Data Analysis & Data Preparation

Existing data is structured, cleaned, checked, and analyzed in depth. The goal is to create a comprehensible and reliable foundation from raw data, enabling targeted answers to specific questions.

This includes exploratory data analysis, data quality testing, merging different data sources, and preparing information for reports, dashboards, automations, or other data processes.

• Data cleansing and data structuring

• Exploratory analysis of data, patterns and anomalies

• targeted analysis to answer specific questions

• Merging Excel, CSV, database or system data

• Checking for completeness, plausibility and data quality

• Preparing data for reporting, automation, or forecasting

Typical result - A clean, understandable and evaluable data basis with clear insights into the respective question.

Dashboards & Reporting

Key figures, trends, and correlations are visualized in clear reports and interactive dashboards. The goal is reporting that is easy to understand and supports everyday decision-making.

The focus is on clearly defined KPIs, a stable data model, and transparent logic. Data sources, transformations, and calculations are transparently documented, ensuring that every metric remains understandable from its source to its presentation.

• Building dashboards and reports

• Development and implementation of KPI structures

• Data modeling for reporting applications

• Visualization of key performance indicators

• Documentation of data sources, transformations, and KPI logics

• Optimization of existing reports

• Automation of recurring evaluations

Typical result - A usable dashboard or report that creates transparency, supports decisions and can be maintained in a traceable manner over the long term.

Data Automation & Data Pipelines

Recurring data and reporting processes are simplified, automated, and made more stable. The goal is to reduce manual work, avoid sources of error, and reliably provide data for reports, analyses, or other systems.

Data can be imported from files, databases, APIs, existing systems, or publicly available web sources, transformed, validated, and further processed in a structured manner. This results in robust processes that can be executed regularly and maintained in a traceable way.

• Automation of manual Excel and reporting processes

• Building simple data pipelines

• Data imports from files, databases, APIs, or existing systems

• Web scraping and structured data collection from suitable web sources

• Transformation and verification of recurring data

• Reduction of manual intermediate steps

• Providing data for reports, tools or analyses

• Documentation of data flows and processing steps

Typical result - A stable data process that reduces manual work, avoids errors, and provides data reliably.

Predictions & Data Tools

Existing data is used to better assess developments and prepare well-informed decisions. This includes simple forecasting models, trend analyses, and evaluations for planning, demand, or capacity.

Additionally, small data-driven applications can be developed that make data directly usable in everyday life. These can be input forms, evaluation tools, or decision-making aids tailored to a specific question.

• Forecasts based on historical data

• Sales, demand, capacity or process analyses

• Trend analyses and evaluation of developments

• Development of small data-driven applications

• Input masks for structured data entry

• Decision-making aids for specific use cases

Typical result - A practical data solution that simplifies planning, analysis or data collection and supports everyday decision-making.