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Becoming a data-driven company with data effectiveness
Recognizing hidden potential, making informed decisions, using growing volumes of data efficiently: Companies today are faced with the challenge of creating real added value from their data.
This is where the concept of the data-driven organization comes in:
Data becomes the central basis for decision-making, processes are made smarter and more customer-oriented, development times are shortened and competitive advantages are secured.
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The advantages of a data-driven organization at a glance
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From challenges to clear solutions – how to make your company data-driven
Data alone does not bring success. The decisive factor is how you use it, organize it and apply it in a targeted manner to solve your business challenges. Use the following approaches to add value to your data strategies, architectures and processes – always with a focus on your business value.
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Data strategy
Data-Aware Architecture
MLOps
Modern data pipelines
Marcel Mikl
Service Lead Data & ML & AI
Let's talk about data effectiveness in your company – for better data-driven decisions and optimized business processes.
Data strategy
Your basis for data-driven success
A successful data strategy is the key factor in transforming your company to be data-driven. It links business goals with data and AI initiatives, creates governance and ensures that data-driven decision-making processes are anchored in the corporate culture. A good data strategy answers key questions:
Your path to a value-creating data strategy
Define business value
Data projects are often technically driven and not aligned with strategic business goals.
Create governance
Unclear responsibilities and roles can lead to fragmented data approaches and a lack of quality.
Promote transformation
Data-driven decisions require a change in mindset and processes – this change is often underestimated.
Plan for sustainability
Pilot projects bring quick success, but the transfer to the entire organization often fails.
Data-Aware Architecture
Think holistically, implement sustainably
A robust and flexible data architecture is essential for successfully building a data-driven organization. It is the basis for transparent data flows, consistent data quality and the seamless integration of modern technologies – while also ensuring that governance and compliance are successfully implemented.
Your path to a data-aware architecture
Overcoming data silos
Many organizations struggle with fragmented data sources, outdated legacy systems and a lack of transparency. This hinders the creation of a unified view of customers, processes or business goals.
Integrate technological diversity
The rapidly growing data platform ecosystem offers countless options, but the selection and integration of these technologies is complex. There is often a lack of clear criteria for combining innovation with long-term stability and maintainability.
Ensure governance and compliance
Companies must ensure data protection requirements and transparency in data origin and use – often in distributed and heterogeneous system landscapes. However, clear data governance is often lacking.
MLOps
From the idea to the real AI application
AI projects often get stuck in the development phase. Although many companies are successfully experimenting with machine learning (ML) models, they face considerable challenges when it comes to putting these models into productive use and operating them effectively in the long term.
Modern MLOps practices close this gap and ensure that AI solutions are not only scalable, but also sustainable and efficient.
Your path to a productive AI application
From experiment to production
ML models often get stuck in the development phase without making the leap into productive environments. There is often a lack of automated processes and standards to make ML pipelines reliable.
Continuous model monitoring
Even productive models lose value if data drift (changes in the data) or model drift (deteriorating results) are not detected in time. Many companies struggle with inadequate monitoring and a lack of feedback loops.
Collaboration and processes in the team
Silos between data science and IT operations prevent interdisciplinary teams from working together efficiently. Consistent processes along the entire ML lifecycle are often lacking, as are MLOps-specific skills.
Modern data pipelines
For stability and scalability
High-quality and scalable data pipelines are the foundation for all data-driven applications, decisions and business models. They make your data reliably available, ensure quality and ensure that you can keep pace with growing requirements.
Your path to a real AI application
Scalability and real-time processing
The amount of data and the number of sources are growing exponentially – and conventional architectures are often not sufficient to ensure an efficient combination of real-time and batch processing.
Data quality and reliability
Without automated quality checks and clearly traceable data lineage, errors can creep in that go unnoticed and jeopardize data-driven decisions. A lack of monitoring also makes it difficult to identify problems quickly.
Strategic integration of modern technologies
Selecting and integrating modern data engineering tools and cloud-native services can be a challenge. You may be wondering where to start or how to establish effective DataOps practices to make your systems flexible and future-proof.
Data strategy
Data-Aware Architecture
MLOps
Modern data pipelines
Data strategy
Your basis for data-driven success
A successful data strategy is the key factor in transforming your company to be data-driven. It links business goals with data and AI initiatives, creates governance and ensures that data-driven decision-making processes are anchored in the corporate culture. A good data strategy answers key questions:
Your path to a value-creating data strategy
Define business value
Data projects are often technically driven and not aligned with strategic business goals.
Create governance
Unclear responsibilities and roles can lead to fragmented data approaches and a lack of quality.
Promote transformation
Data-driven decisions require a change in mindset and processes – this change is often underestimated.
Plan for sustainability
Pilot projects bring quick success, but the transfer to the entire organization often fails.
Data-Aware Architecture
Think holistically, implement sustainably
A robust and flexible data architecture is essential for successfully building a data-driven organization. It is the basis for transparent data flows, consistent data quality and the seamless integration of modern technologies – while also ensuring that governance and compliance are successfully implemented.
Your path to a data-aware architecture
Overcoming data silos
Many organizations struggle with fragmented data sources, outdated legacy systems and a lack of transparency. This hinders the creation of a unified view of customers, processes or business goals.
Integrate technological diversity
The rapidly growing data platform ecosystem offers countless options, but the selection and integration of these technologies is complex. There is often a lack of clear criteria for combining innovation with long-term stability and maintainability.
Ensure governance and compliance
Companies must ensure data protection requirements and transparency in data origin and use – often in distributed and heterogeneous system landscapes. However, clear data governance is often lacking.
MLOps
From the idea to the real AI application
AI projects often get stuck in the development phase. Although many companies are successfully experimenting with machine learning (ML) models, they face considerable challenges when it comes to putting these models into productive use and operating them effectively in the long term.
Modern MLOps practices close this gap and ensure that AI solutions are not only scalable, but also sustainable and efficient.
Your path to a productive AI application
From experiment to production
ML models often get stuck in the development phase without making the leap into productive environments. There is often a lack of automated processes and standards to make ML pipelines reliable.
Continuous model monitoring
Even productive models lose value if data drift (changes in the data) or model drift (deteriorating results) are not detected in time. Many companies struggle with inadequate monitoring and a lack of feedback loops.
Collaboration and processes in the team
Silos between data science and IT operations prevent interdisciplinary teams from working together efficiently. Consistent processes along the entire ML lifecycle are often lacking, as are MLOps-specific skills.
Modern data pipelines
For stability and scalability
High-quality and scalable data pipelines are the foundation for all data-driven applications, decisions and business models. They make your data reliably available, ensure quality and ensure that you can keep pace with growing requirements.
Your path to a real AI application
Scalability and real-time processing
The amount of data and the number of sources are growing exponentially – and conventional architectures are often not sufficient to ensure an efficient combination of real-time and batch processing.
Data quality and reliability
Without automated quality checks and clearly traceable data lineage, errors can creep in that go unnoticed and jeopardize data-driven decisions. A lack of monitoring also makes it difficult to identify problems quickly.
Strategic integration of modern technologies
Selecting and integrating modern data engineering tools and cloud-native services can be a challenge. You may be wondering where to start or how to establish effective DataOps practices to make your systems flexible and future-proof.