ata has become a fundamental element of any organization, yet it remains complex in managing its lifecycle. Its valorization is somewhat akin to the quest for the Holy Grail, making one wonder if it’s just a legend.
The data and IT departments of organizations are sometimes also victims of trends. Each technological era is marked by one or more trends driven by innovation, societal changes, or new needs and business models pushed by organizations.
With increasing digitalization, which accelerated in certain sectors following the COVID-19 pandemic, and data present in every nook and cranny of a company, IT, data, and business leaders are united around the same question: how to valorize data?
In this quest for valorization, and even monetization, data analysis is becoming increasingly important within organizations. To achieve this goal, companies are enhancing their analytical capabilities with technologies like artificial intelligence. With the emergence of technologies such as Machine Learning, the discipline of data science has become a “must-have,” led by data scientists.
However, many organizations realize that mastering data science alone is not enough to valorize their data. What are they missing? Is there an ideal team that could better understand the business challenges of data? Between change management and structural organization, how can we successfully unite around data?
From Theory to Practice
A simple Google search for “data scientist job offer” yields nearly 3 million results. While it’s not just a passing trend, 2020 saw the number of new job offers decline by 45%, according to Indeed. This trend may be explained by the reduction or cessation of AI investments due to the COVID-19 crisis.
This phenomenon is also explained by sometimes incomplete strategies and poorly identified needs, with many organizations just beginning their work around data science. The development and use of AI technologies are still in their infancy. Some companies with data scientists in place struggle to operationalize their skills.
When we look at the volumes of data handled by organizations, the different networks, and architectures, it is not imperative to have a data scientist among the ranks of data experts. For companies managing an astronomical amount of data across multiple channels with a complex structure, the expertise of a data scientist will be very valuable for modeling the data, querying it, and making predictions. One of the first questions to ask is related to data and business needs and the necessity of organizing the structure based on the type of organization and its data strategy.
Companies have also realized that having a data scientist does not solve the problem of valorizing their data. This is partly due to a lack of understanding of the data’s surrounding environment. A data scientist may understand the data but not its purposes, environments, or business applications. Take, for example, a marketing department working on implementing AI to accelerate its web ROI.
Data scientists might develop and implement the algorithm without considering this specific environment and its behavior; if the website takes much longer to load than the algorithm, the combination doesn’t work.
A Holistic Approach to Data Management
Data has become a fundamental element of any organization, yet it remains complex in managing its lifecycle. Its valorization is somewhat akin to the quest for the Holy Grail, making one wonder if it’s just a legend.
This Grail does exist, but like in the story, one must surround oneself with the right data knights. So, what does this new data round table look like? To optimize and leverage data analysis, more and more companies are deciding to centralize the analytical function within a single department and team.
Among the ten data and analytics trends identified by Gartner, central analytics—no longer scattered across different business departments—stands out, with an increasingly important role given to Chief Data Officers.
Centralizing analytics solves communication problems that can arise when this function is dispersed within an organization, creating as many silos as there are departments or stakeholders. With this centralized approach, managing data and its lifecycle and value becomes a collective project, with the Chief Data Officer as the leader.
Moving from a role of influence with little power, the Chief Data Officer becomes a decision-maker. They will be supported by data scientists if necessary, data engineers, and increasingly by business analysts. These analysts play a key role as they bridge the gap between the theory of data and its practical business application, understanding the business expectations related to the data. They will provide the missing key to unlocking the value of data.
This central team, often linked to operations, will work alongside IT, security, and compliance teams to ensure maximum alignment in any data valorization or transformation project, in line with business objectives and needs.