Data Science

Data Science is an interdisciplinary field dedicated to extracting knowledge from data. It leverages methods from statistics, computer science, mathematics, and machine learning to identify patterns and trends, extract insights, and solve complex problems. It encompasses the entire process: from data collection and modeling to delivering results in decision-making processes.

Meaning in detail Contact us now

Distinction from Business Intelligence: While traditional Business Intelligence (BI) primarily analyzes the past ("What happened?"), Data Science uses advanced methods like predictive modeling and machine learning to forecast the future and solve complex problems.

Why Data Science Matters for Your Business

Many executives face a common challenge: data is available, but strategic decisions still rely on gut feeling rather than precise forecasts. Data Science bridges this gap and delivers measurable results.

Three Core Advantages:

From Gut Feeling to Prediction: Make decisions based on validated predictive models rather than assumptions.
Competitive Advantage Through Data: Companies like MediaPrint already use Data Science to increase their advertising revenue by +34%.
Operational Efficiency: Optimize inventory levels, prevent machine failures proactively, and automate quality control.

Core Components and Success Factors

Data Science is the foundation for deploying Artificial Intelligence. Three dimensions are critical for success:

1. Methods
Deploying Machine Learning (ML) models and statistical methods to identify patterns and create forecasts.

2. Data Quality
Solid Data Governance is essential to avoid the risk of "Garbage In, Garbage Out." Results are only as good as the underlying data.

3. Roles and Organization
Specialized Data Scientists develop the models, while Data Engineers provide the necessary infrastructure for scaling solutions.

Common Mistakes When Starting with Data Science

1. Projects Without Clear Business Case
Many companies launch ML models without defining measurable ROI. This leads to technically impressive but commercially worthless results. A Data Science project should always begin with the question: "What specific problem are we solving and how do we measure success?"

2. Underestimating Data Quality
Algorithms cannot compensate for poor data. Without clean, consistent data and Data Governance, even the best models fail. In reality, 70% of the work in Data Science projects lies in data preparation – not in model development.

3. Lack of Organizational Integration
Data Science teams work in isolated "data labs" without integration into business processes. Results remain prototypes that never reach production. Without management support and change management, scaling fails.

Best Practices for Successful Data Science Projects

1. Start with Clearly Defined KPIs
Begin with a concrete business case: "How do we improve forecast accuracy by X%?" or "How do we reduce returns by Y%?" – not "Let's experiment with ML."

2. Parallel Investment in Data Governance
While developing models, you must simultaneously invest in data quality, documentation, and governance. Our Data Strategy approach ensures both go hand in hand.

3. Cross-Functional Teams from Day One
Successful projects unite data scientists, domain experts from the business units, and decision-makers from the start. This collaboration prevents misdevelopment and ensures practical applicability.

4. MVP Approach Instead of Big Bang
Start with a Minimal Viable Product (MVP) that delivers value quickly and is iteratively expanded. Through our ML & AI Readiness methodology, we often deliver initial working prototypes within 8-12 weeks.

💬 Expert Insight

"The most common mistake: Companies start Data Science projects without a solid data foundation. 70% of the work lies in data preparation – not in model development. Without Data Governance, even the best algorithms fail. We therefore always recommend starting with a Data Audit to understand data maturity before making large investments in ML models."

— Thomas Borlik, Managing Partner
The Data Institute

Putting Data Science into Practice

The greatest challenge lies not in theory, but in scalable implementation and strategic integration within the organization.

Two Paths to Get Started:

Already know Data Science is relevant for you?
Let's develop your Data Science roadmap together – from data architecture to production-ready ML models.
→ Schedule a free expert consultation on ML & AI Readiness

Frequently Asked Questions (FAQ)

What is the difference between Data Science and Business Intelligence (BI)?

Data Science uses advanced methods like machine learning and statistics for prediction and problem-solving – looking toward the future. Business Intelligence (BI) primarily focuses on analyzing and visualizing historical data to understand what happened in the past. Data Science goes a step further and answers: "What will happen?" and "What should we do?"

What prerequisites does my company need for Data Science?

The most important prerequisite is not technology, but data quality and organizational integration. You need:

Clean, structured data with clear Data Governance
Defined roles (Data Engineer, Data Scientists, Data Owners)
A clear business goal with measurable ROI
Management support for change processes

The technical infrastructure (cloud, tools) can be built quickly – organizational maturity is the critical success factor.

How long does implementing a Data Science project take?

Duration varies greatly depending on complexity and data availability. Through our MVP strategy (Iteration First), we often deliver initial working prototypes with measurable value within 8 to 12 weeks. Full scaling into production can take 6-12 months, depending on organizational complexity and data maturity.

What does Data Science cost for mid-market companies?

Costs depend heavily on the maturity level of your data and organization. Investments range from smaller pilot projects (starting at €20,000) to comprehensive implementations (€150,000+). We recommend starting with a Data Audit (typically €15,000-30,000) to precisely define costs and expected ROI, avoiding unnecessary initial investments. For most of our projects, investments pay back within 12-18 months.

Can Data Science be used in smaller companies as well?

Yes, absolutely. Even small companies benefit from Data Science, though you should start with focused use cases that deliver value quickly. Instead of building your own Data Science department, you can begin with external expertise and incrementally build competence. Important: Data Science is not an "all-or-nothing" approach. Even individual predictive models can bring significant efficiency gains.