More than three years ago I accepted the challenge to lead the Data & Analytics team at Nexi, one of the most important global players in digital payment systems, working and transforming the company through a modern approach to the use of Data & Artificial Intelligence. In this journey the cultural aspect has been and is fundamental.

Over the past 15 years, there has been a lot of emphasis in all organizations on the role of technology in the transformation driven by Big Data. Certainly, technology plays an important role, but frequent cases of value creation failure are shifting the focus to the priorities for the transformation. It is emerging how organizational models and, above all, the ways in which people interact with data, technology and experts in these areas is increasingly the real strategic success factor.

As Peter Drucker used to say, "culture eats strategy for breakfast" and even in the new world of data and algorithms, this rule holds true.

But what does data culture mean in complex and very dynamic organizations? Our experience leads us to give relevance to these three key aspects:

(a) The ability to integrate relevant new data and algorithms into all key business processes switching from “finding confirmations of C-levels ideas in data” to “analyzing data to drive C-levels decisions”. Key steps in this journey have been building a common semantics of existing data through collaborative knowledge and sharing tools. As well as the creation of topology of data and information flow maps within business processes has been extremely valuable. All this helps us to spread across all the projects the key concept that no data point is useless from an analytical point of view. Building efficient algorithms to help innovation and process and product optimization means don’t ever say “we'll make the data available to data scientists in a second time!”

b) The ability to facilitate teams working on data and algorithms to work well in the company and develop their skills increasing the assets of the company itself and the community in a virtuous circle. This, for example, has led to the creation, since the beginning of Nexi's journey, of a Data Community, which, starting from the central teams (according to a hub & spoke organizational model) has gradually expanded towards those who work within the business and operations units and today consists of almost 100 participants. The Data Community has given rise to many initiatives that are promoting continuous training and the democratization of data throughout the company, such as the Nexi Data Academy. Nexi Data Academy is a set of training courses (online and offline) created by internal data experts themselves using internal data to all company employees on areas ranging from data semantics to business intelligence to issues related to artificial intelligence by leveraging the cloud data lake.

“It is emerging how organizational models and, above all, the ways in which people interact with data, technology and experts in these areas is increasingly the real strategic success factor”

c) The ability to agilize product development and business development projects by applying first of all an iterative approach guided by the information assets that are generated as they interact with the market.

 In parallel to the cultural theme, the world of data & AI driven transformation also leads to a revision of the traditional role of the Manager. Here again, three increasingly strong trends are observed:

(d) Increasingly widespread leadership within data-expert teams. In a very fast-paced and dynamic world, choices, including strategic ones, are increasingly coming from expert teams. But leaders must be able to listen.

e) In an environment of high turnover of strategic human resources, managers must devote more and more time to their role as team motivators and coaches. Having an important network within external communities also becomes a strategic factor in selecting and scaling teams as project activities grow.

f) The democratization process for the dissemination of best practices in all business areas requires managers working in the Data & Analytics area to play a role of mediation and translation of these issues to their peers and to the management board.

This role is often defined as AI & Data translator. Instead of dedicating junior resources to this strategic role, we've been successful when our senior data scientists have been able to dedicate part of their time to this key activity.

For all these reasons, the Data & AI driven transformation is becoming more and more a continuous process and not just a project, and the culture and adaptability of the leaders are increasingly strategic success factors.