#67 12 Questions to Ask in Mentoring Sessions with Felipe Flores – Founder & Podcast Host

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In this episode, Felipe talks about an aspect of leadership, the one-on-one mentorship and feedback session with people on your team. To start, ask your team member what is on their mind and in general, how have things been going? If they do not have anything pressing they want to discuss in the sessions, Felipe turns to set of twelve questions. The first question is what you are most proud of that you have done since we last caught up? These questions are designed as tools for the team member to recognize the need for self-assessment. What they say is not essential; really, the follow-up questions are more necessary to help them uncover themselves. Next, ask what the team member could have done better since the last time you talked. By allowing them to evaluate and think of improving continually, they can learn faster and become more efficient in their work. 

The next few questions require a broader perspective and ask about the team as a whole. It should be clear that everything is a team effort, and all member’s ideas are respected and heard by others on the team. After the team questions, head back to questions about the person and ask what they would like to work on or improve? Then, the next issue will take a lot of trust and rapport with your team member, ask what is one thing that is true that you think I do not want to hear? Question nine is how I can help you to do better? This question has taught Felipe that he is good at the big picture but needs to focus on the details and how the team might achieve the big picture. Finally, the last three questions are asking what they like best and least about the organization and if they are happy at the moment.

Enjoy the show!

We speak about:

  • [01:35] Open-ended questions

  • [02:15] What are you most proud of that you have done since we last caught up?

  • [03:45] What could you have done better?  

  • [05:30] Questions about the team

  • [10:00] What would you like to work on or improve? 

  • [11:40] What is one thing that is true that you think I do not want to hear?

  • [13:10] How can I help you to do better?

  • [15:00] What do you like the least about the team? 

  • [16:00] What do you like best about the team?  

  • [16:35] Are you happy at the moment? 

Resources:

Saturday Night Live

Quotes:

  • “One of the best and quickest ways to learn is to evaluate your efforts continually.” 

  • “Understand the human behind the data scientist.” 

  • “You don’t need to be fixing every person’s problem, but everyone needs help every now and again.”

Thank you to our sponsors:

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RMIT Online Master of Data Science Strategy and Leadership

Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions.

Visit online.rmit.edu.au for more information

Felipe Flores is based in Melbourne, Australia.

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

#66 How to Structure a Data Analytics Team with June Dershewitz – Director of Analytics

June Dershewitz.jpg

June Dershewitz has spent her career driving analytics strategies for major businesses. She's currently Director of Analytics at Twitch, the world's leading video platform and community for gamers (a subsidiary of Amazon). As an analytics practitioner, she builds and leads teams that focus on marketing analytics, product analytics, business intelligence, and data governance. In her prior life as a consultant, she was a member of the leadership team at Semphonic, a prominent analytics consultancy (now part of Ernst & Young). As a long-standing advocate of the analytics community, she was the co-founder of Web Analytics Wednesdays; she's also a Director Emeritus of the Digital Analytics Association and a current Advisory Board Member at Golden Gate University. She holds a BA in Mathematics from Reed College in Portland, Oregon.

 In this episode, June says she fell into data by accident, she has a bachelor's degree in math. She got an idea that she wanted to move to San Francisco and work with a startup. June was invited for an interview as an analyst, and she loved it. It is only a matter of time for June to meet someone before she starts talking about goats. June grew up on a goat farm; her mother was a farmer. After college, June got a job working for a mathematician in Philadelphia. Later, June decided she wanted to work in the industry. She and her boyfriend moved to San Francisco, and both ended up getting jobs with startups. 

June says in startups; people wear many hats. If you come across a problem, you have the liberty to take ownership and solve it. In smaller companies, it is easier to get a holistic view of the workplace. It is possible at more substantial companies too; June currently works for Twitch, which is owned by Amazon. When June arrived, there were 400 employees, but now has over 1500. One skill June has gotten to use to is stepping up to own things. She still feels empowered to solve problems and to take ownership of those problems. Business intelligence means lots of things to lots of people. Before coming to Twitch, June never claimed to own it outright; however, because she is farther along in her career, she has more experience to draw back on. One risk that we have as data people is potentially getting pigeonholed into a thing that we are good at and getting stuck there. June knows a ton about Adobe Analytics; she could have continued to exist in that realm and be just fine. However, she thrives on solving a multitude of problems and being challenged in the workplace. Later, June discussed the hub and spoke organization model, the data quality journey at Twitch, and getting involved in the data science community. 

Enjoy the show!

We speak about:

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

  • [08:20] Solving problems in startups 

  • [09:45] Getting a holistic view in the workplace  

  • [11:20] Feeling unsure about owning a piece of work 

  • [15:30] Business intelligence skillsets for data scientists   

  • [19:35] Clear understanding of data roles in the workplace 

  • [20:55] An overview of June’s teams’ structures 

  • [27:10] Managing career transitions with the hub and spoke model  

  • [29:25] Assigning each person a technical buddy  

  • [32:10] The data quality journey  

  • [41:40] Evolution of data quality at Twitch  

  • [48:00] Becoming involved in the data science community  

  • [53:10] Other ways June stays involved in her communities 

  • [55:20] Advice for breaking into the data science field 

Resources:

June’s LinkedIn: https://www.linkedin.com/in/jdersh

Non-Invasive Data Governance: The Path of Least Resistance and Greatest Success

Data Governance: How to Design, Deploy and Sustain an Effective Data Governance Program

Quotes:

  • “The thing about being a data person at that time was we just had to figure it out.”

  • “I was the vice president of everything that needed to get done.”

  • “At Twitch, we don’t have a clear definition of what a data engineer means.”

  • “We chose to move to an organization model that is hub and spoke.”

  • “Data governance can mean lots of things to lots of people.”

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 will@datasourceservices.com.au

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June Dershewitz is based in San Francisco, California.

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

#63 Set Yourself Multi-Year Professional Challenges with Felipe Flores – Founder & Podcast Host

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In this episode, Anthony Ugoni, one of Australia’s more prominent leaders in analytics interviews Felipe. Felipe came to Australia as a backpacker and ended up falling in love with the place. With Spanish as his first language, the only English he could say was the jacket is black. Then, Felipe explains some of his odd jobs and working freelance IT. At university, Felipe wanted to specialize in data, but all of his friends told him it was dead. So, he ended up specializing in hardware, even though all of his work was in data. When Felipe went to do his thesis, he happened to stumble into a project involving brain wave activity. The electrical engineer did all the research and design, the signals would be passed to Felipe’s computer, where he made his first application of machine learning. 

Then, Felipe explains how he and a colleague of his made the decision to quit their jobs at a small consulting firm. They decided to start their own firm, despite knowing very little about business. The first year they almost went bankrupt about four times and made lots of mistakes. They wanted to be in analytics but were unsure how to sell their services. The two spent six months creating a piece of software. When they went to show prospects they found out people did not like the entire product. So they decided to focus on their consulting business. 

After gaining so many clients, Felipe was literally sleeping in the office and billing over 100 hours each week. Coming up with solutions for the customers needed to be part of the sales process. Do exactly what the customer needs, and do it very quickly to build trust. Although you may be able to do even more for the customers to advance their businesses, target their needs first. After three trusted transactions with the customer, your relationship is built on trust, and then you can start recommending different projects. Eventually, Felipe went back to some of the people who expressed interest in investing in the company over the years and said they had an opportunity to invest. His partner got 51%, and Felipe sold his shares, the company is still going and has been rebranded. Later, Felipe discusses his work as the Executive Director & Head of Data Science at ANZ and explains their supportive work culture. Felipe also reveals the inspiration behind his podcast, Data Futurology, and describes his excitement behind explainable AI. 

Enjoy the show!

We speak about:

  • [02:40] Felipe’s background 

  • [06:10] Education and specializations

  • [14:30] Quick delivery of value  

  • [17:20] A series of odd jobs and IT freelancing  

  • [24:20] Setting up his own consulting company  

  • [33:15] Highs and lows of Clear Blue Water

  • [37:30] Executive Director & Head of Data Science at ANZ

  • [47:55] Supportive and open culture at work 

  • [52:40] Understanding the business at a new job

  • [54:45] Inspiration behind Data Futurology

  • [62:00] Explainable AI 

Resources:

Felipe’s LinkedIn: https://www.linkedin.com/in/felipefloresanalytics/?originalSubdomain=au

Episode #21 Antony Ugoni: https://www.datafuturology.com/podcast/21

Quotes:

  • “If I’m an engineer, people will think I’m smart.”

  • “A colleague of mine and I decided to set up our own consulting company. Professionally, it was the best and worst thing I’ve ever done.”

  • “Sales is built on trust and a human connection.”

  • “I had not done a good job of being a leader and creating a culture.”

  • “How can we make data scientists today, the CEOs of tomorrow?” 

Now you can support Data Futurology on Patreon!

https://www.patreon.com/datafuturology

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 will@datasourceservices.com.au

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Felipe Flores is based in Melbourne, Australia.

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

#62 Full Stack Data Science with Gregory Hill – Global Head of Analytics

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Dr. Gregory Hill leads the Analytics function at Brightstar's Global Services division, developing and delivering their data & analytics strategy, innovation programs, and product development initiatives. He works across their lines of business, including supply chain optimization, product portfolio management, financial services, buy-back and trade-in, leasing, and omnichannel solutions. He also manages Brightstar's analytics team in support of their key global accounts with pre-sales, solution design, and service delivery. His expertise is in the application of advanced analytics techniques (including machine learning, predictive modelling, mathematical optimization, econometrics, and operations research) to commercial problems. These applications span forecasting, pricing, fraud, market segmentation, customer satisfaction, and propensity modelling.

In this episode, Gregory explains how he started in the data space. He was aware of all the theoretical work being done around data but did not know how it worked in an industry aspect. The real challenge of putting mathematical models to practice lies in the organizational and people elements of it. Computer science and electrical engineering do not teach you how to overcome organizational challenges and individual motivations and incentives. Going back to get his Ph.D., Greg wanted to do something requiring qualitative research. So he targeted informational systems and economics. His fieldwork leads him to interview executives of larger banks, publicly listed companies, and government agencies. He came up with an economic framework that improved customer data quality. 

Some problems Greg started looking into while working at Telstra were fixed by using the four P's of marketing. He had an opportunity to learn something new that they did not teach in engineering school. In business, the four P’s are a useful lens to think about commercial problems like product lifecycle management and portfolio optimization for business. They looked at questions around what products will work well in what channels. Previously, this type of merchandising decision making was done by gut feel. Having a data-driven approach was a different way of thinking for the company and the teams. Greg would not recommend someone to gain a Ph.D. to become a data scientist. You can acquire the skills you need outside of academia. Academia will not give you the skills to become successful, a Ph.D. may hinder requiring all the skills to become a data scientist. Later, Greg discusses his appreciation for managing data scientists, being involved in the local data community, and the challenges of working globally. 

Enjoy the show!

We speak about:

  • [02:00] How Greg started in the data space

  • [11:10] Leaving academics and getting involved in the industry  

  • [13:20] Greg’s work background

  • [18:25] The four P’s of marketing

  • [20:40] Transiting from gut instinct to a data-driven approach

  • [27:55] Thinking through cause and effect 

  • [30:45] What Greg’s team looks like

  • [39:00] Lessons learned from managing data scientists  

  • [42:25] Active in local data science meetups + guest speaking  

  • [44:25] Working globally + peeling back opportunities to use data science techniques

Resources:

Greg’s LinkedIn: https://www.linkedin.com/in/gregoryhill/?originalSubdomain=au

Brightstar: https://www.brightstar.com

Quotes:

  • “My thesis was not a project; it was a lifestyle.”

  • “I didn’t want to be an academic, I wanted to get back into the industry.”

  • “It was a combination of arrogance and laziness.”

  • “At the end of the day, it boils down to if I change X, will Y change?”

Now you can support Data Futurology on Patreon!

https://www.patreon.com/datafuturology

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 will@datasourceservices.com.au

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Gregory Hill is based in Melbourne, Australia.

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

#61 Data Science Strategy in the Military, Startups and Tech/Digital Leaders with Sveta Freidman - Data Analytics & Science Director/Mentor

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Sveta Freidman is a data scientist and business intelligence leader with extensive experience in consulting and client-based environments. She has a vast experience working in different industries, including gambling, retail, health, and online businesses (startups). Sveta is a data strategist with a passion for connecting people to the data they need to make decisions, build better products, and execute marketing strategies.

In this episode, Sveta explains why she decided to study statistics, she had a passion for mathematics. During her time in Israel’s military, she collected data from different places and made sense from it. Her commercial experience comes from various startups she joined. When joining a startup, you have to wear many hats. Sometimes you have to be a data engineer, data scientist, or a data analyst. Then, Sveta moved to Australia and found a startup, Envato, where she built all the data from scratch.


Enjoy the show!

We speak about:

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

  • [07:15] Sveta’s professional background

  • [17:10] Investing in local talent

  • [20:45] How to hire for a startup

  • [24:30] Questions for hiring interviews

  • [29:25] Working for Carsales

  • [31:55] People not trusting the data

  • [35:20] Solving the issue of trust

  • [40:30] Finding bias in the data

  • [44:50] Make sure you look at the data every day

Resources:

Sveta’s LinkedIn: https://www.linkedin.com/in/sveta-freidman-5981593

Carsales: https://www.carsales.com.au


Quotes:

  • “Statistics is everywhere.”

  • “You can be a great data scientist, but you need to understand the culture.”

  • “I give the candidate a business problem to see how they will react to it.”

  • “Your algorithms are good as long as your data is good.”

Now you can support Data Futurology on Patreon!

https://www.patreon.com/datafuturology

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 will@datasourceservices.com.au

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Sveta Freidman is based in Melbourne, Victoria, Australia.


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

#58 Explainable AI by Felipe Flores

Felipe Flores.jpg

Today we have a different type of episode, this is a presentation that Felipe did at the Chief Data and Analytics Officer Conference in Canberra, and it is on explainable AI. First, Felipe explains how Amazon used a secret AI recruiting tool that had a bias against women. Also, the U.S. government used an algorithm predicting how likely people in the criminal justice system would reoffend. What they found is that it targeted specific racial groups. The algorithm isn’t racist or sexist, the data is.

Regarding job applications, as your company scales up, the need to automate the process of looking at the applications becomes necessary. Sometimes, bias will creep into the automated decision-making algorithm. The bias can even be narrowed down to the person’s name. For example, somebody with name Felipe might get scored lower than somebody with the name Tyler. Lean into the inequality and predict the bias. You can plug in the CV information, and ask the algorithm to predict the person’s race and gender. Then, find out what key inputs they are flagging to determine this and remove them from the algorithm.

Then, Felipe explains how algorithms can tackle unstructured data approaches. When discussing images, an algorithm was able to correctly identify a wolf from a husky 5 out of 6 times. However, when uncovering how the algorithm determined which was which, it was merely looking at if the animal was in the snow or not. If the picture had snow in it, then it must be a wolf. To determine how this algorithm was functioning, Felipe used LIME - Local Interpretable Model-Agnostic Explanations. It works for classifications and came out of a study from MIT. Later, Felipe discusses using EL15 and how transparency is essential for the public to understand how the algorithms could affect them.

Enjoy the show!

We speak about:

  • [03:40] Large companies and their biases

  • [05:40] Racism and sexism is in our data

  • [08:45] Uncovering inputs of the bias

  • [10:45] Unstructured data approaches

  • [14:30] Using ELI5

  • [19:20] The right to an explanation

Quotes:

  • “We teach our algorithms on how to replicate our decisions.”

  • “The algorithms show the inequality that we have in the world today.”

  • “Explainable AI is more ethical in the sense that it is more transparent.”

  • “Explainable AI helps us avoid blunders and informs us how the algorithm perceives the data.”

Now you can support Data Futurology on Patreon!  

https://www.patreon.com/datafuturology 

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 will@datasourceservices.com.au 

Fyrebox - Make Your Own Quiz!

Felipe Flores is based in Melbourne, Victoria, Australia.

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

#57 How To Go From Academic to Data Science Leader with Yuval Marom - Analytics and Data Science Professional

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Yuval is an Analytics and Data Science professional with extensive commercial and academic experience. His interests and goals are to be working on interesting and practical problems where there is a need to discover and act on meaningful patterns in data, through advanced analytics and data science. I'm the founder and co-organiser of two meetups: Data Science Melbourne and MelbURN, a user group for Melbourne-based users of the R statistical and data mining programming language. 

In this episode, Yuval tells us about how both of his parents are statisticians and inspired him to fall in love with data science. Growing up, he used Pascal to build spaceship games, and it motivated his passion for programming. Eventually, Yuval went for his Ph.D. and focused on applying how animals learn and behave to robotics. Simulated and physical experiments were pretty basic because robotics were not as advanced as they are today. Later, Yuval realized academia was not necessarily his calling, he was more interested in applying solutions to interesting problems. However, in recent years, research innovation and solving problems are becoming much more intertwined. 

Then, Yuval tells us why it essential to embrace simplicity and praises the advantages he reaped by working for a small business. In retrospect, Yuval realized he developed a higher level perspective of business level thinking by working for such a small team; however, he actually turned down a promotion because he was not yet confident in his technical skills. Get as close to the source of data as possible to get a real appreciation for it. Without appreciation, a person will not be successful in a data science managerial position. Doing technical work, Yuval was able to stay connected to the community and hear varying perspectives on how businesses operate and the different flavors of management. Later, Yuval explains his struggles with becoming a manager, the benefits of building connections in the workplace, and the importance of allocating time for professional development. 

 Enjoy the show!

We speak about:

  • [01:40] How Yuval fell in love with data science

  • [05:45] Social learning in biology

  • [08:05] Lessons learned from completing a Ph.D.

  • [13:10] Research innovation vs. solving problems

  • [15:40] Embrace simplicity 

  • [18:00] Small business advantages 

  • [21:45] Skills to develop before management 

  • [26:00] Results oriented work

  • [30:45] Different flavors of management

  • [32:50] Connection to community 

  • [40:20] Learning to interact with stakeholders + managerial skills 

  • [44:00] Benefits of building connections + education 

  • [48:00] Assume people are at work with good intentions 

  • [52:00] Allocate time for professional development 

  • [59:30] Focus on retention

Resources:

Data Science Melbourne 

MelbURN

Yuval’s LinkedIn

University of New South Wales

Tweetable Quotes:

  • “Embrace simplicity or go to academia.”

  • “You have to know the business process before you can make sense of the data.”

  • “Some people strive from innovation, whereas others find satisfaction from solving simple problems and making a difference in an organization.”

  • “If you have the right relationships, you can make anything work.”

  • “It’s important to try different experiences while you’re young, but facing the challenges where you are is equally important.”


Now you can support Data Futurology on Patreon!  

https://www.patreon.com/datafuturology 

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 will@datasourceservices.com.au 

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Yuval Marom is based in Melbourne, Australia.

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



#54 Annie South – General Manager, Data

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Annie South is the General Manager of Data at ME Bank. She is an Information Management professional with twenty years’ experience of complex information environments spanning the full spectrum of structured data to unstructured information. Annie has in-depth technical knowledge of various specialisms, including metadata, data warehousing, data governance, data quality, enterprise architecture, data lineage, Big Data, data analytics, and regulatory requirements.

In this episode, Annie explains the things she does to ensure her career is future ready because nobody can predict what jobs will look like years from now. Do not specialize in a particular technology but specialize in a capability. The technologies that you are using today will not be the technologies they are using tomorrow. If you specialize in a particular technology set, and it is decreasing in popularity, you will end up with fewer opportunities in the market. Annie tells people wanting career advice that when people look at your resume, they are looking for a consistent arc. That could mean staying consistent in an industry or constant engagement in the workforce. Another thing Annie looks for in applicants is kindness, this quality is something that cannot be taught.

To have any sort of success in an organization, you need to have immersed yourself in their environment for a significant amount of time. It has taken Annie a lot of time to grasp her organization’s culture and be able to manage her team efficiently at ME Bank. Annie refers to her team as the librarians, and data scientists are the people who come and read the books. Data is all about human beings being paired with information and knowledge; it helps people live better lives by pairing them with relevant and essential tools. Annie strongly encourages data scientists to follow their passions and continue to look at different opportunities in the field. Later, Annie discusses diversity in the workforce, overcoming discrimination in the workplace, and explains the importance of LinkedIn for social networking.

Enjoy the show!

We speak about:

  • [01:20] How Annie got into the world of data

  • [10:00] Insight for people starting in the data space

  • [12:50] Organizations are not predictable  

  • [14:50] Annie’s team at ME Bank

  • [27:50] Turning recruitment on its head

  • [33:20] Transitioning from teaching to general manager

  • [39:05] Sort out your personality and experiment with leadership

  • [46:30] Imposter syndrome  

  • [49:10] Experimenting with diversity in the workforce

  • [53:30] Challenges with discrimination in the workplace   

  • [61:10] Define yourself; do not be defined by others

Resources:

Annie’s LinkedIn: https://www.linkedin.com/in/annesouth/

ME Bank: https://www.mebank.com.au

IT Jobs Watch: https://www.itjobswatch.co.uk


Quotes:

  • “You are not going to be able to predict the roles that will exist in twelve months.”

  • “Sometimes, a job is better than no job.”

  • “Organizations are extremely complex, quite random chaotic things.”

  • “You will never get ahead by being excellent at what everyone else is doing.”

Now you can support Data Futurology on Patreon!  

https://www.patreon.com/datafuturology 

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 will@datasourceservices.com.au 

Fyrebox - Make Your Own Quiz!

Annie South is based in Melbourne, Australia.

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


#53 Pavel Pleskov – Data Scientist & Kaggle Grandmaster

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Pavel Pleskov is a data scientist at Point API (NLP startup) and currently ranks number 3 out of 109,624 on Kaggle, making him a Grandmaster. Pavel has started companies in the past and has worked in many different industries before becoming a data scientist and Kaggle Grandmaster.

In this episode, Pavel explains his background and how he started in the data science space. When Pavel’s girlfriend went to pursue her master’s degree in London, Pavel began interviewing for a quantitative research job nearby. Turns out, the company was a rival of his current employer, causing him to get fired from his job the next day. Former employees of this job contacted Pavel to ask if they would join their new trading firm and be head of their research team. After doing his job for two years, Pavel knew he was capable of doing it on his own. The company works remotely, and after spending time in bitter Russian winters, Pavel looked to work elsewhere. The ideal country turned out to be Vietnam and was Pavel’s first time outside of Russia.

Then, Pavel started taking machine learning courses and found the platform Kaggle to practice what he has learned. Practically, Kaggle is much more helpful than homework or any online course. The most significant part of his success was spending full-time on Kaggle rather than working another job. Then, Pavel explains the difference between machine learning engineers and researcher data scientists. Later, Pavel reveals his intention behind becoming the very best at his work. The key to success is to find something you like and become the very best at what you are doing. If you are doing something that you do not enjoy, then there is no way you will become the very best. The only way to overcome your obstacles is to enjoy what you are doing.


Enjoy the show!


We speak about:

  • [01:50] How Pavel started in the data space

  • [09:50] Vietnam is an ideal space for working remotely and teaching English

  • [21:40] The moment Pavel found Kaggle

  • [24:20] How Pavel became a data scientist

  • [28:00] Difference between machine learning engineers and researcher data scientists

  • [31:50] Why is it essential to be the very best?

  • [34:20] Machine learning and mathematics

  • [36:45] The early days of Pavel’s Kaggle journey

  • [40:00] Pavel’s favorite part of Kaggle

  • [47:20] The role of automation in Kaggle   

  • [49:40] The steps when approaching a new Kaggle competition

  • [52:55] Think twice before you commit to data science


Resources:

Pavel’s LinkedIn: https://www.linkedin.com/in/ppleskov/?originalSubdomain=ru

Pavel’s Kaggle: https://www.kaggle.com/ppleskov

Pavel’s Twitter: https://twitter.com/ppleskov

Quotes:

  • “It is tricky to work remotely as a data scientist.”

  • “It takes a lot of time to find your passion in life. People spend too much time on things they do not enjoy.”

  • “When I first started Kaggle, I entered any competition and tried to improve my score on the leaderboards.”


Now you can support Data Futurology on Patreon!  

https://www.patreon.com/datafuturology 



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 will@datasourceservices.com.au 

Fyrebox - Make Your Own Quiz!


Pavel Pleskov is based in Moscow, Russia.


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



#52 Influencing Marketing with Data Science with Danielle Timmins – Chief Data Analytics Officer

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Danielle Timmins is the Chief Data Analytics Officer for Free Range Creatives. Free Range Creatives is a digital marketing agency that is deeply rooted in data and analytics. They have a different view on agency life and challenge the existing ways of working. They believe that work should be fun (well, at least most days) and that our work must be insightful, inspirational and effective.

In this episode, Danielle tells us how she did not start in the data space but initially wanted to be a doctor. Danielle ended up getting a Master’s in Economic Psychology, during which she concentrated on the digital side of marketing. This is where Danielle got her exposure to data and started to understand it. Danielle got her first start at an NGO in a marketing position. She would shoot mini-documentaries for television and then moved into a more traditional marketing role. Danielle’s first job as a strategist was down in South Africa where she worked with several different clients. This is when she would start to work with data and incorporate it with strategy.

Danielle says that her most significant learning curve has been communicating related data information to people that do not understand data. She recalls how she was in a meeting and wondered what to do with the numbers that were presented and challenged people on what the data meant and how it was presented. Danielle says that these questions were not really asked at the beginning and said that they only came about once she got into a higher position. She loved sharing how she got to the data and the process, but she learned that most people do not. This opened her eyes to make sure that data is broken down into something exciting and easy to understand.

Later, Danielle explains how her style is more visual than number driven or a standard PowerPoint, she uses a TED Talk presentation. This method utilizes visuals that create excitement; the brain works better with visuals. Usually, there are specific questions that we are trying to answer, and we can help answer these questions with visual stories. There are multiple presentations for different audiences, and you must know this to tell the visual story.

Enjoy the show!

We speak about:

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

•    [03:20] Background and career  

•    [06:20] Deciding what problems to tackle first on the job

•    [08:35] Evolution of marketing   

•    [13:35] Favorite failure

•    [16:50] How to communicate data

•    [18:30] Visual presentation style

•    [19:45] How Danielle creates a story

•    [21:30] How do you structure visuals for executives?

•    [23:10] How do you think people can get better at this skill?

•    [24:45] What is a strategist for data?

•    [27:40] What is the role outside of data?

•    [29:00] The main challenges for Danielle’s clients

•    [32:30] Working with clients on case-by-case basis

•    [33:30] Qualities of a great data scientist  

•    [35:30] What do you think makes a good data leader?

•    [36:15] Current challenges in the data space

•    [37:40] Future challenges for the data space

•    [42:40] Advice for future data scientists and leaders

Resources:

Sexy Little Numbers

Free Range Creatives: https://www.freerangecreatives.co.za/

Danielle’s LinkedIn: https://www.linkedin.com/in/danielletimmins/

Quotes:

•    “There are many different audiences, and you must adapt your story for each one of them.”

•    “Data can sometimes be abused during storytelling, but I hope that over time, this will change and only be used to help support someone.”

•    “All audiences need to be comfortable with what we are doing as data analysists and sharing their work.”

Now you can support Data Futurology on Patreon!  

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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 will@datasourceservices.com.au 

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Danielle Timmins is based in Essen Area, Germany.

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


#51 Peter Elger – Founder and CEO

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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


Resources:

Peter’s LinkedIn: https://www.linkedin.com/in/peterelger/?originalSubdomain=ie

Peter’s Twitter: https://twitter.com/pelger

Peter’s Book: AI as a Service


Quotes:

  • “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!  

https://www.patreon.com/datafuturology 



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 will@datasourceservices.com.au 

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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!



#50 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   

Resources:

Prakash’s LinkedIn: https://www.linkedin.com/in/prakashbaskar/

Khyanafi: http://www.khyanafi.com

Khyanafi’s LinkedIn: https://www.linkedin.com/company/khyanafi/about/

Set up a discovery call with Prakash: https://calendly.com/prakashbaskar/discoverycall

Quotes:

  • “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!  

https://www.patreon.com/datafuturology 


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 will@datasourceservices.com.au 

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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!

#49 Becoming a Kaggle Competition Master with Valeriy Babushkin – Head of Data Science, Kaggle Competition Master (Top 60)

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Valeriy Babushkin is the Head of Data Science at X5 Retail Group where he leads a team of 50+ people (4 departments: Machine Learning, Data Analysis, Computer Vision, R&D) and increases profit in a 25+ billion USD company. Also, Valeriy is a Kaggle competition master; ranking globally in the top 60.

In this episode, Valeriy explains his background and how he started in the data science field. At one point, he received an offer for a senior position at a bank; it was the largest privately owned bank at that time in Russia. Valeriy did not find out that he was doing machine learning until working on it for two years. What someone is doing right now could be pretty close to machine learning, and they don't even know. Then, Valeriy speaks on how trust is essential to the job of a data scientist; not only between you and your boss but between you and other departments. Trust will make your job easier when explaining the data, the results, and how reliable they are for the company. However, if there is an existing data science department in the company, you will not have to work as hard to earn the trust of others because it already exists. Sometimes when data scientists join a company, they think their job will just be to code all day. That is not always the case, you will have to talk to many people and often be a business analyst.

Next, Valeriy discusses setting up teams in the data science space and how many people really need to be involved. For instance, if an algorithm is your product, you will need not only data scientists but product managers, project managers, and software engineers. If you are building the data science department, what do you need to grow? You will need to build a roadmap for the product and know how you want the company to improve. Then, Valeriy explains why reliability is one of the most essential qualities of an employee. For instance, if you have a critical task would you rather give it to an employee with a fifty percent chance of completing it in two weeks or someone who has a ninety-nine percent chance of handling it within six weeks? You will give it to the person that is more reliable, despite the fact it may take them a little longer to complete it. Later, Valeriy reveals the story behind his Kaggle journey and discusses some ethical challenges in the data science industry.

Enjoy the show!

We speak about:

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

  • [06:10] Transiting to working at a bank

  • [11:30] Understanding the business process

  • [15:10] Gaining trust from clients

  • [20:20] Data scientists are business analysts

  • [24:10] Expectations from the job interview

  • [25:50] Starting data science teams

  • [31:40] The type of mindsets to look for in a team member

  • [37:30] Different teams complement each other

  • [40:20] Valeriy’s journey with Kaggle

  • [47:40] Ethical challenges in the industry

  • [51:20] Persistence is key

Resources:

Valeriy’s LinkedIn: https://www.linkedin.com/in/venheads/

Valeriy’s Kaggle: https://www.kaggle.com/venheads

Quotes:

  • “It makes sense to have a job interview which is pretty close to your daily routine, your daily work.”

  • “Before hiring new people, you have to make a path.”

  • “One of the most important qualities for employees to have is reliability.”

  • “It is impossible to work directly with two people if you just spend five minutes a day with each of them.”


Now you can support Data Futurology on Patreon!  

https://www.patreon.com/datafuturology 



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 will@datasourceservices.com.au 

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Valeriy Babushkin is based in Zug, Canton of Zug, Switzerland.



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



#48 Leverage What You Know to Get Your Foot in the Door with Jay Liu - Chief Data Scientist at Digital-Dandelion

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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

Resources:

Jay’s LinkedIn: https://uk.linkedin.com/in/jay-liu-76ab2b8a

Digital-Dandelion: https://www.digital-dandelion.com

Quotes:

  • “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!  

https://www.patreon.com/datafuturology 

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 will@datasourceservices.com.au 

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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!

#47 Transforming Government Organisations with Data Science with Marek Rucinski – Deputy Commissioner, Smarter Data Program

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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  

Resources:

Marek’s LinkedIn: https://www.linkedin.com/in/rucinskimarek/

Quotes:

  • “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!  

https://www.patreon.com/datafuturology 


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 will@datasourceservices.com.au 

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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!



#46 Today is the Best Time to be a Data Scientist with Jonny Bentwood – Global Head of Data & Analytics

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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  

Resources:

Jonny’s LinkedIn: https://uk.linkedin.com/in/jonnybentwood

Jonny’s Twitter https://twitter.com/jonnybentwood?lang=en

Quotes:

  • “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!  

https://www.patreon.com/datafuturology 


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 will@datasourceservices.com.au 

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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!