Data monetization: the 5 rules for extracting new value from your data

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With data monetization - the monetization of data, which is the process of strategic analysis and practical implementation that allows you to use data as a source of revenue - companies can transform data they have collected into monetizable value just like other assets. Data monetization becomes a strategy that allows companies to virtuously recover the investments made on IoT and IIoT infrastructure, systems and platforms, accelerating their evolutionary path and designing new business scenarios.

The value of data is always higher and more tangible

All you have to do is read the figures of the latest market research to understand how the definition of "big data" has now become obsolete. The contribution of data to the economy of a company becomes tangible and, multiplied at a national level, reaches significant GDP values.

  • The volume of data is constantly growing and will triple in the coming years from the current 64.2 ZB (where one zettabyte corresponds, rounded up, to 1 billion Terabytes) to the 181 ZB predicted for 2025 (source: IDC and Statista).
  • Smart objects and IoT and IIoT devices will grow exponentially, permeating everything around us, going from the current 23 billion to the nearly 39 predicted for 2025 to 50 billion connected objects in 2030 (source: Strategy Analytics).
  • Data, applications and their derivatives will come to constitute an important value of national GDP: in 2030 the aggregate value for southern Europe, of which Italy is part, will reach 11.5%, 14.5% in North America and 26.1% in China (source: Bahrain FinTech Bay - PwC).

Data monetization: how to extract value from data

To implement a data monetization strategy, the first question a company must ask itself is what data it needs to grow. Continuing the policy of a data warehouse, or rather of the sterile "storage" in silos, now makes very little sense also given the costs derived from this procedure: in addition to the costs necessary for data collection, those due to their movement to a cloud are added to processing and those for their subsequent distribution.

It, therefore, becomes advantageous to define, in advance, following a precise data strategy, which data will become economically profitable once enriched by analysis and processing, becoming valuable information. To summarise, 5 essential points can be defined for profitable data monetization:

  1. First of all, it is necessary to define what data can be really useful to the company. The heterogeneity of the data creates value but not all data is worth something. To optimise data monetization, it is necessary to make the most of the impact that analysis and automation technologies such as Artificial Intelligence and Machine Learning can bring if properly implemented.
  2. Most of the time available to analysts is spent "cleaning up" data. It is therefore necessary to correctly define the processing frameworks. Evaluation parameters (KPIs) are often not optimised, lowering or even invalidating the quality of data, an indispensable characteristic to give value to the asset.
  3. It is advisable to reduce the movement of data to a cloud by enabling proximity processing thanks to edge computing solutions, which allow a higher quality of data thanks to its detection and processing in real-time, at the same time reducing the costs of transmission and analysis in the cloud.
  4. Defining the level of maturity of the company in terms of analytics and professional capabilities. People are needed who can constantly improve the analysis processes to innovate the core business. According to 2020 research conducted by "the Big Data and Business Analytics Observers of Milan Polytechnic", 39% of Italian companies have stated that they do not have specific internal skills and that they have not carried out any type of experimentation on the subject of AI. An advisors system with your partners allows you to benefit from a higher level of skills, reducing the time to market products.
  5. It may be advantageous to define a data sharing policy not only internally but also externally, involving partners and suppliers. For example, a shared supply chain can create data monetization: 87% of decision-makers would like to rely on external data to expand their business scenarios (source The Insights Professional's Guide to External Data - Forrester 2020).

The companies that manage to monetize their data have higher and sustainable performances on average in the medium and long term. For this to happen, however, the company must open up to the outside, asking for an exchange of skills within a partnership ecosystem, not only in terms of the supply of materials (hardware/software platforms and solutions) but also of skills, exploiting the assets specific to their partners.