Big Data Analytics

Big Data Analytics

6.2 Big Data Analytics

Big data analytics is the science of examining or analyzing large data sets with a variety of data types, that is, structured, semi-structured, or unstructured data, which may be streaming or batch data. Big data analytics allows to make better decisions, find new business opportunities, compete against business rivals, improve performance and efficiency, and reduce cost by using advanced data ana- lytics techniques.

Big data, the data-intensive technology, is the booming technology in science and business. Big data plays a crucial role in every facet of human activities empowered by the technological revolution.

Big data technology assists in:

● Tracking the link clicked on a website by the consumer (which is being tracked by many online retailers to perceive the interests of consumers to take their business enterprises to a different altitude);

● Monitoring the activities of a patient;

● Providing enhanced insight; and

● Process control and business solutions to large enterprises manifesting its ubiq- uitous nature.

Big data technologies are targeted in processing high-volume, high-variety, and high-velocity data sets to extricate the required data value. The role of researchers

in the current scenario is to perceive the essential attributes of big data, the feasi- bility of technological development with big data, and spot out the security and privacy issues with big data. Based on a comprehensive understanding of big data, researchers propose the big data architecture and present the solutions to existing issues and challenges.

The advancement in the emerging big data technology is tightly coupled with the data revolution in social media, which urged the evolution of analytical tools with high performance and scalability and global infrastructure.

Big data analytics is focused on extracting meaningful information using effi- cient algorithms on the captured data to process, analyze, and visualize the data.

This comprises framing the effective algorithm and efficient system to integrate data, analyzing the knowledge thus produced to make business solutions. For instance, in online retailing analyzing the enormous data generated from online transactions is the key to enhance the perception of the merchants into customer behavior and purchasing patterns to make business decisions. Similarly in Facebook pages advertisements appear by analyzing Facebook posts, pictures, and so forth. When using credit cards the credit card providers use a fraud detec- tion check to confirm that the transaction is legitimate. Customers credit scoring is analyzed by financial institutions to predict whether the applicant will default on a loan. To summarize, the impact and importance of analytics have reached a great height with more data being collected. Analytics will still continue to grow until there is a strategic impact in perceiving the hidden knowledge from the data.

The applications of analytics in various sectors involve:

● Marketing (response modeling, retention modeling);

● Risk management (credit risk, operational risk, fraud detection);

● Government sector (money laundering, terrorism detection);

● Web (social media analytics) and more.

Figure 6.1 shows the types of analytics. The four types of analytics are:

1) Descriptive Analytics—Insight into the past;

2) Diagnostic Analytics—Understanding what is happening and why did it happen;

3) Predictive Analytics—Understanding the future; and 4) Prescriptive Analytics—Advice on possible outcomes.

6.2.1  Descriptive Analytics

Descriptive analytics describe, summarize, and visualize massive amounts of raw data into a form that is interpretable by end users. It describes the events that occurred at any point in past and provides insight into what actually has hap- pened in the past. In descriptive analysis, past data are mined to understand the

reason behind the failure or success. It allows users to learn from past perfor- mance or behavior and interpret how they could influence future outcomes. Any kind of historical data can be analyzed to predict future outcome; for example, past usage of electricity can be analyzed to generate power and set the optimal charge per unit for electricity. Also they can be used to categorize consumers based on their purchasing behavior and product preferences. Descriptive analysis finds its application in sales, marketing, finance, and more.

6.2.2  Diagnostic Analytics

Diagnostic analytics is a form of analytics that enables the users to understand what is happening and why did it happen so that a corrective action can be taken if something went wrong. It benefits the decision-makers of the organizations by giving them actionable insights. It is a type of root-cause analysis, investigative, and detective, which determines the factors that contributed to a certain outcome.

Diagnostic analytics is performed using data mining and drill down techniques.

The analysis is used to analyze social media, web data, or click-stream data to find a hidden pattern and consumer data. It provides insights into the behavior of prof- itable as well as non-profitable customers.

Past

ANALYTICS

Future

Descriptive

Diagnostic

Predictive

Prescriptive

Analysis of past data to understand what has happened

Analysis of past data to understand why it happened

Provides a likely scenario of what might happen

Provides recommention/suggestion on what should be done

Figure 6.1  Data analytics.

6.2.3  Predictive Analytics

Predictive analytics provides valuable and actionable insights to companies based on the data by predicting what might happen in the future. It analyzes the data to determine possible future outcomes. Predictive analytics uses many statistical techniques such as machine learning, modeling, artificial intelligence, and data mining to make predictions. It exploits patterns from historical data to determine risks and opportunities. When applied successfully, predictive analytics allows the business to efficiently interpret big data and derive business value from IT assets.

Predictive analytics is applied in health care, customer relationship management, cross-selling, fraud detection, and risk management. For example, it is used to optimize customer relationship management by analyzing customer data and thereby predicting customer behavior. Also, in an organization that offers multiple products to consumers, predictive analytics is used to analyze customer interest, spending patterns, and other behavior through which the organization can effectively cross-sell their products or sell more products to current customers.

6.2.4  Prescriptive Analytics

Prescriptive analytics provides decision support to benefit from the outcome of the analysis. Thus, prescriptive analytics goes beyond just analyzing the data and predicting future outcomes by providing suggestions to extract the benefits and take advantage of the predictions. It provides the organizations with the best option when dealing with a business situation by optimizing the process of deci- sion-making in choosing between the options that are available. It optimizes busi- ness outcomes by combining mathematical models, machine learning algorithms, and historical data. It anticipates what will happen in the future, when will it happen, and why it will happen. Prescriptive analytics are implemented using two primary approaches, namely, simulation and optimization. Predictive analytics as well as prescriptive analytics provide proactive optimization of the best action for the future based on the analysis of a variety of past scenarios. The actual differ- ence lies in the fact that predictive analytics helps the users to model future events, whereas prescriptive analytics guide users on how different actions will affect business and suggest them the optimal choice. Prescriptive analytics finds its applications in pricing, production planning, marketing, financial planning, and supply chain optimization. For example, airline pricing systems use prescriptive analytics to analyze purchase timing, demand level, and other travel factors to present the customers with a pricing list to optimize profit but not losing the cus- tomers and deter sales.

Figure 6.2 shows data analytics where customer behavior is analyzed using the four techniques of analysis. Initially with descriptive analytics customer behavior

is analyzed with past data. Diagnostic analytics is used to analyze and understand customer behavior while predictive analytics is used to predict customer future behavior, and prescriptive analytics is used to influence this future behavior.