Big Data Assessment Quiz

Explanation
The given question asks to identify the option that is not a challenge of big data. The options provided are "Virtually," "Capturing data," "Visualization," and "File storage." Among these options, "Virtually" does not represent a challenge in the context of big data. It is unclear what "Virtually" refers to in this context, but it does not align with the challenges typically associated with big data such as capturing, visualizing, and storing large volumes of data. Therefore, "Virtually" is the correct answer as it does not fit the category of big data challenges.

Explanation
John Mashey is credited with coining the term "big data." He first used the term in the early 1990s to describe the challenges of dealing with large and complex datasets. Mashey's work focused on the need for new tools and techniques to analyze and extract value from these massive amounts of data. His contributions to the field of data science and his use of the term "big data" have had a significant impact on the way we understand and approach data analysis today.

Explanation
The statement suggests that big data often requires new techniques and technologies to analyze and extract insights from large and complex data sets. This implies that big data is typically diverse, complex, and of a massive scale, which aligns with the definition of big data. Therefore, the answer "Often true" is an appropriate response to the statement.

Explanation
The three Vs in big data refer to Velocity, Volume, and Variety. These three factors represent the key characteristics of big data. Velocity refers to the speed at which data is generated and processed, Volume refers to the sheer amount of data being collected, and Variety refers to the diversity of data types and sources. "Virtually" does not fit into this context as it does not represent a characteristic of big data.

Explanation
Virtually is not a concept of big data. Big data refers to the large volume, velocity, and variety of data that cannot be easily managed or analyzed using traditional methods. Digital learning, machine blueprint, and variety are all concepts related to big data. Digital learning refers to the use of technology and digital resources for educational purposes, machine blueprint refers to the design and architecture of a machine or system, and variety refers to the different types and formats of data that are part of big data.

Explanation
The term "big data" was coined in the 1990s. During this time, there was a significant increase in the amount of data being generated and collected due to advancements in technology and the internet. The term was used to describe the challenges and opportunities that arose from the massive amounts of data that organizations were starting to collect. This led to the development of new tools and technologies to store, analyze, and extract insights from this large volume of data.

Explanation
The given options are different systems that could be related to factory work and cyber-physical systems. Out of the options provided, the 6C system is the most suitable because it aligns with the context of the question. The 6C system is a concept that refers to the six key components of cyber-physical systems, which are computation, communication, control, coordination, cognition, and configuration. These components are essential in the integration of physical and computational elements in a factory setting or any system that involves cyber-physical interactions.

Explanation
The use and adoption of big data within governmental processes allows efficiencies in terms of cost, productivity, and innovation. However, the one aspect where it may not provide efficiencies is in terms of value. While big data can provide valuable insights and information, the value derived from it may not always outweigh the costs associated with its collection, storage, and analysis. Therefore, value is the exception in this context.

Explanation
Big data analytics has helped healthcare improve by providing predictive analytics. Predictive analytics uses historical data and statistical algorithms to forecast future outcomes and trends. In healthcare, it can be used to identify patterns and predict patient outcomes, such as the likelihood of developing a certain disease or the effectiveness of a particular treatment. This information can help healthcare professionals make more informed decisions, improve patient care, and allocate resources more efficiently. By leveraging big data and predictive analytics, healthcare organizations can gain valuable insights and improve overall healthcare outcomes.

Explanation
The increase in big data has led to a higher demand for information management specialists. This is because big data requires effective management and analysis in order to extract valuable insights and make informed decisions. Information management specialists have the skills and knowledge to handle large volumes of data, organize it efficiently, and ensure its security and privacy. They are responsible for developing and implementing data management strategies, maintaining data quality, and ensuring compliance with regulations. With the exponential growth of data, the need for these specialists has become crucial in various industries.