In recent months we have put together a series of articles on data, their intrinsic value, and the infinite possibilities of analysis. We have talked about applications serving the people, of vision and knowledge accelerators. But at the end of the day, companies ask themselves: what’s in it for me?
Before profit: the foundations to build on
Let’s take a small step back and start from the 5 key principles which regulate the identity of data, that is the well-known and invoked 5 Vs: Volume, Velocity, Variety, Value and Veracity.
Volume: it is necessary to have a great deal of data. To have value, a datum must be quantitatively consistent and in order to reach a significant quantity it must be collected, saved and made available. Therefore Cloud, rather than physical machines, hybrid structures. A storage space which comes with a certain cost.
Velocity: a datum must be available quickly. If for no other reason than because of its weak nature, as it often traces a behavior, activity or element, a datum sometimes has a very narrow space-time shelf life. Let’s consider, for example, data from IoT sensors, or from social media. Velocity means tracing ability, often in real time, which translates into collection and procurement costs and the ability to receive the datum in real time.
Variety: it is necessary to receive a datum in the most diverse ways possible. To ensure the broadest representation of case studies, we cannot fail to take into account all the data we gather in relation to a phenomenon. Exceptions or peaks cannot be discarded, which in any case represent a portion of data. The datum’s variety is not only given by its changes over time, but also, and above all, by today’s need to capture and interpret data (structured, semi-structured and unstructured) from different sources and which have totally different formats (images, documents, videos, etc.), with a significant storage commitment and analytical capacity.
Value: it is necessary to have a substantial datum and one which has a return value. It is essential that the company should know which data collection will be able to return some business directions.
Veracity: it is necessary to have a datum with an intrinsic truthful content. If we imagine working on different data, from different sources, stored in different databases, with different shelf lives, the truth contained in those data is a variable which should be highly valued. It is necessary to “label” and “weigh” the truth of the data sets, in order to be able to give a “veracity score”, which will then become the indicator of the decisions that companies can make based on the readings of these data. Therefore, an expert capable of providing this information is required.
In practice, we have not even started carrying out an analysis, and we have already begun to spend a lot of money.
What do companies gain from all this? Data Monetization is the answer
Per se, the words Data Monetization are very vague and broad, but they include two elements which interest us: the data collected, which so far have been a cost, and their monetization, i.e. their ability, once aggregated, to generate business.
The highest-profile summary tells us that for companies, data monetization essentially translates into 2 paths:
- the first is internal, focused on data exploitation to improve processes, productivity, products and services, in order to act as an enabler for generating constant dialogue with customers. The possibility of reducing the costs of a process could appear to be rather boring, but the truth is that readings of internal data are a very powerful engine capable of drastically lowering unwanted operational inefficiencies or of being a powerful element of speeding up the process, with a very important impact on its yield. Obviously, the possibility of having a competitive advantage on the market, knowing how to espy and predict the needs of the public with a useful leeway, becomes another important factor for companies. This translates, for example, into suppressing services which customers will tend to discard in the short term and into setting up others which they are starting to demand. Or, for example, into introducing new sales models or customer care services which are totally in line with what the public requires. Not to mention decisions related to elements such as geotargeting and geofencing linked to satisfaction ratings
- the second path is external and involves creating new business by making these data available to customers and partners, perhaps in an anonymous form. Imagine a vendor who is able to inform his/her partners, thanks to georeferencing systems connected to sentiment analysis, which is, for example, the most suitable place to start a certain type of business.
There are many possibilities, but in order to monetize we must never forget the company’s necessity, availability and predisposition to change. Data show us the path. Companies must be ready to follow it.