Peter Elger – Founder and CEO

Peter Elger.jpg

Peter Elger is the founder and CEO of four Theorem; his focus is on delivering business value to his clients through the application of cutting edge serverless cloud architectures and machine learning technology. His experience covers everything from architecting large-scale distributed software systems, to leading the internationally-based teams that built them.

In this episode, Peter tells us how his first real passion was in physics. After graduating with a BSc in Physics and a master’s degree in Computer Science, he worked for several years at the Joint European Torus (JET), the world's largest operational magnetically confined plasma physics / nuclear fusion experiment. They were doing big data, but at the time they did not refer to it as such; they dealt with around four to five terabytes of scientific data. Peter then transitioned to Indigo Stone as a Senior Technical Architect. Indigo Stone was a software disaster recovery firm which exited in 2007 to EMC.

Peter explains how it is essential to keep your technical skills up-to-date and why some of his favorite days are when he gets to code despite being the CEO of his company. If you can actually be the bridge between the business and the technology, you are an invaluable asset to any company. The freedom to innovate is what led Peter to his entrepreneurial ventures; previously, he had no real experience being his own boss. Peter says it is dangerous to think you can do everything; you have might a broad skill set, but you need to recognize that you have gaps. This is why Peter has always started businesses with co-founders. Currently, his co-founder is a world-class technologist and someone who understands the human dimension. All of his current co-founders and people Peter has worked with previously.

Later, Peter explains why he wrote about applying AI to existing platforms in his book, AI as a Service. He also recently wrote a chapter on how to apply machine learning to existing systems and the patterns that arise within two systems. Then, Peter describes why you do not need knowledge of AI to use AI as a service. In the past, everyone wanted their own security department; however, people already have the same technology that is vastly better than your own. This applies to how you can utilize AI within your existing business systems. Peter wants people to be interested in machine learning, but only a basic understanding is essential to use it within a business context. Finally, Peter discusses his approach to finding the most capable team members, being open and upfront with all staff members, and removing the ego to allow his team to perform their best work.

Enjoy the show!

We speak about:

  • [01:45] How Peter started in the data space

  • [06:50] Transition to disaster recovery  

  • [08:55] Interactive radio and marketing applications

  • [13:40] Maintaining a grip with technical skills

  • [16:20] The entrepreneurial bug came organically to Peter

  • [18:40] Transition to entrepreneurship  

  • [21:45] What to look for in a co-founder

  • [26:00] Building analytics with machine learning

  • [29:00] A tale of two technologies

  • [33:00] Applying AI to existing platforms

  • [35:10] Knowledge of AI is not necessary to use AI as a service  

  • [37:50] Capable team members are difficult to find

  • [40:10] Sharing management meetings with all staff members  

  • [44:05] Experiences with handling politics in organizations

  • [48:50] Removing ego + allowing the team to do their best work

  • [50:30] Scheduling work to maximize the impact


Peter’s LinkedIn:

Peter’s Twitter:

Peter’s Book: AI as a Service


  • “Computers don’t lie, and they do what you tell them.”

  • “In order to sell to someone, you need to understand what their problems are.”

  • “Business is all based on delivering value and building relationships.”

  • “Good software engineers have been doing things before they had a name.”

Now you can support Data Futurology on Patreon! 

Thank you to our sponsors: 

UNSW Master of Data Science Online: 

Datasource Services: or email Will Howard on 

Fyrebox - Make Your Own Quiz!

Peter Elger is based in Ireland.

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

Prakash Baskar – Founder and President

Prakash Baskar.jpg

Prakash Baskar is the Founder and President of Khyanafi. He helps data leaders to rapidly transition and accelerate the success of data, analytics, and digital initiatives. Previously, Prakash was the Chief Data Officer at Santander Consumer USA where he led enterprise data governance, risk infrastructure & information (risk data aggregation), data quality, business data strategy & solutions, and business & reporting analysis functions.

In this episode, Prakash tells us how he started in the data space at his university. His role was to determine how students were performing. If they are not performing well, he needed to identify why. The graduation rates were low at the school, so Prakash was tasked with finding out what was the problem. Then, Prakash discusses starting a new job and having little direction about what to do. With everchanging technology, the description of your job will always be changing too. As a person going into any role, understand that you do not have to ask permission all the time. Have a clear idea of what you can do and what you cannot do, then do what you feel is right for the organization. Look for where the opportunities for expansion are and find a way to get results.

If you ask ten people what the role of a Chief Data Officer is, you will get ten different answers. Whatever the CDO does will ultimately be to enable others to receive real benefits out of the data. Just because something is not broken, does not mean it cannot be improved. There are many different routes a person can take to become a CDO; however, you need someone with knowledge in multiple aspects of business, technology, and people management. A CDO needs to create value for the organization; learn the company you are supporting to anticipate the problems they may run into.

Later, Prakash explains how in business, any change is hard. How you embrace the change after it is made is what will differentiate yourself from others. If the change is too complicated, people will shut off. Start off by telling the client what the change will do for them rather than the steps it will take to get there. Some other tips when presenting a significant change is to be realistic with what it will take and make sure not to overpromise. It is imperative to select things that you can quickly do with minimal engagement from their people. Plus, make sure you have updates for the company each month, so they understand what is being revealed from the data. Finally, Prakash discusses how essential it is to move around the organization in order to understand different departments and he reveals the inspiration behind his latest business venture.

Enjoy the show!

We speak about:

  • [01:30] How Prakash started in the data space

  • [03:55] The transition into consulting  

  • [07:50] Deciding what problems to tackle first on the job

  • [10:10] The role of Chief Data Officer  

  • [17:20] Creating value for the organization  

  • [22:10] Businesses getting the maximum benefit from analytical work

  • [31:25] How to determine what to work on first with a company

  • [39:00] Data science conferences are full of CDOs

  • [45:45] Actively moving around the organization

  • [50:20] The inspiration behind Khyanafi

  • [53:50] What do you think makes a great leader in the data space?

  • [55:40] Advice for data scientists   


Prakash’s LinkedIn:


Khyanafi’s LinkedIn:

Set up a discovery call with Prakash:


  • “Your job description is only as good as the time it was written.”

  • “I am not a big believer in five or ten-year plans; I make plans six months at a time.”

  • “CDOs have to know the business! Try and learn how the company operates; you will gain more respect when you take the effort to understand the business.”

Now you can support Data Futurology on Patreon! 

Thank you to our sponsors: 

UNSW Master of Data Science Online: 

Datasource Services: or email Will Howard on 

Fyrebox - Make Your Own Quiz!

Prakash Baskar is based in Columbus, Ohio.

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

Jay Liu - Chief Data Scientist at Digital-Dandelion

jay liu.jpg

Jay Liu is the Chief Data Scientist at Digital-Dandelion specializing in helping insurance, and medical organizations innovate by integrating the latest in Artificial Intelligence (AI), machine learning and big data into their systems. Knowing the best way to learn is by putting your money where your mouth is, Digital-Dandelion launched an online brand and built a customer AI to promote it. There were numerous technical and modeling challenges that were overcome, but in the end, they sold all their stock within three months. They had proven to themselves that customer AI worked. Organizations can have great depth and breadth of customer data from their long-term relationships of selling high-value products and services.

In this episode, Jay explains how he found himself in advertising and started getting fat because of all the Michelin star restaurants his potential clients would treat him to. His data science career began with loyalty cards and being incredibility confident. When someone uses a loyalty card, the company is collecting data. They will know exactly what you purchased and how much you purchased of each item. The customer will be rewarded with monthly coupons. Jay was in charge of coming up with the coupons that were designed to make the customer spend more money in the store. Knowing at least one data programming language will leverage what you have and give you one foot in the door. The best way to get into data science is to know how it will improve the current industry or business you are working for.

Later, Jay explains why QA is a lost skill and the idea that great data scientists have internal discipline. However, there is a race to push the boundaries and become more automated. For example, Facebook collects as much data as possible and thinks about the consequences later. Data is data and people are people. Understanding data is the starting point. Before Jay starts a job, he dives deep and analyzes what every number means to the business with their data collection. Also, Jay considers how to make his bosses job as easy as possible. Overall, the success of his boss will create the most significant impact on his business. If someone has been working at the same job for ten years, they are scared to grow and try something new. Finding a data scientist who has worked at multiple different sizes and types of organizations is the key to finding a well-rounded employee.

Enjoy the show!

We speak about:

  • [01:40] How Jay started in the data space

  • [06:15] Loyalty cards

  • [08:50] QA is a lost skill

  • [10:25] You are your own police

  • [13:30] Ethical considerations

  • [15:10] Transition from marketing to data science

  • [20:40] Putting yourself on the line for the benefit of the company

  • [22:15] Creating change in organizations

  • [27:30] Learning new applications + algorithms

  • [33:50] What makes a great data scientist?

  • [38:15] Delivering results to alleviate pressure


Jay’s LinkedIn:



  • “If you want to get into data science, the best way to do it is to understand how data science can help your current industry.”

  • “I have actually never worked as a full-time employee for more than two years.”

  • “If you want to change, you need to be ready to fight for it.”

Now you can support Data Futurology on Patreon! 

Thank you to our sponsors: 

UNSW Master of Data Science Online: 

Datasource Services: or email Will Howard on 

Fyrebox - Make Your Own Quiz!

Jay Liu is based in London, United Kingdom.

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

Marek Rucinski – Deputy Commissioner, Smarter Data Program

Marek Rucinski 1.jpg

Marek Rucinski is the Deputy Commisioner leading the Smarter Data Program at the Australian Taxation Office (ATO). Marek has taken part and driven the evolution and transformation of Marketing, Analytics, Data and Digital capabilities for over 20 years. This has been done in both industry roles and consulting services capacity, across Australian, Asian and Global clients, across Retail, Telco, Consumer Goods, Financial Services, Mining & Utilities sectors. His passion centers on helping clients change the role of Marketing & Analytics capabilities in Digital and Data age, from activating the capability through acting on insights, to transforming customer experience and the whole business via delivering value across business functions. Prior to ATO & Accenture, Marek lead and created analytics functions and teams in a Retail industry, and developed global corporate strategy frameworks and analytics in a multinational organizations.

In this episode, Marek tells us about how he was always interested in the science behind marketing. Marketing as a discipline has been completely transformed due to the emergence of data as a driver for engagement with the customer. Marek is not a classically trained data scientist; he is a data strategist and can dive deep into the organization’s needs in order to drive value to the customer. Marek tells us how some businesses can struggle with how to handle the findings of research from data scientists. It is essential to translate the potential into targets to create the prize. Leave the ego at the door and find the ability to be critiqued.

Later, Marek tells us how educating businesses on analytics as a mechanical process is essential for them to perceive how the whole thing works. He then explains his transition from consulting to government and how his excitement lies in the play with analytics at an enormous scale. Then, Marek describes how to have each section of the value chain working with purpose and precision. Data has to be trusted, organized, and accessible for the company. A data strategist must consider how the data is being delivered to their client. You want to create products and interactive experiences for the business as opposed to simple spreadsheets. Finally, Marek answers the audience’s questions including what makes a good data scientist and current challenges in the data science industry.

Enjoy the show!

We speak about:

  • [02:10] How Marek started in the data space

  • [05:25] Activating the value from data

  • [08:30] POCs are essential  

  • [09:45] Find people who can create the vision

  • [12:00] Educating businesses on analytics

  • [16:30] Artificial intelligence + automation

  • [18:50] Transition from consulting into government

  • [20:20] Motivations for government work

  • [22:00] Future of ATO

  • [26:15] Continuous production of insights

  • [29:30] Audience questions  


Marek’s LinkedIn:


  • “Good results create more interest which in turn creates traction for new products.”

  • “If you engage the business regarding the value, but then you cannot deliver on the promise, it creates dissonance.”

  • “What separates great data scientists is their ability to communicate what the results actually mean.”

Now you can support Data Futurology on Patreon! 

Thank you to our sponsors: 

UNSW Master of Data Science Online: 

Datasource Services: or email Will Howard on 

Fyrebox - Make Your Own Quiz!

Marek Rucinski is based in Sydney, Australia.

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

Jonny Bentwood – Global Head of Data & Analytics

Jonny Bentwood.jpg

Jonny Bentwood – Global Head of Data & Analytics

Jonny Bentwood is the Global Head of Data & Analytics at Golin. Jonny is an innovative leader with 15+ years of experience in communications - winning, retaining and working for Fortune 100 clients such as Facebook, Unilever, Heineken, Barclays, HP and Microsoft. He has a proven record as a creator of pioneering solutions with ability to transform business to radically impact bottom line. Jonny presents complex information in an engaging and informative style and is a strategic consultant to executives using data to provide guidance on reputational and crisis issues and maximising marketing campaigns.

In this episode, Jonny tells a story about how MTV got in touch with him to apply data in figuring out who would most likely win The Apprentice. After being in the industry for over twenty years, he believes this is the best time to be in data. CMOS are spending more of their money than ever before on analytics. How do data scientist prove their value? People use data purely in a descriptive way. To succeed and bring value to clients, one needs to switch from describing the data to telling the customer what they need to do with the data. Set the goals of who, what, and why to figure out which message will be most useful before you even start. Take it a step further by using prescriptive data and make it predictive. This is where you study what will happen in the future. We are continually absorbing and understanding what things could happen and will happen. This opportunity is essential to identify issues before they occur and fix them.

Later, Jonny explains how understanding the customer requires a customer journey approach to increase marketing efficacy. Instead of doing random stuff, focus objectives with specific tactics and strategies. Something that gets on Jonny’s nerves is when people say it isn’t rocket science. Jonny wants people who do the research and figure out the information that counts. Then, we learn why organizations need to be data-driven. It is essential to train people and give them the technology to improve their jobs and become more efficient. Jonny challenges the status quo in his business. For instance, they have unlimited holiday, and their gender pay gap is positive to women.

Enjoy the show!

We speak about:

  • [01:30] How Jonny started in the data space

  • [04:50] Public relations

  • [06:00] Descriptive, prescriptive, and predictive

  • [08:15] Difference between interesting and useful

  • [10:00] Understanding the customer

  • [15:25] Cultural shift of data in organizations

  • [19:10] Challenging the status quo  

  • [22:40] Shiny object syndrome

  • [26:45] The twenty percent time

  • [30:00] Bringing data application to the masses

  • [34:30] Each stage of the customer journey  

  • [39:30] Getting value for money

  • [42:45] Return on investment

  • [44:15] Data + creativity  


Jonny’s LinkedIn:

Jonny’s Twitter


  • “To be truly smart you need to go from descriptive to prescriptive.”

  • “For a data scientist, the word interesting is one of the worst insults you can get. It has to be useful, what is the point you are trying to make?”

  • “There’s always going to be something else. What you need to do is focus on what you have.”

  • “Some of the best stuff has data infused with creativity.”

Now you can support Data Futurology on Patreon! 

Thank you to our sponsors: 

UNSW Master of Data Science Online: 

Datasource Services: or email Will Howard on 

Fyrebox - Make Your Own Quiz!

Jonny Bentwood is based in London, United Kingdom.

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!