Why is Big Data really big? - Nicolas Glady

Loading...

From the course by ESSEC Business School

Case studies in business analytics with ACCENTURE

оценки

ESSEC Business School

Case studies in business analytics with ACCENTURE

оценки

Course 3 of 4 in the Specialization Strategic Business Analytics

Who is this course for ? This course is RESTRICTED TO LEARNERS ENROLLED IN Strategic Business Analytics SPECIALIZATION as a preparation to the capstone project. During the first two MOOCs, we focused on specific techniques for specific applications. Instead, with this third MOOC, we provide you with different examples to open your mind to different applications from different industries and sectors. The objective is to give you an helicopter overview on what's happening in this field. You will see how the tools presented in the two previous courses of the Specialization are used in real life projects. We want to ignite your reflection process. Hence, you will best make use of the Accenture cases by watching first the MOOC and then investigate by yourself on the different concepts, industries, or challenges that are introduced during the videos. At the end of this course learners will be able to: - identify the possible applications of business analytics, - hence, reflect on the possible solutions and added-value applications that could be proposed for their capstone project. The cases will be presented by senior practitioners from Accenture with different backgrounds in term of industry, function, and country. Special attention will be paid to the "value case" of the issue raised to prepare you for the capstone project of the specialization. About Accenture Accenture is a leading global professional services company, providing a broad range of services and solutions in strategy, consulting, digital, technology and operations. Combining unmatched experience and specialized skills across more than 40 industries and all business functions—underpinned by the world’s largest delivery network—Accenture works at the intersection of business and technology to help clients improve their performance and create sustainable value for their stakeholders. With more than 358,000 people serving clients in more than 120 countries, Accenture drives innovation to improve the way the world works and lives. Visit us at www.accenture.com.

From the lesson

Introduction to case studies in business analytics with Accenture

In this introductory module, Fabrice Marque, Managing Director Customer Strategy Practice Lead for France, Belgium and the Netherlands, also in charge of the ESSEC-Accenture Strategic Business Analytics Chair, will first introduce the MOOC in general. Then Michael Svilar, Global Accenture Data Science Group Lead, will identify the general trends in this sector. In this module, we will cover three different real-life examples. First, Rohit Banerji, Accenture business lead responsible for big data analytics for the resource sector, will present an example from a water utilities company. Second, Cian O’Hare, Managing Director at Accenture Digital, will present a case study from a global communication provider. Finally, Christopher Gray, public service expert at Accenture, will discuss challenges arising in the public sector where Analytics and Big Data can provide effective solutions. At the end of each example there will be quiz questions. Note that those questions may require you to collect additional information from that which was delivered during the videos. Do not hesitate to consult additional books, websites and examples about this topic: some of the answers can actually be found directly thanks to open access research engines or online encyclopedias! The objective with this final MOOC in the Strategic Business Analytics specialization is to assess whether you now master the different concepts that are implemented within this field.

Introduction to Case Studies in Business Analytics with Accenture - Fabrice Marque2:37

Market trends and key challenges in Analytics - Mickael Svilar2:26

Why is Big Data really big? - Nicolas Glady5:50

Winning in Digital: Powered by Analytics - Jean-Pierre Bokobza7:00

Meet the Instructors

Nicolas Glady
Associate professor, at ESSEC Business School
Marketing Department

[MUSIC]

Hello, my name is Nicolas Glady.

I'm professor at the Essec Business School and you may have seen me in the MOOC,

Foundations of Strategic Business Analytics.

Business analytics applications have been around for many, many years.

As you have seen during the other modules, finding groups within data or

doing regressions are [INAUDIBLE], that are well recommended.

And from CRM to operation management, data has been used for

improving the performance of many businesses since at least the 90s.

So is big data really new?

Is the revolution some people claim it is?

I try to explain to you that there is something fundamentally new

in the phenomena we are observe in this video.

And that it's, therefore indeed, a revolution.

Let's take a very concrete example with CRM, customer relationship management,

in the context of a telecom service provider.

Like AT&T, for instance.

And let's imagine that you're interested in four individuals.

The presidential couple, President Obama and the First Lady, and two others.

Let's imagine Felix and his [INAUDIBLE].

These individuals use their phone and hence make calls or transactions.

With a telecom company, for a long time, these companies have been collecting data

about their customers and their transactions.

And so, one of the most classic ways to apportion CRM would be then to collect

a data set with three volumes.

Recency, frequency, and monetary.

One, recency means, how recent was the last transaction?

[INAUDIBLE], two frequency means, how often the individual makes a transaction.

And three monetary means,

what the average transaction value is in dollars, for instance.

It is the very well known RFM data stracture that

will allow marketers to perform RFM segmentation.

And then conduct some specific targeted market actions like course selling or

customer contention for instance.

Moreover, we can see that the data is created and collected and

centralized by the company in a well structured way.

In the years 2000, two phenomena appeared.

What was then called the web 2.0 and social networks.

With web 2.0, anyone could generate content and share it on the web.

The data generation process was decentralized.

It can be pictures, for instance, like the Four More Years picture

of the presidential couple that was taken after the reelection.

Or it can be YouTube videos from famous YouTubers like PewDiePie.

Even your auntie or your grandmother can have a cooking recipe blog nowadays.

And this explains the variety of the very famous 3Vs of big data.

because the source of data can now be anything and in any format.

This is explained by the fact that the data is user generated, and

not only company generated anymore.

This is typically unstructured data like pictures, videos, or

text because it doesn't, for instance, have a predefined format or

data model like an RFM database could.

After web 2.0, the second phenomenon is the central role of online

social networks in these dynamics.

A picture, video, or blog content could be exchanged over networks like Facebook,

Twitter, or Tumblr for instance.

And this explains the velocity, the information

is exchanged faster than before, and the volume of the 3Vs of big data.

The volume may even be explained mathematically.

If you're interested about storing information about an individual,

the quantity of data stored will be proportional to n.

But now if you're interested in the interactions of those individuals because

you know it's better to target customers like PewDiePie for instance, you will

generate data about the connections and not only the individuals anymore.

And if you have n notes in a network,

the number of possible connections is n times n minus one.

So it's similar to n to the square.

It's a quadratic relationship between the number of individuals in a network and

the data generated about their exchanges.

This [INAUDIBLE] calculation explains in part the volume of the 3Vsf.

Before we had data generated by companies that was structured, and

we could have thought to produce reports only once in a while.

Like every week, a month, about our customers.

Now, days of evolution happens.

The data is generated by the users themselves, and in real time.

And can be anything.

Text, pictures, or videos.

Strictly speaking, this is indeed a revolution.

UGC, user generated content,

places the individuals at the core of the data generating posts.

Hence at the center of a business analytics [INAUDIBLE].

Now, that doesn't mean that all of the applications you encounter will

be dealing with [INAUDIBLE].

As a matter of fact, in the last five years,

most business applications are still using classic types of structured data.

But this shift towards more customer centricity however, is a trend,

that is more and more present.

And when you're dealing with strategic business analytics,

using user generated data or not, you should always place the individuals,

customers, consumers, or even citizens are a the center of your analysis.

Ознакомьтесь с нашим каталогом

Присоединяйтесь бесплатно и получайте персонализированные рекомендации, обновления и предложения.

Coursera делает лучшее в мире образование доступным каждому, предлагая онлайн-курсы от ведущих университетов и организаций.

© Coursera Inc., 2018 Все права защищены.

Загрузить из App Store

Загрузить в Google Play