Archive for January, 2014



Data Science is a way of thinking to challenge the yesterday’s way of everything.


By Ali Syed and Dr. Zacharias Voulgaris

Science is one of humanity’s many attempts to make sense of this world and understand the principles behind its various phenomena. It is the methodical way of processing data (collected using natural sensors and instrumentation), extrapolating the rules we derive from them, validating our conclusions, and drawing a path towards new understanding and its application, broadly defined as knowledge. Knowledge is the psychological result of perception and learning and reasoning. As knowledge is quite diverse, humankind decided to classify it into disciplines and domains. Hence we have astronomy, geology, physics, chemistry, etc.[/p]

The amount and complexity of data produced in science, business, and everyday human activity is increasing at staggering rates. The world of today is fast changing leaving behind the ethos of knowledge as we know it. The ever changing data and empiricism on steroids gives knowledge a new meaning which is based on our creativity and deductive reasoning using data.

“What is the object of knowledge?” asks young Grasshopper. “There is no object of knowledge,” replies the old Shaman, “To know is to be able to operate adequately in an individual or cooperative situation.” “So which is more important, to know or to do?” asks young Grasshopper. “All doing is knowing, and all knowing is doing,” replies the Sage, and then continues, “Knowing is an effective action, that is, knowledge operates effectively in the domain of existence of all living creatures.” (Paraphrased from Maturana & Varela, 1992)

Quantification (the act of discovering or expressing the quantity of something) and being empirical has always been the core part of a scientific method. What has changed in the information age as part of human interactions and perception, is that quantification has become more about infusion of technology and data, some might call it neo empiricism.

Realizing that the world around us has moved on, so should our methods and our practices, and our concepts need to evolve with it.  That is the core of data science as we know it. Data Science starts with creative imagination. Our ability to imagine and think beyond the obvious is one of our extraordinary powers as humans. It is why we are different from other beings on the planet. We are creatures of action, we build and create things. We don’t live in the world directly, we live our lives through our imagination, which is full of ideas, concepts, theories and ideologies, grounded through systematic actions.

Everyone wants to be active in the field of data science and most of the people involved in it actually are. However, this is just the first step. If you really want to do data science it is important to be proactive, or to at least take steps towards that. This attitude is what is often referred to in Greek mythology as the “Promethean stance”, since Prometheus was a symbol of forethought and proactive behavior. When the other gods saw humanity as a lost cause, a species slightly more evolved than animals, Prometheus saw the potential in us and decided to sacrifice himself by stealing fire from the gods and giving it to us. Of course this is just a metaphor to show that humanity has a different kind of thinking (fire) which is closer to that of what we refer to as divine and is a godsend. For some people, however, it is a curse, since they don’t know how to use it properly.

Data Science gives us the broadness and complexity to get closer to the verification of reality. One can learn some tools and algorithms to deal with this data deluge and get hired based on a skill-set reflected on a resume, but you will only be able to add real and meaningful value, based on your ability to improvise, adapt, and create.  If we are prepared to give it the needed time and effort, we all can learn and apply data science, accepting the fact that we will make many mistakes along the way, and learn from our errors. Only cynics, brogrammers, pundits and wannabe tech experts benefit from making it the most complex discipline of our time. If you are not prepared to be wrong, you will never come up with anything original and useful. Data science empowers us to be more wrong rather than more accurate, in order to gradually build the means to create useful and meaningful data products.

We believe there is now a potential for data science to move to the next level, if we don’t cling to the status quo of viewing data science as the gatekeeper of yesterday’s scientific methods. This is a belief that is embraced by a number of data scientists, particularly those who shifted to the industry from academia, where challenging the scientific methods of the past is very rare and extremely difficult.

As the applications of data science are quite intriguing and with apparent financial benefits, it has grown to be a popular field and has attracted talented individuals in both academia and the industry, though it is mainly in the latter where data scientists generally build their careers. This accounts for its explosive growth in the past few years and the promise that the right application of such methods will become something really big in the years to come, so big that the demand for data scientists will not be met by the supply. What it boils down to is the generation of a new breed of data scientists that are too skills-centric and have not dwelled much on the mind-set. Soft skills are also imperative for a data scientist, perhaps even more important than the hard ones. Before we end up having an army of tech-savvy data scientists that are nothing more than clever calculators, it is time to take action and ensure that the next generations of data scientists have the mental discipline and maturity to carry the flame of Science to the future. We need to ensure that data science remains a science and does not degrade into some gibberish practice which is more a set of techniques than a discipline.

“The best minds of my generation are thinking about how to make people click ads… That sucks.” Jeff Hammerbacher


Work on stuff that matters. Data Activism for Social Change 

It’s all in the name, it is about attitude and learning and applying data science for a purpose. People talk about data science concepts and all sorts of wonderful things we can do but they never talk about making it meaningful and valuable beyond the interests of a particular company. It sounds a bit naive, and also very self-centred, where we are living a meaningless life. Where we are happy to spend more on the things that don’t really matter but not willing to allocate time to more important and real issues of life.

A change towards a more humanitarian world. 

The social change we collectively desire is not possible without addressing the issues of injustice, poverty, hunger, education for everyone, freedom to live a free life, basically reduce the suffering of the people, because most of the population of the world is suffering. As Martin Luther King eloquently expressed “injustice somewhere is a threat to justice everywhere.” Only a small portion of the total population gets to exploit and use most of the resources of the world, bringing about an imbalance that sows the seeds of all kinds of social and economic problems.

That is why we are promoting data science for humanity, and encourage data activism, the movement to focus the development and application of data science on more meaningful and real human challenges. The path that we have taken and are promoting is not going to be easy until it picks up momentum. Real change is never easy, the people involved have to struggle. But luckily for every one of us, the world is changing so radically that there is already significant change in the way we think, and a growing realization that we can move towards a better world only through application of meaningful data and more sustainable business models.  And that’s where Persontyle fits in. All meaningful changes take time to come to fruition, they don’t happen overnight, it’s a continuous struggle.

We are determined to focus on issues which matter and hope that more and more people will endorse and support in doing what matters. Gradually, there will be a critical mass of people who would be talking about the data activism and application of data science for humanity. All like minded and determined people will join. It’s beyond ourselves, it’s like establishing virtually a new discipline, designed for our time and age. For the gate keepers of the past, it is not going to be easy, and we might get a lot of push back and resistance.  We are determined to overcome against all odds.

Data activism is our humble attempt to give data science a human face. It’s enriched with meaning and purpose, encompassing an educational and revolutionary aspect. To put it simply, in our view, it is data science that is poised towards change, an organic part of a transition to a better society. The latter is not some flaky idea that philosophers like to talk about but a pragmatic and measurable approach that will result in improvement in our society, much like Jacque Fresco’s vision of a better world through the right use of technology. However, data science is not purely technical at its core, even though it makes use of technology for its purposes. Data science is a way of thinking and acting, employing state-of-the-art technology, in order to turn data into actionable and applied understanding that will be useful to the end-user i.e. society. Data activism attempts to ensure that the end-user is not just some corporate or for-profit organization, but its application extends to other places, such as charities and non-profit organizations.

All this is nice and idealistic, but how is it relevant to today’s value-driven society? Well, today’s world is all about creating value for people regardless of whether it is money, reputation, prosperity or other forms of benefit. The altruistic aim of data science may not yield improvement in the bottom line, but it may provide value to everyone involved in it (the organization that provides the data, the people that process it, and the people that make use of the data products as a result). All this will eventually manifest as shared prosperity for everyone.

The possibilities of this movement of sorts are limited only by our imagination. It is important to note that this does not attempt to deprive the industry of its valuable data scientists. It will just make it easier for new individuals in the field to acquire useful hands-on knowledge and experience through their involvement in volunteer work that will be useful to society through non-profits. In this scenario that we envision everyone wins. Isn’t that what Science is all about?

“We are living in the dawn of the big data era, a time in which the vast digitization of our world has created incalculable amounts of information that is now being used to drive our every decision, from what movie we decide to watch this weekend to how we navigate the globe next year. Though data can be immensely transformative, much of the efforts in data science are still focused on first-world gains, such as optimizing ad networks or recommending restaurants. As designers, developers, and scientists, it is not only incumbent upon us to understand how to analyze, understand, and tell stories with data, but also to think about its use in meaningful and socially conscious ways.” Jake Porway


A key challenge for you as a leader is to develop the imagination of your teams. Help them focus on both the hard and the soft stuff. The “hard stuff” is not just some tech skills, and the “soft stuff” is not just “keep calm and carry on innovating” or some such feel good mantra. Instead of issuing detailed instructions, allow your people the freedom to dream, challenge and create. We need institutions to promote  a culture where team members can unleash their creativity and explore the possibilities, which will not only drive better results, it will create teams that are motivated, happy, and are actually having fun instead of being clock-punching drones.


“Innovation” defined as moving the pieces around and adding more processing power is not some Big Idea that will disrupt a broken status quo: that precisely is the broken status quo. If we really want transformation, we have to slog through the hard stuff (history, economics, philosophy, art, ambiguities, contradictions). Bracketing it off to the side to focus just on technology, or just on innovation, actually prevents transformation.” Benjamin Bratton


The next time you are tempted to apply data science, try a different approach. Provide the building blocks and then let your team create their own masterpiece. You may just discover that game-changing innovation is already present. It may just be locked inside a team that needs to be instructed not to just follow the instructions. Always remember that business people don’t need to just ‘understand data scientists better’. Business people need to be data scientists, or at least integrate the data science philosophy in their thinking. If data science is not for thinking, challenging, questioning, and rebelling then it’s just a useless technology to protect mediocrity and status quo.

Credits: Illustration by Decourseyfx. read more