are medical decision supporting, administrator decision support, personal health management, and public epidemic alert.
Big data gathered from heterogeneous sources are utilized to analyze the data and find patterns which can be the solution to cure the ailment and prevent its occurrence in the future.
1.11.2 Telecom
Big data promotes growth and increases profitability across telecom by optimizing the quality of service. It analyzes the network traffic, analyzes the call data in real‐time to detect any fraudulent behavior, allows call center representatives to modify subscribers plan immediately on request, utilizes the insight gained by analyzing the customer behavior and usage to evolve new plans and services to increase profitability, that is, provide personalized service based on consumer interest.
Telecom operators could analyze the customer preferences and behaviors to enable the recommendation engine to match plans to their price preferences and offer better add‐ons. Operators lower the costs to retain the existing customers and identify cross‐selling opportunities to improve or maintain the average revenue per customer and reduce churn. Big data analytics can further be used to improve the customer care services. Automated procedures can be imposed based on the understanding of customers’ repetitive calls to solve specific issues to provide faster resolution. Delivering better customer service compared to its competitors can be a key strategy in attracting customers to their brand. Big data technology optimizes business strategy by setting new business models and higher business targets. Analyzing the sales history of products and services that previously existed allows the operators to predict the outcome or revenue of new services or products to be launched.
Network performance, the operator’s major concern, can be improved with big data analytics by identifying the underlying issue and performing real‐time troubleshooting to fix the issue. Marketing and sales, the major domain of telecom, utilize big data technology to analyze and improve the marketing strategy and increase the sales to increase revenue.
1.11.3 Financial Services
Financial services utilize big data technology in credit risk, wealth management, banking, and foreign exchange to name a few. Risk management is of high priority for a finance organization, and big data is used to manage various types of risks associated with the financial sector. Some of the risks involved in financial organizations are liquidity risk, operational risk, interest rate risk, the impact of natural calamities, the risk of losing valuable customers due to existing competition, and uncertain financial markets. Big data technologies derive solutions in real time resulting in better risk management.
Issuing loans to organizations and individuals is the major sector of business for a financial institution. Issuing loans is primarily done on the basis of creditworthiness of an organization or individual. Big data technology is now being used to find the credit worthiness based on latest business deals of an organization, partnership organizations, and new products that are to be launched. In the case of individuals, the credit worthiness is determined based on their social activity, their interest, and purchasing behavior.
Financial institutions are exposed to fraudulent activities by consumers, which cause heavy losses. Predictive analytics tools of big data are used to identify new patterns of fraud and prevent them. Data from multiples sources such as shopping patterns and previous transactions are correlated to detect and prevent credit card fraud by utilizing in‐memory technology to analyze terabytes of streaming data to detect fraud in real time.
Big data solutions are used in financial institutions call center operations to predict and resolve customer issues before they affect the customer; also, the customers can resolve the issues via self‐service giving them more control. This is to go beyond customer expectations and provide better financial services. Investment guidance is also provided to consumers where wealth management advisors are used to help out consumers for making investments. Now with big data solutions these advisors are armed with insights from the data gathered from multiple sources.
Customer retention is becoming important in the competitive markets, where financial institutions might cut down the rate of interest or offer better products to attract customers. Big data solutions assist the financial institutions to retain the customers by monitoring the customer activity and identify loss of interest in financial institutions personalized offers or if customers liked any of the competitors’ products on social media.
Chapter 1 Refresher
1 Big Data is _________.StructuredSemi‐structuredUnstructuredAll of the aboveAnswer:dExplanation: Big Data is a blanket term for the data that are too large in size, complex in nature, and which may be structured, unstructured, or semi‐structured and arriving at high velocity as well.
2 The hardware used in big data is _________.High‐performance PCsLow‐cost commodity hardwareDumb terminalNone of the aboveAnswer:bExplanation: Big data uses low‐cost commodity hardware to make cost‐effective solutions.
3 What does commodity hardware in the big data world mean?Very cheap hardwareIndustry‐standard hardwareDiscarded hardwareLow specifications industry‐grade hardwareAnswer:dExplanation: Commodity hardware is a low‐cost, low performance, and low specification functional hardware with no distinctive features.
4 What does the term “velocity” in big data mean?Speed of input data generationSpeed of individual machine processorsSpeed of ONLY storing dataSpeed of storing and processing dataAnswer:d
5 What are the data types of big data?Structured dataUnstructured dataSemi‐structured dataAll of the aboveAnswer:dExplanation: Machine‐generated and human‐generated data can be represented by the following primitive types of big dataStructured dataUnstructured dataSemi‐Structured data
6 JSON and XML are examples of _________.Structured dataUnstructured dataSemi‐structured dataNone of the aboveAnswer:cExplanation: Semi‐structured data are that which have a structure but do not fit into the relational database. Semi‐structured data are organized, which makes it easier for analysis when compared to unstructured data. JSON and XML are examples of semi‐structured data.
7 _________ is the process that corrects the errors and inconsistencies.Data cleaningData IntegrationData transformationData reductionAnswer:aExplanation: The data‐cleaning process fills in the missing values, corrects the errors and inconsistencies, and removes redundancy in the data to improve the data quality.
8 __________ is the process of transforming data into an appropriate format that is acceptable by the big data database.Data cleaningData IntegrationData transformationData reductionAnswer:cExplanation: Data transformation refers to transforming or consolidating the data into an appropriate format that is acceptable by the big data database and converting them into logical and meaningful information for data management and analysis.
9 __________ is the process of combining data from different sources to give the end users a unified data view.Data cleaningData integrationData transformationData reductionAnswer:b
10 __________ is the process of collecting the raw data, transmitting the data to a storage platform, and preprocessing them.Data cleaningData integrationData aggregationData reductionAnswer:c
Conceptual Short Questions with Answers
1 What is big data? Big data is a blanket term for the data that are too large in size, complex in nature, which may be structured or unstructured, and arriving at high velocity as well.
2 What are the drawbacks of traditional database that led to the evolution of big data? Below are the limitations
