In July 2017, the concept of Augmented Analytics was first introduced in a research published for Gartner. The topic immediately aroused interest and immediately became an impact trend in the field of Business Intelligence, so much so that just one year later Gartner published the report “Hype Cycle for Analytics and Business Intelligence“.

In its first definition, the concept of Augmented Analytics is described as a new data analysis approach that exploits Machine Learning and natural language (NLG) technologies, in order to automatically identify the most relevant results and independently suggest the concrete actions to be taken. Not bad as a goal …

A new approach

In fact, one of the main current problems is the fact that data tend to become increasingly numerous and increasingly complex. Often it is not possible to integrate them quickly also because of their extreme complexity. The risk of losing information becomes therefore very high, and precisely this complexity does not allow us to explore all the possible opportunities offered by the information available to us. It also increases the likelihood of having as a result just what we “looked for” and not what we “could find”.

We add the fact that, to date, all data extraction and transformation activities are still completely manual (“Artisanal” area of the figure shown below), considerably increasing the possibility of error. Furthermore, finding a data scientist today, a relatively recent profession that requires skills in information technology, statistics and economics, and for which there are still few studies, is really very difficult.

It therefore seems natural that the introduction of analytical tools capable of interacting with the human being in its natural language and autonomously identifying the most significant data without the mediation of analysts and without human intervention in general, becomes a fundamental key for the future of Business Intelligence.

The traditional approach

He is currently approaching traditional projects involving various figures such as analysts, computer scientists, data scientists and managers, involved in the usual decision-making activities that will then lead to the final request. This is followed by various data extraction and cleaning activities performed by computer scientists or technicians with specific skills. We will try to put together these data by linking them together and only then transforming them into information that is more synthetic and “decisive”.

As we can easily imagine the costs and times of such an activity are absolutely remarkable.

The Augmented Analytics approach

With this new approach we try to centralize all the procedures in a single solution, from data collection to analysis processing, to monitoring results. Machine Learning and Artificial Intelligence algorithms are used to automate the procedures making data analysis easier. Billions of data combinations are automatically analyzed, automatically finding correlations and identifying any predictive scenarios.

The future of Augmented Analytics

According to the slide (source Oracle Corporation), there are several levels:

Level 0 – Artisanal: everything is handmade, as in the classic approach. The data model and reporting are the responsibility of IT.

Level 1 – Self Service: data management is still largely manual, but human interaction with data will be done with the natural language (Natural Language Query). Visualizations and graphs will be suggested based on the data we are querying.

Level 2 – Deeper Insights: the first phases of advanced data management are displayed (recommended sources, joins, crowdsourcing suggestions, intelligent cataloging) and increased navigation helps to discover information that would otherwise require a strong human effort.

Level 3 – Data Foundation: data management is increased, corrections and enrichments are automatically identified. New views and new data sets are added.

Level 4 – Collective Intelligence: the system learns metrics and KPIs that alert you when they require your attention. You have both company KPIs and system KPIs. Insights become pervasive, commercial intent passes from an idea to a reality, results are expected, actions are recommended, but humans continue to act.

Level 5 – Autonomous: everything becomes really guided by data, with the best subsequent actions performed on the basis of forecasts, insights and intents. The system is the engine of change.

Where are we at?

I believe that for the moment we have stabilized between the third and the fourth stage, data management starts to be really “augmented”, it is becoming easier to integrate and manage a large amount of information and take the first steps towards automatic identification of KPIs.

Completing the fourth stage and finally reaching the fifth, the “autonomous” total, will be the challenge of the coming years. The way of conceiving Business Intelligence will change drastically, but the challenges to be faced are always more beautiful. We are ready?

In the analytics business and in the business world — business intelligence, analytical devices and BI applications are the main discussed subject. BI is quicker and progressively precise in reporting; data analysis is increasingly vigorous, and forecasting has changed essentially and developed massively. An ever-increasing number of companies have begun to value the worth of business insight (BI) with regards to their decision-making process. The previous couple of years have seen BI systems experience various advancements. With the rising multifaceted nature of the business intelligence environment, the distinguishing proof of trends and market improvements is a key factor in powerful decision-making. It is progressively imperative to utilize the most recent innovations and methodologies so as to adapt to digitalization and market competition. Let’s look at top BI trends that will dominate 2020.

https://www.analyticsinsight.net/top-10-business-intelligence-trends-to-watch-in-2020/

We've all heard a bit about it now: the Data Scientist, for a few years now, is one of the most sought-after pre-treatment figures. Who exactly is he and what exactly does he do? Here are some answers we can find on the web:

What does the Data Scientist do?

A bit statistical, a bit itist, a bit of an economist, but also a marketing expert and a communication enthusiast. He is the data scientist, the job that the Harvard Business Review defined as the "sexiest profession of the 21st century" in 2013. Is he a required professional figure in Italy? And how do you become a data scientist?

According to Claudio Sartori, Scientific Director of the New Master in Data Science of Bologna Business School in an interview reported on sarce.it it is a figure that "requires multidisciplinary skills, because it must not only select, analyze and interpret an ever-widening and complex body of data, but also find the best way to make the processing and results available to the structure for which it works, whether it is a company or a public administration."

The data scientist must first put order in the data, then wonder where his organization wants to go, what information can be useful for his strategy. Finally, it must know how to do but also know how to communicate, make available to management the results of what it has done. The most sophisticated analyses are only useful if they are properly transmitted to those who have to make the decisions, then understood and used to achieve the desired results." […]

In short, the primary task of a data scientist is to explore the data, starting with precise business demands. He is a real investigator and puts all his analytical creativity on the field. Armed with technological tools and machine learning algorithms, he is able to scientifically examine and predict correlations between phenomena that at first analysis are invisible. Its goal is to get insights as accurate as they can give the business an accurate overview of the problem to be solved.

Who is the Data Scientist?

An attempt was made to profile the Data Scientist in the Italian company, the results were reported in the report "The new professionalism and skills for the management of big data", prepared by the Digital Innovation Observers of the Polytechnic University of Milan:

Work within the IT division, an ad hoc business function, or within one of the pre-existing functions. He is a graduate, mostly master's degree, who has often taken training courses in statistics and computer science. He has a background of skills focused primarily on machine learning, analytics and Knowledge Deployment. And he earns on average about 67,000 euros a year, with a bonus that generally stands at 10% of the salary.

It is clear that a strong heterogeneity of skills is essential, from business to programming to technology and especially for the trio of Machine Learning, Analytics and Knowledge Deployment. From a training point of view, it boasts a course of study concluded in the majority of cases (50%) with a master's degree (engineers, economics and computer science go for the most) and accompanied by specialization courses (statistics, computer science and management the most chosen).

The Data Scientist in Italy

Data Scientist is now present in 3 out of 10 companies, but the number of specialists employed full-time is growing at an annual rate of 57%. A sign of a growing corporate sensitivity to the new challenges of the big data boom.

According to data from Robert Half's Technology and IT Salary Guide for 2018, a data scientist's average salary can range, depending on experience, between 100,000 and 168 thousand dollars per year.

Who needs a Data Scientist?

Each sector has its own wealth of data to analyze. Businesses need to analyze their data to make decisions about efficiency, inventory, manufacturing errors, customer loyalty, and so on. In the e-commerce sector, recognizing trends to improve the proposal to the customer, in the financial situation the data of transactions are fundamental assets, but also in the Public Administration you can monitor the general satisfaction of citizens. Health, communication and social networking are other areas where the needs are evident.

Il Sole 24 Ore also reports that in Italy less than one in three large Italian companies has noticed the need to have a Data Scientist inside. Among Italian SMEs, only 34% of these are in a budget dedicated to Analytics.

Conclusions

This figure is referred to as a professional able to range between technical, computer, economic and statistical skills. It's a kind of evolution from the Business Intelligence figure to the Data Scientist. Whereas in the first case you tend to collect requests from the business and then return numbers in output, in the second case it is the Data Scientist who "proposes solutions" starting from a mostly generic input of the business. It collects information on its own and analyzes its correlations, creating new algorithms and applying machine learning techniques.

Sources

https://www.miriade.it/data-science/
http://www.sarce.it/tutti-vogliono-data-scientist-un-mestiere-non-si-improvvisa/
https://www.kdnuggets.com/2018/02/2018-predictions-analytics-data-science-hiring-market.html
https://towardsdatascience.com/what-are-the-skills-needed-to-become-a-data-scientist-in-2018-d037012f1db2
https://www.bigdata4innovation.it/data-science/data-scientist/big-data-cose-cosa-data-scientist/
https://www.cwi.it/cio/data-scientist-chi-e-cosa-fa-e-quanto-guadagna_42108239