Data Intelligence as the main driver of innovation in the banking industry

Anjana Data participated in the recent edition of the Data Bank event to discuss with other data experts about Data Intelligence.

One of the activities in which the Company participated was the panel of experts. “Data Intelligence = Business Intelligence” Moderated by Andreea Niculcea, Director of Digital Transformation at Banca March. Mario de Francisco Ruiz, CEO of Anjana Data, also shared the table with Juan José Pascual Fernández, Data Analyst at BNext, Unai Beato Iglesias, Chief Data Officer at MyInvestor, and Antonio Font Pérez, Director of Business Analytics at Grupo Cajamar.

This panel summarised ideas on Data Intelligence applied to the daily practice of a data professional and how it achieves business objectives alongside a team that must be equally skilled, applying Business Intelligence and Big Data Analytics.

Data Intelligence = Business Intelligence

Big Data in banking is having an impact on the use of technologies that help analyse each user's characteristics and anticipate demand by knowing what they want and what services could be offered. Big Data tools make it possible to determine metrics and actions focused on customer acquisition and loyalty and are very useful for proposing innovation projects that take into account customer benefits and business growth.

In the current banking landscape and in relation to the adoption of these technologies, there is a significant difference between fintech start-ups such as Bnext, and traditional banks such as Grupo Cajamar. The former are companies digital natives, so they have had The former have had data available from the outset, and their biggest challenge is to use it correctly to scale their business, while the latter have had to undergo a complete transformation in the way they use data. To do so, they have had to take on data integration challenges closely linked to omnichannel marketing, in order to deliver their offers and services to customers through different channels in a transparent manner. They have also had to change the name of the Business Intelligence area to Business Analytics, in order to create predictive risk analytics models.

Halfway between the two are banking companies such as MyInvestor, which are newly created but are entering a business with a more traditional culture, such as private banking. In their case, they have had to build the entire architecture for data collection, storage and analysis from scratch in order to meet the challenge of growing into a more automated and efficient bank.

According to Mario de Francisco, the objectives that must always be set for the use of data in the banking industry There are two types:

• Increased efficiency and reduced costs (internal objective)
• Added value to the business and the end customer (external objective)

In order to achieve these objectives, one of the greatest challenges Anjana Data has faced as a company has been identifying the lack of governance among its banking sector clients, from an organisational and data culture perspective, with the aim of adding value to data as another strategic asset.

Similarly, it is very important to understand the organisation's level of maturity and proceed step by step: you cannot successfully undertake Artificial Intelligence initiatives if you have not even established the cross-functional and regular use of Business Intelligence.

CDO and data departments

In this data-driven era, the role of the CDO is one of the most important, as it seeks to meet the need to promote a data-based strategy while complying with the various regulations involved in its management. This profile must balance promoting its use within the company with the risks involved in managing it alongside a relatively new department in any industry.

Data departments are often presented in a cross-functional manner because they serve other areas, such as business, technology, marketing, etc. In some cases, business and technology departments are separate areas, but the idea is to break down that barrier so that they speak the same language and share the same objectives. Ideally, there should be no data office beyond defining organisational, governance and strategic models, but rather data experts should coexist within each area. 

Once the integration of data departments within the company has been achieved, professionals face major challenges such as immediacy (focused on the real time, being in the moment of truth) and proximity to customers through behaviour analysis and data comparison services. At this point, it is essential to discuss functions such as data intelligence management, which allows us to anticipate consumer demand, predict their intentions and make decisions in real time. 

Use cases

Bnext. One of the advantages of using advanced analytics is the development of machine learning models that will enable the business to detect churn focused on retaining potential high-value customers. In this phase, Bnext is applying data science and predictive analytics with the intention of using prescriptive analytics in the future to enable decision-making.

MyInvestor. The current needs of this company are very focused on reporting, as they are still in the early stages of implementing a data culture. However, this point is very important in order to begin working on the challenges of creating understandable, customer-based propensity models.

Cajamar Group. For 17 years, they have been working with the classic machine learning model to evolve into a model that helps detect customer churn, identify potential audiences, segment customers, etc. However, the company's main focus is not so much on the algorithm as on the availability of data, geared towards the business rule of collecting data in real time.

Anjana Data. The same mathematical and statistical techniques of artificial intelligence that are usually applied to find or retain a customer can be used to achieve a good data governance. This makes it possible to find out what data is being used in different databases that are not related in any way, detect ungoverned data, automatically detect personal data in databases, or anticipate bottlenecks in governance procedures, among other objectives that will achieve a positive ROI for the business.

Anjana Data It also welcomed Data Bank attendees at a stand set up to showcase demos of the data governance solution and was one of the main sponsors of the Bootcamp, where it also shared insights with other professional experts in the sector.

If you would like to learn more about the experiences of all the panellists, we invite you to share also an article from Big Data Magazine: Business Intelligence does nothing without data intelligence.