Azure Data Science: 4-weeks Proof of Concept


The aim of the pilot project is to adopt advanced analytics to the specifics of the organization

What do we do during adoption? We evaluate the chance to use data science in your organization. • We find use cases and choose the one that is most promising for the customer. • We evaluate data sources, their quality and integration processes. • We assess competences and identify gaps. • We transfer knowledge on the use of machine learning tools (Machine Learning): ◦ familiarizing with the environment Azure Machine Learning, ◦ familiarizing with the issues related to the preparation of data for analysis, ◦ using of available econometric / machine learning models for a specific business area, ◦ assessment of the quality of the models developed, ◦ testing and implementation of models, ◦ industrialization of models. • We transfer knowledge about integration and automation processes: ◦ familiarizing with the Azure Data Factory work environment, ◦ data flows and their triggers, ◦ automation. • We run and configure Data Science Sandbox: ◦ we run data warehouse and data integration processes (Azure Data Lake, Azure Data Factory), ◦ we launch machine learning service (Azure Machine Learning). • We prepare the selected model of advanced analysis (machine learning model). • We visualize the results in the Power BI report. • We present ways to industrialize the model, promising the highest return on investment. • We present in the form of a workshop the results of adoption data science together with further development recommendations.

Pilot project products • Knowledge transfer related to Data Science Sandbox. • Configured Data Science Sandbox. • A sample machine learning model. • Visualization in the form of a report Power BI. • Final data science adoption assessment report: ◦ business case, ◦ data science cycle, ◦ vision of development, ◦ environmental costs, ◦ team competencies gap, ◦ data science implementation plan.