” Data should be considered a true asset for the company, and should therefore benefit from its own valuation strategy, as with any other asset. “
Business Innovation Director at Business & Decision
Hello Mick. Can you introduce yourself in a few words?
I’m Mick Lévy. I am the Director of Business Innovation for the Business & Decision Group.
I have been working for this consulting and services company specialized in data, for 21 years (soon 22!). Our baseline “We are data” could not be clearer.
We support all of our clients’ projects and transformations around data, from data security to high-value data science and AI, as well as governance, architecture, data intelligence, etc…
Business & Decision has been part of Orange Business Services since 2018. We already had an international scope since we were present in about ten countries, mostly in Europe. We now benefit from a strike force and a support capacity that is even more important than before.
For fifteen years, I was involved in field projects. My role today is to gather the best feedback from all geographies, all sectors, and all sizes of companies that Business & Decision works with. The objective is to share this information with the entire data community, as well as our customers.
In doing so, I can go into detail on some subjects – there are clients that I am still working with on data strategy consulting – but I also have a better overview of what is happening, regardless of the sector or company size.
I also like to launch projects in parallel with my consulting activities. That’s why I started writing this book.
Tell us a bit about the genesis of the book, why did you write it and what are the goals you gave yourself and the readers as well?
I deliberately gave my book a subtitle: “Manifesto for an intensive and responsible use of data and AI by companies”. I started from an observation that I have been making in the field for 20 years: data is massively under-exploited in companies.
We know that all companies have a lot of data. However, only 32% of data is used by companies. A lot of data, therefore, generates value but is under-exploited. This is the starting point of my book: its purpose is to raise awareness on this fact and to give the keys to overcome it.
Also its title – “get your data out of the fridge” – is in line with this idea: all companies have data. They are the only ones who truly know their customers, their employees, their products, their processes, their suppliers… Even Google doesn’t know as much about the company’s operations as the company itself.
So it is up to the company to process all this data, and to create value with it. However, it is too often observed that data is taken cold (we often speak in companies of “cold data”). Data is stored in “informational refrigerators” which are the databases of companies. It is necessary to get the data out of these refrigerators to value it and to create “hot” value for the company.
Were you inspired by real-life experiences for your book? What difficulties did you face in writing it?
I drew a lot of inspiration from all the experiences I had on different projects.
In fact, 20 years of experience in the field allow me to write this book. 20 years helping my clients, advising them on their data strategy, on their projects, being immersed in field projects in a sometimes extremely operational way. Also I have been meeting about a hundred different companies a year for the last five or six years. Each time, there are key questions that come up. I also lead many conferences. All this has allowed me to structure my speech around these issues.
I thought that the book was very good support to make my convictions better known and thus have a stronger impact. Writing a book is a long way to go, it’s a gigantic task to put ideas on paper. Especially since I wanted this book to be very practical so that it could be used directly by companies that want to accelerate their data transformation. There are a lot of examples and quotes from companies in my book. I also wanted to include a catalog of 101 use cases that you can find in the central part of the book, along with 10 fully detailed use cases.
What advice would you give to a company that is starting its data process?
Companies that are only now starting their data process are getting rarer and rarer, and that’s for the best! (laughs)… . At least for the big companies…
In any case, I think that a good starting point should be to evaluate your data maturity and then use this as a starting point to establish a strategy.
It is important to understand that data should be considered a real asset for the company, and should therefore benefit from its own valuation strategy, as for any other asset.
Another important piece of advice is not to neglect what we call acculturation. Acculturation is a word that is too often taken too weakly in my opinion. It is very often understood as training and other actions.
I think it should be understood as a real subject of cultural change. It has to be taken literally: we have to change the culture of the company so that it works better with data and considers data as a real asset.
The third piece of advice: you have to focus very quickly on use cases, and very quickly on value. You have to think about strategy, but you have to convert it into value. You have to think about the long term, but you also have to focus very quickly on the short term, on use cases that quickly bring value, an ROI, and a service delivered. From the very first initiatives, we must be able to show the value that comes from them!
How do you see data practices evolving over the medium term, over two or three years? What will really revolutionize or accelerate data practices in the enterprise?
I see three key topics: the first is the data mesh. We have to take an interest in the data mesh but in all its transforming dimensions. We need to use it as a global framework for data, which also gives us direction in terms of organization, processes, skills management, governance…
How do we transform the company to be truly data-driven? I find that data-driven is too often reduced to other issues. In truth, it is a complete framework to make the company data-driven.
The second angle for companies that want to move towards AI to find value – I think this is the case for all of us – is to work on all the issues of industrialization of AI, scaling up, and so we’re going to focus on the MLOps framework.
The third subject, the most important in my opinion, is “green”, which is becoming essential.
And here, as far as data is concerned, we have to appreciate it on two dimensions.
- First dimension, how to make our projects more frugal?
- Second dimension, which is actually the most important: How to use data and AI to reduce the overall environmental footprint of business operations.
On one side “Green AI” or Greentech and on the other side “AI for green” – How to use AI for green in a general way. And I think the leverage of the second topic “AI for green” is actually huge. AI is going to play a key role in reducing the overall carbon footprint and environmental footprint of companies.
So what is the biggest challenge for you?
It really remains a challenge of business transformation. We’re still coming back to acculturation, and to all the skills issues, obviously. In reality, it is above all a matter of transforming the culture. And that’s why I like the framework that Data Mesh provides. Because it’s a complete framework for the whole company.
Any last words to conclude?
I am passionate about data topics. What drives me, and what we need to pass on, to those of us who are in these data communities, is that it is a subject that lies at the heart of the societal transformations we are experiencing. Let’s see how Big Tech is transforming the world through data. These are environmental issues, economic issues, societal issues, and issues concerning our democracy,
We, who work on data, are in fact at the heart of all these societal transformations: all data players must constantly keep in mind that we have a role that impacts Societies with a capital S and not only our companies.