Big Data Analytics, commonly called Big Data, is the inductive process of examining data sets to uncover information such as patterns, correlations, trends and preferences to make informed business decisions and forecasts.
The data sets used for Big Data are composed by both:
- quantitative data
- qualitative data
The most important thing is that Big Data works with both
- structured data
- non-structured data
This means that, differently from the classic Business Intelligence, Big Data can upload and examine databases made by both
as well as pulling data from
- corporate datasets
- external datasets
What makes this possible is the use of Artificial Intelligence and, in particular, of Deep Learning.
Nowadays, there are 3 main problems that can be solved with AI
- Regression Problems: this could be applied whenever the output of a subset of inputs is a continuous function
- Classification Problems: this could be applied whenever the output of a subset of inputs is a discrete function
- Clustering Problems: this could be applied whenever the output of a subset of inputs is not predictable/known
The 3 problems are solved by using sequences of algorithms called Neural Networks.
Usually, every Neural Network performs 4 activities:
- Data Collection
- Data Cleaning
- Patterns Identification
- Prediction Making
To better understand how Big Data works let’s walk through a process step by step.
One of the most interesting applications of Big Data and AI can be found at Facebook.
Facebook is a social media and social networking service company based in Menlo Park, California.
It was launched for Harvard students in February 2014 by Mark Zuckerberg, Eduardo Saverin, Andrew McCollum, Dustin Moskovitz and Chris Hughes.
During the years, the website opened to other universities and high schools such as other Ivy League schools, MIT, and higher education institutions in the Boston area and in September 2006 it became accessible to anyone who claimed to be at least 13 years old with a valid email.
Facebook grew through the years becoming in 2017 a company with
- More than 2 billion users
- More than US$ 40 billion of Revenues
- More than US$ 15 billion Net Income
- Subsidiaries as Instagram and WhatsApp
Most of the Revenues come from the advertisements that are managed in a dedicated platform called Facebook Ads.
Facebook’s target is to satisfy the advertisers by maximizing the number of clicks on each advertisement (the so-called Click Through Rate or CTR).
For reaching this objective, the company extensively uses Big Data Analytics and Artificial Intelligence.
Having more than 2 billion users, Facebook possesses trillions of digital traces that each person leaves every time an action is performed.
These are hints such as what a user:
- Comments/Interacts with
Moreover, given a user’s past behaviour, there are insights into what he/she may like or purchase.
Facebook’s idea was to teach a machine how to cluster the 2 billion users by taking into account all these data.
In addition to this, the company created a cookie called Facebook Pixel that can be easily installed on every website in order to track also the actions performed by the users when going outside Facebook itself.
This way, if an advertiser is looking for someone similar to who was tracked by the Pixel while buying a product on its website, Facebook Ads is able to provide with a so-called Audience of similar users.
In order to do that, Facebook goes through:
- Step 1. Data Collection:
- from Corporate Datasets stored from Facebook, Instagram and WhatsApp
- from the Pixel installed in the advertiser’s websites
The output of this phase is the Raw Data on which Clusters and Audiences will be calculated
- Step 2. Data Analysis:
- Using a Clustering Analysis, Corporate Datasets are grouped in sets of related objects in such a way that it is possible to explain the behaviour of a group of similar users
- Using Predictive Analytics, clustered Corporate Datasets and Pixel Data are matched by solving a Classification Problem
The output of this phase is an Audience that can be used in order to optimize the budget of the advertiser
Facebook’s usage of Big Data Analytics and AI
- Improved the CTR
- Increased the number of advertisers willing to use Facebook Ads
This led to a Compound Annual Growth Rate (CAGR) of its Revenues of around 50% from 2011 to 2017.
Business Strategy | Product Marketing | Executive Master eCommerce Management | Business Innovation Master | MSc
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