Big Data Analytics, commonly called Big Data, is the inductive process of examining data sets to uncover information such as
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
This means that Big Data allows, for instance, making predictions about sales using both revenues data and users’ activity on social networks.
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. The way for determining such a function is using Supervised/Semi-Supervised/Unsupervised Learning so that the machine can forecast the outcome.
- Classification Problems: this could be applied whenever the output of a subset of inputs is a discrete function. The way for determining such a function is using Supervised/Semi-Supervised/Unsupervised Learning so that the machine can classify the inputs (i.e. 1/0, car/human/bicycle).
- Clustering Problems: this could be applied whenever the output of a subset of inputs is not predictable/known. The way for determining such a function is using Supervised/Semi-Supervised/Unsupervised Learning so that the machine can cluster the outcome.
The 3 problems are solved by using sequences of algorithms and every algorithm is called layer.
The subset of layers needed for performing a task is called Neural Network.
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.
STEP1. DATA COLLECTION
The sources for Big Data Analytics can be several but the most important are:
- Corporate Datasets
- Databases (i.e. Sales, Marketing, Finance, Planning Databases)
- External Datasets
- Sensors (i.e. the ones present/installed on a machine)
- Social Networks
- Documents (i.e. a pdf or an Office file)
- Web Data
The acquisition is made through the part on Neural Networks that perform Data Collection and Data Cleaning. When qualitative/non-structured data are collected, Vision Systems are often involved.
The output of the Data Collection Step is called Raw Data.
STEP 2. DATA ANALYSIS
The input for this step is Raw Data collected in the previous stage.
Raw Data is then processed by Neural Networks that perform Patterns Identification and Prediction Making.
The analysis that can be performed are:
- Predictive Analytics
- Clustering Analysis
Predictive Analytics elaborates Raw Data to make predictions about the future through Regression or Classification algorithms.
Clustering Analysis elaborates Raw Data to group a set of objects in such a way that the ones in the same group are more similar.
The output of Big Data is a Front-end that often performs real-time reporting through
- Visualization Tools
- Online Business Apps
Now that you learned everything about Artificial Intelligence, would you like to go through some real cases?
If the answer is yes, follow this link!
Business Strategy | Product Marketing | Executive Master eCommerce Management | Business Innovation Master | MSc
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