Maximizing the Value of Data : An Interview with Nicolas Morisset

In a context where data plays a central role in the digital transformation of organizations, understanding the challenges related to its management and lifecycle is essential. This interview with our colleague Nicolas Morisset highlights the challenges, developments, and best practices for fully leveraging this new strategic asset, while ensuring responsible and sustainable governance. Enjoy! 

Interviewer:
Hello Nicolas! Can you tell us who you are and what your role is at ADNia powered by COFOMO ?  

Nicolas M.
My name is Nicolas Morisset. I have worked in the data field for over 25 years as a modeler, data architect, and information architect, on operational systems, data warehouses, and data valorization initiatives. I have worked in several sectors, including finance, government, and real estate.
For the past ten years, I have specialized more in  data governance , particularly around the roles, responsibilities, and practices that govern its use. 

Interviewer
: How long have you been with ADNia ?  

Nicolas M.
It’s been five years officially, but I’ve been collaborating with people from ADNia for almost fifteen years. 

Interviewer:
Why do you think we’re talking so much about data today? 

Nicolas M.
It’s a wish for people like me, who have worked in the data world for a long time, that we finally begin to recognize data as a  strategic asset , and no longer just as a by-product of systems. This growing awareness is linked to several factors :  technological evolution, digital transformations, but also the legislative framework, especially with increasing requirements for the protection of personal information, such as Bill 25 in Quebec. 

These developments aim for two objectives : to better protect data while  extracting more value from it . The challenge is to break down silos and decompartmentalize information in order to make it usable securely.    

Artificial intelligence further accentuates this need; data is its fuel, both for training and operational use. This places data quality and management at the heart of concerns.

Interviewer:
What has changed in the way organizations use their data over the past ten years? 

Nicolas M.
Depending on the sector, the evolution is not happening at the same pace, but overall, there is a trend towards a more  proactive use  of data.
The aim is to go beyond static reports and use data to support strategic, tactical, and even operational decisions, often in near real-time, directly by businesspeople. 

Because producing, storing and distributing data is expensive, it becomes essential to  break down  silos and maximize its value in use. 

Interviewer:
What do we mean by data lifecycle and what are the main milestones or stages of this cycle?  

Nicolas M.
The data lifecycle reflects the fact that data is a  living, non-static resource . It follows a path through the organization, and sometimes several organizations (e.g., the government ecosystem), in the form of loops rather than linear phases. 

This cycle begins well before collection, with an  intention , a business need. The data is then created, structured, used, shared, transformed, sometimes for uses not initially intended, until it is archived or deleted. 

The central element of the cycle remains usage . Unlike other resources, data does not disappear when it is used ; it can generate value with each new use, making it a unique asset.  

Interviewer:
Why has data lifecycle management become essential for organizations? 

Nicolas M.
Without management, we end up drowning in an overabundance of data. Many organizations retain large volumes of information without actually using it, which represents a significant cost, both financial and operational. 

Beyond the costs, there are  risks : breaches of confidentiality, security issues, poor data quality, misinformation, flawed decisions, and reputational damage. Certain data, particularly health and financial data, can have serious consequences if mismanaged.  

Good management therefore aims to have  the right data, at the right time, for the right reason and for the right person . 

Interviewer:
What are the main challenges faced by organizations in implementing data lifecycle management?  

Nicolas M.
The main challenge is not technological, but  human . Data is abstract ;  we don’t see it circulating, which makes its journey difficult to understand and control. It passes through systems, organizations, and sometimes jurisdictions, which complicates its management. 

Because the value of data is difficult to measure and attribute directly, it is also more difficult to convince people that it is an investment and not simply a compliance issue. 

Interviewer:
What are the best practices for effectively managing the lifecycle ?  

Nicolas M.
The first step is to  better understand your data : what data is collected, where it flows, and what business activities it’s linked to. Next, you need to break down silos and clarify  roles and responsibilities  in data governance. Many people contribute to the data lifecycle, sometimes without even realizing it. 
 

To begin with, it is best to adopt an  iterative approach : target a subset of data, often reference data, and gradually build organizational maturity.  

Interviewer:
How do you see the evolution of data management in the coming years ?

Nicolas M.
The recognition of data as a valuable asset will continue to grow, particularly with the challenges of security, digital sovereignty, and artificial intelligence.
AI could play a key role by helping to better understand data flows, detect quality issues, and strengthen data governance.

Interviewer:
Why should this issue be a priority for leaders right now ?

Nicolas M.
Because data lifecycle management is a  factor of resilience  in an unstable and rapidly changing world. It allows for better control of risks, costs and quality, while becoming a true  competitive advantage .

AI tools were used to shorten the interview text.