Analytics leaders vs. leaders of analytics, and other trends in analytics leadership
One of the perennial issues in analytics and data science is leadership. There is now general agreement that data and analytics are essential for business success. However, using data and analytics—becoming data-driven—does not happen without analytics leadership. This may come from the top, driven by executives and senior managers. However, it is also important for any manager.
There are many questions about developing analytics leadership, including what areas need to be addressed, and the roles of various players, including universities, businesses, and partners.
The importance of words—and research
Jack Phillips is CEO and co-founder of the International Institute for Analytics (IIA). The IIA provides access to a network of analytics practitioners, experts and academics to help companies to become data-driven. Much of the IIA’s work focuses on analytics leadership, which is also a strong focus for SAS, including through its analytics value training. He draws an interesting distinction in the words used to describe analytics leadership.
“We increasingly see a difference between analytical leadership and ‘leaders of analytics’. The first term applies to non-analytics professionals, while the second applies to the emerging class of (often c-level) leaders of the data, analytics and AI function.”
He also notes a distinction between analytics and other functions in organizations in terms of the volume of research. There is very little information about what skills and behaviors are needed for and by emerging leaders of analytics. Jack sees this as a real problem.
“We know that artificial intelligence, and data and analytics more generally are the future. Understanding what type of leader is needed is also crucial.”
Cutting through the hype
The emphasis for future analytics leaders is very much on how analytics is consumed and deployed to generate business value. At the moment, much of the hype is focused on artificial intelligence (AI), and particularly generative AI like ChatGPT. There is also a lot of enthusiasm for machine learning. However, it is important to be able to ‘walk before you try to run’. The challenge for analytics teams is to ride the wave of enthusiasm in the business, while ensuring that any investment generates value. Jack agrees that this is important.
“There’s nothing like having something that has a whole lot of sizzles and gets the attention of every major journalist and media outlet in the world. That’s what has happened over the past eleven or twelve months with generative AI. The teams we work with are using this opportunity to drive attention toward data quality and traditional analytics as much as this new shiny object.”
Jack comments that the applications for generative AI are interesting, but also limited to certain jobs and activities. He suggests that it is important for analytics leaders to make clear that generative AI may seem new, but it is only an extension of what has been happening for decades. It therefore sits very firmly within analytics—and should not replace basic functions.
“I think you have to recognize that the opportunity for generative AI is very limited compared to the potential of broad data and analytics in most organizations. Basic business intelligence and reporting can take many enterprises a very long way.”
Investing in analytics leadership
The next question is what qualities are needed from analytics leaders—and who should take responsibility for developing them? Jack’s views are simple.
“Our research has shown that analytics leaders need an enterprise mindset, evangelist mindset, change agent mindset, people mindset, growth mindset, and technical mindset.”
Paul Hansen, from the Norwegian Business School, states that business knowledge is key for working with analytics. Riaan de Jongh, who has previously joined us to talk about the data science skills gap, suggests that analytics students should be required to do projects in industry to develop that knowledge. Jack agrees that the responsibility for developing crucial skills must be shared between education and industry.
“Higher education plays a crucial role in training future leaders. Einstein said that information is not knowledge, and the only source of knowledge is experience. We should see the split in responsibilities as universities provide the information, but enterprises have to provide the experience over time to develop new leaders.”
There is very little doubt that the highest performing enterprises when it comes to analytics are heavily investing in talent development, particularly through partnerships with local universities. Enterprises that are not doing this are falling behind. Development of data science skills—and especially analytics leadership skills—must be a shared responsibility.
