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One of the elements that is often mentioned as an advantage when it comes to digital is that everything done on digital channels can be measured unlike what happens on traditional channels. If we consider the famous saying “If you can’t measure it, you can’t manage it” it is clear how decisive this fact can be. Thanks to measurement the possibility of making the right choices (effectiveness) and using the available resources well (efficiency) is significantly increased.

Yet, every time we discuss with a room of managers and entrepreneurs, when asked the fateful question “How many of you have configured a monitoring system such as Google Analytics on your company website and regularly consult it?”, the raised hands are very few. Or rather, Google Analytics is usually there, but consulting it regularly is another thing….

This happens because the fact of being able to measure, alone, is not enough. We also need to know, for example, what is worth measuring and what is not, that is, what are the variables directly linked to the company’s business and which therefore must be included in the control system. The question is not trivial because it postulates the existence (or non-existence) of a conceptual reference model for the functioning of the company.

In practice, Google Analytics gives you a series of values, parameters, and variables. But you must be able to find the ones you really need in your specific context. And this is a completely different story. In fact, it is one thing to have parameters such as reach, bounce rate, and the percentage of direct traffic available, it is another thing to know what these variables are connected to, what their physiological values are in the different contexts in which the measurements are carried out, and what their positive or negative variation implies.

Today we are bombarded with data, which arrives from analytics systems, from social channel measurement platforms, or online monitoring platforms focused on reputation. But all this data is just raw material that must be placed in the right contexts to be transformed into information and be truly useful for decision-making.

Put like this it seems very simple, almost elementary. After all, today we talk about big data, sophisticated predictive analysis techniques, and learning algorithms based on inferential statistical analyses. But our position, after many years of experience with companies of all complexity and sizes, is that there is a strong, enormous need to work on so-called data literacy, to develop and consolidate a true culture of measurement. Starting, or restarting, from the basics of mathematics and descriptive statistics can allow a very significant leap in quality in the managerial capabilities of many companies.