All technical systems automatically log their activities. This creates data that we can collect and evaluate. But why should we do that? Well, let’s take the example of online banking. This offering from our bank does not consist of a single IT component, but rather several different systems. When we access online banking with our web browser, we first see the web page. This website is provided by a web server. Our customer data, on the other hand, is stored in a database that is kept secure in a protected area of the bank. The actual process of transferring funds from one account to another is then handled by a mainframe computer in the background. All these technical components are connected via other IT systems such as routers or switches.
So what happens if we as a customer have problems accessing online banking? Then the responsible employees in the bank’s IT department have to check what the fault is. This can be done very easily if you have access to the activity logs, i.e. the data of all as of these IT systems. This allows IT staff to very quickly identify whether the problem is with the web server, the database, the mainframe, or perhaps a general malfunction of the Internet.
A Swiss bank demonstrated this very nicely a few years ago. Those responsible there realized that when online banking was disrupted, there was initially an increased number of messages with the word “Rädle “on Twitter. The Swiss use the word “Rädle” to refer to the spinning circle that indicates that a web page should be loaded, but this process takes longer than expected. So the word Rädle on Twitter was a kind of early indicator of online banking disruption. The bank’s IT department connected the data from its Twitter account with data from its web servers, its databases and its mainframes. In the event of an unusually frequent occurrence of the word Rädle, those responsible are automatically alerted and can set about troubleshooting their various IT systems in order to remedy the fault as quickly as possible.
In this example, we can see very nicely how the analysis of data can be used to both facilitate the work for one’s own employees and to improve the offerings for customers. The bottom line is that companies save a lot of money, have satisfied customers and reduce the workload of their employees.