What is Big Data Analytics? Types, Importance, Applications, Models

What is Big Data Analytics?

The act of gathering, organising, and analysing massive data sets in order to identify distinct patterns and other important information is known as big data analytics.

Big data analytics is a combination of technologies and approaches that necessitate new forms of integration in order to reveal big hidden values from vast datasets that are different from the norm, more complicated, and on a massive scale. It mostly focuses on tackling new or existing issues in more efficient and effective ways.

Types of Big Data Analytics

There are four types of big data analytics, which are as follows:

Descriptive analysis

It can be defined as condensing the existing data to get a better understanding of what is going on using business intelligence tools. This helps to get an idea about what happened in the past and if it was as expected or not.

For example, a coffee shop may learn how many customers they served in the time duration of 9 a.m. to 11 a.m. and which coffee was ordered the most. So, this analysis answers questions like “What happened?”, but is not capable to answer more deep questions like “Why it happened?”. Due to this reason, companies which are highly data-driven don’t rely just on descriptive analysis, but rather combine it with other analyses to get detailed results.

Diagnostic analysis

With the availability of historical data, diagnostic analysis can be used to find the answer to the question “Why it happened?”. Diagnostic analysis provides a way to dig deeper by drilling down and find out patterns and dependencies. The result of this analysis is often a predefined report structure, such as RCA (Root Cause Analysis) report.

For example, if the coffee shop owner experiences a heavy rush on someday and finds he was unable to provide quality service, the diagnostic report can help him find out why it went wrong. Attribute importance, principle components analysis, sensitivity analysis, and conjoint analysis are some techniques that use diagnostic analysis. The diagnostic analysis also includes training algorithms for classification and regression.

Predictive Analysis

Predictive analysis can be defined as the process of focusing on predicting the possible outcome using machine–learning techniques like SVM, random forests and statistical models. It tries to forecast on the basis of previous data and scenarios. So, this is used to find answers to questions like “What is likely to happen?”.

For example, a hotel chain owner might ramp down promotional offers during a restive season of rains in a coastal area. This is based on the predictions that there are going to be fewer footfalls due to heavy rain.

However, it must not be understood that this analysis can predict whether an event will occur in future or not. It merely is able to predict the probability that an event will occur. If predictive analysis model is tuned properly based on historical data, it can be used to support complex predictions in marketing and sales.

Prescriptive Analytics

Prescriptive analytics is a method of analysing data and making immediate recommendations on how to improve company processes to meet a variety of expected results. Prescriptive analytics, in essence, takes “what we know” (data), analyses it thoroughly to anticipate what could happen, and then recommends the best next moves based on educated simulations.

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Need and Importance of Big Data Analytics

According to Atul Butte, Stanford, “Hiding within those mounds of data is the knowledge that could change the life of a patient, or change the world.” So, the real power of Big Data lies in its analysis.

Processing, studying and implementing the conclusions derived from the analysis of Big Data help you to collect accurate data, take timely and more informed strategic decisions, target the right set of audience and customers, increase benefits, and reduce wastage and costs. The right analysis of the available data can improve major business processes in various ways.

For example, in a manufacturing unit, data analytics can improve the functioning of the following processes:

• Procurement: To find out which suppliers are more efficient and cost-effective in delivering products on time
  
  
• Product development: To draw insights on innovative product and service formats and designs for enhancing the development process and coming up with demanded products
  
  
• Manufacturing: To identify machinery and process variations that may be indicators of quality problems
  
  
• Distribution: To enhance supply chain activities and standardise optimal inventory levels vis-à-vis various external factors such as weather, holidays, economy, etc.
  
  
• Marketing: To identify which marketing campaigns will be the most effective in driving and engaging customers and understanding customer behaviors and channel behaviours
  
  
• Price management: To optimise prices based on the analysis of external factors
  
  
• Merchandising: To improve merchandise breakdown on the basis of current buying patterns and increase inventory levels and product interest insights on the basis of the basis of the analysis of various customer behaviors
  
  
• Sales: To optimise assignment of sales resources and accounts, product mix, and other operations
  
  
• Store operations: To adjust inventory levels on the basis of predicted buying patterns, study of demographics, weather, key events, and other factors
  
  
• Human resources: To find out the characteristics and behaviors of successful and effective employees, as well as other employee insights for managing talent better

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Application of Big Data analytics in Industries

Every business and industry today is affected by and benefitted from Big Data analytics in multiple ways. A closer look at some specific industries will help you to understand the application of Big Data in these sectors:

Transportation

Big Data has greatly improved transportation services. The data containing traffic information is analysed to identify traffic jam areas. Suitable steps can then be taken, on the basis of this analysis, to keep the traffic moving in such areas. Distributed sensors are installed in handheld devices, on the roads and on vehicles to provide real-time traffic information. This information is analysed and disseminated to commuters and also to the traffic control authority.

Education

Big Data has transformed the modern day education processes through innovative approaches, such as e-learning for teachers to analyse the students’ ability to comprehend and thus impart education effectively in accordance with each student’s needs.

The analysis is done by studying the responses to questions, recording the time consumed in attempting those questions, and analysing other behavioral signals of the students. Big Data also assists in analysing the requirements and finding easy and innovative ways of imparting education, especially distance learning over vast geographical areas.

Travel

The travel industry also uses Big Data to conduct business. It maintains complete details of all the customer records that are then analysed to determine certain behavioural patterns in customers. For example, in the airline industry, Big Data is analysed for identifying personal preferences or spotting which passengers like to have window seats for short-haul flights and aisle seats for long-haul flights. This helps airlines to offer the similar seats to customers when they make a fresh booking with the airways.

Some airlines also apply analytics to pricing, inventory, and advertising for improving customer experiences, leading to more customer satisfaction, and hence, more business. Some airlines even go to the length of evaluating customers who tend to miss their flights. They try to help such customers by delaying the flights or booking them on another flight.

Government

Big Data has come to play an important role in almost all the undertaking and processes of government. For instance, Indian government body, UIDAI was able to successfully implement Aadhar card using big data technologies that includes millions of citizen registration by performing trillions of data matches every day.

Analysis of Big Data promotes clarity and transparency in various government processes and helps in:

• Taking timely and informed decisions about various issues
• Identifying flaws and loopholes in processes and taking preventive or corrective measures on time
• Assessing the areas of improvement in various sectors such as education, health, defense, and research
• Using budgets more judiciously and reducing unnecessary wastage and costs
• Preventing fraudulent practices in various sectors

Healthcare

In healthcare, the pharmacy and medical device companies use Big Data to improve their research and development practices, while health insurance companies use it to determine patient-specific treatment therapy modes that promise the best results.

Big Data also helps researchers to work towards eliminating healthcare-related challenges before they become real problems. Big Data helps doctors to analyse the requirement and medical history of every patient and provide individualistic services to them, depending on their medical condition.

Telecom

The mobile revolution and the Internet usage on mobile phones have led to a tremendous increase in the amount of data generated in the telecom sector. Managing this huge pool of data has almost become a challenge for the telecom industry.

For example, in Europe, there is a compulsion on the telecom companies to keep data of their customers for at least six months and maximum up to two years. Now, all this collection, storage, and maintenance of data would just be a waste of time and resources unless we could derive any significant benefits from this data.

Big Data analytics allows telecom industries to utilise this data for extracting meaningful information that could be used to gain crucial business insights that help industries in enhancing their performance, improving customer services, maintaining their hold on the market, and generating more business opportunities.

Consumer goods industry

Consumer goods companies generate huge volumes of data in varied formats from different sources, such as transactions, billing details, feedback forms, etc. This data needs to be organised and analysed in a systemic manner in order to derive any meaningful information from it.

For example, the data generated from the Point-of-Sale (POS) systems provides significant real-time information about customers’ preferences, current market trends, the increase and decrease in demand of different products at different regions, etc. This information helps organisations to predict any possible fluctuations in prices of goods and make purchases accordingly.

It also helps marketing teams in taking suitable actions rapidly if there is a deviation in the expected sales of a product, thus, preventing any further losses to the company. Therefore, we can say that Big Data analytics allows organisations to gain better business insights and take informed and timely decisions.

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Business Analytics Models

Business Analytics (BA) frequently utilises numerous quantitative tools to convert Data into meaningful information for making informed business decisions. These tools can be further categorised into tools for data mining, operations research, statistics and simulation. Statistics for instance, can be helpful in gathering, articulating and understanding Big Data as part of the descriptive analytical model.

A Business Analytics model assists organisations in making a move which yields fruitful results. Here, we will discuss the two most commonly used analytical models by analysts across the globe as standard analysis factors – SWOT and PESTEL analysis.

SWOT Analysis Model

SWOT stands for Strengths, Weaknesses, Opportunities, and Threats. As evident from the abbreviation, an organisation uses SWOT analysis to figure out its greatest extremes – strengths to help it stand even in the toughest of times, weaknesses that may lead to its failure, opportunities that may help in realising its full potential and finally the threats to the businesses that may end up exploiting its weaknesses and may turn its strengths into weakness.

Table shows the SWOT diagram:

	Strengths	Weaknesses
Opportunities	What new opportunities, our forces allow us to seize? What can we can do better? How we can stand apart from our competitors? Had there been any changes in the market recently? How can these benefit? What are the major external trends that will influence the future market?	forces so that we seize new opportunities? What does our business lack to compete with our strongest competitor? Are our resources limited? What are the hidden resources of our company whose potential we don’t see yet? What changes in values, behaviours and speech should we initiate?
Threats	How can we exploit our forces to turn threats into opportunities? Which are the markets to reinvent and those to create from A to Z? Can we create a new market instead of competing? Who are the “non-buyers “? How could they become new customers?	Do these threats can turn our weaknesses into forces? How our weaknesses can turn the threats into opportunities? Which factors should we control to prevent these threats in the future? Is there anything that deteriorates our revenues or profits? And how can we change this?

Businesses that have been in market for long should conduct SWOT analysis periodically to evaluate the impact of the changing situations in the market, getting around the newer business models and respond actively.

On the other hand, new starters should include SWOT as their planning process. SWOT is not necessarily a pan-organisation process; rather each of the organisation’s departments can have their own dedicated SWOT, such as Marketing SWOT, Operational SWOT, Sales SWOT, etc.

Consider an example of the implementation of SWOT analysis in the organisation, Apple Inc. Apple was incorporated in 1995 after a long battle with the existing stakeholders who had control over the shares and stocks. Post return to the computing market, facing a mighty challenger in Microsoft, Apple did not take them head-on as most would have expected.

Rather, it realised the opportunities and laid back on the threats part since they had ‘nothing to lose’. Apple identified opportunities in newer areas of the technology, while the world was considering computers as the lone IT revolution torch-bearer.

PESTEL Model

PESTEL stands for Political, Economic, Social, Technological, Legal and Environmental. PESTEL analysis is a method for figuring out external impacts on a business. In some countries, legal and environmental parts are combined in the social, legal, political and economic part.

Hence, they use PEST. The sample PESTEL analysis is shown in below table:

P	E	S	T	E	L
Government Policy Political stability Corruption Foreign trade policy Tax policy Labour law Trade restrictions	Economic growth Exchange rates Interest rates Inflation rates Disposable income Unemployment rates	Population growth rate Age distribution Career attitudes Safety emphasis Health consciousness Lifestyle attitudes Cultural barriers	Technology incentives Level of innovation Automation R&D activity Technological change Technologicaawareness	Weather Climate Environmenatal policies Climate change Pressures from NGO’s	Discrimination laws Antitrust laws Employment laws Consumer protection laws Copyright and patent laws Health and safety laws

The benefits of PESTEL analysis are as follows:

• Political factors: These are government regulations in different countries related to employment, tax, environment, trade and government stability.
  
  
• Economic factors: These factors affect the purchasing power and cost of capital of a corporation, such as economic growth, inflation, currency exchange and interest rates.
  
  
• Social factors: These influence the consumer’s requirements and the possible market size for an organisation’s products and services. These factors include age demographics, population growth and healthcare.
  
  
• Technological factors: These influence the barricades to entry, investment decisions related to buying and innovation, such as investment incentives, automation and the adaptability quotient for the technology.
  
  
• Environmental factors: These influence mainly the marketers with respect to various environmental factors and policies of a specific country.
  
  
• Legal factors: These influence the business decisions of an organisation with respect to various legal factors such as discrimination laws, antitrust laws, employment laws, consumer protection laws etc. of a specific country.

Also, it is a point worth noticing that the six components of the PESTEL model vary in meaning on the basis of business type. For example, social factors are more important to a consumer-oriented business at the customer’s side of the supply chain. On the other hand, political factors play their role more for an aerospace manufacturer or a defence contracting firm.