SMAC-Social Mobile Analytics and Cloud

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Analytics Tutorial

Welcome to the third lesson ‘Analytics’ of SMAC - Social, Mobile, Analytics, and Cloud tutorial which is part of the ‘Social, Mobile, Analytics, and Cloud course’ offered by Simplilearn. This lesson focuses on the ‘A’ of SMAC that stands for Analytics, also known as Big Data Analytics.

Let us look at the objectives of this lesson in the next section.

Objectives

After completing this lesson, you will be able to:

  • Define Big Data

  • Explain digital footprint

  • Describe Big Data Analytics

  • List the emerging trends in Analytics

  • Discuss the challenges and future of Analytics.

Let us discuss Big Data in the following sections.

Big Data

The term ‘Big Data’ is commonly used to describe the rapid growth and vast availability of datasets in the form of structured, semi-structured, and unstructured data.

Structured data

Structured data refers to the data that is represented in a tabular format. For example, MySQL databases.

Semi-structured data

Semi-structured data refers to the data that does not have a formal data model. For example, XML files.

Unstructured data

In unstructured data, there is no structured or predefined data model. The data is integrated by the processing systems as provided by the source and does not follow any predefined format. For example, Text files and web server logs.

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In the next section, we will discuss the characteristics of Big Data.

Characteristics of Big Data

Big Data has three characteristics: variety, volume, and velocity. Each of these characteristics overcome challenges of managing data by traditional data processing applications.

Variety

Variety indicates data received from different sources such as traditional databases, web server logs, social media, and so on.

Volume

Volume implies the large data size ranging from terabytes to zettabytes and more.

Velocity

Velocity indicates the huge volume of data arriving from different sources at high speed or in the fraction of a Nanosecond. A digital footprint is the source of Big Data, which will be explained in the next section.

Digital Footprint

A digital footprint is a term used to describe the main source of Big Data. It refers to the data trail or traces left by users in the digital environment. Digital footprints are captured from user interaction with the digital environment, in any form of information transmission such as:

  • Internet

  • Cellphones

  • Television

  • Registration forms, or any other.

Let us now learn about the two categories of digital footprints in the next section.

Categories of Digital Footprint

There are two categories of digital footprints, active and passive.

Active digital footprints

Active digital footprints are created when a user intentionally shares personal data to share information via websites or social media. For example, posting images or videos on social media sites, creating websites, and writing comments on other people's' social media pages.

Passive digital footprint

A passive digital footprint of an individual is created by others or gathered from the activities performed by an individual without the intention of sharing personal data.

For example, other’s comments on your social media page, visiting websites, and online shopping activities.

So far, we have discussed Big Data, let us now move on and learn about Big Data Analytics in the following sections.

Big Data Analytics

Big Data Analytics, in the context of business, refers to the collection and analysis of large datasets available to discover hidden insights, intelligence, and other useful business data.

Big Data Analytics offers several organizational benefits such as new business insights and market opportunities, access to huge databases, enhanced operational efficiency, and better customer experience.

Let us understand how different industries are using Analytics in the next section.

Analytics in Industries

Examples of how Big Data Analytics is used in some of the major industries are:

Banking & Finance

The Banking and Finance industry uses Analytics for customer retention and cross-selling. Detection of error and fraud is also made easy through Analytics.

Retail

The Retail industry uses Analytics for better supply chain and inventory management. Customer care facilities are also enhanced with the help of Analytics.

Information Technology

Analytics lead to the emergence of new technologies and enhanced software and program management in the Information Technology industry.

Healthcare

The Healthcare industry uses Analytics to provide E-healthcare facilities. Maintaining patients’ track records have become easier with the help of Analytics.

Let us look at some emerging trends in Analytics in the subsequent section.

Emerging Trends in Analytics

The focus of the industry has shifted from ‘what is’ Big Data to ‘what can be done with Big Data.’ Organizations are constantly researching and employing different Big Data solutions to gain a competitive edge.

Some of the emerging trends in Analytics are as follows:

Big data-as-a-service: Moving Big Data to Cloud platform leads to a new model called Big Data-as-a-service. In the Big Data environment, the data grows extensively and therefore needs specialized platforms to store and handle it, such as Hadoop Distributed File System (HDFS, MapReduce, and so on.

Predictive Analysis: It refers to the process of identifying meaningful patterns of Big Data to predict future events. By using the Big Data insights, organizations can predict the future, which helps in decision-making.

Real-time Analytics: With real-time analytics, organizations are able to identify any security violations and suspicious trades or transactions. Big Data-based security systems are being implemented in many organizations to secure their IT systems.

Big Data and Mobile: With the arrival of new technologies, Mobile platforms have started contributing to the Big Data space. Mobiles mainly serve two purposes: they are the source of data and also the mode of delivery.

Let’s find out the challenges faced by analytics users in the next section.

Analytics - Challenges

The challenges faced by Analytics users are as follows:

Customer behavior patterns

Identifying customer behavior patterns is one of the major challenges in Analytics as customer behavior data is mostly disjointed.

Optimized usage of Cloud-based offerings

Optimized usage of cloud-based offerings in terms of ease of data access and safety of the content shared is another challenge in Analytics. This is especially in cases where public and private clouds overlap.

Common data-driven pattern

The next challenge is building a data-driven pattern that becomes a compulsory need for any business. Since organizations vary in terms of size, products, services, location, and so on, it is a challenge for Analytics to build a common pattern that can be used universally.

Keeping up the pace with technology

Keeping up with the pace of fast-growing technology and tools is another challenge for Analytics.

In the subsequent section, we will discuss the future of Analytics.

Future of Analytics

Analytics is gaining momentum rapidly and organizations have started to use Big Data for their business. Some of the predictions for Analytics are as follows:

  • Technologies that can overcome Big Data challenges will become predominant and universal.

  • Insights from Big Data will be widely used in the decision-making process.

  • The database systems, and data processing and reporting tools will be integrated into one package.

  • The concept of numbers-as-output, that is analytical results in the form of numbers and percentages, will fade slowly and these numbers will be visualized and presented in the form of images, insights, and action plans.

In the next section, let us find out how Analytics is used in the real-world situation with the help of a case study.

Case Study

Let us look at a scenario below.

Scenario

One of the fast-growing online travel agencies wanted an efficient tool to analyze the bookings and inventory data to improve their system of many buses operating services in more than 12,000 routes.

How Analytics helped

The company decided to adopt Google’s BigQuery, which is a web service that enables interactive analysis of extensively large datasets.

According to the BigQuery analyses, customers searched for seats in the busses but could either not find any seats or very fewer seats were available. This resulted in the company adding more seats to their busses.

Outcomes

As a result, the travel agency:

  • Analyzed datasets as huge as 2 terabytes, in less than 30 seconds

  • Spent 80% less than they would have on other infrastructure

  • Gained stronghold on the bookings and inventory data

  • Strengthened the company by enhancing customer service and trade quality In the next section, we will understand the job market scenario and salary trends in the field of Analytics.

Analytics - Jobs and Salary

According to Data Jobs, the average salary of an individual with Big Data Analytics skill ranges from $70,000 to $115,000, which further increases with experience. The breakdown of Analytics-related job titles is illustrated in the section.

‘Developers’ is the most popular job with 42% demand. The demand pattern for other Analytics-related jobs in terms of percentage are also shown.

Let us proceed to the next section and get familiar with the learning path in the Analytics field.

Analytics - Learning Path

The learning path in the field of Analytics is given below.

The Foundation level

The Foundation level comprises the non-certification courses:

  • Business Analytics Foundation – R Language

  • Business Analytics Foundation – SAS

The Advanced level

The Advanced level courses comprise two non-certification courses:

  • Big Data and Hadoop Developer

  • Big Data and Hadoop Administrator.

The Expert level

The Expert level includes the following certification courses:

  • SAS Certified Clinical Trials Programmer

  • SAS Certified Predictive Modeler using SAS Enterprise Miner 7

  • SAS Certified Statistical Business Analyst: Regression and Modeling

  • SAS Certified BI Content Developer

  • SAS Certified Data Integration Developer

  • SAS Certified Platform Administrator.

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Summary

Let us summarize what we have learned in this lesson.

  • Big Data is a common term used to describe the rapid growth and vast availability of data.

  • Digital footprint refers to the data trail or traces left by users in the digital environment.

  • Big Data Analytics refers to the collection and analysis of any large dataset available to discover hidden insights, intelligence, and other useful business data.

  • Big Data-as-a-Service, Predictive Analysis, Real-time Analytics, and Big Data and Mobile are the emerging trends in Analytics.

Conclusion

This concludes the lesson on Analytics. In the next lesson, we will focus on Cloud Computing.

  • Disclaimer
  • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.

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