Innovation is a choice

There’s nothing like a week in another city to get the innovation juices flowing, and London Fintech Week was exactly that (thank you Luis and team for another week of great debate, networking and insights).

Source: Pexels.com/photo-512249/

There’s nothing like a week in another city to get the innovation juices flowing, and London Fintech Week was exactly that (thank you Luis and team for another week of great debate, networking and insights).

So, what is new in the world of Fintech? Well, if the speakers and panelists are to believed, and the messages were far too similar and consistent for them not to be…

…AI is playing an increasingly important role in the world and indeed in the world of banks – …  . It is clear that to win in the investment banking game will still require smart people – but we must couple smart banker types with AIs and we must change our definition of “banker types” to include engineers and mathematical PHDs.

…Blockchain is here, and it’s all grown up. No longer a concept for alternative funding and the underworld, the cryptocurrency conversation is upping the volume at the highest levels with countries like Canada, the UK and Singapore all running projects, and banks of all sizes experimenting and building applications both in crypto-coins and blockchain technologies. Even the highly volatile crypto-currency prices over the week did nothing to dampen the enthusiasm. With the rise of open source, I expect we will see increasing opportunities to move from our existing centralized models to new blockchain enabled ones in many economies and industries.

…Trust is no longer about relationships, nor the strength of your brand. It’s about ease of use and, increasingly, peer review. Customers are no longer seeking a similar experience to the one they get from other banking brands, they’re looking for an experience like they get from the mega brands like Apple and Amazon. Banks are going to need to up their game – and quickly!

Clients know that if they are not paying, they are the product. Both banks and clients know the power of their data – what will this mean in the future? How will this change their expectations of service? Security now becomes as important as service; will clients demand due diligence of their service providers to ensure that their data is secure?

…Innovation is a choice – it doesn’t just happen. This is potentially the most important message of all. Entities that are leading in the start-up and innovation space are choosing to  – they are seeing the possibilities that innovators bring and are finding creative ways to enable them. The innovation choice is being made at the highest level – countries like India, Canada, the UK, Germany, the Netherlands, and China are all facilitating innovation communities and the start-ups and banks coming out of those countries are moving faster than others because of it. It is a choice because there are millions of reasons and costs involved with creating change, but forward-thinking leaders recognize the importance and their choice to enable, means they are leaping ahead.

…It’s organizational cultures that will make the space for innovation and those cultures look to leaders for the messages they need. Coincidentally, I just finished reading “Under the Hood” by Stan Slap where he describes how to maximise business performance. Culture understands leadership motivators beyond words and culture works exceptionally hard to protect its own existence – so innovation will simply not happen without leaders giving the right messages. Innovation is a choice leaders have to make and their actions will send the clear message.

Everything we know, the way we work and the way we behave was all once created as a leadership or cultural choice. In this exponential era, we will need to change the stories we tell, the way in which we work, the technologies we believe in. It’s ridiculously exciting – and it’s moving…well, exponentially! At last years’ event, there was talk of what blockchain is and what AI could conceivably do, this year it was all about what businesses are being built on these technologies. I can’t wait to see what the next year brings!

by Liesl Bebb-McKay

 

An Ode to the Weird, and how to manage them

Naked, he ran through the dark streets of Syracuse. His damp footfalls and shrill screams echoing off the Sicilian architecture. While the footfalls have been forgotten, Archimedes’ alleged cries of, “Eureka!”, are still synonymous with the theory of displacement nearly 2,500 years later. Wow, he must have been a weirdo to run screaming through the streets, but undoubtedly a genius weirdo.

Naked, he ran through the dark streets of Syracuse.  His damp footfalls and shrill screams echoing off the Sicilian architecture. While the footfalls have been forgotten, Archimedes’ alleged cries of, “Eureka!”, are still synonymous with the theory of displacement nearly 2,500 years later. Wow, he must have been a weirdo to run screaming through the streets, but undoubtedly a genius weirdo.

Henry Ford was renowned for his love of carrots and once sponsored a 14-course meal with every course containing carrots. Thomas Edison, once deliberately and macabrely electrocuted an elephant called Topsy, and let’s not even address Albert Einstein’s choice of hairstyle.

Aristotle once famously stated that, “there is no great genius without some touch of madness”.

But, if this is true, how can the madness/weirdness be appropriately ‘managed’ to benefit from those who are able to see the world through different eyes and bring innovation to the corporate space?

The first thing to note is that trying to overly control a creative genius, using the same rules and policies that have been applied to the rest of the corporation, is going to ensure that you stifle any potential for positive disruption. This is not to say that rules, policies, and that demonised word, ‘governance’ do not have a place, but rather that they should be appropriate for the required outcomes, not just a generic ‘one-size-fits-all’ approach. For example, if you expect your creative geniuses to deliver quickly and maintain momentum, don’t force them to use archaic procurement processes that require completion of forms, in triplicate, with 17 signatures each. Tell them what needs to be achieved, but don’t tell them how to do it.

Next, you need to make them feel valued. It is an unfortunate reality that creative geniuses often come with an oversized ego. But this should be acknowledged, rather than ignored in a vain attempt to contain that ego. If not, they will leave the organisation.

Arguably worse than that, they may stay, but their spirits will leave, which only results in a chair being occupied, but very little more than that. If you think a genius with a large ego is hard to manage, try managing a disengaged genius, or moreover a genius hell-bent on exacting revenge on an oppressive manager. Give them the freedom to experiment and the opportunity to truly see their ideas fly.

So, in summary, you need to control that which does not like to be controlled. Easy, right?

Actually, it can be, just ensure there is a clear vision of the end state and the bare minimum of rules to support that. After that, just let your creative geniuses feel valuable. And, let’s face it, if they are geniuses then they are indeed incredibly valuable to the future of your company.

by Brad Carter

A curriculum for growing your data science skills (almost) for free

With the plethora of free (or at least reasonably priced) high-quality massive open online courses (MOOCs), free online textbooks, tutorials, the tools available for aspirant data science apprentices are many and varied. From taking courses offered by Coursera to freely available eBooks and code examples to download from Github, there are many useful resources at our disposal.

I hear and I forget. I see and I remember. I do and I understand – Confucius

With the plethora of free (or at least reasonably priced) high-quality massive open online courses (MOOCs), free online textbooks, tutorials, the tools available for aspirant data science apprentices are many and varied. From taking courses offered by Coursera to freely available eBooks and code examples to download from Github, there are many useful resources at our disposal.

Demand for data science skills remains consistently high. IBM predicts that appetite for data scientists will grow 28% by 2020. Job postings for data science skills in South Africa are rising rapidly as companies begin to realise the true value of their data initiatives.

According to IBM, the current most desirable and lucrative skills include machine learning, data science, Hadoop, Hive, Pig and MapReduce. It is interesting to note just how many data engineering type skills are in demand. I recently started to set up a data lab at the Foundery based on the Hortonworks distribution of Hadoop, and I can understand why this is true – (big) data engineering is unnecessarily complicated!

Over the last few years, I have completed (and sometimes part-completed) some data science MOOCs and tutorials. I have downloaded free eBooks and textbooks – some good and some not so good. These, along with the MOOCs, have become my primary source of knowledge and skills development in the data science domain. I am finding this form of online learning to be a very efficient and effective way to grow my knowledge and expertise. However, my choice of which courses to do has been haphazard at best and having this much choice has also made it difficult to find the right courses to pursue, often leading to me abandoning classes or not learning as well as I should.

The purpose of this blog, therefore, is twofold: to create a thoughtful and considered curriculum that I can follow to elevate my data science mastery and to share with you some of the resources that I have collated in researching this proposed curricula. Whether you are a seasoned data science expert, or an absolute beginner in the field, I believe there is value from some, if not all, of the topics in the curriculum.

sourced from http://blogs.edweek.org/edweek/edtechresearcher/2014/07/moocs_and_the_science_of_learning.html

The ultimate ambition of completing this proposed curriculum is to vastly (and more efficiently) improve my mathematical, statistics, algorithmic development, programming and data visualisation skills to go from a journeyman level understanding of data science to full-on mastery of advanced data science concepts.

I want to DO so that I can better UNDERSTAND. Eventually, I’d like to understand and implement advanced machine learning and deep learning concepts (both from a theoretical and practical perspective) as well as obtain more in-depth expertise in big data technology. I also aim to improve my data visualisation skills so that I can have more impactful, interesting and valuable discussions with our business stakeholders and clients.

The day that I can have a debate with my maths colleagues about advanced mathematical concepts, compete with the computer scientists on Hackerrank coding challenges, run my models on a big data platform that I have set up, create a beautiful and insightful visualizations AND make this all understandable to my wife and daughter is the day when I know I have been successful in this endeavour.

I proposed this curriculum based on the skills that are commonly acknowledged to be required for data science as well as on course ratings, popularity, participant reviews and cost. I have tried to be as focussed as possible and my thinking is that this is the most efficient plan to get deep data science skills.

This curriculum will be based on open-source programming languages only, namely Python and R. My initial focus will be on improving my Python skills where possible as I want to get this up to a level where I can implement Python-based machine learning models in NumPy/SciPy. I do acknowledge, however, that for many of the stats and maths related courses, R is often preferred and in that event, I will switch.

Given my work commitments and the fact that we have a new (and very loud) addition to our family, I think that I would likely only be able to devote 10 hours a week to this challenge. My proposed timetable will, therefore, be based on this estimate. The current estimate to fully complete the curriculum is at 110 weeks or just over 2 years! This is going to be a long journey…

https://unsplash.com/collections/136866/journey?photo=7RIm0GqvvkM

I plan to update this blog periodically as and when I complete a course. My updates will include a more detailed summary of the course, an in-depth review and score, how much it cost me as well as tracking how long the course took to complete relative to the advised timeframe provided by the course facilitators. My time estimates will be slightly more conservative relative to the time estimates for each course as, in my experience, it always takes longer than suggested.

Thank you for reading this far. If you wish to join me in growing your data science skills (almost for free) and help keep me honest and accountable in completing this curriculum, then please do read on.

Data Science Curriculum

0. Supplementary resources and setup

Sticking to the blog’s theme of finding low-cost resources for this curriculum wherever possible, I have found a few high-quality free online maths and stats textbooks. These will serve as useful reference material for the bulk of the curriculum. They are:

  • Think Stats – a freely downloadable introductory book on Probability and Statistics for Python. Code examples and solutions are provided via the book’s Github repository.
  • An Introduction to Statistical Learning with Applications in R – another freely available book that is described as the “how to” manual for statistical learning. This textbook provides an introduction to machine learning and contains code examples written in R. There is online course material that accompany this book and this can be found here as well as here. I will use this manual and potentially their associated MOOCs as a reference when I begin the machine learning component of this curriculum.
  • Introduction to Linear Algebra is the accompanying linear algebra reference book for the MIT Open Courseware MOOC. This book will also have to be purchased should the MOOC require this.
  • Although not free, Python Machine Learning by Sebastian Rashka has good reviews as a reference book for machine learning applications in Python. The book also has an accompanying code repository on Github.
https://unsplash.com/collections/488/books-libraries-paper?photo=6ywyo2qtaZ8

1.  start by focusing on maths and stats

The first section of the curriculum will allow us to concentrate on redeveloping fundamentals in mathematics and statistics as they relate to data science. University, over a decade ago now, was the last time I did any proper maths (yes, engineering maths is ‘proper mathematics’ to all you engineering-maths naysayers).

Regarding learning the mathematics and statistics required for data science and machine learning, I will focus on the following courses.

  • Statistics with R Specialisation – There were many courses available to improve my stats knowledge. Ultimately, I settled on this Coursera specialisation by Duke University as it seemed the most comprehensive and the textbook seems a good companion book. This specialisation comprises 5 courses – Introduction to Probability and Data, Inferential Statistics, Linear Regression and Modelling, Bayesian Statistics and a capstone project written in R. Each course will take 5 weeks and will require 5-7 hours of effort per week. I will use this set of courses to improve my R skills, and I will audit courses if possible or I may have to pay for the specialisation. [Total time estimate: 250 hours]
  • Multivariable Calculus – (Ohio State University) referencing the Multivariable calculus sections of the Khan Academy where required. This highly rated course (average rating of 4.8 out of 5 stars) will provide me with a refresher of calculus and will take approximately 25 hours to watch all the videos. I think I can safely add the same amount of time to go through all the tutorials and exercises putting the length of this study at 50 hours. [Total time estimate: 50 hours]
  • Linear Algebra – (MIT Open Courseware) referencing the Linear Algebra sections of the Khan Academy where required. I don’t know how long this should take to complete, so I will base my estimate on the previous courses estimate of 50 hours. I chose this course as the lecturer of the Linear Algebra textbook, MIT Professor Gilbert Strang, conducts this MOOC. [Total time estimate: 50 hours]

2.  Time to improve my management skills of data science projects, experiments and teams

A large part of work at my previous employer and at my current job at the Foundery is to manage various data science projects and teams. I have a lot of practical experience in this domain, but I don’t think it would hurt to go back and refresh some of the core concepts that relate to effective data science project management. To this end, I managed to find an appropriate Coursera specialisation that aims to help data science project managers “assemble the right team”, “ask the right questions”, and “avoid the mistakes that derail data science project”.

  • Executive Data Science Specialization – John Hopkins University. The entire specialisation is only 5 weeks long, and requires 4-6 hours a week of effort. The courses that are on offer are titled “A Crash Course in Data Science”, “Building a Data Science Team”, “Managing Data Analysis”, “Data Science in Real Life” and “Executive Data Science Capstone”. I wasn’t able to obtain rating information for this specialisation. [Total time estimate: 50 hours]

 3.  Improve my computer science and software engineering skills

When I first started out, I managed to pick up a few Unix skills (just enough to be dangerous as evidenced when I once took out a production server with an errant Unix command). Since then, and over time, I have lost the little that I knew (luckily for production support teams).

New and exciting software engineering paradigms have emerged, such as DevOps and code repository solutions like Github are now commonly used in both the data science and development industries. As such, I thought that some study in this domain would be useful in my journey.

I would also like to increase my knowledge of data structures and algorithms from both a practical and theoretical perspective. To this end, I have found an exciting and challenging University of California San Diego Coursera specialisation called “Master Algorithmic Programming Techniques”.

The courses that I am planning to complete to improve my computer science and software engineering skills are:

  • How to use Git and GitHub – a freely available course offered by Udacity with input from Github. This course is a 3-week MOOC and is rated 4.5 out of 5 stars out of 41 student reviews and will require 6 hours of commitment per week. This course introduces Git and GitHub and will help me to learn how to use better source control, which in turn will greatly assist with project delivery of medium to large sized data science projects. [Total time estimate: 30 hours]
  • Introduction to Linux – a freely available course from edX. This is an 8-week course rated 4 out of 5 stars by 118 student reviews with over 250 000+ students enrolled. Thoroughly covering this material will take between 40-60 hours per the course notes. Gaining a firm understanding of Linux will allow me more control when using the open source data science environments and tools. [Total time estimate: 60 hours]
  • Introduction to DevOpsUdacity. This free course introduces that concept of DevOps and explains how to implement continuous integration, continuous testing, continuous deployment and release management processes into your development workflow. I am very interested to see how this could be applied to the data science world. The course does not have a rating and is 3 weeks in length requiring 2-3 hours per week of effort. [Total time estimate: 10 hours]
  • Master Algorithmic Programming Techniques – This Coursera specialisation by the University of California San Diego comprises 6 courses —Algorithmic Toolbox, Data Structures, Algorithms on Graphs, Algorithms on Strings, Advanced Algorithms and Complexity, Genome Assembly Programming Challenge Each course is 4 weeks of study, 4-8 hours per week. The individual courses were rated between 3.5 – 4.5 stars.

What excited me about this specialisation is that I would get an opportunity to learn and implement over 100 algorithms in a programming language of my choice from the ground up. I think that this would certainly improve both my knowledge about algorithms as well as my programming skills.

After looking a bit deeper at the course structure, it seems as if this specialisation is paid for at $49 per month until you complete it. So, the faster I do this, the cheaper it’ll be – nice incentive! [Total time estimate: 235 hours]

4.  Improve my base data science skills and up my Python coding abilities

At this stage of the curriculum, I would have solidified my maths and stats skills, improved my computer science and software engineering skillset, and brushed up on some data science project management theory. Before embarking on intensive machine learning material, I think that it might be a good decision to get back to basics and look at improving my base data science and visualisation skills and upping my Python coding abilities while at it.

One of my goals for this curriculum was to improve my communication skills by becoming a real data story-teller. An effective way to do this is to learn how to visualise data in a more concise, meaningful and, I guess, beautiful manner. I say beautiful because of an amazing data visualisation website called Information is Beautiful. Check it out; you won’t regret it.

  • Learning Python for Data Analysis and Visualisation – Udemy. Jose Portilla’s Udemy course is highly rated at 4.6 stars out of 5 from over 4 220 student reviews. Over 47 812 students have enrolled in the course. The length of the videos on this course is 21 hours, so until I can estimate this better, I will add 100% to my time estimate for completing the course.  The course is focussed on Python and introduces topics such as Numpy, Pandas, manipulating data, data visualisation, machine learning, basic stats, SQL and web scraping. Udemy often run specials on their courses, so I expect to pick this one up between $10 and $20. [Total time estimate: 50 hours]
  • Data Visualization and D3.js – Communicating with DataUdacity. This free course is part of Udacity’s Data Analyst nanodegree programme. The course provides a background in visualisation fundamentals, data visualisation design principles and will teach you D3.js. It is an intermediate level course that will take approximately 7 weeks to complete at 4-6 hours per week. [Total time estimate: 50 hours]
  • HackerRank challenges – HackerRank is a website that provides a very entertaining, gamified way to learn how to code. HackerRank offers daily, weekly and monthly coding challenges that reward you for solving a problem. The difficulty of the questions ranges from “Easy’ to “Hard”, and I plan to use this to test my new-and-improved Python skills. Every now and then I will use this form of learning Python as a “break” from the academic slog. [Total time estimate: n/a]

5.  Learn the basics of machine learning from both a practical and theoretical perspective

The resurgence of machine learning (the science of “teaching” computers to act without explicitly being programmed) is one of the key factors in the popularity of data science and drives many of the biggest companies today including the likes of Google, Facebook and Amazon. Machine learning is used in many recent innovations including self-driving cars, natural language processing, advances in medical diagnoses to name a few. It is a fascinating field, and as such, I want to gain a solid foundational understanding of this topic. It will also lay the foundation to understand the more advanced machine learning theory such as deep learning, reinforcement learning and probabilistic graphical models.

Machine Learning – Stanford University. Taught by Andrew Ng, this 10-week course is one of Coursera’s most popular courses and is rated 4.9 out of 5 from 39 267 student reviews. A commitment of 4-6 hours per week will be required.

Andrew Ng provides a comprehensive and beginner-friendly introduction to machine learning, data mining and pattern recognition and is based on several case studies and real-world applications. Supervised and unsupervised learning algorithms are explained and implemented from first principles, and machine learning best practices are discussed.

This course is a rite of passage for all aspirant data scientists and is a must-do. If you are on a more advanced level of machine learning understanding, look for the handouts of the CS229 Machine Learning course taught at Stanford (also by Andrew Ng) for further material. [Total time estimate: 80 hours]

Machine Learning A-Z Hands-On Python & R In Data ScienceUdemy. This course is a highly rated, practical machine learning course on Coursera. It is rated 4.5 stars out of 5 based on 11 798 student reviews. 86 456 students had signed up to this course at the time of writing. The videos total 41 hours and as before I will double this for my effort estimate.

The course is very hands on, and comprehensively covers topics such as data pre-processing, regression, classification, clustering, association rule learning, reinforcement learning, natural language processing, deep learning, dimensionality reduction and model selection. It can be completed in either R or Python. Again, I will look to pick this one up on a special for between $10 – $20. [Total time estimate: 80 hours]

6.  Our capstone project – let’s dive into the deep learning end

We have finally made it to what I regard as the curriculum’s capstone project – a practical course on deep learning:

  • Practical Deep Learning For Coders, Part 1 – fast.ai. Out of all the courses that I have looked at, I am probably the most excited about this one. Fast.ai’s deep learning course is a very different MOOC to the rest in that the content is taught top down rather than bottom up. What this means is that you are taught how to use deep learning to solve a problem in week 1 but only taught why it works in week 2.

The course is run by Jeremy Howard who has won many Kaggle challenges and is an expert in this field. The problems solved and datasets used in this course comes from previously run Kaggle challenges, which allows you to easily benchmark your solution to the best submitted entries.

A significant time commitment is required for this course – 10 hours a week for 7 weeks. The course teaches you some cool stuff such as such as how to set up a GPU server in the cloud using Amazon Web Services, and how to use the Python Keras library. As per the homepage for Keras, “Keras is a high-level neural networks AP developed with a focus on enabling fast experimentation. It is written in Python and is capable of running on top of either TensorFlow, CNTK or Theano.

As Jeremy Howard says, all you need to succeed in this course is pragmatic programming, tenacity, an open mind and high school math so good luck and well done on getting to this stage! [Total time estimate: 100 hours]

https://unsplash.com/collections/579786/knowledge-is-power?photo=esCc1qx6TVw

7.  Conclusion

So, we have finally made it to the end – well done! I have reviewed countless number courses in compiling this curriculum, and there were so many more that I wanted to add, including these more advanced topics:

I have also not touched on related topics such as big data, data engineering, data management, data modelling nor database theory of structured and unstructured sets of data. An understanding of these topics is nonetheless vital to understand the end-end spectrum that makes up the data analytics continuum. Nor have I chatted about the myriad data science tutorials and Kaggle-like data science challenges out there.

I intend to look at relevant tutorials and Kaggle problems where they relate to parts of this curriculum and where possible I will try implement some of these solutions on a big-data platform. While discussing this topic with one of my colleagues, he suggested also trying to build something big enough that encompasses all the above so that I can have an end-target in mind, don’t get bored and implement something that I am passionate about from the ground up. This is certainly something that I will also consider.

This challenge will start on 10 July 2017. According to my estimate, this curriculum will take 110 weeks or just over 2 years!! As daunting as this sounds, I take heart from Andrew Ng, the machine learning expert, when he said the following in an interview with Forbes magazine:

In addition to work ethic, learning continuously and working very hard to keep on learning is essential. One of the challenges of learning is that it has almost no short-term rewards. You can spend all weekend studying, and then on Monday your boss does not know you worked so hard. Also, you are not that much better at your job because you only studied hard for one or two days. The secret to learning is to not do it only for a weekend, but week after week for a year, or week after week for a decade. The time scale is measured in months or years, not in weeks. I believe in building organizations that invest in every employee. If someone joins us, I can look them in the eye and say, “If you come work with me, I promise that in six months you will know a lot more and you will be much better at doing this type of work than you are today

I hope that this quote resonates with you too and that the blog has helped or motivated you to improve your data science skills. Thank you for reading this and please keep me honest in terms of completing this challenge. Please post a comment if you think I should add to or change the curriculum in any way, and post your own course reviews — let me know if there are any other books and textbooks that I should consider. Expect updates soon!

by Nicholas Simigiannis

 

 

Finding your True North

In RMB’s world, and indeed in corporate and investment banking in South Africa, the Foundery is an unusual paradigm. On the one hand, the Foundery is a fintech, aiming to disrupt the financial services industry with innovative and novel products and services, but on the other hand, the Foundery is very much rooted as the digital innovation unit of RMB — one of the very incumbents which stand to be disrupted by the fintechs.

In RMB’s world, and indeed in corporate and investment banking in South Africa, the Foundery is an unusual paradigm. On the one hand, the Foundery is a fintech, aiming to disrupt the financial services industry with innovative and novel products and services, but on the other hand, the Foundery is very much rooted as the digital innovation unit of RMB — one of the very incumbents which stand to be disrupted by the fintechs.

There are many things that are unique about the Foundery, not least of which is its position at the intersection between fintech startup and banking incumbent, but most pertinently is its mission to completely change and reimagine the corporate and investment bank of the future.

This is a monumental goal and certainly not something that can be achieved without great effort. It may be worth asking, why not go for something smaller or easier? Why not chase the untapped profits or go after the opportune inefficiencies in traditional banking

The Foundery’s mission is what we call its True North. This is what gives the Foundery its identity and guides its actions. Without it, the Foundery would be another player in the fintech space, but with our True North, the Foundery is a fintech with purpose.

https://www.jobmastermagnets.com/fun-facts-about-magnets

Earth’s geographic North Pole

WHAT IS TRUE NORTH?

In astronomy, True North is the direction along the earth’s surface which points towards the geographic North Pole of the earth. This seems reasonable as geographic north is the northernmost point of the earth, so why call it True North when it is already north? Why the extra qualification?

The reason is that compasses and maps point to a slightly different north pole, what we call magnetic north and grid north respectively. These differences arise out of the slightly irregular shape and magnetic distribution of planet Earth.

http://gisgeography.com/magnetic-north-vs-geographic-true-pole/

The difference between the true north and the magnetic north

True North is important in astronomy because it serves as a reference by which we can measure the position of every object in the universe relative to its point of observation on Earth. This takes us back to the analogy of the Foundery’s True North and what we mean by the concept of True North:

Your True North can be thought of as your fundamental purpose that guides everything you do.

Just like the Earth’s True North is used by astronomers to map the night sky, your True North is what informs your goals and your decisions. It is the guiding principles by which you can make your biggest and most impactful choices.

IDENTIFYING YOUR TRUE NORTH

The concept of your own True North is something which is quite abstract and extremely daunting. What does it mean to have a fundamental purpose? Where does it come from? What am I meant to do with it? How do I know if I even have one? These questions are all relevant and aren’t easy to answer. In fact, there are no obvious or immediate answers — it’s up to you to decide what they are.

The good news is that we can learn from the geographic North Pole and extend the analogy. In practice, while we can’t rely on compasses or maps to navigate to the Earth’s True North, astronomers have been using the North Star, also known as Polaris, to mark the location of true north since the time of the Ancient Greeks. The North Star lies almost exactly “above” the geographic North Pole. Furthermore, the North Star is visible throughout the year (as long as you’re in the northern hemisphere*) and is therefore a reliable, although ancient, method of navigating to true north.

http://earthsky.org/astronomy-essentials/north-star-movement

The North Star: a time lapse image shows how the North Star rotates tightly around the celestial North Pole (in line with True North), while other stars rotate with a much wider radius.

Extending this analogy to your own True North, the challenge then becomes to find your North Star. What is that thing, whether it is abstract or tangible, that points to your True North? This could be a person, it could be your family, it could be your talents, it could be your hobbies or whatever it is that affirms that you doing what you love and that gives you a sense of purpose.

In the Foundery’s case, True North is our mission to change the world of banking, and the North Star is the people who have stepped up to the challenge to make this mission possible. Without the North Star, the Foundery would never be able to find its True North. Now the challenge is up to you — go out there and find your own North Star and, ultimately, your True North.

* Unfortunately there is no equivalent star in the southern hemisphere

by Jonathan Sinai

Human-centered design

Gone are the days when design used to be about aesthetic execution, when the main focus was to get as much work out the door as possible so that there was more time for more work — when brands spoke down to their consumers instead of speaking to them.

Gone are the days when design used to be about aesthetic execution, when the main focus was to get as much work out the door as possible so that there was more time for more work — when brands spoke down to their consumers instead of speaking to them.

Now most brands are waking up to the fact that we are living in an ever-changing, ever-growing, fast-paced world where the consumer has access to all kinds of information literally at their fingertips. And design plays a crucial part in the world we live in today. From the food we eat to the information we choose to consume online, design is everywhere.

Brands are cottoning on to the fact that their customers are more informed than ever before, and so big brands like Apple, Google, Uber, Airbnb, Facebook etc. are placing the consumer at the core of everything they do. This means that brands are now allowing their customers to decide on the type of content they want to consume and then designing for that. In the product design space this is so important — keeping the consumer at the center of everything that you do for a better experience.

Human-centered design is all about developing good relationships with your customer by delivering a high quality product that through prototyping and testing results in an emotional connection between the customer and the product.

                              Fig. 1 The human centered design pyramid (source: Giacomin, 2014)

Good design needs to be able to answer a set of key questions to facilitate this connection.

  • Who is the consumer?  Does the design reflect the user characteristics?
  • What are the consumers’ goals when using the product?
  • What is their experience when using the product?
  • What are the goals of using this specific product or service?
  • When and how does the consumer interact with the product design?
  • What do consumers think about the product or the design?
  • Why does the consumer want to use this product or design?
  • http://www.designorate.com/characteristics-of-human-centered-design/

 

Consumer feedback is key in ensuring that the design continually improves. And so, unlike before, the design process is never complete. Especially in the product design space, it is very important to keep iterating and making your product better with each iteration. Part of achieving that emotional connection with the product is about designing experiences as opposed to designing products.

The commonly used tools in building a human-centered approach are:

  • Personas;
  • Scenarios; and
  • Use cases.

 

Persona: This refers to creating fictional character that could potentially interact with your product. This usually includes their age, race, gender, location etc. Basically, it’s the target audience.

Scenarios: This would be the possible scenario of the persona using your product.

Use cases: This refers to the feedback gathered from the Persona through the Scenarios

It’s time that brands start immersing themselves in the worlds of their consumers if they want to remain relevant.

by James Mokhasi

Ideas

With the ubiquity of electronics, going from an idea to a working prototype has become cheaper and easier than ever before. The biggest problem with ideas is that everyone has them but most people do not have the drive and persistence needed in order to turn their ideas into a reality. To add further insult to injury it is also a challenge ensuring that the ideas one has chosen are good and not just a waste of time and money.

Source: https://static.pexels.com/photos/192637/pexels-photo-192637.jpeg

With the ubiquity of electronics, going from an idea to a working prototype has become cheaper and easier than ever before. The biggest problem with ideas is that everyone has them but most people do not have the drive and persistence needed in order to turn their ideas into a reality. To add further insult to injury it is also a challenge ensuring that the ideas one has chosen are good and not just a waste of time and money.

Idea selection

In terms of selecting an idea it is worth taking a step back and examining examples of good ideas and what made them so successful. Throughout history man has not changed too much from an evolutionary perspective. As a result, man’s desires and needs have also not changed too much either. The key thing that has changed is the technology available that that has enabled us to implement concepts to fulfill these needs and desires in different ways. Fiat money was first developed not because it was a good idea but rather because it became impractical to carry heavy goods and gold around to barter with. Another more recent example would be the development of Google and Wikipedia. Prior to the internet people would have used encyclopedias and libraries to research anything they needed, but the advent of the internet has allowed Google and Wikipedia to be developed to more efficiently and broadly spread this knowledge. With this observation in mind, that an idea can be successful if people find it useful, we get a tool we can use to help sift through our ideas. To use this we can just check if an idea we have will be useful to enough people, and if so, see how technology can help us pull this off to great effect.

Execution

Once an idea has been selected the execution stage can begin. Many methodologies have arisen to make the process of building ideas more scientific. Lean startup methodologies are one of the popular approaches in the startup space while agile provides similar concepts for software development. No matter the approach generally they encourage people to come up with a hypothesis and decide on the smallest possible chunk of this hypothesis they need in order to make what is known as the MVP or minimum viable product. All bloat is removed in favour of the smallest possible grain of the idea that we can build so that we can get it into the hands of customers as fast as possible. Small development cycles are advocated so that we can get feedback on the idea quickly and based on the feedback validate our hypothesis, and tweak it a bit more or completely change direction by pivoting.

One story that illustrates the power of small iterations comes from a book called: “Art & Fear: Observations on the Perils (and Rewards) of Artmaking” by David Bayles and Ted Orland:

The ceramics teacher announced on opening day that he was dividing the class into two groups. All those on the left side of the studio, he said, would be graded solely on the quantity of work they produced, all those on the right solely on its quality. His procedure was simple: on the final day of class he would bring in his bathroom scales and weigh the work of the “quantity” group: fifty pound of pots rated an “A”, forty pounds a “B”, and so on. Those being graded on “quality”, however, needed to produce only one pot – albeit a perfect one – to get an “A”. Well, came grading time and a curious fact emerged: the works of highest quality were all produced by the group being graded for quantity. It seems that while the “quantity” group was busily churning out piles of work – and learning from their mistakes – the “quality” group had sat theorizing about perfection, and in the end had little more to show for their efforts than grandiose theories and a pile of dead clay.

What we can infer from this is that the faster we can test more ideas, the faster we can start perfecting our process and in so doing eventually hit upon the best ideas.

Constraints

When building something it is very valuable to draw a line in the sand in terms of both time and money. If we have no deadline we may never finish, so putting a firm deadline in the sand helps us weed out unnecessary features to end up with our MVP and pushes us to make our development cycles as short as possible. Y Combinator (a company that provides early stage funding and assistance to startups) for example gives companies they fund just enough money to act as seed funding and 10 weeks to build a working prototype after which they present it to potential investors and acquirers. With unlimited funds and time, we are more likely to keep adding unnecessary features and deviate away from the MVP we decided upfront.

On a much smaller scale and from a personal perspective I decided I wanted to start building up an online presence with my own personal blog. I wasted time getting lost in the details and the technologies available without writing a single article. In the end I gave myself a deadline of two weeks from that point and decided my main aim was about the articles I wanted to start writing and not so much about the technology behind it. So I ended up using the cloud computing provider DigitalOcean and used one of their pre-built vanilla Ghost blogging platform deployments to get up and running ASAP. In the end putting this time constraint in place forced me to get on the right track.

Coming up with good ideas is tougher than it may seem. Many people have ideas but not all that many can go from idea to finished product. By looking at existing ideas one can get a feel for what makes a good idea — generally it is something that people really need as they find it useful. A number of methodologies have come to light which guide in validating an idea as fast as possible. Giving ourselves constraints helps keeps us honest and working towards a reasonable deadline. In the end if we can iterate through our ideas and validate them as fast as possible we are more likely to come upon a successful one. Thomas Edison sums it up best in his response to a reporter on their jeering comment about the number of times he failed: “I have not failed. I’ve just found 10,000 ways that won’t work.”

 

by  Yair Mark

Do corporates need garages?

Innovation is easy right? You throw a few super smart, socially awkward people into a garage and wait until they emerge with some new technology that will change the world. And, of course, that’ll take their earthly belongings from a stash of Led Zeppelin vinyls, a collection of well-worn t-shirts, and no doubt one or two student loans (for degrees they never actually finished) to billions of dollars.

Would you like a garage with that?

Innovation is easy right? You throw a few super smart, socially awkward people into a garage and wait until they emerge with some new technology that will change the world. And, of course, that’ll take their earthly belongings from a stash of Led Zeppelin vinyls, a collection of well-worn t-shirts, and no doubt one or two student loans (for degrees they never actually finished) to billions of dollars. This worked for Apple, Amazon, Google, HP and Microsoft, so surely it’ll work for everyone right?

Proximity to Business

But what if you’re not a new kid on the block, but rather, are one of the incumbents of the industry? How feasible is it to confine a portion of your company to a dingy garage, and keep them running on a diet of stale pizza and a steady stream of lofty ideals? There is a school of thought that advocates a very similar approach to this, albeit more grown-up. Whereby a portion of the company is carved out, or formed, and given autonomy to experiment, invent and innovate to their heart’s content- unencumbered by the drudgery of meetings about meetings, and without any expectation of immediate results or potentially any results at all. The hope being that, in time, the gamble will pay-off sling-shotting the company to the forefront of a bold new wave within the industry.

At the other end of the potential scale, and it should be viewed as a scale (see below), is an internal entity that is clearly part of the organization, and targets the short time-to-value, incremental, mildly-disruptive types of innovation. This is sometimes appropriate, especially for innovation that focuses on links within an existing value chain. To use a simplistic example, from the automotive industry, it is exceptionally hard to invent a new type of indicator stalk without having a steering wheel, or steering wheel column, to attach any prototypes to, nor any actual indicator lights and electrical system to test whether it even works. And as your value chain gets more complex, it gets exponentially more difficult.

So perhaps the most critical element in choosing an approach should be dictated by what you’re wanting to innovate. Too often people are given the broad directive to innovate, without any specific focus, and with no appreciation of the independence of the portion they need to innovate. Big corporates got big because of a certain set of competencies, so often, to avoid throwing the baby out with the bath water, they’d opt to innovate portions of an existing value chain and that would then require closer collaboration (left edge of the scale above). One caveat though, is that you may need to rely on the parts of the value chain, and by implication the people running those parts, to test your innovation. An innovation, that may very well be trying to disrupt another portion for which they are also accountable, so they may actually prove to be obstacles to innovation. It is the corporate equivalent of attempting to get turkeys to vote for Christmas.

Reputation of Innovation Arm

The reputation of your innovation arm also dictates the most appropriate innovation portfolio. If your innovation arm is yet to win over the skeptics in the mothership company, then you may need some quick, incremental wins before you’ve earned your freedom to go after the long-shots. Obviously there are ways to circumvent this, such as ensuring that Innovation teams report directly into the CEO, and using the subsequent hierarchical power to build their reputation, and its associated freedom to innovate. However, innovation teams’ reporting lines would need a blog of its own to fully explore.

Harvard Business Review, published a seminal article that divided innovation up into Core, Adjacent and Transformational (see right, with some additions to the original HBR diagram). They found that different industries, and companies with different levels of maturity, would find a different mix between these 3 types appropriate. However, if an innovation arm still needs to build its reputation then it may well be advised to more heavily weight core innovation, and then, as their reputation for delivering value grows, they can move towards a higher proportion of adjacent and transformational innovation types.

Take-outs:

  • Garage style innovation may not be appropriate for corporates.
  • Clearly define what you’re wanting to innovate (part of a value-chain or long-shot), and choose the appropriate proximity based on your intentions. Take note of corporate culture here too- turkeys won’t vote for Christmas.
  • Consider your innovation arm’s internal reputation in selecting your innovation portfolio.

by Brad Carter

The Power of the Unconversation

On the 9th of March 2017 twelve enthusiastic Foundery members attended DevConf 2017, South Africa’s biggest community driven software development conference: an event that promised learning, inspiration and networking.

Courtesy of DevConf 2017 (devconf.co.za)

On the 9th of March 2017 twelve enthusiastic Foundery members attended DevConf 2017, South Africa’s biggest community driven software development conference: an event that promised learning, inspiration and networking.

With a multi-tracked event such as this one there is usually something for everyone, and yet if you speak to serial conference attendees (guilty as charged), the talks aren’t the greatest reason to attend.

People like me go to conferences in part for the scheduled content, but mostly for the unscheduled conversations in the passage en route to a talk or around a cocktail table during a break. The “unconversations”, I’m calling them. It’s the conference equivalent of another well-known creative outlet: “water cooler conversations”.

I’ll admit that I’m a bit of a conference butterfly – actively seeking out these “unconversations” so that I can join them. I especially take note as crowds disappear into conference rooms. I’m drawn to the groups of people who stay behind wherever they might have gathered. That’s where I’m almost guaranteed to participate in really interesting discussions and learn something new. When I attend conferences, it’s this organic and informal style of collaborative enquiry I look forward to the most.

Courtesy of DevConf 2017 (devconf.co.za)

Ironically it was one of the DevConf talks that helped me understand why these “unconversations” tend to work so well as creative spaces. In his talk on Mob Programming, Mark Pearl mentioned a study conducted by the American Psychological Association which established that groups of 3-5 people perform better on complex problem solving than the smartest person in the group could perform on their own. See “references” for more information.

Loosely translated, a group of people has a better shot of solving a complex problem together than if they tried to solve it independently.

As a Mob Programming enthusiast myself, this makes complete sense to me. What’s interesting is that this research is not new, yet many organisations still discourage “expensive” group-work and continue to reward individual performance, and I can see why. For people with similar upbringings and educational backgrounds to mine, this is the comfort zone. We default to working alone and feel a sense of accomplishment when we achieve success individually. As children we were told to solve problems and find answers on our own. Receiving help was a sign of weakness, and copying was forbidden.

In contrast, the disruptive organisations of the last few decades encourage the complete opposite. These organisations recognise the value of problem-solving with groups of people who have varying, and even conflicting, perspectives. There’s no time for old-school mindsets that favour individual efforts over collaboration. We need to cheat where it’s appropriate by knowing who can help us and what existing ideas we can leverage.

I don’t mean to trivialise it. There’s a bit more involved than just creating opportunities for people to solve problems in groups. According to the book “Collective Genius”, innovative companies such as Google have developed three important organisational capabilities: creative abrasion (idea generation by encouraging conflict and high quality feedback), creative agility (hypothesizing, experimenting, learning and adapting) and creative resolution (deciding on a solution after taking new knowledge into account) all supported by a unique style of leadership. The case studies are incredibly motivating.

Since joining the Foundery I’m discovering that we are practicing these things every day, and the amazing ideas and products born from our “collective genius” serve as confirmation that we’re on the right track. Is it always easy? No, absolutely not. It’s requires a great deal of mindfulness.

When I’m reflective I notice that the greatest ideas and most creative solutions I’ve brought to life were conceived with input from others. Many of the dots I connected for the first time happened during completely unlikely meetings of minds, and some through passionate differences of opinion. In an environment that calls for constant collaboration, it’s wonderfully refreshing to find that the “unconversations” I enjoy so much are happening all around me, every day.

And so long as I’m participating, I am always reminded that together we are more capable of solving really complex problems than the smartest one among us, and I’m becoming more and more OK with that.

References:

By Candice Mesk

 

The Doosra

Working in an investment bank over the past decade has provided the opportunity for many interesting conversations around what the value to society of an investment bank represents. Often the model of a “zero sum game” is proposed which suggests that finance often doesn’t add much – in terms of the transactions that banks facilitate, someone is a winner and someone else is the loser, there is no net gain to the world. Other purists would argue something along the lines of efficient allocation of resources. That initially sounded a bit too creative for my more linear reasoning, but after years in the trenches, it has developed an intuitive ring of truth to it.

Working in an investment bank over the past decade has provided the opportunity for many interesting conversations around what the value to society of an investment bank represents. Often the model of a “zero sum game” is proposed which suggests that finance often doesn’t add much – in terms of the transactions that banks facilitate, someone is a winner and someone else is the loser, there is no net gain to the world. Other purists would argue something along the lines of efficient allocation of resources. That initially sounded a bit too creative for my more linear reasoning, but after years in the trenches, it has developed an intuitive ring of truth to it.

Similarly, digital disruption suffers a questionable motive. For some enterprises, such as Uber, it may appear that the shiny plaything of some young geeks on the west coast of america has been allowed to plough through the livelihoods of real people with real jobs and families around the world. When applying such thinking to digital disruption in the realm of investment banking, the question arises as to whether there is any real value that this rather obscure digital offspring of an already often questioned enterprise can produce.

At times this line of thinking led me to check my own passion for this “new vector of commerce”. How do I ensure that my natural fascination with some “new and shiny” geek toy is not diverting what should be a cold, objective application of technology to investment banking, rather than being an excuse to pursue disruption for its own sake. How do we ensure a golden thread of validity and meaning to this exercise.

I started thinking about Google, and how I could justify what value they might have brought to the world (and not just their shareholders). I won’t pretend that I spent much time on this question, but I did come to the following example. Google maps is a fantastic application, and I probably initially loved it more for the fact that in this we have an application that is bringing the real world (travel, maps, my phone, my car) together with the digital world (the internet, GPS technology, cloud based algorithms).

However, it is a tool that many people use, and its value extends beyond that initial fascination. I have considered that in a very real way there are likely to be hundreds of millions of people that might use google maps every day to guide them on an optimal route in their cars. And, true to form, it manages to do this: either by advising detours around potential traffic jams, or by merely showing quicker routes that save time.

That extra time in traffic that has been avoided represents a very real saving in carbon emissions into the atmosphere, and real energy that would have been wasted pumping cylinders up and down in an idling vehicle. This is not a zero sum equation where google benefits and many small companies lose out. This is a very real benefit to the world where increased efficiency reduces the amount of wasted energy, and wasted time of humans. This is a net positive game to the world. In some respect the world of humans win, and the domain of entropy loses – if we are forced to put a name to it.

Personally I would feel deeply gratified if I could produce such a result that created a new benefit to either the world, or at the very least some small piece of it.

Interestingly enough, this speaks to an underlying theme which appeals to many people that are attracted to incubators of disruption, such as the Foundery. Many people do really feel that they would like to be part of something that changes the world. Perhaps this is because such incubators invoke the perceived “spirit” of Google, Facebook and other silicon valley heroes as an inspirational rally cry. I believe that the example of google maps does show that the present opportunity of disruptive technology can represent a possibility for such very real efficiencies and benefits to be created. Perhaps those seemingly naive passions that are stirred in the incubatees are valid, and should be released to find their form in the world.

So how do we harness this latent energy? Where do we direct it for the best chance of success?

Some of the technologies to be harnessed, and which represent the opportunity of disruptive technology:

  1. IoT (the internet of things):

At its most simple, this means that various electronic components have become sufficiently small, powerful and most importantly, cheap. It can become possible and economically viable to monitor the temperature, humidity, soil hydration of every single plant in a field of a farm. To measure the status of every machine on a production line in a small factory in the east rand, without bankrupting the owner with implementation costs.

Apart from sensors, there are actuators in the world such as smart locks, smart lights and the smart home which enable real-world actions to be driven and controlled from the internet. Together these provide the mechanism for the real world to be accessible to the digital world.

This extends beyond the “real“ real world: there are changes at play, not too far under the surface of the modern financial system, that are turning the real world of financial “things” (shares, bonds, financial contracts) into the internet world of financial “things” (dematerialised and digitised shares, bonds online, financial contracts online).

There are also actuators in this world, such as electronic trading venues and platforms which enable manipulation of digital financial contracts by digital actors of finance.

  1. Data is free:

The cost per megabyte of storage continues to drop exponentially, and online providers are able to offer services on a rental basis that would have been inconceivable a decade ago. The ubiquity of cheap and fast bandwidth enables this even more so.

  1. Computation is cheaper than ever, and simple to locate with cloud based infrastructure:

Moore’s law continues unabated, providing computational power that drops in cost by the day. Notwithstanding the promise of quantum computing which seems around the corner

  1. The technologies to utilize are powerful, free and easy to learn:

If you have not yet done so, have a sojourn on the internet across such topics as python, tensorflow, quandl, airflow and github. These represent free, open-source (largely) capabilities to harness the technologies above and make them your plaything. Not only that, the amount of free resources “out there” which can help you master each of these is astounding.

A brief exercise into trying to automate my house using python has revealed hundreds of youtube videos of similarly obsessed crazies presenting fantastic applications of python to automating everything from their garage doors, fishtanks, pool chlorine management systems, alarms etc. These youtube videos are short, to the point, educational, free and most importantly crowd moderated – all the other python home automation geeks have ensured that all the very good videos are upvoted and easily found; and the least fit are doomed to obscurity.

This represents another perhaps unforeseen benefit of the internet which is crowd-sourced, crowd-moderated, efficient and specific education. JIT learning (“just in time learning”) which means being able to learn everything that you need to accomplish a task five minutes before you need to solve it, and perhaps to forget everything almost immediately once you have solved it…. (That is an interesting paradigm to counter traditional education).

( P.S. if you have kids, or want to learn other stuff, checkout https://www.khanacademy.org/ )

Given the above points, it has never been easier for someone to create a capability to source information in real time from the real world, store that information online, apply unheard of computing power to that information using new, powerful and easy programming languages which can be learned online in a short period of time.

It might be a moot point that is valid at every point in time in every generation, but it has never been easier and cheaper to try out an idea online and see if it has legs.

So we have identified people with passion, a means of delivery and so now … what?

Those of you that are paying attention would realise that I have skirted the question of whether we have added any real value to the world, or feel that we can? Time will tell, and I would hate to let the cat out of the bag too early. But there is one thing that is true: if you are one of those misguided, geek-friendly, meaning-seeking, after hours change agents, or if you have an idea that could change the world, come and talk to us … the door is always open.

by Glenn Brickhill

Design Indaba made me do it –

This was the mantra for the 22nd annual Design Indaba conference, hosted by the beautiful city of Cape Town at the Artscape theater.

This was the mantra for the 22nd annual Design Indaba conference, hosted by the beautiful city of Cape Town at the Artscape theater.

The Design Indaba Conference has grown to become one of the world’s leading design events and hosts more than 40 speakers and 2 500 delegates. It draws creatives from all spheres and industries to come together under one roof to share knowledge, inspire and to collaborate with one another.

We talked, mingled and networked; filing our inspiration tanks. There were graffiti artists, dj’s, musicians, sculptors and various sponsor pop-ups and activation units, inviting us into this world of endless possibility and creativity.

Contrary to current perception, Design Indaba is not a conference ONLY for creatives – it is for everyone, from any field of expertise that would like to ignite their senses and intrigue their minds. It’s a jam packed 3 days and I believe that there is something that will speak to anyone’s core. This year was my first Design Indaba and it was a truly immersive experience, exceeding all my expectations.

The main highlight for me, wasn’t the skill or talent of all these amazing people (even though that was incredible) – but rather their thinking, this really stood out to me; they took us on a journey through the lens and into their magical minds!

Ultimately, Design Indaba wants to change the thinking of the world, one conference at a time, one creative at a time, and one business at a time.

It will take a generation of creative thinkers and implementers to see a turnaround. Design Indaba’s primary aim therefore is “to advance the cause of design as a communication fundamental, a business imperative and a powerful tool in industry and commerce, awakening and driving a demand for investment in intellectual capital”.

Investing nearly two decades in this vision, Design Indaba has championed the creative revolution. Here are some of my highlights from the 3-day event (content supplied from the Design Indaba weekly mailer):

The enchanted forest – Can beauty redeem us?

We were welcomed into the Design Indaba Festival 2017 through an enchanted forest of massive tree sculptors that were beautiful and surreal.

These tree sculptures were on exhibition the entire conference and created a magical ambience to the atmosphere in the festival court yard. I felt like I was walking around in a world that was a mash-up of the movies, Labyrinth and Alice in Wonderland (Tim Burton version).

Read more >

Capturing Cape Town’s scent with Kaja Solgaard Dahl

The thank-you gift for the festival this year was created by this designer, Kaja Dahl, she is fascinated with creativity that uplifts our experience and affect the senses directly.

Her process and the end-product is captivating and just incredible. She truly did capture the scent of Cape Town –whimsical, fresh, enlighten, yet eccentric.

Read more >

Masters in the art of freestyling it

One of my main highlights of the festival was the amazing group called Freestyle Love Supreme. They would wrap up each day with freestyle rap and beat boxing. They were so entertaining and funny, I laughed so hard that may face hurt.

The Design Indaba team chatted to Freestyle Love Supreme ahead of their Design Indaba daily wrap ups and once-off performance on the Thursday at Nightscape.

Read more >

 

 

Swahili launches on Duolingo

At Design Indaba 2017, Luis Von Ahn launches the first African language course on Duolingo. The audience went wild when he told us, he then went on to say that the second African language they will be launching will be Zulu. We can’t wait to see more African languages on this amazing app.

Read more >

Arch For Arch: A coda for Design Indaba Festival Day 3

The spectacular finale of the 2017 Conference and a tribute to Archbishop Desmond Tutu. It was a great honor and privilege for me to be a part of this amazing ceremony and to hear the incredible and humble, Archbishop Desmond Tutu talk. It was a great way to end the amazing festival, I left feeling inspired

Read more >

Thank you for the wonderful experience and we are looking forward to where they go from here.

So, if you think that design indaba isn’t for you – think again. Book your ticket for next year and immerse yourself.

by Mari-Liza Monteiro