Big Data Analytics

Big Data Analytics

Big data analytics examines large and different types of data to uncover hidden patterns, correlations, and other insights. Basically, Big Data Analytics is largely used by companies to facilitate their growth and development. This majorly involves applying various data mining algorithms on the given set of data, which will then aid them in better decision making.

Stages in Big Data Analytics

These are the following stages involved in the Big Data Analytics process:

Big Data Analytics: There are four general categories of analytics that are distinguished by the results they produce:

1. Descriptive Analytics        .......... hindsight

2. Diagnostic Analytics         ..........  insight

3. Predictive Analytics          ..........  insight

4. Prescriptive Analytics      ..........  foresight

Note:

Online Analytical Processing (OLAP) –
Online Analytical Processing consists of a type of software tools that are used for data analysis for business decisions. OLAP provides an environment to get insights from the database retrieved from multiple database systems at one time.

Examples – Any type of Data warehouse system is an OLAP system. Uses of OLAP are as follows:

• Spotify analyzed songs by users to come up with the personalized homepage of their songs and playlist.

• Netflix movie recommendation system.

Online Transaction Processing (OLTP) –
Online transaction processing provides transaction-oriented applications in a 3-tier architecture. OLTP administers day to day transaction of an organization.

Examples – Uses of OLTP are as follows:

• ATM center is an OLTP application.

• OLTP handles the ACID properties during data transaction via the application.

• It’s also used for Online banking, Online airline ticket booking, sending a text message, add a book to the shopping cart.

1. Descriptive Analytics :

Descriptive analytics is carried out to answer questions about events that have already occurred. This form of analytics contextualizes data to generate information.

The reports are generally static in nature and display historical data that is presented in the form of data grids or charts. Queries are executed on operational data stores from within an enterprise, 

For example, Online Transaction Processing (OLTP) , a Customer Relationship Management system (CRM) , Enterprise Resource Planning (ERP) system.

Sample questions can include:

• What was the sales volume over the past 12 months?

•     What is the number of support calls received as categorized by severity and geographic location?

• What is the monthly commission earned by each sales agent?

It is estimated that 80% of generated analytics results are descriptive in nature.

2. Diagnostic Analytics :

Diagnostic Analytics aims to determine the cause of a phenomenon that occurred in the past using questions that focus on the reason behind the event. 

The goal of this type of analytics is to determine what information is related to the phenomenon in order to enable answering questions that seek to determine why something has occurred.

Diagnostic analytics usually requires collecting data from multiple sources and storing it in a structure that lends itself to performing drill-down and roll-up analysis.

Diagnostic analytics results are viewed via interactive visualization tools that enable users to identify trends and patterns. The executed queries are more complex compared to those of descriptive analytics and are performed on multidimensional data held in analytic processing systems.

Such questions include:

• Why were Q2 sales less than Q1 sales?

• Why have there been more support calls originating from the Eastern region than from the Western region?

• Why was there an increase in patient readmission rates over the past three months?

3. Predictive Analytics :

Predictive analytics are carried out in an attempt to determine the outcome of an event that might occur in the future. With predictive analytics, information is enhanced to generate knowledge that conveys how that information is related.

It is important to understand that the models used for predictive analytics have implicit dependencies on the conditions under which the past events occurred. If these underlying conditions change, then the models that make predictions need to be updated.

Predictive analytics try to predict the outcomes of events, and predictions are made based on patterns, trends, and exceptions found in historical and current data. This can lead to the identification of both risks and opportunities. 

This kind of analytics involves the use of large datasets comprised of internal and external data and various data analysis techniques.

Questions are usually formulated such as :

• What are the chances that a customer will default on a loan if they have missed a monthly payment?

• What will be the patient survival rate if Drug B is administered instead of Drug A?

• If a customer has purchased Products A and B, what are the chances that they will also purchase Product C?

4. Prescriptive Analytics :

Prescriptive analytics builds upon the results of predictive analytics by prescribing actions that should be taken. 

Prescriptive analytics provide more value than any other type of analytics and correspondingly require the most advanced skillset, as well as specialized software and tools.

Internal data might include current and historical sales data, customer information, product data, and business rules. 

External data may include social media data, weather forecasts and government-produced demographic data. 

Prescriptive analytics involves the use of business rules and large amounts of internal and external data to simulate outcomes and prescribe the best course of action.

Sample questions may include:

• Among three drugs, which one provides the best results?

• When is the best time to trade a particular stock?

Business Intelligence (BI) :

BI enables an organization to gain insight into the performance of an enterprise by analyzing data generated by its business processes and information systems.

BI applies analytics to large amounts of data across the enterprise, which has typically been consolidated into an enterprise data warehouse to run analytical queries.

The output of BI can be surfaced to a dashboard that allows managers to access and analyze the results and potentially refine the analytic queries to further explore the data.

Tools used in Big Data Analytics