Data Science Ethics: Without Conscience It Is But The Ruin of the Soul

Data Science is on the agenda but what about Data Science Ethics? The twin motors of data and information technology are driving innovation forward in most every aspect of human enterprise. In a similar fashion, Data Science today profoundly influences how business is done in fields as diverse as the life sciences, smart cities, and transportation

As cogent as these directions have become, the dangers of data science without ethical considerations is as equally apparent – whether it be the protection of personally identifiable data, implicit bias in automated decision-making, the illusion of free choice in psychographics, the social impacts of automation, or the apparent divorce of truth and trust in virtual communication.  

Justifying the need for focus on the Data Science Ethics goes beyond a balance sheet of these opportunities and challenges, for the practice of data science challenges our perceptions of what it means to be human.

This contribution was inspired by Julie Compagny’s noteworthy article on the Digital Me Up. Digital Me up is the blog of our students from the Advanced Master’s in Digital Business Strategy of Grenoble Management School, where I teach and Julie is one of the students from the 2018-2019 academic year. This topic will be on the menu of next month’s lecture about “Technology and Innovation”, which I will deliver on the premises of Vinci’s incubator, aptly named Leonard.

Data Science ethics and its influences on today’s business practices

Data Science Ethics
Pr Lee Schlenker with Vinci’s #Weareleonard Camille Desforges is preparing is Apr 5th Offsite lecture. The Advanced Master’s in Digital Business Strategy of Grenoble Ecole de Management will be hosted by Vinci on that date.
if ethics is defined as shared values that help humanity differentiate right from wrong, the increasing digitalization of human activity shapes the very definitions of how we evaluate the world around usClick To Tweet

Margo Boenig-Liptsin’s points out that our ever-increasing reliance on information technology has fundamentally transformed traditional concepts of “privacy”, “fairness” and “representation”, not to mention “free choice”, “truth” and “trust”.These mutations underline the increasing footprint and responsibilities of data science – beyond the bytes and bits, data science shakes the perceptual foundations of value, community, and equity.  If academia has been quick to establish Data Science programs around statistics, computation and software engineering, few programs address the larger societal concerns of data science, and fewer still analyse how responsible data practices can be conditioned and even encouraged. Let’s sketch out the contours of this challenge. Read more

How Tom Tom achieved its digital transformation with Big Data – #bigdataparis

Big data is more than ever on the agenda of those marketers who are on the warpath of data-driven marketing. It’s the 6th year I’ve been active (from a content marketing perspective) in this area and I find it always more exciting, year after year. On March 12, I attended the Big Data Paris 2019 keynote entitled “How Tom Tom has evolved from a navigation company to a big data company”.

Tom Tom lives and breathes Big Data

The speaker was Alain de Taeye, Founder of TeleAtlas, Member of the Management Board, Tom Tom. For those who wouldn’t know, Tom Tom is a Dutch company. His pitch was instrumental in my understanding how Big Data moved from a technical topic into a full fledged transformational toolbox reshaping entire industries and businesses.

Such was the promise of Big Data six years ago or more, but few are able to show such impressive results as Tom Tom. Here is how they turned from a B2C company selling navigation devices into a B2B data-driven money machine.  

Tom Tom Big Data
Alain de Taeye is right: we do live in a world driven by Big Data and Tom Tom is leading the show.

The Big data revolution was the subtitle of de Taeye’s presentation and God knows this was a revolution! Tom Tom has changed radically. Here is his account thanks to my notes taken during his presentation. 

Making navigation easier with Big Data 

“At this moment in Paris we are collecting data from users and this is used to make navigation easier” Mr de Taeye said as an introduction to his speech. Tom Tom is well known for its maps and rapidly evolved into a technology company. “The company’s maps aren’t ‘ordinary maps’” however, Mr de Taeye went on.  Read more

Data-driven Marketing: norm among pundits and enigma among marketers

It has become a common parlance amongst marketers lately; there’s hardly any marketing without data. Data lays down the bed-rock for successful marketing campaigns, and it has become a discipline within itself. In simple terms, I would like to present an overview of how to approach data-driven marketing.

data-driven marketing
Data is a precious resource for marketers

To know your customer is fundamental, and to do that, there’s a multitude of questions that marketers must know the answers to. The data from these answers, collected from various primary and secondary sources, guides the marketing strategy of firms. The analyses derived from this precious real-time customer data can, therefore, determine the success of marketing campaigns.

There must be a proper methodology that caters to the requirements of marketers when it comes to data-driven marketing. Let’s try and dissect this topic to gain some insights into the world of data and how marketers can unveil its benefits for executing their marketing campaigns.

What data do we really need?

All the collected data isn’t really useful for marketers. It’s therefore paramount that marketers know what data they should collect, segregate and work on. Keeping unnecessary data will only help increase the complexity of analysis.

But, what’s really useful? Useful data is one that attends to the marketing functions of understanding your customer better: knowing when, where and through what channels to reach him/her at the minimum possible cost.

Read more

Why should future managers invest in Data Science?

The Business Analytics Institute will once again be hosting its Summer School in Data Science for Management in Bayonne, France from July 2 to 11 2018.  In this exclusive five-part series on Why Future Managers Should Invest in Data Science, we will explore the BAI’s unique value proposition around improving managerial decision-making.

Early this month, president Emmanuel Macron pledged to make France a major international hub of Artificial Intelligence.The French government has committed €1.5 billion over five years to support research in the field, encourage startups, and collect data that can be used safely by organizations and individuals alike.  With all the buzz concerning artificial intelligence, machine learning, and deep learning what exactly is the vision and value of each? More importantly, what can you learn about AI from the BAI in general, and our Summer School in particular?

What do we mean by Artificial Intelligence?

Coined by John McCarthy over sixty year ago, the term Artificial Intelligence refers to the ability of information technology to perform tasks commonly associated with human reasoning. Data scientists feed the development of AI through the identification of pertinent and meaningful information in both small and Big Data sets. Despite the hype, current implementations of AI are limited to “Narrow AI” – which concerns a computer program capable of performing a particular task well (Chess, image recognition, translations, sales predictions…).

Discussions of “General AI” which refer to a machine’s ability to do perform a wide variety of tasks as well or better than its human counterparts are still science fiction – “robots” won’t be replacing managers any time soon.  Finally, the concept of “Super AI” first introduced by Nick Bostrom, suggests algorithms capable of redefining the problems we are trying to solve – an idea that is unfortunately as intriguing as it is unreachable today. Machine learning, deep learning, and process mining are all approaches to implementing artificial intelligence

What exactly is Machine Learning

Machine learning refers to computer programs that can learn from the data set with which it is associated. Machine learning involves training an algorithm to identify relationships in the data without having to explicitly program all the potential cases and outcomes. There are four main categories of machine learning: supervised (in which the data includes “labels” indicating the relative importance of its attributes), unsupervised (involving “raw” unclassified data), semi-supervised (including both labeled and unlabeled data) and reinforced (which trains algorithms using a reward system). Within each category, data scientists work with a number of methodologies and models including regression, clustering, Bayesian networks, support vector machines, and random forest.

Each methodology incorporates a set of classifiers that the computer understands (the notion of representation), a scoring function (for evaluation) and a search method (for optimization). The choice of the methodology depends on the nature of the problem “environment” under study (deterministic or stochastic), the quality of the data (nominal, ordinal, ratio, structured, unstructured…), and the model’s parameters (the assumed properties of the data imposed by the model, the training data, and the problem at hand). In each case the machine “learns” as the pertinence and the efficiency of the algorithm is adjusted to the properties of the data.

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 What is the difference between Machine Learning and Deep Learning?

Deep learning is one specific approach to machine learning. Deep learning involves the study and design of machine algorithms to produce better representations of the data at multiple levels of abstraction.  Deep learning mimics the structure and functions of our own brain, involving how neurons, nerve fibers, and synapses interact to process information in the hippocampus. Artificial Neural Networks (ANNs) are algorithms in which “neurons” have discrete layers and connections to other “neurons”. Each layer picks out a specific feature to learn, such as curves/edges in image recognition. It’s this layering that gives deep learning its name, depth is created by using multiple layers as opposed to a single layer. Read more

Why should future managers invest in Data Science ?

The Business Analytics Institute will once again be hosting its Summer School in Data Science for Management in Bayonne, France from July 2 to 11 2018.  In this exclusive five-part series, we will explore the BAI’s unique value proposition around improving managerial decision-making.

Ever since the Harvard Business Review declared a few years back that Data Science was the “sexiest job on Earth”, students have flocked by the thousands in pursuit of degrees in the field. Recent estimates suggest that there are roughly fifteen thousand qualified data scientists working today – a pale comparison to the eighty-one thousand openings for data scientists on LinkedIn this week. Alphabet’s Eric Schmidt recently summed up the situation, “a basic understanding of data analytics is incredibly important for this next generation of young people…”As a future manager, why should you seize this opportunity by enrolling in BAI’s Summer School on Data Science for Management?

Why are companies so interested in data?

Klaus Schwab suggests that data and analytics have provided the foundations of a Fourth Industrial Revolution.  Statista’s figures back up this claim – the data centric GAFA corporations accounted for $229 billion of revenue in 2017.The European Commission estimates that the value of personalized data will reach 1 trillion euros by 2020, almost 8% of the EU’s GDP.[v] Cap Gemini’s survey of corporate business executives found that 61% of the respondents “acknowledge that big data is becoming as valuable to their businesses as their existing products and services.Given that the volume of data companies capture grows by an average of 40% a year, it is not surprising that Data Scientists are in such high demand..

Why some much fuss about data?

Data is no longer a simple by-product of a business process, it has become the lifeblood of modern enterprise.  Riley Newman of Wave Capital reminds us that data isn’t just data — it is an imperfect mirror of our customer conversations. If captured and nurtured correctly, data reflects our understanding of current economic realities, and help us predict and influence how consumers will value their experiences over time. Data in itself has no intrinsic value, value is produced in the platforms, processes, and mindsets we put in place to capture and influence customer experiences. Unlike investments in capital goods, the value of these “intangible” digital properties grows over time through network effects as more and more people use them.

What is Data Science?

Data Science is less about theory than it is about practice, integrating these decision-making fundamentals into the way we work.  Data Science is less concerned with what we do (descriptive) than what we could (predictive) or should do (prescriptive analytics). Today, organizations are looking for data scientists in industries ranging from the health sciences to finance, IT, and the media and public service. The scarcity of specialists to fill these openings is largely because Data Science is marketed as a mashup of analytical, business, and technical skills that are rarely found in any one profile. The only common denominator is a universal vocation of using data to learn about real life business challenges.   Read more