The rise of Artificial Intelligence and megadata now offers new prospects for economic development. Meeting with Gregory Serrano, co-founder of Invenis, a software that uses AI and Big Data to analyze data.
Big Data, Artificial Intelligence… Currently, these terms intrigue, question, and even frighten by their unknowns. In the business world, what types of challenges do these technologies address ?
“Data” is an other word to say “information”. Information has always helped to communicate, but also to make decisions. Thanks to it, a person, a company, an organization… makes choices with full knowledge of the facts. Previously, data were difficult to report. However, over the last fifty years, with the evolution of computer science and the arrival of the Internet, it has become not only easy to store them, but also to share them. And within companies, there is a lot of very rich information, especially about customers. All this data that comes from consumers has a particular value: it allows us to offer the right product at the right time to the right target via the right communication channel. It is the job of a company : designing and selling a maximum of goods and/or services that correspond to customers’ expectations and thus increase their satisfaction. In order to fulfill this objective, a company’s marketing team retrieves precise information, among others on the CRM databases. These information make it possible to better understand behaviors, needs and desires of customers and to offer them an adapted offer. We can cite other equally widespread use cases such as predictive maintenance, process or flow/stock optimization, etc.
Then there is data and BIG data : technologies, but also the volume of data, have evolved enormously in recent years. There are more and more elements to deal with, even too many. Old techniques don’t allow us to analyze these ever-increasing quantities of information. Technologically, we had to innovate.
This brings us to artificial intelligence: which means mechanical algorithms of management and reflection, created by man, but not directly linked to him. These algorithms allow a computer to process problems automatically and judiciously, without human intervention. AI therefore helps to do mass, intelligent, descriptive and predictive work. However, without data, no Artificial Intelligence. We need to feed the algorithms with data.
In summary, data and new technologies help companies make informed decisions.
What does Invenis propose as solutions to the problems that emerge from Big Data ?
We created Invenis, a data analysis software, at the end of 2015. Today, either you do relatively simple data processing (traditional Business Intelligence), but then you are limited because you cannot process large volumes, nor use prediction algorithms… Either you have access to Big Data IA technologies, but which are very complex. They require the help of specialised service providers or, for sufficiently large companies, data scientists. The problem is that there are very few. As a result, their expertise is expensive.
We are the first to offer a business intelligence solution that integrates Big Data and IA technologies, simple enough for everyone be able to use it. The real issue is to democratize access to these technologies, so that all teams can analyze all types of data and perform intelligent analyses without being an IT expert.
In the long run, won’t this have an impact on the data scientist business ?
On the contrary. The profession as such is relatively recent. Data Scientists are experts, super-experts. Their work’s heart is not cleaning or creating data sets. Their job is to build algorithms, or use them to make them as efficient as possible. We are used to having them process data on customer databases, whereas this is the job of a business analyst. We have therefore adopted the concept of “Citizen Data Scientist”, launched in 2015 by Gartner : which means that the business analyst, or even the business teams, will be able to do the same work as a Data Scientist without having the same skills. How? By using software such as Invenis, which allow to make these complicated data analyses, without having the complexity of these new technologies. This also helps the Data Scientist, as he will be able to concentrate on his core business.
How does data improve business performance ?
Business performance is mainly based on lower costs or higher sales. Let’s take the example of an industrialist who makes, say, corks. He makes several hundred million a year. It therefore has production lines: between the input data1and the output data2 of a chain, a correlation is made. Then we play with the input variables and see what result comes out. This will lower costs. Why? Why? Because thanks to Artificial Intelligence, we are able to determine the variables which are most important, and thus to modify them to pass for example from 10% to 5% of waste rate. Another example is that we are currently working with a company that is sending data from its construction sites en masse. This information will help optimize their work, the use of machines, processes, etc.
Today, is it still possible to boost your business without focusing your strategy on data? Or has it become essential ?
Obviously, there is more to running a business than data. Except that processing the data is not new. What is, is the volume, the value of this information, the way to study it. But a company manager or any other business manager has always processed the data. We all do it, every day, even if only to choose a direction. Data processing is nothing more and nothing less than decision support. It just so happens that we have access to more information and we have more means to analyse it. In the United States, Data Driven3 companies are growing eight times faster than average. Companies that establish strategies oriented towards data exploitation therefore have every chance of becoming leaders in their market. Indeed, it helps to better manage your business, both in terms of costs and performance.
What matters is usage. The data and the AI, without innovation of use, have no interest. It’s a question of “what’s the point ?” not “how does it work ? ». Companies need to understand and accept the “what’s the point ? ». Namely : decision support for a more efficient business.
In which industries does a company need data most ?
Everyone needs it in general. But there are indeed sectors for which this is essential. Before quoting them, let us try to understand why : one of the first criteria is to ask ourselves what is the digital maturity of the domain concerned. It’s nice to need it, and even want it, but if you don’t have the necessary information systems, or even the legal or regulatory environment, it’s complicated. The second criterion : having data and an immediate operational need.
To quote the four/five sectors most in phase according to IDC4, there is first of all Retail, notably with e-commerce: there is so much exploitable information that it is a challenge to believe that it has no interest. Everyone talks about pure players5, but those called “brick-and-mortar”6 need them more than ever. Then comes industry: processing industry, traditional extraction, manufacturing… and by extension connected objects (which go back a lot of data). The banking world follows, where there is a lot of information but two constraints: the regulatory framework and the SI6maturity. We can also mention defence and security, as well as government organisations. The latter realized a real need in terms of digital transformation. This involves, in particular, programmes and innovation processes. Finally there is health. This sector has a strong potential because it brings out a lot of important, even vital information, and a great need to put data at the heart of its activity to meet patients’ needs.
Invenis :
www.invenis.co
@invenis_co
Clémence d’Halluin
2. Output data : all quality information.
3. Data Driven Enterprise : a “data-driven” enterprise that relies on data analysis to make decisions and guide its evolution.
4. IDC : International Data Corporation, the world’s leading consulting and research group in information technology markets.
5. Pure player : actor exercising his commercial activity only on Internet.
6. Brick and mortar : as opposed to the pure player, a traditional sales company.
7. SI : information system. By “IS maturity”, we mean adapted, up-to-date and efficient information systems.