Founder of Data Science Institute, Kevin McIsaac
After spending 15 years as an analyst at IBRS and Meta Group as an expert in technology infrastructure and datacentres, Kevin McIsaac began to ponder the overriding question - what about the actual data?
“The evidence shows that half the organisations that build data infrastructures actually derive no value. You need to have a data scientist to explore it, and then you can gain insight,” he said.
This lead him to launch boutique consultancy firm, Data Science Institute in January, after discovering the possibilities of the growing industry.
“There really isn't a standard place to find data scientists. As I researched more into the field and the role of the data scientist, the more excited I became by the potential,” he said.
McIsaac’s extensive experience as an analyst, working with business leaders to understand their problems, paired with his background in science, armed with a PhD in quantum mechanics, meant he could combine forces and become involved in a new venture of high value to businesses.
“You need to be able to sit down with a business person and speak in their language. You also have to be able to take what used to be a very complicated piece of implementation and come back in simple terms to describe it to the business,” he said.
According to McIsaac, there are three skill sets that make up the criterion of a data scientist including a mathematics, science and programming background paired with business intelligence.
“In my case, fortunately, I have the business skills with 15 years of experience as an analyst at a modern strategic consultancy and as a former scientist, the mathematics is very straightforward for me personally.
In getting his company off the ground, McIsaac spoke of his experience using Kaggel, a competition website where companies upload data and ask competitors to solve a problem.
“In January, Telstra put up data and wanted people to predict network altitudes. As a data scientist it’s kind of a problem in a bottle, but with real world data and competing with tens of thousands of other people. I’ve been in about four of five different competitions and it gave me a practical experience as a data scientist solving real world problems,” he said.
Currently, McIsaac works alongside one other within the Data Science Institute and has two unnamed customers he is working with.
“Where we are at today is just starting off. I am working on landing a few more contracts and developing methodologies. Later on, we will need some data engineers who we will go out and hire.”
McIsaac said the next steps in progression surround the development of methodologies to help business understand the value of data science.
"When you start thinking about predictive analytics you can speak to a business person about high level use cases like lead scoring," he said.
"A company may be getting 50,000 leads coming through a website and the call centre calls them, but less than half of the people are ready to buy a product.
With predictive analytics, we can create models which can predict who is ready to buy right now. I can then also take the people who are not yet ready to buy and move them to more nurturing programs such as email marketing," he added.
“However, nobody wants to spend a million dollars to find out that it fails, so our methodology is that we do very rapid prototypes where we take the idea, we get the data however we can, we identify the hypothesis that drives that model, then we build and validate the model and see whether or not there is a business case that we can build into production.”
Going forward, McIsaac said his vision for the consultancy is to keep it ‘boutique’ size.
“Currently there are 2 people in the business. Maybe that will be ten in 15 months. I have no ambitions to become a massive company. I really want to offer people a partnership type of experience where they can do great work and be paid according to their skillset and how and what they deliver.”
