TheBigDataChef, 2019

In the fast-changing world of technologies, there is a lack of catch up with the change and adaptation in new IT trends such as AI and Machine Learning. Even though both bring outstanding features and advantages, people still don’t realize their usage. Some heavyweight analytical agencies show the results that in many companies nowadays Machine Learning makes 76% of its sales.

One of the criteria of successful data analysis is always a volume of collected datasets. If you have a big amount of collected data, you are able to generate more precise mathematical models for forecasts, find more correlations and all kind of KPIs. But different companies should have different datasets, structure and goals. So, it makes the adaptation the model for certain needs very hard. There is the challenge, where Machine learning can help. Instead of the one-time model it provides the agile system, which can handle the whole class of the issues.

The idea behind the ML is quite simple: at first stage you train the model – system consumes the data records with the known results and forms it’s internal structure; when it’s ready – system is able to consume the new data records and return the expected result.

This time, we partnered with RS Finance, who offers data intelligence for its clients. Businesses can have a detailed analysis of their development, growth and comprehensive reports about their activity. Data is taken from various sources (financial tools, ERPs, e-commerce etc) and stored into the Data Lake – a storage of prepared data, able to be analyzed. After the analysis, results are used to build reports, which are storing into the Microsoft PowerBI. Clients get access to dashboard with the list of reports, possibility to view any, request new reports, share it with team members.

The Big Data Chef passed the UAT (user acceptance test) successfully and was decently appreciated. The second phase of development is on the planning stage now. It is meant to enlarge the app functionality and to optimize its working process due to the users’ feedbacks and wishes.