**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.
- What are Augmented Analytics for?
- Augmented Analytics applications
- The Business Intelligence revolution
What are Augmented Analytics for?
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.
Augmented Analytics applications
The “augmented” analyses act throughout the data lifecycle. Nowadays (the technology is constantly evolving!) it is possible to identify three main applications:
- Augmented Data Preparation: in this case, Artificial Intelligence algorithms are applied in the preliminary phase of data analysis. Data cleaning and normalization, evaluation of missing data, identification of outliers, preparation of Data Modeling: these are some activities that can be accelerated by intelligent algorithms. These capabilities have an impact not only on the time and efficiency of Data Scientists work, but they enhance the possibility of less experienced figures to create their own views independently.
- Augmented Data Science and Machine Learning: guess if instead of having to choose an algorithm, you are only asked to specify the question to answer, for example “I would like to create targeted groups of my customers”. Thanks to the augmented approach, the tools can be autonomous in going to use (exemplifying, in this case) a clustering algorithm and return the results.
- Conversational Analytics: speaking in your natural language with the Data Analysis and Visualization tools, having at your disposal a constantly updated and informed Virtual Assistant. The Virtual Assistant is able to start from the question, to identify the analyzes to be performed and to create the best visualization to represent the insight. This “augmented” challenge is still underway, but this is certainly the direction!
The revolution of Business Intelligence
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.