The Dimensions Of An Effective Data Science Team

Organisations worldwide are increasingly looking to data science teams to provide business insight, understand customer behaviour and drive new product development. The broad field of Artificial Intelligence (AI) including Machine Learning (ML) and Deep Learning (DL) is exploding both in terms of academic research and business implementation. Some of the world’s biggest companies including Google, Facebook, Uber, Airbnb, and Goldman Sachs derive much of their value from data science effectiveness. These companies use data in very creative ways and are able to generate massive amounts of competitive advantage and business insight through the effective use of data.

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The Need for Data Science

Organisations worldwide are increasingly looking to data science teams to provide business insight, understand customer behaviour and drive new product development. The broad field of Artificial Intelligence (AI) including Machine Learning (ML) and Deep Learning (DL) is exploding both in terms of academic research and business implementation. Some of the world’s biggest companies including Google, Facebook, Uber, Airbnb, and Goldman Sachs derive much of their value from data science effectiveness. These companies use data in very creative ways and are able to generate massive amounts of competitive advantage and business insight through the effective use of data.

Have you ever wondered how Google Maps predicts traffic? How does Facebook know your preferences so accurately? Why would Google give a platform as powerful as Gmail away for free? Having data and a great idea is a start – but the likes of Facebook’s and Google’s have figured out that a key step in the creation of amazing data products (and the resultant business value generation) is the formation of highly effective, aligned and organisationally-supported data science teams.

Effective Data Science Teams

How exactly have these leading data companies of the world established effective data science teams? What skills are required and what technologies have they employed? What processes do they have in place to enable effective data science? What cultures, behaviours and habits have been embraced by their staff and how have they set up their data science teams for success? The focus of this blog is to better understand at a high level what makes up an effective data science team and to discuss some practical steps to consider. This blog also poses several open-ended questions worth thinking about. Later blogs in this series will go into more detail in each of the dimensions discussed below.

Drew Harry, Director of Science at Twitch wrote an excellent article titled “Highly Effective Data Science teams”. He states that “Great data science work is built on a hierarchy of basic needs: powerful data infrastructure that is well maintained, protection from ad-hoc distractions, high-quality data, strong team research processes, and access to open-minded decision-makers with high leverage problems to solve” [1].

In my opinion, this definition accurately describes the various dimensions that are necessary for data science teams to be effective. As such, I would like to attempt to decompose this quote further and try to understand it in more detail.

Drew Harry’s Hierarchy of Basic Data Science Needs

Great data science requires powerful data infrastructure

A common pitfall of data science teams is that they are sometimes forced either through lack of resources or through lack of understanding of the role of data scientists, to do time-intensive data wrangling activities (sourcing, cleaning, preparing data). Additionally, data scientists are often asked to complete ad-hoc requests and build business intelligence reports. These tasks should ideally be removed from the responsibilities of a data science team to allow them to focus on their core capabilities: that is utilising their mathematical and statistical abilities to solve challenging business problems and find interesting patterns in data rather than expending their efforts on housekeeping work. To do this, ideally data scientists should be supported by a dedicated team of data engineers. Data engineers typically build robust data infrastructures and architectures, implement tools to assist with data acquisition, data modeling, ETL, data architecture etc.

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An example of this is at Facebook, a world leader in data engineering. Just imagine for a second the technical challenges inherent in providing over one billion people a personalised homepage full of various posts, photos and videos on a near-real time basis. To do this, Facebook runs one of the world’s largest data warehouses storing over 300 petabytes of data [2] and employs a range of powerful and sophisticated data processing techniques and tools [3]. This data engineering capability enables thousands of Facebook employees to effectively use their data to focus on value enhancing activities for the company without worrying about the nuts and bolts of how the data got there.

I realise that we are not all blessed with the resources and data talent inherent in Silicon Valley firms such as Facebook. Our data landscapes are often siloed and our IT support teams where data engineers traditionally reside mainly focus on keeping the lights on and putting out fires. But this model has to change – set up your data science teams to have the best chance of success. Co-opt a data engineer onto the data science team. If this is not possible due to resource constraints then at least provide your data scientists with the tools to easily create ETL code and rapidly spin up bespoke data warehouses thus enabling them with rapid experimentation execution capability. Whatever you do, don’t let them be bogged down in operational data sludge.

Great data science requires easily accessible, high-quality data

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Data should be trusted, and be of a high quality. Additionally, there should be enough data available to allow data scientists to be able to execute experiments. Data should be easily accessible, and the team should have processing power capable of running complex code in reasonable time frames. Data scientists should, within legal boundaries, have easy, autonomous, access to data. Data science teams should not be precluded from the use of data on production systems and mechanisms need to be put in place to allow for this rather than being banned from use just because “hey – this is production – don’t you dare touch!”

In order to support their army of business users and data scientists, eBay, one of the world’s largest auction and shopping sites, has successfully implemented a data analytics sandbox environment separate from the company’s production systems. eBay allows employees that want to analyse and explore data to create large virtual data marts inside their data warehouse. These sandboxes are walled off areas that offer a safe environment for data scientists to experiment with both internal data from the organisation as well as providing them with the ability to ingest other types of external data sources.

I would encourage you to explore the creation of such environments in your own organisations in order to provide your data science teams with easily accessible, high quality data that does not threaten production systems. It must be noted that to support this kind of environment, your data architecture must allow for the integration of all of the organisation’s (and other external) data – both structured and unstructured. As an example, eBay has an integrated data architecture that comprises of an enterprise data warehouse that stores transactional data, a separate Teradata deep storage data base which stores semi-structured data as well as a Hadoop implementation for unstructured data [4]. Other organisations are creating “data lakes” that allow raw, structured and unstructured data to be stored in a vast, low-cost data stores. The point is that the creation of such integrated data environments goes hand in hand with providing your data science team with analytics sandbox environments. As an aside, all the efforts going into your data management and data compliance projects will also greatly assist in this regard.

Great data science requires access to open-minded decision-makers with high leverage problems to solve

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DJ Patel stated that “A data-driven organisation acquires, processes, and leverages data in a timely fashion to create efficiencies, iterate on and develop new products, and navigate the competitive landscape” [5]. This culture of being data-driven needs to be driven from the top down. As an example, Airbnb promotes a data-driven culture and uses data as a vital input in their decision-making process [6]. They use analytics in their everyday operations, conduct experiments to test various hypotheses, and build statistical models to generate business insights to great success.

Data science initiatives should always be supported by top-level organisational decision-makers. These leaders must be able to articulate the value that data science has brought to their business [1]. Wherever possible, co-create analytics solutions with your key business stakeholders.  Make them your product owners and provide feedback on insights to them on a regular basis. This will help keep the business context front of mind and allows them to experience the power and value of data science directly. Organisational decision-makers will also have the deepest understanding of company strategy and performance and can thus direct data science efforts to problems with the highest business impact.

Great data science requires strong team research processes

Data science teams should have strong operational research capabilities and robust internal processes. This will enable the team to be able to execute controlled experiments with high levels of confidence in their results. Effective internal processes can assist in promoting a culture of being able to fail fast, fail quickly and provide valuable feedback into the business experiment/data science loop. Google and Facebook have mastered this in their ability to amongst other things; aggregate vast quantities of anonymised data, conduct rapid experiments and share these insights internally with their partners thus generating substantial revenues in the process.

Think of this as employing robust software engineering principles to your data science practice. Ensure that your documentation is up to date and of a high standard. Ensure that there is a process for code review, and that you are able to correctly interpret the results that you are seeing in the data. Test the impact of this analysis with your key stakeholders. As Drew Harry states, “controlled experimentation is the most critical tool in data science’s arsenal and a team that doesn’t make regular use of it is doing something wrong” [1].

In Closing

This blog is based on a decomposition of Drew Harry’s definition of what enables great data science teams. It provides a few examples of companies doing this well and some practical steps and open-ended questions to consider.

To summarise: A well-balanced and effective data science team requires a data engineering team to support them from a data infrastructure and architecture perspective. They require large amounts of data that is accurate and trusted. They require data to be easily accessible and need some level of autonomy in accessing data. Top level decision makers need to buy into the value of data science and have an open mind when analysing the results of data science experiments. These leaders also need to be promoting a data-driven culture and provide the data science team with challenging and valuable business problems. Data science teams also need to keep their house clean and have adequate internal processes to execute accurate and effective experiments which will allow them to fail and learn quickly and ultimately become trusted business advisors.

Some Final Questions Worth Considering and Next Steps

In writing this, some intriguing questions come to mind: Surely there is an African context to consider here? What are we doing well on the African continent and how can we start becoming exporters of effective data science practices and talent. Other questions that come to mind include: To what end does all of the above need to be in place at once? What is the right mix of data scientists/engineers and analysts? What is the optimal mix of permanent, contractor and crowd-sourced resources (e.g. Kaggle-like initiatives [7])? Academia, consultancies and research houses are beating the drum of how important it is to be data-driven, but to what extent is this always necessary? Are there some problems that shouldn’t be using data as an input? Should we be purchasing external data to augment the internal data that we have, and if so, what data should we be purchasing? One of our competitors recently launched an advertising campaign explicitly stating that their customers are “more than just data” so does this imply that some sort of “data fatigue” is setting in for our clients?

My next blog will explore in more detail, the ideal skillsets required in a data engineering team and how data engineering can be practically implemented in an organisation’s data science strategy. I will also attempt to tackle some of the pertinent open-ended questions mentioned above.

The dimensions discussed in this blog are by no means exhaustive, and there are certainly more questions than answers at this stage. I would love to see your comments on how you may have seen data science being implemented effectively in your organisations or some vexing questions that you would like to discuss.

References

[1] https://medium.com/mit-media-lab/highly-effective-data-science-teams-e90bb13bb709

[2] https://blog.keen.io/architecture-of-giants-data-stacks-at-facebook-netflix-airbnb-and-pinterest-9b7cd881af54

[3] https://www.wired.com/2013/02/facebook-data-team/

[4] http://searchbusinessanalytics.techtarget.com/feature/Data-sandboxes-help-analysts-dig-deep-into-corporate-info

[5] https://books.google.co.za/books?id=wZHe0t4ZgWoC&printsec=frontcover#v=onepage&q&f=false

[6] https://medium.com/airbnb-engineering/data-infrastructure-at-airbnb-8adfb34f169c?s=keen-io

[7] https://www.kaggle.com/

by Nicholas Simigiannis

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

The essence of a FinTech team

Along my short career I find myself wondering what the keys to success are. I have come to the realization that though the media will tell us stories of successful individuals, few key inventions were conceptualized and industrialized by just one person. So what makes a successful team and how would you put one together?

Along my short career I find myself wondering what the keys to success are. I have come to the realization that though the media will tell us stories of successful individuals, few key inventions were conceptualized and industrialized by just one person. So what makes a successful team and how would you put one together?

The idealist within me wishes that I could provide a recipe for the ideal FinTech team. I would like to be able to say in order to revolutionize the world you need 5 analysts, 10 developers and 17 Data scientists but this still wouldn’t guarantee success. So what is the essence of a Fintech team? I may not have all the answers but I do think there are some common elements in truly successful teams.

Purpose

The word purpose is over used but misunderstood. The true meaning of the word took on a new meaning when described by Viktor Frankl in his 1946 classic, “Man’s Search for Meaning” within the context of a World War 2 prisoner camp. Viktor was a neurologist and psychiatrist who was captured and lived in a prisoner of war camp. He shares his observation on the elements of motivation and depression that he observed in his fellow prisoners.  Personally I think Viktor does a better job at explaining it than I could.

Viktor explains that the reason people survived the Holocaust is they had something else to live for, a true purpose. Sometimes this was as simple as a desire to see their family again, in other cases it was more complex. It is this motivation by purpose is that I believe galvanizes a team.

Salim Ismail insists that all start-ups set a multi transformational purpose. These purpose statements need so be short and to the point so that there is no room for misinterpretation. If your purpose cannot be stated in one sentence, then it has not been distilled into its essence. This helps focus all team members at the same goal. Most importantly it means that all team members should believe in the purpose. Getting this right is almost impossible but I would be willing to bet that successful teams have gotten this right. My memory takes me back to South Africa’s 1995 Rugby World Cup winning team who went through the entire tournament with the purpose statement of “one team, one nation.” A purpose that resonates so strongly in all individuals within the team makes it impossible to fail.

http://www.sport24.co.za/Rugby/Springbok-Heritage/1995-RWC-squad-honoured-for-greatest-day-in-SA-rugby-history-20150624

People

I was in awe of these start-up stories outlining how a group of people started a multi-billion-dollar company in their garage.  In the past few years I found myself in the proverbial garage of multiple different acquaintances and friends, it was only then that I realized what was driving this behavior. I found myself drawn to this group merely because we were enjoying the hard work and the time we were spending with each other. It is easier to accomplish a complicated and long goal when you have good people around you that you connect with. I’m not at all saying that you need to be best friends with all your team members but I do believe that you need to find some commonality to have a human connection.

What about skills?

I’m am by no means diminishing the need for skilled people in your team. I am however making an assertion that even if you have the best skills, without a purpose and connected team you are doomed to fail. Pay more attention to the qualitative things when setting up the team. The things we take for granted like the feeling when you walk through the office doors, the vibe in the room, the “nice to have” social interactions.

So I guess my recipe is this:

Find a purpose that resonates with you. Then find a group of people that you can connect with. If the purpose resonates with your team, I believe you have a good chance of success.

by Tyrone Naidoo

Link to video: https://www.youtube.com/watch?v=fD1512_XJEw

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

 

 

 

 

The changing world around programmers

In today’s ever-changing world, we find that businesses have become more concerned about what you can do rather than what qualification you have.

Gabriel blogIn today’s ever-changing world, we find that businesses have become more concerned about what you can do rather than what qualification you have. This paradigm is becoming more apparent as companies have an unbelievable shortage of decent coders who are able to deliver to their expectations. This gap in the employment market is increasing as the average university turnout of BSc Computer Science graduates is far less than actual demand.

 This situation has led the industry to change the way they look at qualifications and to focus more on a person’s ability to code and learn. If you are a self-taught coder and have an understanding of industry-relevant technology, you are in a much better position than someone who still has to go into university and learn coding there for the first time. A few companies are willing to take the risk of hiring someone without formal coding qualifications, and have reaped the rewards in taking those risks. The coders that they hire generally seem to be more aware of what new technology is available, and are more willing to learn something new in order to help them grow further.

 We are starting to see a paradigm shift in the industry and the way in which people think. The stack overflow statistics show that the proportion of self-taught developers increased from 41.8% in 2015 to 69.1% in 2016. This shows that a lot of developers are self-taught and a lot more people are teaching themselves how to code each year. People who start to code from a young age show such passion for coding and in combination with their curiosity for learning something new, their love for it speaks volumes. To have the ability to create anything that they can think of on a PC, and to manipulate a PC to behave like they want it to and have a visual representation of this, is unbelievable.

 For those interested in teaching themselves how to code there are many websites to look at. Here is a list of 10 places you can learn coding from, but I will list the top 3 places that I learnt the most from:

Those websites have their own way of teaching code and if youcombine this with some Youtube videos from CS50 and MIT OpenCourseWare you will be all set to learn at your own pace. Hackerrank is a good way to test everything you learnt and you can see how you rank against the world.

 WeThinkCode_ is an institution to learn coding, for anyone from ages 17-35 years old. Their thinking is that you do not need to have a formal qualification to be a world class coder. More institutes like this are opening across the world. Having a wide age gap illustrates that you are never too old to learn how to code. There are also more and more coding education opportunities for young people. It is really easy to learn how to code from a young age as that is when your mind is at its prime to learn new things and adjust to constant change.

 In a programmer’s world you are constantly learning new things and this is what makes our jobs exciting.

The world is ever-evolving and we all need to keep adjusting our mindsets on how we look at things, otherwise we will be left behind while everyone moves forward.

By Gabriel Groener

The Modern Programmer

IT professionals often don’t get an honest portrayal in the entertainment industry and, for better or worse, the mass perception of Computer Science has been influenced by what people see on their TV screens. Either we sit in a dingy dark room, littered with empty energy drink cans, staring at a terminal with green font flashing and passing by at light speed – with sound effects, or we are cool rich guys creating programs that become self-aware.

IT professionals often don’t get an honest portrayal in the entertainment industry and, for better or worse, the mass perception of Computer Science has been influenced by what people see on their TV screens. Either we sit in a dingy dark room, littered with empty energy drink cans, staring at a terminal with green font flashing and passing by at light speed – with sound effects, or we are cool rich guys creating programs that become self-aware. There really isn’t a middle ground and these perceptions either drive people to developing an insatiable curiosity in the field or becoming fearful and believing that they aren’t mentally fit to join the club.

http://i.imgur.com/heb9csO.jpg
http://i.imgur.com/heb9csO.jpg

The demographic of the modern programmer isn’t what it was back in the 70’s. Most IT professionals were – well…Professionals. They were mathematicians, engineers, scientists, accountants, etc. often in their 30’s or 40’s. The programming industry was almost 50% women. What on earth happened?

Well, I have a theory. Computer Science (CS) wasn’t a course at any universities at that time, so youngsters really had no way of entering the field. Not to mention the fact that what they called a computer back then isn’t what we have today. They were big, expensive and obviously fewer. There were no operating systems. They wrote code by hand which was then converted into punch cards that could be fed into the computer and you had better pray that what you wrote was correct – which, if you code, you know it often isn’t – because then you would have to start that lengthy process from scratch. Blessed are those that came before us, for they were a resilient few. By the time we had a CS course it was the 80’s and young adults could learn how to code.

http://i.imgur.com/27vs3iD.jpg
http://i.imgur.com/27vs3iD.jpg

The 80’s was definitely one of the most defining times in modern history. We saw technology really being embraced in the media. Back to the Future, Ghostbusters, Star Wars, Terminator and many more franchises showed us a world of technology that seemed almost impossible. In lots of ways we are still catching up the imaginations of the filmmakers and science fiction writers. But I find this time very interesting because it gave birth to the geek culture which has lasted to this day. This culture was very young and male dominated. It was a kind of cult to those who were part of it. This must have driven the women away. Women in general still don’t get the culture. Heck, even I don’t get it to the degree of hardcore followers. Now think about how we perceive these “geeks” in society. Beady eyed, brace faced, drooling, good-grade-getting teens with bad acne (is there good acne?) and thick glasses, always getting bullied by the “jocks”. Truth is, in a quest to fit in, teens only hang out with the group that they relate to and/or accepts them. Learning became the uncool thing and Disco was in. The media neatly crafted and packaged nerd culture. Being a cool kid meant you didn’t even greet the nerd – unless shoving someone into a wall counted as a greeting. And so that was that. Programmers were part of a culture that embraced creativity, logic and intelligence and frowned upon anything less, because in order to be a programmer you needed to love learning and solving problems. Being a cool kid meant you had to love partying, gossip and creating problems.

http://www.philiployd.com/wp-content/uploads/2016/04/geek.jpg
http://www.philiployd.com/wp-content/uploads/2016/04/geek.jpg

Things have changed somewhat. Programmers today come in different shapes and sizes. Still not many hourglass shapes, but we’re getting there. The next generation of teens will definitely be more in-tune with technology and the true culture of the geek or the “hacker”. Those that fail to see the power of new technologies will be left behind. Computers are so much more accessible and all schools are starting to teach coding. With innovative colleges like We Think Code and 42, the future of what we perceive as an IT professional will be completely different to what we have today.

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It’s now up to us to make sure that our kids become programmers rather than the programmed. It’s in the small things that we spot the young coder. The little kid that breaks his/her toys to find out how they work. Kids are naturally curious and it’s up to us to nurture that curiosity and not reprimand or punish them for it. We interact with technology every day and we would only be empowering them by encouraging them to learn how to control that technology as creators in the same way that we might teach them how to play a musical instrument. I envision a world where the modern programmer is anyone, in a society that frowns on those that shun learning. Let’s make it happen.

by Sherwin Hulley

A case of keeping up with the Joneses?

The world is changing. Drones are fighting in armies, driverless cars are no longer a fiction of someone’s imagination, robots have the ability to outsmart humans in offering legal and financial advice. Exciting? Most definitely. But for a developing country such as South Africa there is also a sense of uneasiness.

South Africa and the Fourth Industrial Revolution

Source: genesisnanotech.com
Source: genesisnanotech.com

The world is changing. Drones are fighting in armies, driverless cars are no longer a fiction of someone’s imagination, robots have the ability to outsmart humans in offering legal and financial advice. Exciting? Most definitely. But for a developing country such as South Africa there is also a sense of uneasiness. How will our economy keep up with a changing world while having to fight poverty, inequality, corruption? As one of the most unequal societies of the current age one has to wonder what technological advances will do to the already high Gini coefficient? Are we entering a dystopian world order where some countries will ride the wave of a revolution and others will be left behind, feeding on the scraps?

Prof. Klaus Schwab from the World Economic Forum has proposed that a fourth industrial revolution is imminent. An industrial revolution is seen as a change in the basic economic structure, driven by innovation and invention. In the late 18th century coal and locomotives introduced the first industrial revolution with mechanization being the key driver.  The fourth industrial revolution will fundamentally change the world that we live in through so-called cyber physical systems where the natural, human and digital world meet. In short, extreme automation and connectivity might mean that boundary lines between humans and technology are blurred with concepts such as artificial intelligence, virtual reality and the internet of things taking civilization where it has not gone before.

While South Africa’s economy relies heavily on its natural resources the question is whether the country has progressed sufficiently on the ladder of previous industrial revolutions, or rather whether a platform has been created from which to launch into the rising age. One simply has to look at the state of railways and even the huge amount of manufacturing that happens off-shore to wonder whether South Africa has been able to progress industrially with the rest of the world. UBS highlights in a white paper (Davos 2016 White Paper) that South Africa is behind the curve with regard to the evolving of manufacturing along with demographics. In effect, the failure to move to high level of manufacturing with the demographic prime could indicate that SA has not adequately adapted to the second and third industrial revolutions.

The question remains whether the fourth industrial revolution would be a case of keeping up with the Joneses or whether it would be possible to fast track development in order to launch South Africa into the new era.

Africa is a continent that upholds a certain reputation for innovation particularly to overcome obstacles often created by the lack of development in a certain area. The continent is also seen as an early adopter of technology, with success stories of financial applications such as M-Pesa flying the flag for an innovative continent. Pockets of excellence mean that it is not all doom and gloom for South Africa. The financial sector is one of the best in the world and may well be a field in which new technologies will get traction.

The WEF looks favourably towards South Africa in terms of innovation and new fourth industrial revolution indexes in their Global Competitiveness Report (2016-2017) (WEF – Global Competitiveness Report ) with the following global ranks: innovation and sophistication (31st), business dynamism (50th) and innovation capacity (38th ) perhaps indicative of the silver lining of promise that the country will punch above its weight in the revolution.

Although ranked low in basic requirements and some efficiency parameters indicating a lag in fully adapting to previous industrial revolutions, positive innovation rankings paired with the right industry, such as financial markets, could position South Africa favourably in terms of moving towards the fourth industrial revolution.

It would be easy for South Africa to adopt a fearful approach towards the new era of automation and robotics. In a country with high unemployment low-skilled workers are likely to face a world where the job-market has no space for them. Some pessimists are predicting a world where humans are not needed. The reality is that change is coming and being a country that is able to adapt is non-negotiable. Education has been a pain point and South Africa is ranked a low 123d on the Global Competitiveness index for Health and Primary Education. Education is a critical factor to the challenges that a new revolution would bring forth and ensuring that a new generation can hold their own in a changing world will make the difference between those running the race well and those falling out along the way. Adapting education models is an important measure to be put in place if South Africa wants to approach the fourth industrial revolution with courage.

In our experience, it is evident that successful disruption is often birthed within a start-up atmosphere. Where current incumbents, especially in the financial sector, are faced with issues such as integration and legacy systems, a start-up with a dedicated and talented team and a great innovative idea have a greenfield approach that lends itself toward disruption.

Drawing the parallel to country-wide development there is a case for cheering on Africa and South Africa’s jump to the fourth Industrial Revolution modelled as a start-up with leeway to embrace the new era and its corresponding shifting boundaries. Leapfrogging some of the essential factors of previous revolutions toward a new disruptive way of using computer systems to address fundamental social problems. But investments in skills, particularly software development and the required infrastructure will need to be fast-tracked to create an enabling environment for the innovative nature of South Africans – to not just keep up with the rest of the world but to leverage a strong innovative culture and excel.

                               by Charlotte Hauman

 

Our book is yet unwritten

2016 was a year of discovery, of adventure, of breaking boundaries. For many it’s been a year of unparalleled innovation – especially for those of us that live in experimental spaces. We’ve long known that innovation is for the brave – those souls who dare to speak out, the curious ones asking “But who says?”.
As I reflect on bravery or courage or heroism, it dawns on me that bravery in any of its forms is remarkably like crazy – or is this simply a matter of perspective?

2016 was a year of discovery, of adventure, of breaking boundaries.  For many it’s been a year of unparalleled innovation – especially for those of us that live in experimental spaces. We’ve long known that innovation is for the brave – those souls who dare to speak out, the curious ones asking “But who says?”

img_6768As I reflect on bravery or courage or heroism, it dawns on me that bravery in any of its forms is remarkably like crazy – or is this simply a matter of perspective? Much of our lives as innovators requires us to quiet the voices in our heads yelling out “You can’t do that! It’s crazy!”. And it’s exactly this act of changing perspective that allows us to see possibility and create a new future – to disrupt our worlds. It takes a special kind of crazy to question assumptions that are years old, to challenge ideals and concepts that work well enough, to be that person in the room asking “why?”

In Adam Grant’s “Originals” (if you haven’t read it yet, what are you waiting for? It’s incredible!), he speaks about “Vuja De” –  the obvious reverse of Déjà vu – the concept of facing something familiar but seeing it with a fresh perspective that enables new insights into old problems.

In today’s world of work, one of the biggest issues we face is creating spaces where people can bring their excellence, where the uniqueness of the individual can be expressed to create winning innovation.  How do we create that winning culture?

For years we’ve followed the rules on how “work” is, a kind of imaginary Encyclopaedia Britannica of how we work. But that imaginary book was written before “we” were working! It was written before many of “us” entered the world of work! Us being women and millennials and innovators and also closet creatives, and evening gardeners and day-time-suit-wearing-iron-men and also… well, most everyone.

Let’s face it, this book was written for a bunch of folk who are now in the minority. And don’t get me wrong, it worked really really well back then, but for “us” in the workplace now, it really does fall short. Many of us feel that our workplaces just don’t enable the way we need to work. So why then are we still using that imaginary book as our core reference guide?

That way of work was perfect for specific workplaces, for a workforce that were all very similar (or were told that they had to be) and for a time that was, well…industrial revolution. We’re in a whole new time, with a whole new workforce, and yet – there is no new book!  We have moved from a world where work was about creating consistency, to a world where work is about embracing each individual’s unique contribution and, if we wish to see that reality, it means we are going to need that bravery to change our worlds of work.

img_6779And it’s right about at this point that I hear Natasha Bedingfield belting out “I’m just beginning, the pen’s in my hand, ending unplanned” and then…a great big ol’ penny drops…it’s time to do some re-writing!

In 2017 I’m keen to see these new chapters take shape.  Let’s take the time to write “the Wikipedia of work” for our future, one that works for us, one that creates space for innovation, for creativity, one that allows every person to thrive, one that isn’t creating a whole workforce of ill-fitting pegs.

We have already rewritten the chapter on dynamic working (literally rewritten), but there are still many chapters that we haven’t even begun to write. We’ve only just started the chapters on what the world of work look could like for single moms? What about the chapters on working dads? Or insomniacs? Or those that live far from their workplaces? Or nocturnals?

And what about the chapter on success? Does it still mean becoming the CEO? Really? What is success if you believe in balancing family and sport and work and creative hobbies? What could that chapter look like?

And what is a career? Is it really a straight-line 20-year plan? What if there was a chapter on changing careers mid-way? Or one on taking a break from your career? Or one on how to come back after a break?

Now is the time for a massive cultural innovation.  It’s the time for new chapters. It’s time for all you brave crazies out there to start recreating, it’s time to get writing. Take it home Natasha… “Live your life with arms wide open, today is where your book begins, the rest is still unwritten”

by Liesl Bebb-McKay

Intentional Simplicity

I believe that in this time we live in more than any other that came before it we are dealing with exponentials. Exponential growth, exponential challenges and exponential solutions. This theme manifests into both daily life and our businesses, and since our businesses are technology driven into technology too.

intentional-simplicity-v3I believe that in this time we live in more than any other that came before  it we are dealing with exponentials. Exponential growth, exponential challenges and exponential solutions. This theme manifests into both daily life and our businesses, and since our businesses are technology driven into technology too. Exponential translates to the sheer number of technology choices we now have when solving the needs of our businesses. While in principle I believe this is positive it also brings with it the potential for complexity. Complexity inherently is required in some cases, it’s the unnecessary complexity we need to guard against.

Another consideration is the way we approach problems. How is it that we find ourselves with “legacy solutions”? For me it’s quite simply two things: firstly, we’ve allowed ourselves to reach that position and secondly we have not been consciously optimising for the right things. What do I mean by this?  Simply put, life is about choices – if we consciously or unconsciously chose to allow legacy this was our doing. Often legacy is the unintended outcome of optimising for only a few dimensions such a delivery velocity, business functionality over sustainability. This talks to organizational culture and whether a culture of refactoring exists – this however is not the only consideration.

So the meta question for me becomes: since we have been building software for decades what can be done differently to achieve a different and more sustainable outcome. As it happens there is much research on the subject of IT complexity. My experience is that IT complexity mirrors business complexity – this my interpretation of Conway’s Law. I believe that while this is true, how we consciously promote intentional simplicity comes down to how we think, design, architect and ultimately build.

The fundamental shift that one needs to make is to move away from viewing businesses as being supported by platforms towards a capability view of an organisation. The capability organisational approach prescribes that you need to break down your business into capabilities. Capabilities are at a granularity where they describe related sets of business functions, for example: valuation and settlement are two capabilities in a bank. Once you have done your business architecture work we can start talking about how you apply a new architecture approach. Enter the Snowman or Simple Iterative Partitions (SIP) architecture:

 

intentional-simplicity-v4

The Snowman Architecture talks about creating “Snowmen” for each capability – essentially a small system for each capability. Business functionality is achieved by linking the required capabilities together by means of a messaging bus in accordance with a defined business process.

The head of the snowman contains the Business Architecture – practically what this contains is business logic which itself is comprised of the capability business process and related business rules. The belly of the snowman holds the Technical Architecture or technical implementation of this business logic as it pertains to the capability business process. The arms of the snowman represent the Service Architecture or service endpoints by which bidirectional communication is achieved through well-defined interfaces and through which snowmen interact. The base of the snowman encapsulates the Data Architecture which contains the data itself as well as the mechanism through which the belly of the snowman interacts. It must be noted that the head of the snowman must only interact with the belly which in turn interacts with the base – in this way we ensure design consistency.

Right so we’ve been through the theory, lets walk through a practical example of a snowman built for a valuation capability – a simple example. The capability is required to provided valuations of products given product characteristics and market data. Given this need the service endpoints (arms) of the snowman have a method that describes the interface that allows products to be valued given characteristics C and market data M. This method is implemented in the belly of the snowman, in the implementation the belly queries the head of the snowman for the valuation algorithm given C and M. The head applies the relevant business logic to return the correct algorithm. Once returned this algorithm is executed and both the method input and outputs stored in the data layer (base) of the snowman before the valuation result is returned by means of the service endpoint.

So I understand the approach, can you articulate some of the benefits? Sure, following the example above we can easily test our valuation capability. We understand its interfaces and expected outputs for a given data set. When we change our mind on how valuation will be done we simply build a new valuation snowman with the same interfaces and plug it in alongside the existing one for parallel run before we ultimately switch off the old snowman. We understand dependencies as these are articulated in the service data contract. Capabilities are highly re-usable. The most powerful value proposition for the Snowman approach is that we can achieve true composition – in other words solutions can be composed for business by linking capabilities. The outcome of this composition ability is that it allows us to move product to market at great velocity.

Is the Snowman the end, certainly not, adoption of this approach talks to the conscious mind shift that we’ve made towards intentional simplicity.

by Jason Suttie

Walking The Spine

In my previous post I introduced the Spine Model – a philosophy of thought for Agile companies. Let’s Walk the Spine to show how philosophy can be applied to the real world.

Walking The Spine

In my previous post I introduced the Spine Model – a philosophy of thought for Agile companies. Let’s Walk the Spine to show how this philosophy can be applied to the real world.

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Start with the Need

blog-post-visuals-05-oct-needs

Over the years I have been all over the world and have observed many teams and their ways of work. An organizational pattern that always seems to surface is what I call “Cargo Culting” – see https://en.wikipedia.org/wiki/Cargo_cult_programming. Simply put the pattern is where people conduct rituals e.g. follow processes without any understanding of what the purpose of this work is. Typically, how this comes about is that a person establishes the work needed to solve a problem at a point in time. This work process is then adopted by the team. At some point the person who established the process moves on yet there may no longer be a need for the work.

I’ve seen accountants doing arduous reconciliations that were replaced by a new system yet they continued with these process regardless. Similar things happen in software development teams where the team follows a ritual but has no idea why. When questioned the people usually say “Because we’ve always done this/followed this process”. The wisdom here is that it’s often very hard for people to see clearly when they are in a system, an external view sometimes provided by a coach can provide insight for change that may have dramatic impact. Be clear on the need that is being solved for.

What is valued in the context of this need?

I believe that all of us behave according to our value drivers. Being conscious of one’s own value drivers as well as the value drivers of others, the team and the organization is possibly one of the most useful insights a person can have. Also having the insight around whether people associate value to themselves by what they do versus who they are (human doing versus human being) is critical. In our competitive workplace I frequently observe an imbalance wherein people associate all of their value to what they do, then in the absence of the work they find themselves lost. Values are also not binary, for example a team may favour flow over simplicity – it’s an “and” discussion.

Making “Principle” decisions

blog-post-visuals-05-oct-princaplesPrinciples are a great tool to guide decision making. I recently was involved in organizing a large industry event with a number of peer organizations. Half way through the exercise we came to the realization that we disagreed on a number of principle issues. It’s harder to address these issues when you are half pregnant, my takeout is that one should always do an Agile Bootstrap when forming a new team. Agile Bootstraps typically allow for a safe space where team members can share their backgrounds and where the team can walk the spine together – through this process it raises where there is disagreement.

Practical steps

blog-post-visuals-05-oct-practicesIn the world of knowledge workers that we find ourselves in, it is imperative that the team is allowed to align their chosen practices to satisfy the ultimate need or outcome. If you have hired smart people who can solve problems with creative thought, then why on earth would you dictate a solution? The first caveat is that in large enterprises there are some metrics that need to be provided by the team that are common across each team so that management can obtain a holistic view of the system. The second caveat is that some practices are just inappropriate for some problems – see Cynefin framework: https://en.wikipedia.org/wiki/Cynefin_Framework. The summary of this is that it really isn’t wise to use Waterfall for work that is not highly repeatable.

A man with a Tool

blog-post-visuals-05-oct-toolsInterestingly this is where many people start. They think that choosing a technology or tool will solve all of their problems. Unfortunately, this is a cognitive distortion. Tools don’t solve problems; humans do so don’t expect tooling to have any effect on the behaviour of the people in the system. Having said this if one adopts best practice then common sense must apply. We are in the world of DevOps and there are best of breed tools in each part of the pipeline. Do some research, your time will be well spent.

The last point to make is that when you have conversations be clear where you are having the conversation – the Spine again is useful for this.

by Jason Suttie