The Rise of the Chief Data Scientist
In the past, hiring a chief data scientist was considered a luxury. Now it is a "must-have" position, especially as organizations accelerate their digital transformations in a trickier post-pandemic environment.
- By Ira Cohen
- February 19, 2021
The importance of data science and analytics has skyrocketed in the chaotic post-COVID-19 world as organizations realize they must become more data-driven to survive and embrace digital transformation. With corporate data fueling major decisions at the board of directors and C-suite levels, the need for greater senior leadership at the data science level becomes even more critical.
It has become evident that there needs to be a leader who can do more than just define and prioritize important analytics. Enterprises need a chief data scientist who can serve as the key go-between for C-suite executives and data science teams, a translator who bridges strategy and execution -- someone who can find the data gold mines required to fuel business and digital transformation while guiding technical data science teams to find the gold in the first place.
Right now, organizations are investing heavily in the chief data scientist role. This individual manages a range of data-driven functions, including overseeing data management, creating data strategy, and improving data quality. They also help their organizations extract the most valuable and relevant insights from their data, leveraging data analytics and business intelligence (BI). In this capacity, the chief data scientist has a far deeper understanding of how AI and machine learning (ML) can improve data management than the CTO, who has a broader knowledge base but not the deeper expertise.
This is critical as ML has emerged as a key driver in improving data quality and access as navigating the journey from big data ideas to real-world machine learning implementation is a challenging endeavor. In this scenario, the chief data scientist serves as the trusted navigator, understanding that data is the fuel for key initiatives, knowing the non-deterministic risk of developing those capabilities. Moreover, this individual can manage the expectations of C-suite executives, helping them better understand the reality of what ML can accomplish while mitigating the risks associated with data-driven initiatives.
In the past, hiring a chief data scientist was considered a luxury. Now it is a "must-have" position, especially as organizations accelerate their digital transformations in a post-pandemic environment. Companies are engaging customers and partners in new and different ways in the digital world, creating new business models and finding faster ways to bring products to market. These initiatives require more complex data strategies that must be created and managed by a true data science leader.
When it comes to making important company decisions, boards of directors and C-suite executives are relying more heavily on the chief data scientist. In fact, IDC recently completed a study that revelated that 59 percent of chief data scientists now report to their CEO or another top executive in the C-suite. The data science role has come a long way over the past three years.
This year, one of chief data scientists' top priorities will be finding ways to use machine learning to solve critical business problems caused by the COVID-19 pandemic and the recession. One of those problems is churn prediction -- particularly when organizations must predict when their customers are most likely to leave. This type of forecast requires stellar analysis expertise as well as different levels of technical and data science knowledge, which is where the chief data scientist truly shines.
When it comes to hiring the best chief data scientist, companies should look for experienced professionals who understand the balance between fostering creative innovation and pragmatic solutions. Look for the individual who is a researcher at heart, who loves to explore different problem sets and data-driven solutions but who can also deliver the real-world solutions that solve the organization's business problems. This person is equally comfortable giving guidance to C-suite executive teams while rallying data science teams to uncover the best solutions.
Today's modern chief data scientist will also fully understand how to deploy ML and AI. Machine learning has emerged as the most important weapon in the data science arsenal. Many data science teams are currently facing a build-versus-buy debate regarding ML, particularly as they evaluate new products and services that offer ML capabilities. Forward-thinking data scientists may opt to buy machine learning capabilities, knowing the considerable time and expense required to create a new product from scratch -- one that may not deliver as much value as the time and effort they put into it.
This year, chief data scientists will need to further extend their considerable influence across their organizations during a pivotal time in the global economy. There will be more pressure on them to find breakthrough solutions, and in doing so, it will be challenging to keep their data science teams from going down too many rabbit holes, chasing the wrong data. At the same time, they will be empowered to leverage corporate data assets to make critical decisions that help their organizations and people thrive in a modern digital world.
Ira Cohen is co-founder and chief data scientist of Anodot, in charge of inventing and developing its real-time multivariate anomaly detection algorithms. He holds a Ph.D. in machine learning from the University of Illinois at Urbana-Champaign and has over 15 years of industry experience. You can reach the author via email, Twitter, or LinkedIn.