Advanced data analysis

How do we work with clients?

We aim to adopt advanced analytics to the specifics of a given organisation. We show the possibility of creating a suitable working environment for advanced analytics in an organisation, commonly referred to as a Data Science Sandbox. This environment is equipped with a set of tools to support advanced analyses, including the preparation of data, models, reports and industrialisation of models.

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.

Why Azure?

Microsoft Azure is a state-of-the-art cloud environment that offers unlimited scalability, strong performance, large storage capacity, and security. With Azure, you can optimise the cost of your IT environment while providing fast access to a variety of services.

Why now?

Most organisations operate in a competitive environment that can change rapidly. Accurate predictions and quick decisions are often key to business development. Supporting digital transformation with solutions related to advanced analytics can result in measurable business benefits in a short period of time.