The sharp increase in the mass of digital data produced and the development of IT tools for storing and analyzing them offer many concrete applications for economic players, in particular for banks. What is Big Data? Why analyze Big Data? Should we be afraid of Big Data? What are the prospects for banking Big Data?
What Is Big Data?
Big Data: The Simple Definition
With the development of new technologies, the Internet, and social networks, the production of digital data is constantly growing. The expression “Big Data” (translated into French by “mégadata” or “massive data”) designates the heterogeneous mass of digital data produced by companies and individuals whose characteristics (huge volume, diversity of form, speed of processing ) require increasingly sophisticated specific IT storage and analysis tools.
Where Does Big Data Come From?
The digital data produced comes in part from devices connected to mobile telephone networks and the Internet.
Thus, smartphones, tablets and computers transmit data relating to their users during the following actions: emission of GPS signals from smartphones, internet browsing, use of search engines, messages left on social networks, download and use of applications, an online publication of photos and videos, purchases on e-commerce sites, etc. Likewise, bank cards transmit data when used for withdrawals or payments, for example.
The intelligent objects connected transmit data on the use made of some consumer’s everyday objects (e.g., for a car, chip connected indicating the route and the distance travelled and the average speed).
Apart from connected devices, Big Data data comes from a wide variety of sources: demographic data, climate data, scientific and medical data, energy consumption data, data from transport networks, frequentation of public places, etc. An important new source of data: open data, i.e. data sharing of the state, public institutions and communities.
All of this data provides information on the users of the devices, their movements, their centers of interest, their consumption habits, their hobbies, their projects, etc. But also information on how infrastructure, machines and devices are used.
With the constant increase in the number of Internet and mobile phone users, the volume of digital data is growing dramatically.
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Big Data Analysis
What Is Big Data Analysis?
Innovative storage solutions (cloud computing, hybrid supercomputers, etc.) coupled with software using sophisticated computer algorithms allow the analysis of these large volumes of digital data. These tools are designed to detect relevant information and establish correlations between it. If data analysis, also called “Data Mining,” already existed in many companies, this activity took on a new dimension with the arrival of Big Data. Today we speak of data science.
One of the current challenges of Big Data is the development of complex tools making it possible to process and better visualize, analyze and catalogue vast flows of data.
What Is Extensive Data Analysis Used For?
This analysis allows, for example:
- Understand the needs of individuals and the constraints of users;
- To adapt infrastructures, networks and services (in particular public services) according to their use;
- To assist the decision-making of the various economic actors (companies, administration);
- Analyze and anticipate consumer behaviour (predictive analysis);
- To facilitate the evaluation of services;
- Improve the use of machines and devices (improvement of performance, prevention of breakdowns, maintenance).
Concrete Applications Of Big Data
In the health sector, for example, Big Data promotes preventive and personalized medicine. Thus, the analysis of Internet users’ searches on a search engine has already made it possible to detect the arrival of an influenza epidemic more quickly. Shortly, connected devices should enable the continuous analysis of patient biometric data.
In the transport sector, the analysis of Big Data data (data from public transport passes, geolocation of people and cars, etc.) makes it possible to model populations’ movements to adapt infrastructures and services. (timetables and frequency of trains, for example).
In energy management, data analysis from Big Data is used to manage complex energy networks via intelligent grids that use computer technologies to optimize the production, distribution, and consumption of electricity. ”electricity.
Likewise, data analysis from sensors on aircraft (flight data) combined with weather data makes it possible to modify flight lanes to achieve fuel savings and improve aircraft design and maintenance.
There are concrete uses of Big Data in many other fields: scientific research, marketing, sustainable development, commerce, education, leisure, security, etc.
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Should We Be Afraid Of Big Data?
The use of Big Data is strictly regulated. In France, the operators involved in collecting and analyzing data are subject to the supervision of the National Commission for Informatics and Liberties ( CNIL ). The Data Protection Act regulates the use of personal data. This law specifies that personal data must be collected and processed with a specific objective: only relevant data for a defined use can be collected. The law also recognizes the right of everyone to be informed about the collection and use of their data. In principle, each person can decide for himself the use of the data concerning him.
Therefore, big data is subject to the requirements of the CNIL, and its uses are directly concerned by the legislative framework in force.
Note: new European regulation on protecting personal data will come into force on May 25, 2018. This regulation provides for increased transparency on the use of personal data collected. In particular, new obligations will be imposed on operators collecting personal data: they will have an obligation to ensure the consent of individuals (and to prove it) for the collection and processing of their data. Data. They will also have to put all the necessary devices to secure this data against risks such as loss, theft or even disclosure.
Right To Be Forgotten And The Internet
The Court of Justice of the European Union obliges search engines to implement a “right to be forgotten,” i.e. a deletion of personal data at the request of users.
The challenges of Big Data in the banking sector
Data To Improve Customer Relations
The banks’ Big Data strategy aims to improve their customers’ knowledge and establish a closer link to respond more appropriately to their needs (customer satisfaction).
Concretely, this involves the immediate personalization of the services and products offered by using the data sources to which the customer has authorized access. We talk about “predictive marketing.”
Certain banking products (for example, a mortgage loan offer ) can thus be highlighted on the bank’s website during its consultation by the client according to his projects identified thanks to Big Data (for example, a project of acquisition of real estate identified because the client has consulted real estate ad websites).
Customer satisfaction is also improved thanks to the adaptation of communication processes depending, in particular, on the customer’s use of social networks. Therefore, big data is part of banks omni channel communication strategy to adapt to their customers’ communication habits and preferences.
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The Fight Against Bank Fraud Thanks To Big Data
In the fight against credit card fraud, thanks to Big Data, the banks aim to cross-check the information related to a request for payment authorization by credit card with the customer’s history and buyer profile. And its activity on social networks (which can allow it to be geolocated in particular). In case of doubt, additional authentication would then be necessary.
Big Data And Personal Data Protection
In line with the requirements formulated by the CNIL, companies wishing to use Big Data technologies must respect certain principles in the use of personal data:
- Bank transparency on the use of stored and analyzed data;
- Respect for confidentiality and privacy (the data remains internal to the bank and is not subject to commercial processing);
- Development of sophisticated security systems to limit the risk of data hacking.
Big Data at LCL
At LCL, algorithms study customer browsing on the LCL.fr bank website.
One of the objectives pursued is to improve customer knowledge to respond in the most appropriate way to their needs.
Another objective is to develop the bank’s website by identifying the most visited pages, the most used functions, etc. Highlighting the strengths of the site allows for a better customer experience.
In addition, to improve its customer relationship, LCL offers an efficiency assessment. For customers, this involves evaluating their satisfaction with their banking relationship.
The Effectiveness Review questionnaire has 3 questions. Through the first question, the customer rates their predisposition to recommend the bank to those around them on a scale of 0 to 10. The following two questions are open and allow the customer to detail the reasons for which he gave the mark on the one hand, and on the other hand, to provide suggestions for improvement. The comments collected are then analyzed using algorithms to identify, from the verbatim, the reasons for satisfaction and dissatisfaction to improve processes and capitalize on good practices.
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