Big Data Applications: a Framework of interest: the impact of data analytics and the angle of the changing profile of future manager-leaders

By Prof. Nicolas Glady, Doctor in econometrics, Associate Professor at ESSEC Business School and the Accenture Strategic Business Analytics Chair holder. 

Professor Nicolas Glady
Professor Nicolas Glady

Big Data is the new digital buzzword. However, professionals are still wondering what kinds of practical applications Big Data could have for their business. Is it possible to articulate a framework that will help us to develop a better understanding of the usefulness of Big Data?

Big Data: A revolution in data generating process and network architecture

Data-driven processes have been around for many years. Among other examples, one could think of marketing applications (CRM), supply chain optimisation, or any type of forecasts. So, let us focus on what is really new in this new phenomenon: the prominent role of users in the data generating process and the network architecture.

First, the nature of the data generating process has changed. The source is no longer IT systems controlled by companies but users themselves, which now generate new type of contents: texts, photos, videos, etc. Users are therefore generating data (User Generated Content) with an unprecedented volume and variety.

Second, the dynamic of the data transmission changed as well. Before, the n users of a service were interacting with the service provider, generating n transactions. Nowadays, with Facebook, Twitter, Reddit, etc, users are communicating with one another allowing the number of transactions being n times n. In a network, data is exchanged faster.

These two phenomena explain the famous “3V” characteristics of Big Data: Volume, Velocity and Variety.

Accordingly, a framework of the applications of Big Data can be analysed regarding two dimensions: (1) the nature of the network, and (2) the proximity of the solution with the end-user.

Nicolas Glady Council on Business & Society

Types of networks: Human or Machine?

We obviously think first of social networks (like Facebook or Twitter), where users interact (H2H). These are networks where the elements (“nodes”) are human beings. Generating information as they go, users make available a range of information about their habits, their uses and needs.

In contrast, a second type of network is the network of machines (M2M). It is to be found, for example, in the industry, where the elements of industrial systems or production lines constantly send information to know the state of these different elements in real time. This kind of “machine to machine” network is part of the Internet of Things, and allows a wide variety of applications. These applications range from home automation (entirety of your appliances connected and, thus, “smart”) to industry processes (parts of aircraft engines continuously monitored), through “Smart Cities” (connected, automated infrastructure and resource management).

Between these two extremes, we can distinguish connections between humans and machines (H2M), like “wearable technologies”: Google Glass, connected watches, smart textiles, etc. This new family of technologies allows us to imagine a variety of applications (particularly in terms of comfort, health and performance measurement) worthy of the movie Minority Report or the TV show Black Mirror.

Proximity to end-user: Front-end to Back-end

Online social networks, wearable technologies, or the Internet of Things are at the Front-end of Big Data revolution. And data generated by those interfaces enable many applications.

There is a first type of usages of Big Data which aims at optimising the existing solutions. It leverages data about processes that are identified, but where most of the information is typically out of reach of standard IT systems (i.e. “known unknowns”). It offers new insights on the existing solutions and allows optimisation of those processes: H2H data allows better targeting in marketing, risk management, or improved fraud detection. H2M data may be used to improve performances or health. M2M data may be used to optimise a production process or monitor defaults. This intermediary stage is a Middle-end usage of data.

At a core level (Back-end), unsupervised learning enables machine learning techniques to use Big Data to identify new patterns and develop new solutions that we were not even aware could exist (i.e. “unknown unknowns”). It can lead to the most disruptive innovations: creating new products or targeting new markets.

Two dimensions: type of networks and proximity to end-user

This two-dimension framework allows us to first better identify what is new in Big Data, and most importantly what is not. Secondly, what are – or could be – its applications. We see that those new types of interfaces (Front-end) offer a new way to approach the world, and the data generated offers a wide range of applications: from the improvement of experiences or existing solutions (Middle-end) to the development of new services (Back-end.)

This article was originally published in French on Harvard Business Review France on the 20/05/2014. You can access it here.

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