All You’ve Ever Wanted To Know About Tuition Fees In Business Education

A while ago I delivered a speech on financing of Higher Education, wherein I emphasized that education entails a cost. Once this fact is comprehended, it is wise enough to walk the roads of strategic thinking to timely secure the funding options for tuition fees. This would ensure that each element of the French higher education is strengthened not only for the good of all its stakeholders (students, families, teachers, support staff, etc.), but also for companies and the country as a whole. 

To state a precise number, let me mention that the annual cost of graduate studies, whether private or public, is €13873. This accurate figure, calculated by the OECD, perfectly highlights the importance of this subject. But to our amazement, this topic is a taboo in some countries (see below). In such countries, the fact that the state is the main donor has caused the public to believe that studies are “free” because of redistribution.

Why education has a cost 

financing education
Need funding to graduate

Regardless of its status, just like any business or organization, an educational institution bears a range of expenses that span multiple dimensions:

  • investment expenditure (premises, infrastructure, information system, etc.)
  • operating expenses divided between:
    • salaries (teachers, support staff and others)
    • classic operations
    • specific functions (direct cost of training – case purchases for a business school, for example)

Let’s also take a note of the fact that whenever an institution of higher education operates as a business in the high value-added services sector, then it’s a rule that 60% of its budget is spent on the salaries of its staff. Read more

A Publisher’s 7 key steps to succeed through Digital

What are the signs of digital success for a Publishing company? One of the best manifestations of a successful digital transformation is portrayed by the famous German media publisher Ebner Publishing Group that has had remarkable success in the past few years. In order to learn more about and get an insight into the company’s digital transformation, I interviewed Dominik Grau, the Chief Innovation Officer of the group.

Ebner Verlag GmbH & Co. has a history of around 200 years and is a family-owned media group operating in 11 countries having headquarters in Ulm, Germany. The company operates in niches and is a special interest company, publishing magazines related to firefighters, fashion, music, IT, etc. The group has more than 80 magazines and 60 websites to its name. Dominik, who has more than 15 years of experience in the media industry worked as the Managing Director of Ebner’s New York office prior to joining the German office as the CIO.

What has been the major pain-point for Ebner Publishing in the Internet era?

DG: In 2011, Ebner Publishing witnessed that the print and traditional publishing businesses were going down as there was a shift of people’s interest from print to digital. The audiences were now using social media platforms, apps and other online services. People had internet where they read whatever they wanted to, and this didn’t go well for print media businesses since their audience numbers and revenues were going down.

Dominik Grau on digital success for Ebner Publishing Group
Dominik Grau delivering keynote at eZ Conference Cologne June 2018

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Improving your customer experience with a chatbot: 4 tips to remember

Chatbots or conversational robots seem to have a bright future ahead: It is projected that by 2020, 80% of companies would be using them for their interactions with customers . As a result, the chatbot market is expected to grow by 37% in the next 4 years . But, what is the reality? Are there any constraints? How to set up a chatbot? Faced with these questions, Claire Sorel of Visionary Marketing interviewed Frédéric Canevet, Product Manager at Eloquant.com (customer relations specialist).

Tip # 1: Make a qualitative and quantitative assessment of the most relevant use case, then customize the conversation design accordingly

 

The problem must be voluminous and recurrent. Frederic reminds us that it’s not a question of volume only, sometimes a FAQ can be sufficient, but value is also important. The good news is that value is attainable, thanks to a connection with the information system the bot is able to deliver personalized answers in a very simple way to the users. It is therefore important to frame the use case appropriately.

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Top 3 retro innovation tips by Pixminds at #Vivatech

It is time we talk innovation, particularly retro innovation, as that is what we derived from our interview with Pixminds at Vivatech 2018. Pixminds holding is a group that brings together several subsidiaries comprising of a game distribution company that caters to a broad socio-demographic group in France and a manufacturing company that designs retro innovation driven pinball machine and arcade kiosks.

Retro Innovation
Arcade games reinvented by Pixminds holding, thanks to Retro Innovation

 

Thirdly, it also encompasses an innovation company (ARK) which develops, files and manages a certain number of patents. LEXIP is another of the group companies which develops arcade games. It is trying to build up its products by incorporating retro innovation mechanisms. Our meeting with Mr. Hugo Loi of LEXIP led to the unveiling of top 3 tips that he believes are needed to make fruitful the retro innovation dynamics: begin with passion and a driving personal experience, observe the improvement opportunities that might come up during the utilization of products and lastly, make beautiful encounters and build relationships with like-minded people. 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.

Source : en.proft.me

 

 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