Back in January, I attended #NRF2023 in New York. We ‘rode the wave’ of retail there, with a SAS-run bootcamp and Store tours. It was great to be able to meet the retail community in person, and also work with partners including Amazon Web Services, project44 and Cosmo Tech. One reason that the bootcamp itself was successful was that is was run together with the Eco-system and more focusing on Challenges and how to solve it with help of technology. Combining Hard and Soft skills. Visitors at NRF was also able to visit our booth to surf the supply chain by playing an interactive game or listen to some of our customers telling their stories. We were also treated to partner presentations and software demos (and you can still see the sessions on demand).
More recently, I was at the University of Borås for its ITSM Borås conference. The event theme was value creation in a digital age, but with a particular focus on digitisation, artificial intelligence, and skills needs. I was invited to take part in a panel event talking about skills supply. The event website makes a strong case for digitisation as a force for change and transformation across industries and sectors. However, change by itself is not enough.
Creating value for customers
The key message from both events was that organizations need to be able to harness digital technologies to create value for themselves and their customers. That means bringing together knowledge and collaboration—and not just collaboration between people, but between people and technology. It is now essential that people and machines work together to create maximum value for both companies and customers.
However, this may be easier said than done. This need for a different form of collaboration creates a demand for a whole new set of skills. The question is how best to spread the use of analytics and artificial intelligence (AI) across the whole organization, in a process known as democratization of AI. It is essentially a skills challenge. A recent #SASchat talked about this topic, and the place of seasonal schools in democratization of AI.
Understanding and delivering democratization
The first step is to agree what we mean by democratization. I believe that it is the spread of AI to a wider use base. This means not just the technology itself, but also knowledge and understanding of it. As Jurgen Kaselowsky said in the SASchat, we must demystify AI, because it is becoming part of everyday life.
More importantly, however, AI is a crucial way for organizations to get a better picture of their customers. That is essential if they are to create better value for those customers. The gap now is not really understanding of the need to use data. Instead, it is about having the skills to ask the right questions, and then understand the answers. Organizations must be able to help their employees to acquire the skills that they need to do this effectively.
I think there are two essential elements to this. The first is buy-in across the organization, including from management. Everyone needs to understand why democratization of AI is important. Data science must not be seen as the province of analysts. The second element is training and skills development. If you leave people to develop the skills themselves, it is unlikely to happen. With the best will in the world, the ‘day job’ tends to take over, and there just aren’t enough hours available.
SAS seasonal schools were created to address both. They provide an overview of analytics and AI, and therefore spread basic knowledge and understanding. In effect, they allow people to get sufficient taste to decide whether they wish to become data scientists, or citizen data scientists (business users of analytics). Importantly, no prior statistical knowledge is necessary, and these schools therefore open data science to anyone and everyone.
A useful experience
The feedback from the seasonal schools is that students find them useful. Some students have gone on to take further certification courses or qualifications. Importantly, they reported that the knowledge they gained at seasonal school enabled them to identify suitable courses for further learning. The overall impression seems to be that seasonal schools are a genuine enabler for those who have not previously done much or even any data science or statistics.
I think it’s worth closing this article with a thought from Glyn Townsend at the end of the #SASchat. He points out that it is essential to address the stereotype of data science being for ‘maths geeks’. Data is now everywhere. Understanding it and how to generate and harness insights from it is crucial to generating value. Seasonal schools are one way to do this, and to achieve reskilling.
