Data Futurology - Leadership And Strategy in Artificial Intelligence, Machine Learning, Data Science

Christoph Molnar is a data scientist and Ph.D. candidate in interpretable machine learning. He is interested in making the decisions from algorithms more understandable for humans. Christoph is passionate about using statistics and machine learning on data to make humans and machines smarter.

In this episode, Christoph explains how he decided to study statistics at university, which eventually led him to his passion for machine learning and data. Starting out studying with a senior researcher gave Christoph exposure to many different projects. It is an excellent program for students and companies whom both benefit greatly. Christoph learned so much about statistics that he would not have been able to acquire otherwise. The clients got nine hours of consulting for free, which is very valuable for their businesses. When Christoph started his statistical consulting career, he did patient analysis to assess if a medication was affecting the spine. He found this very interesting as it differed significantly from his previous consulting.

When labeling data, Christoph says to label and always compare continuously. For instance, when a student labeled one photo, later on, Christoph would show a student the same photo and see if it got labeled identically. Sometimes people will see the same image but label it differently; so, this is one thing you can do to ensure labeling data is going smoothly. If you have multiple labelers, you will need to compare how each labeler will mark the same photo. Do not be blind to the quality of your data; it is easy to adjust the numbers.

Then, Christoph speaks about pursuing his Ph.D. in Interpretable Machine Learning. He publishes his book, Interpretable Machine Learning, on his website chapter by chapter. Christoph gets feedback and uses it while continuing his writing on future chapters. Learning about interpretable machine learning is not exactly present at university now. Some schools and professors are starting to integrate it into the curriculum. Stay tuned to hear Christoph discuss accumulated local effects, deep learning, and his book, Interpretable Machine Learning.

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Steve Monaghan is the Chief Digital Officer at Riyad Bank. He has a series of investments in banking and insurance using AI and new technologies in order to improve efficiency.

He started out as a commercial pilot working 22 hours a day, running the aviation company during the day and flying at night. His first contact with data came when he started using spreadsheets to automate tasks and accelerate the time in which he got them done. He was able to quote 3 days before any of his competitors and his business boomed. This gave him a great insight on the impact technology can have.

His career then moved from flying into technology. Within this industry, he has learned that people have a learning curve and for them to adopt change, they first need to understand it. You need to show people how processes and systems work for them to understand and accept them. That’s the reason why he thinks curiosity is such an important trait.

For Steve, the three core laws of technology that drive data science and AI are:

He believes these 3 laws encompass the ability you have to assimilate learning into knowledge. The faster you do that, the bigger the advantage you have over your competitors. It’s almost as if you live in their future because you’ll be able to see things they won't see for a period of time.

Stay tuned to learn more about the impressive way he looks at what you can do with technology, the results he’s had and how he leverages AI in business.

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Stuart Long is the CTO Hewlett Packard Enterprise. In his role, he identifies different product portfolios and pulls together solutions for different customer sets, such as public health, education, finance, retail, mining, and determines how to develop the solutions from HP’s product portfolio across all the different stacks.

In this episode, Stuart tells us how HPE are moving the analytics to the edge, making it actionable with the ability to link data across the organization and across multiple organizations with a new solution out of Europe; Gaia X.  He says that with today’s privacy regulations, it’s now about federating your data and then making that data available to people but still within your control. But you can’t do it alone – you need an ecosystem of partners and products to do that.  And that’s exactly what HPE has done.

They have built a whole range of products to do what they call ‘federated data analytics’. This allows you to look where your data is and start to then understand where you want to analyse that data, and what data flows you need to provide, and how you want to basically store and categorize that data. There are a number of what they call “architectures”, and underneath those architectures, they are developing products that fit nicely within those to enable the customers to look at how to deploy these new systems.

Finally, Stuart tells us what excites him most about the opportunities ahead for Gaia and the data economy as a whole and HPE’s plans for Gaia in the next 3-5 years.  He says the ability to see a whole new range of services be made available very quickly, and different ways of utilizing those services - is what he calls ‘service chaining’. Giving the consumer a way of being able to choose from multiple different providers and customize their own different services and chain them together.

Stuart believes it’s really about developing the whole ecosystem and bringing on different partners.  We will start to see more edge to quarter cloud type environments where organizations will now have a lot more edge processing. Companies will become more and more digital, and services will become more and more digital. He tells us that there will be some interesting opportunities for organisations and with this level of innovation, there is going to be good and bad, but it’s about making sure that you can optimize the good and limit the bad.

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Read the full episode summary here.

Organisations work hard on creating and building data science capabilities and models that will provide a real positive impact for their users. However, the feedback we’ve had from the Data Futurology audience shows there is a struggle to get ROI from their AI. So what do you do when your models and AI efforts are not living up to their promises? This conversation explores how to ensure that your AI projects don’t fall flat, but actually deliver business value.

In this episode, we hear from Scott Hubbard, Director of HPE Ezmeral Software Business Group, South Pacific and Ridhav Mahajan, Solutions Engineering Leader, HPE Ezmeral Asia Pacific. They have recently been part of setting up a start-up within HPE, that focuses on cutting-edge stacks in the AI, ML and data space. Though this startup is fairly new, the technology behind their platform, Ezmeral, has been in production for over 10 years now!

One of the current challenges organisations face is accessing all of their data. Whether it’s stored in silos in the cloud or on-premises. As data grows exponentially, being able to scale a platform almost infinitely to keep up with that demand is a vital aspect. To address this, they have worked on what they call Data Fabric, a layer under a data platform with the ability to unify data sets across an organisation and bring data silos together so you can have a single data source.

Tune in to hear Scott and Ridhav expand on the adoption of AI in an organisation and share their industry experience with us.

If you want to learn more about leveraging your data with AI, join the HPE Ezmeral Asia Pacific Launch to hear our host, Felipe Flores, share his insights on Getting Value From AI At Scale. Save your spot here! 

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Read the full episode summary here.

If you are trying to choose what skills you should build as part of your career, no single answer fits everyone. It depends on your strengths and ambitions. Your strengths are the things you are naturally good at. If you're not sure what your strengths are, then think about the things that people come and ask you about. When someone asks you how you do something, most likely that is one of your strengths.

Also, think about where you want to end up in your career. For instance, if you're going to become a manager, find a way to be a leader on a project and develop your soft influence. Hard influence is something that comes from authority. Whereas soft influence is something that we can build upon. Find a way to lead a project among your peers. Taking the lead will allow you to practice your influence, leadership, and management skills. A group of people will follow the person with an organized and well-thought-out plan; be that person!

Another tip - do not be the person that blindly follows what they enjoy; it's not a strategic way to go about crafting your career. Felipe has seen people have a successful career doing work that they enjoy. However, they will eventually lose their love for it. Plus, if you only follow what you enjoy, then you run the risk of getting pigeonholed in an area that may not have future growth. Also, it might be an area that loses demand and importance; it can make your skills irrelevant.

At the end of the day, we need generalists. Companies need people who are knowledgeable in the end to end process. As an industry, data scientists are in high demand. Plus, they need people who have a mix of skills. There are always more roles being added because the industry is starting to understand that a great deal of knowledge is required to build a successful data science team.

Enjoy the show!

Read the full episode summary here: Ep 108

Thank you to our sponsor, Talent Insights Group.

In part 2 of our interview with Abhi Seth, he tells us that part of his role is to really drive scale for analytics across the ten businesses within TE. We learn how the adoption of analytics is enabled throughout the organization and a big component of that is driving, understanding and building capability within the Centre of Excellence (COE).

He says the first 90 days in the COE is about building capability and being able to have experts in data science, cloud, dev ops, ML Ops, data visualization, user experience, storytelling, and data engineering. Abhi says his focus is now on building a small COE team within each of the businesses and moving to a  “hub & spoke” model as the analytical maturity of the organisation improves.

Abhi goes on to tell us about how he creates and enables “seed teams'' and how it’s important to ensure the problems you're solving are creating value for the company and are tied to a strategy. He also says you should have a committed executive sponsor.

Throughout the episode we discuss the results of our poll questions:

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Read the full episode summary here: Ep 163

Thank you to our sponsor, Talent Insights Group.

Abhi Seth started his journey as an industrial engineer which he describes as his passion, and then found his way into research. His PhD is in mechanical engineering, and human-computer interactions, which he says was kind of a double major PhD.

Abhi started working with virtual reality applications for mechanical engineering and says back then it was about how to simulate environments and models where you create a suspension of disbelief for a user. So he set out to prove that VR was also valuable in solving real industrial problems which led him to join the Global R&D centre for Caterpillar, the world’s largest mining construction equipment manufacturer.

Abhi currently leads a global data analytics team for TE Connectivity, a $14B electronics manufacturing company. He says he is always up for a challenge and learning something new, one of the reasons he decided to join TE Connectivity.

We learn from Abhi about the purpose of centres of excellence, the differences between data science COE, and a centralized department and how do you measure success with analytics.

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Read the full episode summary here: Ep 162

Thank you to our sponsor, Talent Insights Group.

In part two of the Ultimate Uplevel for Business-focused Data Professionals, with Lillian Pierson, we learn about her data action strategy plan. She created a data evaluation use case workbox with 31 use cases broken down by industry and function and tells us that if you are innovative then it's actually everything you could possibly need. You don't need to read 100 use cases.

Lillian says you should survey industry use cases to find what's possible. Take stock of your company, look at where the biggest gap for a data solution is, and then assess possible options against use cases. Aim for projects that will make an impact within 3 months.

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Read the full episode summary here: Ep 161

Thanks to our sponsor Talent Insights Group!

We are joined by Lillian Pierson, a very well-known figure in the data space. She started her own business as a data consultant around 2013 and eventually started helping others become better data leaders and entrepreneurs. Currently, she has helped train and educate over a million data professionals! Lillian is not only a great data business mentor, she is also the CEO of Data Mania and a data product manager.

She touches on the exponential growth the data science industry has had in the past few years and shares how hard it was for her to get useful data content online when she first started. That’s part of why she is interested in helping others and making knowledge available to everyone.

Quotes:

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Read the full episode summary here: Ep 160

Thanks to our sponsor Talent Insights Group!

In part 2 of this episode, Nitish shares his thoughts on how to ensure that the data scientists expertise are well utilized. He says their key metric last year for the entire team was to hit exactly one number - 100 active users for their data platform with only 50 data scientists! He also says organizations should focus on enabling self service across the business.

Nitish talks about the data support channel at Afterpay which allows for anyone to ask questions about the data and the data platform. He says find the people naturally drawn to the data and support them with assistance, training and coaching.

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Read the full episode summary here: Ep 159

Thanks to our sponsor Talent Insights Group!