Last Updated on August 22, 2022 by rida
We can define Analytics as the set of data necessary for the discovery and analysis of particular behavior patterns or demographic data. In more technical terms, data in its numerical and natural state is transformed, through Analytics, into insights – which we could translate as “insights” – necessary for marketers and companies to make strategic operational and marketing decisions.
Like Analytics in a general sense, Real Time Analytics – that is, the insights proposed in real time regarding the performance of a given connected instrument – represent a perfect fusion of science, technique and technology and include different approaches:
- Pure mathematics
- Business intelligence
To these, the human component will add its own considerations and skills in marketing , sociology , communication techniques and even psychology to create highly performing strategies on digital tools.
Why Real Time Analytics are so important
The importance of Real Time Analytics lies first of all in the speed with which they are able to provide specific information, currently considered crucial and indispensable not only for the study of marketing projects, but also for complex business strategies .
Not only that: on a technological level, Real Time Analytics allow you to benefit from a wide range of projects ranging from predictive maintenance to fraud identification , from internal analysis to personalized advertising , from redemption of campaigns to integration of marketing, from the evolution of products or services to the study of new customer care systems and so on.
When applied to the world of the Internet of Things in particular, Real Time Analytics produces an incredible advantage that affects the value chain as a whole . In the event that these tools are in fact integrated into connected systems and objects (whether they are B2C or part of the structures evolved to Industry 4.0), the amount of data (in terms of volume, differentiation and quality) that they will produce will inevitably have a significant impact on the business. model of the company.
If, on the other hand, we imagine Real Time Analytics in the context of complex marketing projects , their typical application will be that of Proximity Marketing , i.e. a proximity marketing that hits, according to specific actions, leads and potential audiences that are nearby. of certain shops with the ultimate aim of inviting to purchase. This marketing approach is typically achieved through targeted and personalized promotions according to the characteristics of the target to be hit.
To conclude, it is important to highlight that there are four different data analysis speeds , according to what was released by the Big Data Analytics & Business Intelligence Observatory and which refer to a rather recent study, dated 2018.
- Batch analysis : ie according to predefined regular intervals. Typically, data collected throughout the day can be processed overnight.
- Near Real Time : the frequency of analysis and data updating is reduced compared to the Batch mode and can be over an interval of hours or even minutes.
- Real Time : the data are collected in real time and their analysis takes place whenever the need is felt.
- Streaming : data is collected in a continuous and uninterrupted flow and analyzed continuously. Obviously, this solution provides for a system capable of supporting a very heavy activity and which usually takes the form of a set of sensors capable of identifying constantly variable values.
Currently, Italy seems more and more turned to Near Real Time analytics systems , but in the near future the need to set up Real Time Analytics projects will become more and more important: this will only be possible with the help of technologies and infrastructures. highly performing both in terms of data collection and querying and use of information.