**We have been talking about Big Data for many years now. As you know, Big Data refers to data that have at least one of these characteristics: Volume, Velocity or Variety. For some years we have started talking more about Analytics, in order to emphasize the need to transform data into information, and therefore value, for companies and public administrations.
Looking at the future and current trends, there is a new expression entering in the language of practitioners: Augmented Analytics. What does it mean? The term was coined in 2017 by Gartner and, as defined, it refers to the use of technologies and methodologies such as Machine Learning and Artificial Intelligence to support data activities. For instance, data preparation, insight generation and explanation. The use of Augmented Analytics increases the ability of people - in particular, non-expert users in data exploration and analysis - to extract valuable information from Data Analytics.
To understand why the future belongs to “Augmented” Analytics, let’s make an example: Marketing function. According to Gartner analysts, in a sample of 400 large companies, only 54% of marketing decisions actually depend on the results of Analytics, although this area is high connected to Analytics investment decisions. Furthermore, it remains very challenging to be able to quantify in depth the relationship between the carried-out analysis, the information collected and the business results. Finally, the Chief Marketing Officers complain about the poor data quality and therefore they are reluctantin “entrusting” their decisions to Analytics.
On one side, it is necessary to invest in the expansion of skills and in the building of a data-driven approach within Business functions - Marketing, Finance, Production and so on -. On the other hand, tools that make use of Augmented Analytics can represent a real point of conjunction between two worlds (and two languages) that sometimes seem irreconcilable: Data Science and business objectives.
The “augmented” analyses act throughout the data lifecycle. Nowadays (the technology is constantly evolving!) it is possible to identify three main applications:
Augmented Analytics, as we have described in this article, can represent a real revolution of traditional Business Intelligence. Taking up a Gartner metaphor, the advent of Big Data has placed the accent on the depth of analysis, on the types of data available and more generally on the complexity of the information assets. This evolution has contributed to create a haystack in which large companies Business Owners and, even more, functional managers of small and medium companies tend to get lost. “Augmented” Analytics has the task, like a big magnet, to help business figures find the needle in the haystack, accelerating and simplifying data engineering and data analysis phases, in order to leave more space and time to higher added value components: interpretation of insights and effective use of results in decision-making process.
As Datrix, we strongly believe in this revolution and we demonstrate it daily, working on the development of platforms that are easy to use but sophisticated in their analytical capacity. An example is DataLysm, a Predictive Marketing platform focused on predicting the behavior of your customers and prospects. DataLysm has, first of all, the aim to facilitate the integration and data preparation phases, then provides Machine Learning algorithms capable of identifying the probability of a single user to perform an action (for example, to leave the company or to carry out purchase) and lastly it provides the users with business suggestions.