Erweiterte Datenanalyse

We assess the opportunity to use data science in the organisation.

• We find use cases and select the most promising one for the client.
• We assess data sources and their quality, as well as integration processes.
• We assess competencies and identify gaps.
• We transfer knowledge concerning the use of a machine learning tool:
  • familiarisation with the Azure Machine Learning environment,
  • familiarisation with issues concerning the preparation of data for analysis,
  • use of available econometric / machine learning models in a specific business area,
  • assessing the quality of developed models,
  • testing and implementation of models,
  • industrialisation of models.
• We transfer knowledge regarding integration and automation processes:
  • familiarisation with the Azure Data Factory work environment,
  • data flows and their triggers,
  • automation.
• We launch and configure the Sandbox Data Science environment:
  • we launch a data warehouse and data integration processes (Azure Data Lake, Azure Data Factory),
  • we launch a machine learning service (Azure Machine Learning).
• We prepare a selected advanced analysis model (machine learning model).
• We visualise results in a Power BI report.
• We present ways to industrialise the model that promises the highest return on investment.
• We present the results of data science adoption in the form of a workshop with recommendations for future development.

• • Knowledge transfer related to Sandbox Data Science.
  • Configured Sandbox Data Science.
  • An example machine learning model.
  • Visualisation in the form of a Power BI report.
  • Final evaluation report on the adoption of data science in the organisation:
    • business case,
    • data science cycle,
    • vision of development,
    • costs of environment,
    • missing competencies of the team,
    • a plan for implementing data science in the organisation.