As the name suggests, speakers and audiences of the summit are the leaders of data division within a company such as Head of Data and CDO, who are in a decision-making role, and it was a great opportunity to identify the difference between data leaders and data "players" (e.g., data scientist, analyst, ML engineer), and rethink their responsibilities.
In the sessions, there were two particularly important topics:
- How to build data organization(s) within a company, in consideration of diversity.
- How to make real-world data project successful.
Good Data Scientist != Good Data Leader
First of all, a key responsibility of data leaders is to translate business needs into data requirements, and successful data organization and project must be led by those who are capable to accomplish it.
On that point, it is important that good data scientists cannot always be good data leaders; a data scientist is a person who is working on data itself to make the outcomes, whereas a leader is in charge of the more strategic decision-making process before aggregating data and playing with it.
To give an example, in a panel session "How can you organize your data office," Head of Customer Insight, Analytics and Reporting at AXA Investment Managers mentioned that, the biggest challenge at the very initial phase of their data efforts was gathering multiple data sources across the company. We can easily imagine it does require someone to strategically communicate with a variety of stakeholders and carefully plan the whole implementation process.
Meanwhile, Global Head of Data, Analytics & AI at Volkswagen Financial Services emphasized the importance of end-to-end automation of data science and machine learning workflow. Making sure company-wide business requirements is a crucial first step that has to be done by the leaders.
Thus, for the reasons that I heard from the panel, a leader's role is different from what we normally talk about data science.
Enabling Data Scientists to Make Actionable Insights
Once the business problems and data requirements become clear, it's time to pass the baton to the "players" to move forward, and the ultimate goal of data science is to make actionable recommendations for the business personnel.
Importantly, most real-world data-related business problems can be solvable by a combination of simple mathematical formulas and ML models; as Head of Data and Insights at Virgin Atlantic stated in the panel, 99% of data scientists in an organization do not necessarily have to hold a Ph.D. to achieve their goal.
1% of Ph.D.-grade data scientists could help to resolve unique, challenging problems, but we need to consider the balance between model complexity, explainability, and prediction accuracy. Here, an optimal choice should be derived as a consequence of the leader's Biz/Data translation process.
Therefore, data leader's responsibility differs from data scientist's one, and their work helps data scientists perform to the best of their potential for making the value.
As a real-world story, my favorite session at Data Leaders Summit 2019 was "F1 journey to build its own Business Data Analytics capabilities". Since F1 has re-branded in 2017/2018 and changed from a motorsport company to a comprehensive entertainment brand, providing not only the races but online media and gaming content, many data challenges had arisen. Their Senior Analytics Director demonstrated how they overcome the situation, and it was a good example of showing the power of successful data organization and project.
Overall, even though I'm not actually a "data leader", the summit was highly informative and insightful to see the data problems from the business perspective. I can confidently say the inputs become valuable when I think about my career.