A Data-Driven Company is the name given to companies that use data as a fundamental asset in their decision-making in all their processes within the company. They do this optimally through the use of advanced analytical techniques such as Artificial Intelligence. As has been said before, it is vital for companies, becoming a matter of survival.
A data-driven Company is also the latest book recently published by Dr. Richard Benjamins, Chief AI & Data Strategist at Telefónica, with the aim of helping companies accelerate their data-driven transformation. The book also includes mini articles by industry experts that provide a transversal view of the major issues companies face in this process.
What I liked most is that it is real. It deals, in a pragmatic way, with the most important questions that companies face in their data-driven transformation process, avoiding the euphoria and overselling that surrounds the world of Artificial Intelligence.
This book, full of experience and advice, helps to identify and recognise the challenges they face and the decisions to be made. Different alternatives are presented and analysed for each of them, facilitating the selection of the alternative that best suits the company according to its particular characteristics. Structured in 21 lessons aggregated in 5 thematic parts that I will summarise at a high level in this post.
Transforming the company
The first part focuses on organisational transformation, focusing on the company’s organisational chart and the relationships between areas that ensure the accomplishment of goals. Starting with the key profile, the Chief Data Officer (CDO), to the relationships between the areas of data, IT, Artificial Intelligence and the CDO itself. These relationships will be established depending on their maturity and for this it is convenient to have maturity measurement methodologies as indicated in the book.
This first part is aimed at the management committees of companies or those responsible for their organisation, so that they can successfully plan the transformation. The remaining parts are aimed at the people who must lead this data-driven transformation within their companies, with the CDO being the main but not the only target of these lessons.
Use cases accelerate transformation
The second part deals with the business and financing of transformation plans. The first step is to establish the selection of the use cases, an arduous task due to the lack of knowledge and uncertainty of the new technologies to be used, and the measurement of the economic impact of the use case to be developed.
At this point, I liked the proposed measurement approach: comparing the economic impact of the use case, both in terms of cost reduction and increased revenue, with the impact if the analytical use case were not available. This is not easy to do, so it is necessary to establish the measurements prior to the use case in order to be able to measure the economic impact. But it is not only economic impact that we get from use cases. Each use case has an impact on the cultural change of the company, although this is difficult to measure, it is undeniable that successful use cases accelerate the transformation of the company.
Technology, a key pillar in this transformation
The third part is focused on technology, one of the fundamental pillars of the data-driven transformation. Apart from the well-known debate between Cloud and On-premises (with permission of hybrid systems), also known as Capex to Opex transformation strategy, other key aspects in the development of the technological strategy are discussed.
Deciding to establish a local or global strategy in the administration and management of data and in the development of advanced analytics may seem easy, but there are many aspects that must be taken into account, such as technological maturity or the impact on data governance, among others. Even contemplating the use of MLaaS, Machine Learning as a Service for more mature organisations.
People at the heart of transformation
The fourth part is all about the people, which in my opinion is the most important pillar to address in the data-driven transformation of companies. The book does not focus on hiring architects, engineers or data scientists to execute the use cases, but on managing the members of the company who must participate in the transformation through the democratisation of data thanks to Data Literacy programmes and Self-services tools. These tools should enable the entire company to extract the maximum value from data, each at his or her level of knowledge and skills.
A lesson is also reserved for managing employee scepticism, which is very common in transformation plans and is often the main challenge in transformation. We must be able to identify and reverse it as early as possible.
In this fourth part we find what for me is the most important lesson: how to create “momentum with data”, in other words, how to approach the execution of use cases, not from the planning of tasks and activities, but from the management of expectations and the management of those involved or affected within the company. A good strategy of interactions and communications that creates positive momentum is key to the success of the use cases and, therefore, of the transformation.
Without ignoring the responsibility
Part five focuses on the responsibility of data-driven companies in the face of the societal challenges presented by Artificial Intelligence. Issues such as discrimination and bias in algorithms, the appropriateness of the use of black box algorithms, data privacy and security, and the use of autonomous decisions are presented in the book to ensure that those in charge of managing teams using Artificial Intelligence are aware of the impact of their actions. To address these challenges, companies are developing their AI ethics principles to mitigate the aforementioned risks.
In short, it is a must-have book for anyone who wants to lead or participate in the data-driven transformation process of their company or public administration and wants to anticipate the challenges that will arise thanks to the experience of all the people who have collaborated in the book.
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