From vision to a practical GenBI roadmap.
GenBI & Dashboards Gaining clear answers from your data
What if you could simply ask your data questions?
Generative Business Intelligence (GenBI) makes exactly that possible. Instead of working your way through complex dashboards, you receive precise answers in natural language — quickly, intuitively, and accessible to everyone. This turns data analysis into a genuine dialogue that unlocks new perspectives.
Take your data to the next level with GenBI.
Our services at a glance
How can GenBI be successfully implemented?
- We analyse your data landscape and work with you to develop a GenBI strategy that is perfectly aligned with your technical and organisational requirements.
- Together, we identify the right GenBI tools and integrate them in a way that allows you to analyse data effortlessly through natural language and put valuable insights directly into action.
- We train your teams and foster a culture in which data and AI naturally work hand in hand.
From tool selection to the cloud – everything you need to modernise your BI
- We assess technologies and tools such as Amazon Quick, Tableau, or Power BI to ensure your BI platform is perfectly aligned with your requirements.
- We efficiently migrate existing BI systems to the cloud, transferring only the reports that truly matter — reducing complexity and lowering costs.
- We deliver modern cloud analytics solutions that centralise your data and provide transparent, up-to-date insights at any time.
Reliable data as the foundation for intelligent analytics
- Clean data you can rely on: By automatically cleansing and enriching your data, we create a robust foundation for accurate and trustworthy analyses.
- End-to-end transparency: Clear data lineage ensures you can always trace where your data comes from and how it is processed.
- Secure use of AI and GenAI: We advise you on data protection and compliance (e.g. GDPR, AI Act) and show you how to use AI solutions safely and responsibly.
From manual work to intelligent automation
- By using generative AI, reports and visualisations are created automatically. This saves valuable time and significantly reduces manual effort in the BI front end.
- Self-service analytics through natural language: Instead of complex queries, you can simply ask your data questions in natural language — making data analysis intuitive and accessible to all employees.
- Agent-driven workflows with AWS Quick: AWS Quick brings data, tools, and processes together in a central workspace. AI agents answer questions in natural language and automate tasks such as report creation and data analysis.
Planning & Implementation
Technology, architecture and implementation, all from a single source.
GenBI Consulting
Business Intelligence reimagined
Business intelligence is a central component of modern corporate management today. However, traditional dashboards and static reports are increasingly reaching their limits. Technological progress – particularly in the field of generative AI – is opening up entirely new possibilities: data becomes conversational, analyses are automated and insights are immediately actionable.
With GenBI, we transform your data landscape from reactive reporting to intelligent, proactive decision support. We guide you from strategic alignment to successful implementation, bridging the gap between business objectives and technological execution.
How GenBI Works in Practice
GenBI makes data analysis accessible to everyone. Instead of manually evaluating dashboards or formulating complex queries, employees engage in a direct dialogue with their data.
Questions such as "How is revenue trending in product segment X?" or "Which factors are influencing margins in the Southern region?" are posed in natural language and answered within seconds through visual and context-related insights. GenBI identifies relevant correlations, highlights deviations and provides structured summaries – transforming raw data into actionable decision-making impulses.
Added Value through Intelligent Automation
With generative AI, the focus shifts from manual report creation to automated, intelligent insights. Reports are generated faster, enriched with context and deliver more than just raw figures. GenBI detects anomalies, identifies patterns and supports forecasts as well as scenario analyses. At the same time, manual effort in the BI frontend is significantly reduced, allowing specialist departments more time for strategic questions.
Reliable Data as a Foundation
The quality of results depends significantly on the underlying data architecture. For this reason, we do not view GenBI in isolation but as an integral part of your entire data landscape.
We support you in harmonising data sources, further developing existing architectures and establishing a consistent "Single Point of Truth". Through clear data lineage and structured governance, we create transparency and trust. Regulatory requirements such as the GDPR and the AI Act are also taken into account from the outset. This creates a secure foundation for the responsible use of generative AI.
Strategic Architecture and Integration
GenBI only unfolds its full potential through seamless integration into existing systems. We analyse your current BI and data architecture – from on-premises environments to modern cloud platforms – and develop integration concepts that intelligently connect frontend, backend and automation.
In doing so, we also evaluate forward-looking approaches such as a Unified AI Workspace, where data, AI models and automation processes are centrally orchestrated. The goal is a scalable, flexible architecture that keeps pace with technological developments.
Planning & Implementation
From Vision to Impact
GenBI enables companies to understand data faster and make informed decisions with measurable added value. In practice, however, initiatives often fail due to manual effort, unclear objectives or a lack of integration into existing BI architectures – this is exactly where we come in.
We support you from the planning stage to the implementation of your GenBI solution, focusing on automation, efficiency and ROI. By automating dashboard creation and migrating legacy systems to modern, cost-effective cloud BI platforms, we reduce reporting efforts and create a scalable foundation for data-driven innovation.
Our Roadmap to Realisation
The implementation of Generative BI requires more than new tools. Success depends on a structured approach that connects business objectives, technical architecture and organisational acceptance.
Our approach comprises:
- Defining clear target visions and use cases with measurable added value
- Analysing your existing BI and data architecture
- Developing a scalable cloud and integration strategy
- Technical implementation and integration into existing systems
- Enablement of specialist departments and sustainable anchoring within the company
Technology & Platform Strategy
For the implementation of Generative BI, we rely on proven BI and cloud platforms that are specifically enhanced with generative functions. Our selection remains technology-agnostic and is oriented towards your existing system landscape as well as your strategic goals.
Depending on requirements, we combine analysis, visualisation and data engineering platforms into a high-performance overall solution, including:
- Cloud-based BI platforms such as Amazon QuickSight or Tableau
- Data warehouse and analytics solutions such as Amazon Redshift, Amazon Athena, Google BigQuery or Databricks
- Integration and automation components for AI-supported workflows
AI-powered Workflows & Automation
The primary added value of Generative BI stems from the automation of operational analysis and reporting processes. Generative AI enables the automated creation of dashboards and reports, the transparent explanation of deviations and the efficient derivation of scenarios and forecasts.
Reusable, AI-powered workflows can be integrated into existing processes in a structured manner. Agent-based functions support specialist departments in their daily work and significantly reduce manual reporting tasks. This results in more efficient workflows and accelerated decision-making processes.
Example: Generative BI with Amazon Quick
Platforms such as Amazon Quick allow business intelligence and generative AI to be combined within an integrated environment. Analysis, insight generation and automation interlock seamlessly. Business users receive context-related insights, can query data using natural language and generate automated reports – without having to switch between different tools. Furthermore, reusable AI-powered workflows enable the enterprise-wide scaling of analysis and decision-making processes.
Data Governance & Compliance
Trust as the foundation for Generative BI
The potential of Generative BI only unfolds on the basis of reliable data. One of the greatest challenges in using generative AI is the risk of so-called hallucinations – plausible but incorrect results. Without a stable data basis and clear governance, GenBI applications quickly lose credibility.
For this reason, we combine intelligent automation with consistent data governance. We secure data quality, guarantee the traceability of data flows and ensure compliance with regulatory requirements such as the GDPR and the AI Act. In doing so, we create trustworthy, compliant Generative BI solutions – while simultaneously strengthening the data literacy of your organisation.
Governance & Data Quality as a Foundation
High data quality is the fundamental prerequisite for resilient analyses and AI-powered evaluations. Incomplete or erroneous data undermines trust and sustainably impairs decision-making capabilities. For this reason, we support companies in establishing and further developing structured data governance models, ensuring consistent and verified datasets and providing transparent traceability of all data flows through data lineage. At the same time, we optimise metadata management and data catalogues to create a clear structure and unambiguous responsibilities. Generative BI can specifically support these processes — for example, through anomaly detection or automated metadata enrichment — but always remains embedded within clear and reliable governance structures.
Compliance, Security & Data Sovereignty
Regulatory requirements from the GDPR or the AI Act present companies with new challenges. Uncertainty regarding data protection and AI regulation often hampers innovation and the productive use of Generative AI.
We provide certainty by advising on regulatory and ethical frameworks and implementing technical measures such as pseudonymization, anonymization, or the aggregation of sensitive data. Furthermore, we develop architectural concepts for on-premises or European sovereign cloud environments and define clear regulations for data sovereignty and model usage. In doing so, we enable the responsible and productive application of Generative AI – without compromising on data protection and compliance.
Reliable AI & Data Literacy
Generative AI models can produce incorrect or biased information if they are not based on a verified data foundation.
We develop controlled GenBI architectures in which AI models only access reliable, approved data. If the data basis is insufficient, the systems deliberately provide no answer rather than generating erroneous results.
At the same time, we systematically promote data literacy within your organisation. Sustainable success with Generative BI only occurs when employees can correctly interpret, critically scrutinise and responsibly utilise data.
Interested?
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Why Choose Woodmark?
For over 25 years, we have been supporting companies in creating genuine added value from their data. With Generative BI, we go one step further: we make data understandable, accessible and directly actionable for better day-to-day decision-making.
Rather than relying on off-the-shelf products, we work alongside you to develop an approach that truly fits your organisation – from strategy through to implementation. Practical, efficient and with a clear focus on impact.
Let us discover together the potential hidden within your data.
Success stories from our clients
Driving sovereign, AI-driven logistics optimization with AWS & Woodmark
To replace fragmented legacy systems, Woodmark and AWS developed a sovereign data platform for next-gen assistance systems. The solution combines real-time streaming with strict EU governance (TISAX, GDPR) and automated compliance pipelines. The result: Maximum data control in Europe, significantly accelerated deployment cycles, and a highly available, resilient infrastructure for the mobility of tomorrow.
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Sovereign AWS cloud architecture for next-gen driver assistance systems
Faber Group faced the challenge of transforming fragmented legacy logistics systems into a future-proof, EU-compliant structure. Together with Woodmark and AWS, a sovereign data platform was developed that utilizes AI and real-time analytics to optimize route planning, reduce CO₂ emissions, and achieve savings of €3.2 million through strict governance and automated compliance.
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How Data Analytics Is Revolutionising Training and Health in Sport
After the sporting restart, Düsseldorfer EG faced an extraordinary challenge: building a squad from scratch – under intense time pressure. Together with Woodmark, the traditional club relied on AI-driven analyses to precisely identify player profiles, assess market opportunities more quickly, and make well-informed decisions.
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How AI is accelerating the fresh start of Düsseldorfer Eislauf-Gemeinschaft (DEG)
After their sporting restart, Düsseldorfer EG faced an extraordinary challenge: building a squad from scratch – under intense time pressure. Together with Woodmark, the traditional club turned to AI-driven analysis to identify player profiles with precision, assess market opportunities more quickly, and make well-founded decisions.
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GDPR- & NIS2-Compliant Data Platform
A leading company in the airline catering industry is laying the foundation for tomorrow’s inflight services with a modern data platform on AWS and Databricks. Scalable, secure and future-focused, it enables data-driven innovation, greater transparency and noticeably more efficient processes across the entire value chain.
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Enhanced Customer Service with an AI Chatbot
With a GenAI bot for automated responses, Quirion speeds up support, eases the burden on teams, and boosts customer satisfaction. Fast. Scalable. Future-proof.
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AI-powered analysis of legal documents with AWS
Imagine being able to analyse legal documents like NDAs in seconds – completely free from manual errors and endless hours of work. By leveraging AI and AWS Bedrock, we have developed a solution that automates the extraction of key contract information.
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AI & Amazon Q: The intelligent platform for data-driven ice hockey scouting
With an AI-powered platform based on Amazon Q, IceHawk UG optimises its scouting and controlling: relevant data is centralised, player and opponent analyses are automated, and decisions are made based on solid foundations. A step towards data-driven, future-proof club development.
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SV Darmstadt 98 improves player analysis with AI technology
Experience the future of sports data analysis with an AI-powered solution from AWS. The coaching staff and scouts can now easily ask questions like "Who is the best substitute for an injured player?" via voice query.
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Development of a Data Governance Framework for an MS Purview-based data platform
Through the new Data Governance Framework, including the Data Marketplace, a systematic improvement in data and metadata quality in the Data Lakehouse was achieved.
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AI-supported tender optimization for construction companies
Currently, reviewing tender documents takes several days. AWS's Gen-AI solution offers significant time savings and enables faster decisions through clear executive summaries
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Introduction of Databricks platform at municipal utilities
The introduction of the Databricks platform at the municipal utilities enables the IT department to address all use cases from the specialist departments, break down existing silos, and strengthen the reputation of IT.
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GenAI route generation with AWS and the D2E program
SilverRail Technologies aims to redefine the travel experience for its customers and has laid the foundation for a data-driven, AI-powered travel assistant.
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Higher efficiency through process automation in AWS
Woodmark has supported NHD Beteiligungs GmbH in reducing operational effort through the use of automation in AWS. This resulted in improved operational processes, reduced downtime, and lower costs in the AWS environment.
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Migration and modernisation of the Redshift data warehouse
Our client Tom Tailor was able to create a 'single point of truth' for business logic and KPIs with the migration and modernization of the Redshift data warehouse. This now enables more accurate data-driven decision-making.
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Introduction of Amazon QuickSight for better comparability of staff and branches
A butcher shop lacks established branch and personnel reporting, and the branch managers are using an outdated analysis system. With the implementation of Amazon QuickSight, fast evaluations are now possible.
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Insurance group migrates from heterogeneous BI landscape to Amazon QuickSight
An internationally operating insurance group saves license costs through the introduction of Amazon QuickSight and can focus on the content thanks to structured data visualisation
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SV Darmstadt 98 is cloudifying its scouting data with AWS Glue.
SV Darmstadt 98 improves match data analysis with Amazon QuickSight
The Bundesliga club was able to significantly improve data quality in the areas of athletics and scouting through centralized data visualization and the introduction of a QuickSight dashboard.
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GenAI-supported generation of Ninox database models in AWS
The goal of the software developer arcRider is to simplify the creation of Ninox database models in AWS with GenAI and to increase customer satisfaction...
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Route optimization of a logistics company with AWS SageMaker
The Faber Group BV offers pallet and pooling services. Due to the increasing complexity of supply chains and rising fuel prices, the company wants to optimize its route planning...
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Introduction and coaching of the SAFe methodology
FinTech startup with 50 developers aims to improve customer satisfaction with product quality and enhance internal collaboration through the use of SAFe and SCRUM methodologies...
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Automated preprocessing of incoming email customer inquiries in inside sales
In the inside sales department, a five-figure number of email inquiries are received annually, which are processed with a very high manual effort...
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Digitalisation of processes / target concept for the specialist departments
Shared responsibility for complex processes by two different authorities with many manual processes, decentralized data storage...
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Integration of affiliate, website analytics, SEA, and SAP into marketing analytics
Data from affiliate marketing, website analytics, and SEA are manually analyzed side by side...
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IT Architecture Support für die ‚New Car Architecture 2025‘
The need for a data-driven automotive industry requires new data architectures in the vehicle and in the backend.
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Architecture of a group data warehouse and project coaching
There is a need for a group-wide data warehouse as the foundation for further BI applications.
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Platform for automated creation of data-driven exposés for customer advisory services
Desire for digitization, i.e., data-driven and automated customer advisory services in the healthcare sector...
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Data governance concept at an automotive manufacturer
The data lake is fed from various systems worldwide. Efficient utilization of the data lake therefore requires a company-wide, international data governance concept...
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Concept and pilot implementation of the Alation Data Catalog
For the large data lake with many diverse and unstructured data, central management and a consistently established data governance are required...
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Standard reporting and data analysis in the business department with Tableau
The reporting should be made more efficient, enabling self-service analytics and relieving the IT department...
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Data-driven marketing for predicting the online customer journey with Dataiku
There is a need for detailed analyses and forecasts of purchasing behavior, particularly for predicting the transformation of visitors into customers...
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KPIs and visual analytics for optimising the planning of a transportation company
The transportation company is looking for an automated, data-driven approach to assess the impact of individual construction projects on delays and optimize future planning.
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High performance through auto-scaling of the open-source services Jitsi and BigBlueButton
Covid-19 requires the rapid digitisation of school education. The open-source services Jitsi and BigBlueButton (bbb) have proven effective, but they are not automatically scalable...
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Process transparency through analytical visualization of additive manufacturing
The sensor data is available in various formats and resolutions and cannot be used for real-time analysis and quality monitoring
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From Data Lab to 'Smart Data Factory' – Innovative data strategy based on Docker
Managing non-financial risks requires a new data strategy. IT and the compliance department are seeking a way to collaboratively and iteratively implement data-driven risk analyses.
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Cloud analytics unlocks vehicle data globally for R&D and quality management.
Systematic use of vehicle data requires powerful cloud analytics, machine learning, and advanced visualisation.
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Selection of a BI solution for near-realtime analytics in production
Optimising the manufacturing plant requires the use of sensor measurement data from the production process in real time.
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Prediction and prevention of component failures based on sensor data
Real-time analytics are needed to effectively utilise the vast amount of machine data from the relevant production machines.
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Design Thinking enhances innovation capability and opens up new revenue streams
Market dynamics require greater innovation capability.
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Training a decision model for churn prediction
600 million data records of sales activities and purchase transactions.
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Smart data platform as a data and data value broker
Looking for a data hub that enables data-driven business models.
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Strategic mid- and long-term planning
Various data sources should be consolidated into a strategic overall picture and allow for detailed analysis...
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Building a hybrid architecture for data analysis
The system landscape for data analysis has become outdated and consists of isolated solutions...
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Simulating and optimising airport charges
Simulation of various fee scenarios considering multiple variables such as noise and pollutant emissions, noise classes, night-time and other surcharges.
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Integration and development of an advanced analytics platform
Aluminium wheels are produced using the low-pressure casting process, and after casting, they undergo an X-ray inspection.
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Geo-fencing engine as a service based on HERE geo-data
Geo-services have become essential for modern automobiles.
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Development of the backend for driver assistance systems
Vehicles can currently only use local sensors for information gathering.
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Development of a software framework for sensor data analysis
Data from over 170 channels in a proprietary format need to be stored, processed, and visually analysed.
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Development of sensor processing chains on the HERE OLP
The driver assistance systems are to be enhanced and improved with insights from the sensor data.
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Building a data warehouse for enterprise-wide reporting using CI/CD
A new Data Warehouse (DWH) is required for future bank management. The implementation will use the technologies Exasol and Tableau, supplemented by Jenkins and Docker for Continuous Integration (CI) and Continuous Delivery (CD).
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Implementation of Planning Analytics and Tableau Self-Service
Instead of time-consuming planning in Excel, both central departments and business units should be able to quickly and independently create analyses of the business plan and business performance in the future...
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Implementation of an organisational concept and process model for a Data Science Lab
The organisational structures and project processes of the existing BI Competence Center conflict with the requirements of Data Science projects.
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Concept and implementation of a hybrid Big Data & BI architecture
The analytical architecture landscape has reached the end of its lifecycle after 15 years of use.
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Sales planning and forecasting with predictive planning
Long production times (at least 9 months) require very accurate forecasting and planning of delivery quantities to avoid shortages.
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Introduction of cost planning for development costs
The planning of approximately 8,000 development projects is carried out in various Excel-based applications.
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Sales planning and production planning
Sales planning is carried out using Excel (across more than 20 workbooks).
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Maintenance and operations of management information systems
Expiry of the existing contracts for maintenance and operations for the applications of the automotive bank.
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Real-time streaming of Teleloq vehicle data with Kafka and Storm
Functional improvements are to be achieved for each market and vehicle model.
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Setting up an HDP DEV Cluster - operation and onboarding of use cases
Hadoop development clusters distributed across multiple business areas.
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AfterSales potential and information system
The profit share in the After Sales segment for automotive manufacturers is over 50%. Therefore, the potential in the After Sales area should be better utilised.
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Achieving service goals through process implementation
No transparency regarding the services provided by the Competence Center in the SAP BI Shared Service area.
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Concept for establishing a BI Competence Center
High coordination effort, long project start-up times, lack of technical expertise, and support quality.
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Data warehouse system landscape using the Data Vault approach
A regulatory authority with over ~400 insurance companies requires an information system with:
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Building a Self-Service Analytics Platform with Tableau
Complex requirements for data visualization cannot be implemented with the existing BI tools.
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Building a Self-Service Analytics Platform with Tableau
The existing BI tool cannot meet the complex requirements for data visualisation.
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Introduction of the Notation Concept according to IBCS® for Status Presentations
Preparing the weekly and monthly reports takes approximately 2 days each.
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IBM Cognos Mobile Reporting for Sales
The field sales representatives are often on the road and at customer sites. During this time, direct access to sales analytics is not possible.
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Implementation of use cases with Microsoft HDInsight and Azure
The department has no way to analyse data across applications.
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KPI - Dashboard zur Unternehmenssteuerung
Der Kunde nutzt unterschiedliche Software Systeme zur Analyse seiner wichtigsten Unternehmens-Kennzahlen. Dies führte zu zeitaufwendigen Analysen
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Building a Big Data Platform with Fraud Detection
There are no self-learning fraud detection mechanisms that examine transaction data for fraud patterns in real-time.
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Building a Big Data Platform for the Corporation
In order to offer and implement Big Data projects within the Group in a professional service environment in the future, a Big Data Service and Operations Platform should be created for two components: IBM SPSS and Hadoop 2.1
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Building a BI Solution - Talent Management System International
The qualification of potential touchpoints, customers, and prospects is not optimally established to manage and monitor the services offered in the retail channels.
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Introduction of an IT Service Financial Management Platform
IT organisation in over 50 countries with Excel-based IT cost and billing planning.
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