MBA MCQ
Big Data, Cloud, and Analytics
FreshtoHome is an app-based grocery delivery company operating in the major cities in South India. It has been generating a large volume of data from customer interactions. In one such initiative, it wanted to find out the most frequent customer complaints based on the analysis of customer complaints and looking for similar words and descriptions. After analysis, the company identified the top 5 frequent customer complaints and started working on them. This has increased customer satisfaction and facilitated customer retention.
Which of the analytics` approaches is used for the current business situation at FreshtoHome?
a.Business Intelligence
b.Big data BI
c.Big analytics
d.Big data analytics
e.Structured predictive analytics
The answer is d) Big data analytics.
Big data analytics is the process of collecting, storing, analyzing, and interpreting large datasets to gain insights into customer behavior, identify trends, and make better business decisions. In this case, FreshtoHome used big data analytics to analyze customer complaints to identify the most frequent ones. This information was then used to improve customer satisfaction and facilitate customer retention.
Business intelligence (BI) is a broader term that refers to the use of data and analytics to improve decision-making. BI can use a variety of data sources, including customer complaints, but it typically does not involve the analysis of large datasets.
Big data BI is a subset of big data analytics that focuses on the use of big data to improve decision-making. Big data BI typically uses more sophisticated analytical techniques than traditional BI, such as machine learning and artificial intelligence.
Big analytics is a term that is sometimes used interchangeably with big data analytics. However, big analytics can also refer to the use of big data for other purposes, such as fraud detection and risk assessment.
Structured predictive analytics is a type of analytics that uses statistical models to predict future outcomes. Structured predictive analytics is typically used for tasks such as forecasting demand or predicting customer churn. In the case of FreshtoHome, the company used big data analytics to identify the most frequent customer complaints. This is a clear example of big data analytics, as the company was using large datasets to gain insights into customer behavior.
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LinkedIn has introduced a feature “Jobs you may be interested in”. This feature analyses the Big Data available with it obtained from a large pool of recruiters and members seeking jobs. The analytics algorithm uses attributes like Skills, locations, jobs, and recommendations as obtained from the profile of the LinkedIn Member and also from the PDF document containing the detailed resume. From the textual descriptions few key features are extracted and identified as tags. These tags are matched with the extracted tags from the job postings.
Which of the following type of Big Data describes the tag data being processed at LinkedIn?
a.Structured
b.Semi-structured
c.Relational Data
d.Unstructured data
e.Meta Data
The answer is b) Semi-structured.
Semi-structured data is a type of data that has some structure, but not as much as structured data. This type of data often contains tags, labels, or markers that provide some organization and context. Examples of semi-structured data include XML files, JSON data, and log files.
In the case of LinkedIn, the tag data being processed is semi-structured. This is because the tags are extracted from textual descriptions, which are not as structured as a database table. The tags are also not as unstructured as the text itself, as they have been identified as key features.
Structured data is a type of data that has a fixed format, such as a database table. Relational data is a specific type of structured data that is organized into tables, with each row representing a record and each column representing a field. Unstructured data is a type of data that does not have a fixed format. Meta data is data about data. It is often used to describe the structure and content of other data.
In the case of LinkedIn, the tag data is not structured in a fixed format, such as a database table. However, it is not as unstructured as the text itself, as the tags have been identified as key features. Therefore, the tag data is semi-structured.
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Amazon, the e-commerce major, has been implementing big Data Analytics on the huge volume of data it has been collecting with user interactions to gain insights into customer interests. They have a system where the buyers rate a product based on their experience with the product. Using the rating data, the company stores more of such items to quickly fulfil customer orders for those products. In one of the reviews, the company realized the movement of some of the products with high customer ratings was slow. On analysis, they found the user ratings may be manipulated.
Which of the following fundamental characteristics of Big Data is being addressed by the engineers at Amazon?
a.Volume
b.Variety
c.Velocity
d.Veracity
e.Value
The answer is d) Veracity.
Veracity is the quality of being truthful or trustworthy. In the context of big data, veracity refers to the accuracy and reliability of the data. In the case of Amazon, the engineers are concerned about the veracity of the user ratings, as they believe that some of the ratings may be manipulated.
The other fundamental characteristics of big data are:
Volume: The volume of big data refers to the sheer amount of data that is being generated. In the case of Amazon, the company is collecting a huge volume of data from user interactions, such as product ratings, reviews, and searches.
Variety: The variety of big data refers to the different types of data that is being collected. In the case of Amazon, the company is collecting a variety of data, such as structured data (product ratings), unstructured data (product reviews), and semi-structured data (search queries).
Velocity: The velocity of big data refers to the speed at which the data is being generated and collected. In the case of Amazon, the company is collecting data in real-time, as users are interacting with the website and app.
Value: The value of big data refers to the insights that can be gained from the data. In the case of Amazon, the company is using big data analytics to gain insights into customer interests and improve its product offerings.
Therefore, the engineers at Amazon are addressing the veracity of the big data, as they believe that some of the data may be manipulated. This is important, as the company relies on the data to make decisions about its product offerings and inventory.
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General Motors, the auto Major introduced a customer support service called OnStar. It provides support taking inputs from the sensors located in the vehicle. The data collected is analysed to obtain geospatial and location-based intelligence and this is passed on to the customer in real time so that the customer can get real time support for a service problem encountered or potential problems. The data analysts have to address the challenge of this kind of data coming in a stream from various sensors and needs to be analysed appropriately.
Which of the following fundamental characteristics of Big Data is being addressed by the engineers at GM?
a.Volume
b.Variety
c.Velocity
d.Veracity
e.Value
The answer is c) Velocity.
Velocity refers to the speed at which data is generated and collected. In the case of General Motors, the data is coming in a stream from various sensors in the vehicle. This means that the data is being generated and collected at a very high speed. The data analysts at GM need to be able to collect and analyze this data in real time in order to provide timely support to customers.
The other fundamental characteristics of big data are:
Volume: The volume of big data refers to the sheer amount of data that is being generated. In the case of General Motors, the company is collecting a large volume of data from the sensors in the vehicle.
Variety: The variety of big data refers to the different types of data that is being collected. In the case of General Motors, the company is collecting a variety of data, such as sensor data, geospatial data, and location data.
Veracity: The veracity of big data refers to the accuracy and reliability of the data. In the case of General Motors, the company needs to ensure that the data from the sensors is accurate and reliable in order to provide accurate support to customers.
Value: The value of big data refers to the insights that can be gained from the data. In the case of General Motors, the company can use the data from the sensors to improve its customer support services and to prevent potential problems.
Therefore, the engineers at GM are addressing the velocity of the big data, as the data is coming in a stream and needs to be analyzed in real time. This is important, as the company needs to provide timely support to customers.
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UnityPoint Health, a network of healthcare facilities, wanted to understand why patients are being readmitted. They simply asked them, “Why do you think you are back?” The answers varied from not having the means to buy medication to not being able to book follow-up appointments. UnityPoint Health aggregated these answers into a predictive model that would assign a readmission risk score to every visiting patient. Based on these risk scores they were in constant touch with patients with high admission risk and prevented readmissions and thereby penalties from Insurance companies.
Which of the analytics` approaches is used for the current business situation at UnityPoint Health?
a.Business Intelligence
b.Big data BI
c.Big analytics
d.Big data analytics
e.Structured predictive analytics
The answer is e) Structured predictive analytics.
Structured predictive analytics is a type of analytics that uses statistical models to predict future outcomes. In the case of UnityPoint Health, the company used structured predictive analytics to create a model that would predict which patients were at risk of being readmitted to the hospital. The model was based on data from patients who had been readmitted in the past, as well as data from patients who had not been readmitted. The model was then used to assign a readmission risk score to every visiting patient. Patients with high readmission risk scores were then contacted by the hospital and offered additional support, such as help with medication or follow-up appointments. This helped to reduce the number of readmissions and saved the hospital money.
Business intelligence (BI) is a broader term that refers to the use of data and analytics to improve decision-making. BI can use a variety of data sources, including structured data, unstructured data, and semi-structured data. However, BI typically does not involve the use of predictive analytics.
Big data BI is a subset of big data analytics that focuses on the use of big data to improve decision-making. Big data BI typically uses more sophisticated analytical techniques than traditional BI, such as machine learning and artificial intelligence. However, big data BI does not typically involve the use of predictive analytics.
Big analytics is a term that is sometimes used interchangeably with big data analytics. However, big analytics can also refer to the use of big data for other purposes, such as fraud detection and risk assessment.
In the case of UnityPoint Health, the company used structured predictive analytics to create a model that would predict which patients were at risk of being readmitted to the hospital. This is a clear example of structured predictive analytics, as the company was using statistical models to predict future outcomes.
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