How are projects usually structured and how does the composition of an analytics/data science team look like?
Mostly, we develop novel analytical product and services for our clients using advanced analytics or machine learning. In Accenture we typically do this using an “Analytics POD” team composition. This is a mixed team of analytics and business people, working tightly and independently together every day to solve the analytics tasks.
Which skills would you regard as vital for your current job role?
As a data scientists you need of course to understand the data, but it is as important to understand the client’s situation and interpret what the data/insights can do for the client. In addition, analytics like all consulting, is a people’s business, so the most vital part is really dealing the people and motivating and growing people.
What was your career path to becoming Analytics Lead? Which steps gave you the most important learning experiences for your career?
I have always done analytics and been interested in what can be achieved with better insights. I started my career in university and then moved to banking, always within analytics functions. I think, the key for my career has been that I have interested in both pure analytics, as a technical discipline, as well as in results of the analytics, as in what is the impact for the end-user of the analytics.
You have spent two decades working with data. How do you see the development of data science/analytics over the next years?
There has been a big boom on analytics in recent years and lot of hype around AI, deep learning, etc. The scope of analytics both in terms of types of data and in areas where analytics can be applied has expanded rapidly, and at the same time the development of tools and methods has made some parts of analytics easier. There is a diversification of analytics roles happening, there will be the "basic" data scientist/business analyst that will use ready-made basic analytical blocks, there will be the more technical expert data scientists performing deeper analytics, and there will be new roles like insights interpreter bridging the gap between business and analytics.
Which three pieces of advice would you give to aspiring data scientists?
Analytics is a broad field, find what really interests you and become good at it.
Learn the theory from formal education, get the practical knowledge from an older practitioner/mentor.
Try out different things and have fun.
