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  

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 

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!




Warwick Graco - Senior Director Data Science

Warwick Graco.jpg

Warwick Graco is the Senior Director of Data Science at the Australian Taxation Office (ATO). He has worked in defence, health, and taxation and has been involved in analytics for 25 years. He is a practicing analytics professional and is currently convenor of the Whole of Government Data Analytics Centre of Excellence and is a senior data scientist in Data Science and Special Acquisition Group of the Smarter Data Program of the ATO. He has a BSc from the University of New South Wales and a Ph.D. from the University of New England Australia. His professional interests include organisational innovation and learning, organisational decision making and analytics.

In this episode, Warwick tells us how he got started in data research the skills gained that led him to his successes today. Warwick explains why transparency is a business requirement for software and tools in the data science field. People with more analytical backgrounds will be more willing to accept an opaque solution over a transparent solution. When analytics was in the early stages, some organisations pushed back from data science; feeling they were on top of their portfolio and did not need any outside resources. No matter what results Warwick would come up with for these organisations, they would continue to have the same attitudes. Since 2010, there has been a shift in attitudes because data science has shifted from the background to the foreground.


Then, Warwick tells us the difference between good support and lousy support in the workplace. While Warwick was working with organisations, instead of providing results, he did the reverse. Ask the organisation what they want rather than telling them the findings. Providing the outputs clients wish to see led to incremental improvements built into their business intelligence reports. Warwick also explains why you can no longer be a data scientist; you will need to learn and master the domain of your work. For instance, Warwick learned everything about ophthalmology while working on data science with an ophthalmologist. Later, Warwick explains his process of publishing research, improving privacy concerns, and automated supports.

Enjoy the show!

We speak about:

• [02:20] How Warwick started in data science

• [05:55] Aptitude for research

• [08:40] Purpose-built software + decision trees

• [12:20] Accepting opaque solutions vs. transparent solutions

• [16:45] Pushback of data analytics

• [21:15] Difference between good support and bad support on the job

• [25:25] Necessity to learn the domain first

• [29:00] How to learn on the job

• [32:20] Process of publishing research

• [41:50] Improving legal and privacy concerns

• [44:25] Automated support + decision-making operations

• [52:40] Developing an analytical + practical mindset

• [58:10] Hyperspecialized

• [64:30] Moving toward data + analytics as a service

• [66:25] Advice from Warwick

Resources:

Datasource Services

University of New South Wales

Warwick’s LinkedIn: https://www.linkedin.com/in/warwick-graco-4a27044/


Quotes:

  • “Unless you have the support of those whom you are working for, your chances of success are probably fairly slim.”

  • “You can no longer just be a data scientist; you have to work in a particular area and build up the knowledge first.”

  • “I define an expert as someone who has profound knowledge in an area. They are very good at coming up with solutions to solve problems quicker than those without that deep knowledge.”

  • “Talent identification means identifying what people have a gift for, giving them the right experiences to bring their gifts to sharp focus, and having them use it in the best possible way to benefit everyone.”

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!

Warwick Graco is based in Canberra, Australia.

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

Caroline Worboys - Data Expert, Investor, Advisor, COO & Vice Chair

Caroline Worboys.jpg

Caroline Worboys is a data expert, investor, advisor, COO at Outra & Vice Chair at DMA Group. She has been working in the data industry for over 30 years. In this time, she’s had a fascinating journey. She has worked, created, mentored and consulted through many data driven organisations. She’s played all the different roles: technical lead, a business lead, a founder and investor.

While Caroline doesn’t describe herself as a data scientist and didn’t go to university, she has always worked with data and has a wealth of experience. She started in the field by working with consumer data for direct marketing and progressed to the point where she founded and sold several successful data related start-ups. Currently, she is the founder and COO of Outra.

In this episode, we talk about what it was like being a woman in technology in the 80’s, how the use of data has progressed over the years and how she keeps her team focused on the goal of doing things faster than other companies.

We speak about:

  • How Caroline got started in data (03:02)

  • What she learnt from observing senior colleagues and what it was like being a woman in technology in the 80s (05:38)

  • Using customer data in order to target people at the right time (07:46)

  • The principles of working with consumer data hasn’t changed (10:04)

  • How the care and attention required for direct mail has now been lost with email and digital marketing (11:09)

  • The importance of being curious and learning (12:31)

  • Starting her own business and finding a different way to charge customers (13:46)

  • Advice for young people and why it’s important to seek people for advice (21:34)

  • Personal drivers to start her business (23:35)

  • How her business innovated as technology changed (25:10)

  • The challenge of using data to actually solve problems (30:29)

  • Considerations when choosing her team (35:48)

  • The recruitment process is like for Caroline’s company (39:00)

  • How Caroline keeps her team focused on the goal of doing things faster than other companies (41:40)

  • The difficulties of work/ life balance (44:16)

  • Considerations for being a leader in the data space (47:03)

  • The importance of thinking about the type of data you want to work with (51:43)


Quotes:

  • “Seek out people who have really, honestly read the book and seen the movie and been there. Because they can stop you from going down a whole bunch of dead ends.”

  • “You can’t scale and have thousands of relationships with thousands of people. But you can create a culture, and processes below that culture, that are scalable.”

Links:

Outra

https://outra.co.uk

Actico

https://www.actico.com/

SmartFocus

https://www.smartfocus.com/


News International

https://en.wikipedia.org/wiki/News_UK

Transunion (Formerly Call Credit)

https://www.transunion.co.uk/

Barclays

https://www.barclays.co.uk/

Monzo

https://monzo.com/

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!

Kevin Harrison is based in Concord, California, USA.

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





Kevin Harrison - Chief Data Officer and Deputy Chief Information Office

Kevin Harrison - Chief Data Officer and Deputy Chief Information Office.jpg

Kevin Harrison is working as Chief Data Officer and Deputy Chief Information Officer for the City of Oakland in California. Prior to this he worked as the first ever Chief Data Officer for the State of Illinois. During that time he designed the blueprint for the State Data Practice. Operating under the new Department of Innovation and Technology agency, he implemented an enterprise approach to Business Intelligence and Data Analytics, covering all 60 State Agencies to create a collaborative and sharing environment across the state. Having worked with multiple organisations, Kevin has been able to handle different types of challenges in our industry. In today’s episode, Kevin shares the strategies he applied to move from smaller projects to bigger ones. How he has been able to help organisations increase their market share and improve operations. Kevin also shares why he thinks changing the perception of organisations about data and educating them about tools in the space is so important. He further talks about data governance and possible changes in role of the data scientist role in future. 

We speak about:

01:55 Professional background of Kevin

06:30 Why data is important?

07:20 Evolution of Data warehousing

10:00 How organizations are utilizing the data?

11:39 As data officer, how to help organizations to improve their data capabilities?

13:00 Building trust is crucial for project success

13:30 Transition from small to bigger project

16:12 Challenges faced as data consultant

19:00 Educating about the change coming to data science

21:00 Process of data strategy for organizations

23:50 Why so many data warehousing failed?

26:00 Importance of data governance

27:10 Biggest problem in data governance

31:56 Role of data storage

35:15 Challenges faced from moving to another industry/sector

38:42 Qualities data scientist should have

41:43 Future of data science

42:30 Advice to the listeners

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!

Kevin Harrison is based in Concord, California, USA.

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

Michael Tamir - Head of Data Science & Data Science Lecturer

Michael Tamir- Head of Data Science.jpg

Mike serves as Head of Data Science at Uber ATG and lecturer for UC Berkeley iSchool Data Science master’s program.  Mike has led several teams of Data Scientists in the bay area as Chief Data Scientist for InterTrust and Takt, Director of Data Sciences for MetaScale, and Chief Science Officer for Galvanize he oversaw all data science product development and created the MS in Data Science program in partnership with UNH.  Mike began his career in academia serving as a mathematics teaching fellow for Columbia University and graduate student at the University of Pittsburgh. His early research focused on developing the epsilon-anchor methodology for resolving both an inconsistency he highlighted in the dynamics of Einstein’s general relativity theory and the convergence of “large N” Monte Carlo simulations in Statistical Mechanics’ universality models of criticality phenomena.

In this episode, Michael talks about how he accidentally got into data and his work with simulation. Then, Michael discusses his background in data science product development and data science education. He reveals all the mistakes he made with his transition from academics to industry. Also, Michael explains some software engineering challenges he faced during his time in industry and solutions he ended up needing to be successful. Later, Michael tells us what attracted him to data science education and how he balances industry projects with his teachings. Rapid growth is a challenge with technology management because your skillset will get rusty as the technology advances. Lastly, Michael talks fake news, bootstrapping, and Fake or Fact.

We speak about:

[00:20] Michael accidentally got into data

[02:15] About Michael Tamir

[03:40] Transition to industry

[06:40] Software engineering challenges

[08:45] Data Science Education

[15:15] Adaptive learning

[17:15] Team management

[19:05] Challenges with rapid growth

[24:25] Fake news

[27:25] Toughest challenge

[28:50] Fake or Fact

[31:20] Listener questions

Mike's quotes from the episode:

“You have to be really careful about what you do and what you do not teach in order to make sure students are successful in the long-term.”

“Decisions are going to be best made by those who are closest to the ground.”

“You’re not going to be the expert in every group you are managing.”

“I take full responsibility for any failures with the algorithm.”

“Most of my time is spent on my day job.” 

“Find out what you enjoy about data science skills; find the role that is looking for those skills.”

“I enjoy the science and making sure we are asking the questions in a scientifically sound way.”

Connect:

Twitter - https://twitter.com/MikeTamir

LinkedIn – https://www.linkedin.com/in/miketamir/

Website - http://www.fakeorfact.org

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!

Michael Tamir is based in San Francisco Bay Area, USA.

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

David Niemi - VP Measurement and Evaluation

David Niemi - VP Measurement and Evaluation

David Niemi is Vice President of Measurement and Evaluation at Kaplan, Inc., where he oversees efforts to improve the quality of measurement across all education units, evaluate the effectiveness of curricula and instruction, and study the impact of innovative products and strategies.

Previously he was Vice President Evaluation and Research, at K12 Inc., where he directed assessment development and validation, evaluation of products and services, and research studies used to drive curriculum development. He has been a co-principal investigator for a number of large-scale assessment research projects funded by the U.S. Department of Education and the National Science Foundation and has collaborated on Department of Defence training studies. As a researcher and professor at UCLA and the University of Missouri, respectively, he has also managed assessment research and development studies in school districts across the U.S. and has trained thousands of teachers and other professionals to design and use assessments more effectively.

David's new book is:

Learning Analytics in Education: Experts Explain How To Use Data To Understand and Increase Learner Success

New technologies, better measures and more data, all related to learning, hold the promise of helping educators increase their students’ success. The relatively new field of learning analytics has developed to help educators understand and use the increasing amounts of evidence from learners’ experiences. How can educators harness access to greater data to improve learning on a large scale?

Learning Analytics in Education is a new book written by a broad range of experts who explain their methods, describe examples, and point out new underpinnings for the field. The collected essays show how learning analytics can improve the chances of success for all learners through deeper understanding of the academic, social-emotional, motivational, identity and meta-cognitive context each learner uniquely brings.

The collection was edited by four noted educational experts including David Niemi, vice president of measurement and evaluation at Kaplan, Inc., the global educational services company well-known for using advanced learning science and learning engineering methods in its programs and products.

"At Kaplan, we've been invested in using learning science and data analytics for several years to help us design courses and refine instructional methods to help students achieve better outcomes," explains Niemi. "Educators today face accelerating change as education undergoes a fundamental transformation driven by the replacement of traditional analog tools by digital systems and expansive data inputs." He adds, "Understanding how to use these new streams of available data to best guide student learning is the essential point of the book."

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!

David Niemi is based in Valencia, California, USA.

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

Kjersten Moody - Chief Data and Analytics Officer

Kjersten Moody - Chief Data and Analytics Officer

Kjersten is a graduate of the University of Chicago and has a proven track record in modernizing and scaling operations, executing mission-critical business initiatives, and achieving profitability objectives. An energetic leader with a focus on people development, diversity, and inclusion Kjersten demonstrates the ability to effectively lead and work in highly complex environments.


We speak about:

• [00:20] About Kjersten Moody

• [04:45] Love for data

• [06:40] Transition to technology consulting

• [09:50] Lessons learned early on

• [13:15] Leadership took the time

• [14:40] Kjersten’s leadership style

• [15:35] Transition to healthcare

• [18:00] Lessons learned in consulting

• [20:00] Building teams

• [22:15] Qualifications for individuals

• [29:10] Data strategy 

• [33:00] Data governance

• [38:00] Understanding the business aspects 

• [45:20] Financial impacts

• [48:20] Listener questions

Some of Kjersten's quotes from the episode:

  1. “Challenges are a constant in a domain such as data science.”

  2. “Diversity is an attribute of the team. It’s the diversity of experiences, culture, and thought.”

  3. “The process of matching price to risk is inherently done through data.”

  4. “Data strategy is interpreted in many different ways.” 

  5. “The leader needs to be able to work in a trusted way with business leaders and general managers.”

 

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!

Kjersten is based in Chicago, Illinois

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

 

Matt Kuperholz - Partner and Chief Data Scientist

Matt Kuperholz - Partner and Chief Data Scientist

Matt Kuperholz. Matt currently works for PWC as a Partner in their Analytic Intelligence Area and is their Chief Data scientist. With a background in both actuary and computer science, Matt has been working with data for over 20 years. He ran his own company in the early 2000s which included working with Deloitte Australia as they started to look at how to use data science in their business. He is now a is a partner and chief data scientist at PWC Australia. An expert in planning, executing and communicating the results of advanced analytics projects, Matt’s area of specialisation is the application of artificial intelligence and machine learning technologies to detailed and complex data.

We speak about:

· Matt’s love for computers and he he got to where he is now (00:12)

· How Matt’s interest in computers led to a love for data (06:28)

· Matt’s interest in martial arts and why a diversity of people matters (08:19)

· Smell-testing the quality of a number, and the importance of attention to detail (09:40)

· Working with limited time on a mainframe and how Matt coped with limited resources (12:09)

· The early days of using AI and what it was like working in a start-up in the late 90s (15:04)

· The importance of well prepared data (16:56)

· How Matt keeps up to date with data and technology (21:17)

· How Matt chooses what problems to tackle (23:26)

· What it was like working with Deloitte (26:03)

· How data can integrate into other areas of a business (28:32)

· Starting with the real world problem before focusing on the data (30:26)

· A recent project Matt has worked on exploring what trust looks like in a digital world (35:11)

· The idea of responsible AI and how we develop checks and regulation (41:41)

· How technologies are growing exponentially and causing a fast changing world (49:45)

· How Matt follows his curiosity and how this has led to opportunities (52:05)

· Why the data industry is worth getting into (54:48)

· The importance of finding what you are into and staying true to yourself (55:53)

 

Connect:

Twitter - https://twitter.com/datafuturology

Instagram - https://www.instagram.com/datafuturology/

Facebook - https://www.facebook.com/datafuturology

 

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!

 

Matt 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!

How To Build a World Class Data Science Team

Felipe Flores-How To Build a World Class Data Science Team.jpg

In this episode, I talk about data scientists and ways you can attract the best talent to your team. Instead of telling your employees what they can do better, make them curious as to what they could do better. Then, I reveal the three things to look for when analyzing your pool of applicants. Once you have your team, now what? Once you have a decent pay settled, I explain the three things you will need to have for a capable team. Later, I tell you the elements, as a manager, you should be doing as rarely as possible.

In This Episode:

• [02:45] How to attract data scientists to your team?

• [04:45] The three things to look for from your pool of applicants

• [07:05] Adversity; test how they would react 

• [11:00] Three things needed to run an effective team

• [18:00] Managers should be doing this as rarely as possible

Creating a Data Team Session Quotes:

1. “Create a learning environment and continually challenging projects to focus on their development.”

2. “People should be open-minded and willing to learn; I test this in two different ways.”

3. “A lot of people come with technical skills from other countries.”

4. “They had to code it live with about eight people watching them, no pressure!”

5. “You know the answer, and you want to tell them to get to the outcome quickly. That’s an urge you have to roll back and fight against.” 

6. “Purpose is really what gets us out of bed every day.”

7. “Make yourself redundant as quickly as possible.”

Resources Mentioned: 

Drive: The Surprising Truth About What Motivates Us

Connect:

Twitter - https://twitter.com/datafuturology

Instagram - https://www.instagram.com/datafuturology/

Facebook - https://www.facebook.com/datafuturology

 

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 is based in Melbourne, Australia

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. It really helps new data scientists find us. Thank you so much, and enjoy the show!

 

Dr. Kristen Sosulski - Associate Professor of Data Visualization NYU Stern; Director, Learning Science Lab; Author of Data Visualization Made Simple and Consultant

Dr. Kristen Sosulski - Data Visualization

Dr Kristen Sosulski is an Associate Professor of Information Systems at New York University’s Stern School of Business. She teaches MBA, undergraduate, executive, and online courses in data visualization and computer programming. She is also the Director of the Learning Science Lab for the NYU Stern where she leads teams in design immersive learning environments for professional business school education. 

We speak about:

• Kristen’s journey from doing her undergraduate in Information Systems at NYU Stern School of Business to being a professor there teaching Data Visualization (00:17)

• How Kristen’s love of technology led to an interest in using technology to help students learn (01:38)

• The challenges of trying to create an immersive learning environment in the late 90s (02:41)

• What led to Kristen working with data visualization (03:38)

• How Kristen thinks about data visualization and designing data graphics (06:14)

• Some guidelines and thoughts on presenting data to an audience (08:03)

• How people learn to improve their data graphics (11:15)

• The importance of showing your work and getting feedback (14:18)

• The challenges Kristen finds when consulting for companies in data visualisation (17:08)

• The value of data visualization in a data driven organisation (19:54)

• Why Kristen wrote her book on data visualization and why she included case studies (21:14)

• Some resources that Kristen created for the book (23:40)

• Her work in building NYU’s online education and the use of learning analytics (27:11)

• Why there needs to be more training in how to visualize data and to understand what it means (30:10)

• Designing a dashboard for user driven storytelling (33:41)

• How Kristen would like data visualization to evolve in the future (36:44)

• Mistakes people make when creating visualizations (38:51)

• How Kristen developed and improves her work and the value of sharing your mistakes (41:33)

• The importance of understanding what your data means in the real world (42:49)

Links:

Data Visualization Made Simple: Insights into Becoming Visual by Kristen Sosulski

https://www.amazon.com/Data-Visualization-Made-Simple-Insights/dp1138503916

The Online Certificate in Visualizing Data

Taught by Kristen Sosulski via NYU Stern School of Business

https://www.stern.nyu.edu/programs-admissions/online-certificate-courses/visualizing-data

Connect:

Twitter - https://twitter.com/datafuturology

Instagram - https://www.instagram.com/datafuturology/

Facebook - https://www.facebook.com/datafuturology

 

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!

Kristen is based in New York, USA.

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. It really helps new data scientists find us. Thank you so much, and enjoy the show!

Kickstarting 2019 With A Look Back at 2018 (Part 2): Episodes 19 to 34

Felipe Flores.jpg

A lot of listeners have asked what have been my takeaways from the 30+ discussions with the guests on this podcast so far. To launch 2019 I’ve done a look back at all episodes from 2018. This is part 2 where I discuss episodes 19 to 34.

I hope you enjoy my recollection of these conversations. I’d love to hear what were your favourite takeaways!

Connect:

Twitter - https://twitter.com/datafuturology

Instagram - https://www.instagram.com/datafuturology/

Facebook - https://www.facebook.com/datafuturology

 

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 is based in Melbourne, Australia

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. It really helps new data scientists find us. Thank you so much, and enjoy the show!

Kickstarting 2019 With A Look Back at 2018 (Part 1): Episodes 1 to 18

Kickstarting 2019 With A Look Back at 2018 (Part 1): Episodes 1 to 18

A lot of listeners have asked what have been my takeaway points from the 30+ discussions with the guests on this podcast so far. To launch 2019 I’ve done a look back at all episodes from 2018. This is part 1 where I discuss episodes 1 to 18.

I hope you enjoy my recollection of these conversations. I’d love to hear what were your favourite takeaways!

Connect:

Twitter - https://twitter.com/datafuturology

Instagram - https://www.instagram.com/datafuturology/

Facebook - https://www.facebook.com/datafuturology

 

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 is based in Melbourne, Australia



And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. It really helps new data scientists find us. Thank you so much, and enjoy the show!

#34 Sally Grove - General Manager of Insights

Ep34 Sally Grove - Melbourne, Au1.jpg

Sally is the General Manager of Insights at the Australian Motoring Services. She previously spent 10 years working in banking and today she shares her story.

We speak about:

* Fraud analytics in big banks

* End to end analytics

* Importance of fast feedback loops

* Shocks of early working life

* Balancing speed & accuracy

* 80/20 vs 95/5

* Exposures in strategy & politics

* Helping the business ask the right questions

* Leading with the work

* Career breaks: how to

* Importance of working on yourself

* Advantages of medium sized companies

* Creating a data strategy

* Balancing tactical solutions, strategic initiatives and team development

* Self service analytics

* Educating business stakeholders & getting their feedback

* Ability to ask anything from everyone

* Data science is like medicine

* Leveraging multiple dimensions for career development

* Knowledge sharing sessions

* Getting analytics a seat at the table

Sally is based in Melbourne, Australia

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. It really helps new data scientists find us. Thank you so much, and enjoy the show!

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!

#33 Graeme McDermott - Chief Data Officer

Ep33 Graeme McDermott - London, UK.jpg

Graeme started in actuarial science and developed a love for algorithms and automation. He worked in data warehousing before moving into data analytics. He spent 16 years in several Head of Data roles at The Automobile Association (AA) before joining Addison Lee as their Chief Data Officer, where he is today.

We speak about:

* What is actuarial science

* Data warehousing & GIS systems

* Overview of the Chief Data Officer role

* Automation in the data space

* How to build a data warehouse

* The difference between a data warehouse, data lake and virtual data warehouse

* Starting data work with business problems/questions

* How to deliver value to the business

* Balancing tactical project delivery with strategic work

* Enabling self service data analytics

* Prioritising & sizing up work

* Modern styles of work in data

* Data governance: creating a plan

* Creating a data strategy

* How to get to a head of role

* Team building

* Networking

Graeme is based in London, Greater London, United Kingdom

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. It really helps new data scientists find us. Thank you so much, and enjoy the show!

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!

#32 Carole Wai Hai - Head of Data Science & Analytics

Ep32 Carole Wai Hai - Berlin, Germany.jpg

Carole had an unusual path into data science. She's worked as a content project manager, in strategic planning and in sales before getting into data through Business Intelligence at Fyber where she eventually became their Head of Analytics. Today she is the Head of Data Science & Analytics at Tenjin.

We speak about:

* The strengths of being a generalist

* Upskilling throughout your career

* Focus on self service reporting

* The skills needed in a BI team

* Creating internal user groups to share knowledge

* Convincing people to get training on the tools required to do their job better

* The benefits of gaining a reputation internally

* Setting a strategy for data teams

* The importance of data modelling skills in data teams

* Learning technology on the job when you're background is not technology

* Monthly meeting with key departments to review all dashboards in the department

* Working remotely in global companies

* Metrics about user behaviour

* Offering analytics for many customers with the same problem/need

* How to develop consulting skills

* The platinum rule - book on communication style

* The leadership challenge - book recommendation

* What it's like working in startups

* How to recover from being a workaholic

Carole is based in Berlin Area, Germany

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. It really helps new data scientists find us. Thank you so much, and enjoy the show!

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!

#31 Scott Wilson - Founder & CEO

Ep31 Scott Wilson - Sandringham, VIC.jpg

Scott started his career pushing trolleys at Woolworths. In his career he rose to management levels in retail with Woolworths, consumer goods with Kraft Foods, Fonterra SPC and PZ Cussons, then in media with 21st Century Fox. He then became the CEO of iSelect, a role he left earlier this year to start his own AI company Wilson AI.

We speak about:

* Focus on customer needs

* Digitising industries to access more data

* Helping companies in multiple industries to begin their data analytics journey

* How to differentiate your company when competitors have access to the same data

* How to overcome being "data rich but insight poor"

* Changing industry power dynamics through data

* Creating new teams to create value from data

* The importance of storytelling in data science

* Defining objectives with your data analytics communication

* Educating industries to use data more effectively

* Understanding costs & priorities across the value chain to make better decisions

* Eliminating your biases when dealing with customers

* Process re-engineering & AI

* How to think outside of the building

* How to start an AI company

* The importance of translating between business and technical

* How to connect data science and the boardroom

* The importance of data science education in an organisations journey

* How to achieve a wider spread adoption of AI

* Focusing on cost & revenue with data science for maximum impact

* Resist the urge to boil the ocean

* The role of a CEO in a publicly listed company

* Focusing on the top 3 business priorities

* Productionising AI & monitoring unintended consequences

Scott is based in Sandringham, Victoria, Australia

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. It really helps new data scientists find us. Thank you so much, and enjoy the show!

#30 Aaron Black - Chief Data Officer

Ep30 Aaron Black - Washington DC Metro Area.jpg

Aaron started his career working in accounting and building management information systems (MIS). He had his own company, worked in multiple industries and then got into biology and genomics. Today he is the Chief Data Officer at the Inova Translational Medicine Institute.

We speak about:

* How to take research into scaled applications

* The importance of sharing your knowledge and helping others understand

* Why you're only as good as your team members

* How to engage many different types of stakeholders

* Challenges of data management in healthcare

* Data governance & provenance in healthcare

* Data monetization & it's stigma in healthcare

* The benefits of data sharing consortiums

* The potential of genomic & DNA data

* Handling algorithm biases

* Enabling reproducible research through data

* Why "perfection is the enemy of good"

* The importance of creating & sharing your mental models

Resources:

Weapons of Math Destruction

https://weaponsofmathdestructionbook.com/

Evernote

https://evernote.com/

Real time board

https://realtimeboard.com/

Mind jet - mind mapping

https://www.mindjet.com/

Aaron is based in Washington DC Metro Area, USA

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. It really helps new data scientists find us. Thank you so much, and enjoy the show!

#29 Dr. Klaus Ifflander - Chief Analytics Officer

Ep29 Klaus Ifflander - Berlin, Germany.jpg

Klaus started his career doing internships at Yahoo! and the port of Hamburg. He worked as a consultant and completed a PhD in Quantitative Marketing. Today he is the Chief Analytics Officer at YAS.life

We speak about:

* The importance of getting applied experience as early as possible

* Defining KPIs for businesses

* Using data to change organisational behaviour and increase safety

* How to navigate organisations to create data definitions

* Realities of consulting: positives and negatives

* Why large companies require so much custom work

* How to help people and organisations that don't know what they want

* Helping organisations in progressing through their analytics journey

* How to overcome technical challenges with creative solutions in your projects

* Why honesty within yourself and others is imperative in your work

* How to provide customers what they need instead of what they want

* The importance of hard and soft metrics when measuring value

* Applying soft skills in data science

* How to find what will be valuable for your customers

* Expanding your interest with a postgraduate degree

* How your social surroundings affect your purchase decisions

* Using soft skills for data acquisition

* What is eigenvector centrality and what is it used for?

* How product reviews influence your buying decisions

* How to create experiments in business

* Pricing models in the steel business

* Data science in fitness startups

Klaus is based in the Berlin Area, Germany.

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. It really helps new data scientists find us. Thank you so much, and enjoy the show!

#28 Jennifer Prendki - VP of Machine Learning & Data Strategist

Ep28 Jennifer Prendki - Mountain View, CA.jpg

Jennifer started her career as a particle physicist before becoming a data scientist. After gaining experience in many fields including high frequency algorithmic trading & advertising, she was Atlassian's first Chief Data Scientist. Today she is the VP of Machine Learning at Figure Eight and an Expert and Advisor at the International Institute for Analytics.

We speak about:

* How to see the results of your work sooner and faster

* The importance of choosing your manager

* Making data strategy decisions for companies that are very immature in their approach to data

* Building data science teams from scratch

* Combining impostor syndrome and leaps of faith for your benefit

* The importance of making mistakes to be successful

* What having a great data culture really means

* How to convince peers and supervisors on the benefits and the path of data strategy

* Differences between having a technical and non-technical manager

* Combining technical abilities and business sense

* The importance of customer contact for technical people

* Focus on the impact and outcome of everything that you're building

* How to keep the balance in teams

* Pleasing customers vs product intuition

* How to drive and create a data driven culture

* How to create scale with your data science efforts

* How to build your data science team

* Data engineering vs Machine learning engineer

* How to keep talent

* How can data scientists learn the skills for business leadership

* Active learning and building products for data scientists

Jennifer is based in Mountain View, California

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. It really helps new data scientists find us. Thank you so much, and enjoy the show!

#27 Dr. Mark Nasila - Chief Analytics Officer

Ep27 Dr. Mark Nasila.jpg

Mark used to be a statistics lecturer at Nelson Mandela University in South Africa. He then joined First National Bank as a quantitative analyst where he climbed through the ranks to Head of Advanced Analytics and beyond. Today he is the Chief Analytics Officer at FNB.

We speak about:

* Predicting what the customer is calling about

* Improving compliance in banking through analytics

* Creating and driving a data strategy across an organisation

* Using analytics to look after customers in better ways

* How to create and measure economic value from data

* How to find meaning in your work

* Understanding your value across the entire value chain

* Creating a culture of collaboration that's not afraid to fail

* Working with tertiary institutions to identify talent

* What to test when interviewing data scientists

* How to structure your team & work with stakeholders

* The importance of data governance

* How to implement and socialise the solutions created by the team for maximum impact

* The importance of mentoring and growing people

* The difference between head of analytics and chief analytical officer

Mark is based in the Johannesburg Area, South Africa

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. It really helps new data scientists find us. Thank you so much, and enjoy the show!