• Customer experience framework as a definition of a customer-centric business model 
• Business model canvas and business case to ensure sustainable success 
• Transformation plan as the basis for a smooth transition of the business model 

• Improved customer relations through better customer satisfaction 
• New revenue streams based on a redefined business model 
• New value pools by leveraging customer insights 
• Reduced time to market based on better efficiency 
• Resilience by directly addressing customer needs 

• Defined Customer Interaction Strategy 
• Willingness to redefine the existing business model 
• Availability of key experts and support of management 

• Digitalization and connection of customer touchpoints in one learning system 
• Creation of reach, optimization of the sales funnel, and expansion of the brand's ecosystem 
• Continuous improvement through analytics-based automation and innovation 

• Improvement of customer retention rate through an increase in conquest and retention 
• Improvement of NPS based on superior customer experiences 
• More revenue per sale based on optimized sales and marketing performance 

• Availability of data and access to relevant experts 
• Support of top management 

• A well-defined roadmap and sustained support for implementing recommended data strategy initiatives and realizing set goals and objectives 

• Seamlessly integrating data initiatives with overarching business objectives in a data-centric business approach 
• Fostering a data-centric culture and cultivating a data-literate workforce
• Streamlining data collection and deploying data-enabling tools & IT architecture
• Achieving a comprehensive data maturity assessment, showcasing strengths and opportunities for growth
• Focused attention on high-impact data strategy initiatives to deliver transformative outcomes 

• Receive a unique sustainability maturity profile, with an overarching ESG score across 16 categories 
• Get best-in-class insights accompanied by tailored recommendations and concrete next steps 

• Factory layout validation and optimization 
• End-to-end planning process (greenfield and brownfield) 
• Development of location, building, and infrastructure requirements 

• Reduced planning time and increased quality 
• Decrease capital expenditure by optimizing floor space, number of machines, and assets through the use of digital twin (e.g. material flow simulation, 3D model) 
• Improve performance of the factory and its processes (validated by simulation) 
• End-to-end transparency across assets and improved utilization of those assets 
• Increase energy efficiency of factories and production networks 

• Consulting and Integration

• Reduced risk and complexity
• Holistic, tailored end-to-end solutions

The client's business was undergoing drastic changes in light of the energy transition, leading to significant losses.

We jointly defined a holistic and aspirational turnaround masterplan considering both growth and cost improvement levers. Following our one-team approach, we supported the creation of measures to achieve the ambitious targets set for a short and mid-term timeframe.

The client successfully achieved a turnaround, resulting in double-digit profit margins and growth.

The energy ministry of a country requires a holistic energy transition strategy to drive nationwide policy instruments for decarbonization (net zero).

We jointly defined an energy transition vision with the client, establishing optimal decarbonization targets, including renewable energy generation, energy efficiency, and e-mobility. We developed an implementation roadmap and secured policy buy-in from key stakeholders in charge.

A policy outlook until 2030, with dedicated measures and accountabilities defined per policy area, enabling a net-zero target by a specific date.

Data baselining, also known as data landscaping, is our approach to processing and visually organizing data in an intuitive and easy-to-understand way. This involves developing diagrams, maps, charts, and dictionaries to show the relationships between different data elements and entities. After identifying the appropriate data (see "Minimum Viable Data"), we process, prepare, clean, and align it for analysis. This typically includes data profiling, cleansing, and transformation to ensure accuracy, consistency, and completeness of the data. 

We use strategic data analysis to identify and target our clients' Minimum Viable Data (MVD), which is the minimum amount of data needed to achieve a specific objective or goal. The concept of MVD is often used in agile development methodologies to quickly develop and release products by focusing on the minimum set of features required to meet customer needs. 

Regarding the application of data analytics, MVD refers to the minimum amount of data necessary to gain insights or make informed decisions. This involves identifying the most important data variables, reducing data redundancy, and eliminating extraneous data points that do not contribute to the analysis. 

The use of MVD can streamline the data collection process, reduce the cost and time required for data storage and processing, and improve the accuracy and relevance of the analysis. By focusing on the minimum amount of data required, data analysts and decision-makers can avoid information overload and being distracted by irrelevant or inconsequential data. 

However, it is important to note that MVD should not be seen as a rigid or fixed requirement. As business needs and objectives change, the minimum viable data may also change, and additional data may be required to gain deeper insights or achieve more ambitious goals. 

We employ data governance as the process of managing and ensuring the quality, availability, integrity, and security of an organization's data. We work with clients to define policies, procedures, and standards for data management, as well as assigning responsibilities for data management and ensuring compliance with regulatory requirements. 

The data governance process typically includes the following steps: 

• Defining data policies: Establishing policies that govern the management of data, including data quality, data security, data privacy, and data retention. 

• Establishing data standards: Defining data standards that outline the rules for data collection, storage, and use, such as data formats, data definitions, and data validation rules. 
• Assigning data ownership: Identifying data owners who are responsible for managing specific data sets and assigning data stewards who are responsible for ensuring the quality and accuracy of the data. 
• Establishing data processes: Developing processes for managing data throughout its lifecycle, including data collection, data storage, data processing, and data dissemination. 
• Ensuring data security: Implementing measures to protect data from unauthorized access, such as encryption, access controls, and data backup and recovery. 
• Monitoring data quality: Regularly monitoring data quality to ensure that data is accurate, complete, and consistent. 

• Auditing data compliance: Conducting regular audits to ensure compliance with regulatory requirements and internal policies and procedures. 
• Continuously improving: Continuously reviewing and improving the data governance process to ensure that it remains effective and efficient in meeting the organization's data management needs. 

Our approach is a true end-to-end process, from the initial identification of value through to the implementation of applied analytical solutions. We work with our clients from initial discovery to POC (Alpha) and MVP (Beta) to full-scale implementation and support. 

Our analytics development process is a framework for designing, developing, and implementing analytics solutions for specific business problems or opportunities. The process involves several stages that build on each other to create a successful analytics solution. These include problem definition, data collection, data preparation, data exploration, modeling, evaluation, and deployment. 

Overall, the analytics development process is iterative and involves multiple cycles of refining and improving the analytics solution. The key to success is to ensure that the analytics solution is aligned with the business problem and stakeholders' needs and that the insights generated are actionable and can drive business value. 

The IoT digital core for industry is a framework for building out the value of data that customers already have. Integrating and exposing this data with ease, and creating value use cases and analytics without the focus on the technology, allows accelerators to quickly begin the value of data. 

The IoT digital core and data use cases & analytics are two essential components of IoT technology. They work together to collect and analyze data from connected devices and sensors in real-time. 

IoT Digital Core: The IoT Digital Core is the foundation of an IoT system. It consists of the hardware and software that manage the flow of data from connected devices to the cloud. The digital core includes the devices themselves, as well as the gateways and edge devices that are used to collect data and transmit it to the cloud. In addition to hardware, the digital core also includes software such as operating systems, security software, and data management systems. Instead of focusing on technology, this is an accelerated framework approach to bringing value from your data, with blueprint integration adapters, data models, and workflows. 

Data Analytics: Once data is collected by the digital core, it is processed and analyzed using data analytics tools. Data analytics involves applying statistical and machine learning algorithms to identify patterns and insights in the data. These insights can be used to optimize industrial processes, improve product quality, and reduce downtime. Data analytics can also predict equipment failures and maintenance needs. 

Together, the IoT Digital Core and Data Analytics form a powerful system for managing and analyzing IoT data. By collecting and analyzing data in real-time, businesses can gain valuable insights into their industrial processes and make data-driven decisions to improve efficiency, productivity, and profitability.