Michael Brand has over 25 years of cutting-edge, international industry experience in advanced analytics, machine learning, artificial intelligence, machine vision, and natural language processing, Dr. Brand’s data expertise is both uniquely wide and uniquely deep. He served as Chief Data Scientist at Telstra Corporation, as Senior Principal Data Scientist at Pivotal, as Chief Scientist at Verint Systems, as CTO Group Algorithm Leader at PrimeSense Ltd (in the machine-vision team that developed the Xbox Kinect), and as Director of the Monash Centre for Data Science in his role as Associate Professor of Data Science at Monash University. He has developed solutions at every scale from on-chip to Big Data, from real-time to high-powered computing, and made industry-defining contributions that have earned him 16 patents (more pending), garnered many prestigious industry and academic awards, and underline $100Ms/pa revenues and $100Ms in valuation for the companies he worked with.
In this episode, Michael explains how he started in the data space by doing a little bit of programming. At the start of his career, he was one of the first people to look at continuous blood pressure measurements. When you go to the doctor, and they measure your blood pressure, it’s a number. Although, nobody knows how to interpret this number. It was assumed this number would be reasonably consistent throughout the day; however, everything that we do influences our blood pressure. For instance, Michael found that when a person wakes up, their blood pressure spikes so high that if a doctor saw that number, they would assume the patient had a heart attack. Then, Michael found himself serving in the Israeli army in a unit dedicated to data science for a full seven years. At the time, they had to invent a lot of the theory, data management, and data science.
Later, Michael explains his work at Verint Systems. Like all other vendors, the worst their testing set is, the better their results are going to be. They do not need to go back and reconsider if the numbers are right or wrong, there is no incentive. Data science needs to be heading toward a world where it is done in a regulated and transparent way. There is nobody that can see the hump in their own back, no one is perfect. It is challenging to convince companies that they need an external review. There is a mental shift that needs to happen, data science is not a form of magic. Organizations spend millions of dollars on data science, and it increases their risk based on research. Michael knows many large teams that have been closed down because companies are not seeing the value. The reason why it is not happening more is that they do not even realize their data science teams are losing them money. Stay tuned to hear Michael discuss data governance, data rights, and the services Michael’s company offers.
Enjoy the show!
We speak about:
[01:45] How Michael started in the data space
[05:35] Capturing brand new blood pressure data
[09:15] What you buy and eat depends on the weather
[15:10] Working with data science in the Israeli army
[19:40] Engineering approach vs. the scientific method approach
[28:45] When is the deep learning madness going to end?
[31:00] Working at Verint Systems
[36:05] The core of what Michael currently does
[43:20] Tools to ensure secrecy
[47:20] Making strategic decisions with data science
[50:00] Every company needs a data strategy
[54:15] Where does data governance play a role in an organization?
[63:10] The need to start talking about data rights
[69:20] Listener questions
[76:00] Michael has imposter syndrome
Michael’s LinkedIn: https://www.linkedin.com/in/michael-brand-b230736/
Otzma’s LinkedIn: https://www.linkedin.com/company/otzma-analytics/about/
Otzma Analytics: https://otzmaanalytics.com
“When you have data that nobody has ever looked at before, you will see stuff that nobody has ever seen before.”
“We are in a world where we are pushed towards thinking of data science as a form of engineering.”
“You can outsource a lot of things, but you should do your own testing.”
“Every data you encounter is different; the value is understanding how that data is different.”
Thank you to our sponsors:
UNSW Master of Data Science Online: studyonline.unsw.edu.au
Datasource Services: datasourceservices.com.au or email Will Howard on email@example.com
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Michael Brand is based in Melbourne, Australia.
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