Why Big Data and Where Did it Come From?

Why Big Data and Where Did it Come From? >> Introduction to Big Data

1. Which of the following is an example of big data utilized in action today?

• The Internet
• Wi-Fi Networks
• Individual, Unconnected Hospital Databases
• Social Media

2. What reasoning was given for the following: why is the “data storage to price ratio” relevant to big data?

• It isn’t, it was just an arbitrary example of big data usage.
• Companies can’t afford to own, maintain, and spend the energy to support large data storage unless the cost is sufficiently low.
• Larger storage means easier accessibility to big data for every user because it allows users to download in bulk.
• Lower prices mean larger storage becomes easier to access for everyone, creating bigger amounts of data for client-facing services to work with.

3. What is the best description of personalized marketing enabled by big data?

• Marketing to each customer on an individual level and suiting to their needs.
• Being able to obtain and use customer information for groups of consumers and utilize them for marketing needs.
• Being able to use personalized data from every single customer for personalized marketing needs.

4. Of the following, which are some examples of personalized marketing related to big data?

• News outlets gathering information from the internet in order to report them to the public.
• A survey that asks your age and markets to you a specific brand.
• Facebook revealing posts that cater towards similar interests.

5. What is the workflow for working with big data?

• Theory -> Models -> Precise Advice
• Extrapolation -> Understanding -> Reproducing
• Big Data -> Better Models -> Higher Precision

6. Which is the most compelling reason why mobile advertising is related to big data?

• Since almost everyone owns a cell/mobile phone, the mobile advertising market is large and thus requires big data to contain all the information.
• Mobile advertising allows massive cellular/mobile texting to a wide audience, thus providing large amounts of data.
• Mobile advertising in and of itself is always associated with big data.
• Mobile advertising benefits from data integration with location which requires big data.

7. What are the three types of diverse data sources?

• Machine Data, Organizational Data, and People
• Machine Data, Map Data, and Social Media
• Sensor Data, Organizational Data, and Social Media
• Information Networks, Map Data, and People

8. What is an example of machine data?

• Weather station sensor output.
• Sorted data from Amazon regarding customer info.
• Social Media

9. What is an example of organizational data?

• Satellite Data
• Disease data from Center for Disease Control.
• Social Media

10. Of the three data sources, which is the hardest to implement and streamline into a model?

• Machine Data
• People
• Organizational Data

11. Which of the following summarizes the process of using data streams?

• Integration -> Personalization -> Precision
• Big Data -> Better Models -> Higher Precision
• Theory -> Models -> Precise Advice
• Extrapolation -> Understanding -> Reproducing

12. Where does the real value of big data often come from?

• Using the three major data sources: Machines, People, and Organizations.
• Combining streams of data and analyzing them for new insights.
• Having data-enabled decisions and actions from the insights of new data.
• Size of the data.

13. What does it mean for a device to be “smart”?

• Collect data and services autonomously.
• Having a specific processing speed in order to keep up with the demands of data processing.
• Must have a way to interact with the user.  

14. What does the term “in situ” mean in the context of big data?

• Accelerometers.
• The sensors used in airplanes to measure altitude.
• In the situation
• Bringing the computation to the location of the data.

15. Which of the following are reasons mentioned for why data generated by people are hard to process? Choose all that apply.

• The velocity of the data is very high.
• Very unstructured data.
• Skilled people to analyze the data are hard to come by.
• They cannot be modeled and stored.

16. What is the purpose of retrieval and storage; pre-processing; and analysis in order to convert multiple data sources into valuable data?

• Since the multi-layered process is built into the Neo4j database connection.
• To enable ETL methods.
• Designed to work like the ETL process.
• To allow scalable analytical solutions to big data.

17. Which of the following are benefits of organization-generated data? Choose all that apply.

• High Velocity
• Improved Safety
• Better Profit Margins
• Higher Sales
• Customer Satisfaction

18. What are data silos and why are they bad?  

• Data produced from an organization that is spread out. Bad because it creates unsynchronized and invisible data.  
• A giant centralized database to house all the data produces within an organization. Bad because it is hard to maintain as highly structured data.  
• Highly unstructured data. Bad because it does not provide meaningful results for organizations.  
• A giant centralized database to house all the data production within an organization. Bad because it hinders opportunity for data generation.  

19. Which of the following are benefits of data integration? Choose all that apply.

• Reduce data complexity.
• Adds value to big data.
• Unify your data system.
• Monitoring of data.
• Increase data availability.
• Increase data collaboration.