Our colleagues turn to us when they are interested in any figures in our division – how many passengers bought a ticket on a given day? What is the failure rate of our subway gates in England for the past month? What is the success rate of the first-level technical support team in resolving customer issues? And many other tricky questions to which we seek answers in large amounts of data.

The great thing is that we play at work – with numbers, tables, and graphs, which we summarize in various reports. We also engage in active data analysis and search for various correlations and useful connections. For example, using machine learning algorithms, we have identified the factors that most influence the failure rate of our TVMs (i.e., automatic Ticket Vending Machines). In practice, this means we can predict which devices will need more servicing, allowing us to plan the number of preventive service inspections. Recently, we have been devoting most of our energy to building a data warehouse for our customers' needs.