Wayne Thomson:
I am Looking for Analytical Blood Group AB

Interview with SAS's Chief Data Scientist at Analytics Experience 2016 event where he explained some basics on how to use analytics in business.

By Miran Varga, originally published: IKT Finance on November 17, 2016.

Today businesses are literally shocked about the amount and importance of data. What does this flood of data mean to business analytics?

The data are the basis for analytics. The more data sources a company has the better as it can deliver better business decisions. The importance is not only about the data being structured or unstructured, companies are increasingly dealing with different data such as images and even sound. Shops, government, telecoms and other major companies have created true data lakes where many hidden treasures can be found.

Companies need data scientist more than they need different analytical tools and functions if they want to get the best out of their data.

How do you separate between useful and useless information?

Algorithms have become so good during the last years that they exclude most of the bad data - e.g. incomplete data - themselves. Especially effective are the functions that use the techniques of deep learning to detect a variety of scams. In fact, the companies have several functions and tools at their disposal but to truly make them work and get the best from the data they need data scientists. Only qualitative information, advanced tools and experts in the field of analytics can give the businesses owners a really good insight into the future.

How do companies "activate" their data?

There are many ways companies can use the data to fuel business development. Several companies use analytical solutions to develop a variety of models to predict the future – then they test their formulas on real data and figure out if he predictive model works in line with expectations. The true challenge is to do this in real time, on the edge and at the client side. This is also one of the requirements of the internet of things.

Analytical solutions, however, are not almighty. How far they are from this ideal?

I am convinced that Picasso had a picture of the art form he wanted to create in his head before he set his tools of the piece of marble. He had only to remove the bad information and disclose of the art. Our goal is to get all possible information from the artificial intelligence and deliver the best possible result. I am convinced that we will see this happen in this century. Machines will therefore behave like people ...

If doesn’t matter if the data is structured or unstructured, we are often dealing with images and even sound.

Do you think the people are still the weakest link when it comes to analytics? How do you select your employees?

For me there are two types of data scientists. I need both types within the team. The first type are thoroughbred analysts, let’s call them type A, and the others are builders, e.g. developers and programmers, so type B. I'm most happy when I find a person who has the analytical blood group AB. It is true that some employees can be taught other skills, but basically the difference between computer scientists and analysts is really big and when it comes to my work I simply need a mixture of both types of people and their knowledge.

What will we be able to do with analytics tomorrow?

Among the most promising areas for advanced analytics are location-targeted mobile advertising, mobile apps, monetization of analytically processed data, several opportunities in the Internet of Things, co-operation of autonomous vehicles, predictive maintenance... The fourth industrial revolution will also bring a number of changes to the social and economic systems that shape our lives and the way we live. For all this, of course, a lot of credit goes to advanced analytics.

Wayne Thomson

Thompson is one of the early pioneers of business predictive analytics and is a globally renowned presenter, teacher, practitioner and innovator in the field of predictive analytics technology. He has worked alongside the world’s biggest and most challenging organizations to help them harness analytics to increase their performance. Over the course of his 23-year tenure at SAS, he has been credited with bringing to market landmark SAS analytics technologies.

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