// Data analysis

Data Science Adoption

Most organisations set themselves two goals: the first is generating profit and the second is optimising costs and risk. Modern Business Intelligence tools definitely facilitate the achievement of these goals. Organisations realise that launching a modern analytical platform and industrialisation of analyses and models is often a major challenge.

An experienced partner, with experience gained in dozens of projects, plays an important role in this process and can effectively help with the introduction of advanced analytical solutions, quickly demonstrating measurable benefits.


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.
Biznesowa analiza danych

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.

Additional information

A data science adoption pilot project lasts 4 weeks.
These types of projects usually start with the production of a minimum product to quickly show the benefit of the implementation. The cost can be EUR 20,000–30,000 net, depending on the scope. Our experience indicates that the vendor’s investment funds are a great support in the launch of such projects, which allow you, among others, to launch cloud subscriptions and finance the implementation partner services. As a partner with the highest competencies, we support our customers in obtaining such investment funds.
The cost of an Azure service depends on various factors, such as the number of users, number of instances, computing power and data volume. During the pilot project, we will show you how the cost can be optimised and what cost value can be expected during the production use of Sandbox Data Science. Based on our experience, the cost of Azure services during the pilot project can be between EUR 1500 and 2500 net. A production use of Sandbox Data Science can generate an average cost of EUR 4500–7500 net per month.
  • Sales forecasts.
  • Analysis of shopping basket and customer behaviour.
  • Recommendations concerning the best offer, product, content.
  • Price optimisation.
  • Optimisation of inventory management.
  • Recommendations for adjusting the size of the sales force to the demand at the point of sale.
  • Analysis of the economic efficiency of products on the shelves and categorisation of products.
  • Automation of shelf audits based on image analysis.
  • Abuse detection.

Deep data analysis and visualisation of results and recommendations.

  • Azure Data Factory
  • Azure Data Lake Storage
  • Azure Machine Learning
  • Azure Databricks
  • Azure Synapse Analytics
  • Azure Analysis Services
  • Microsoft Power BI