

    In concrete terms, the added value for the company was reflected in critical business indicators: Parts availability, also known as the service level, improved significantly, the Inventory turnover was optimised and lost sales from avoiding empty warehouses could be drastically reduced. This illustrates how our advanced data-driven solutions can help companies transform their business processes and gain a competitive advantage in the market.

    Curious now? Let us show you what sets us apart from other companies and how we can help you achieve your goals.

    Your expert

    Michael Scharpf - Key Account Manager

    Michael Scharpf

    Key Account Manager | Alexander Thamm GmbH