Page 113 - Data Science class 11
P. 113

5.  Why does an organisation implement data governance framework?
              Ans.  An organisation implements  data  governance  framework so as to achieve better command over its data  assets,
                  including the methods, technologies, and behaviours  around the proper management of data. It also deals with the
                  security, privacy, integrity, and management of data flowing in and out of the organisation.
            B.  Long answer type questions:

               1.  What are some of the benefits of implementing data governance framework?          [CBSE Handbook]
              Ans.  Some benefits of implementing a data governance framework are:
                  •  Procedures around regulation and compliance activities become exact.

                  •  There is greater transparency within data-related activities.
                  •  Increase in value of organisation’s data.
                  •  Better resolution of issues around current data.
               2.  Write a short note on ITA-2000. Also mention why the data privacy rules therein, are described as too harsh, by few
                  Indian and US firms.
              Ans.  The Information Technology Act, 2000 (also known as ITA-2000, or the IT Act) is an Act of the Indian Parliament (No 21
                  of 2000) notified on 17 October 2000.
                  The data privacy rules introduced in the Act in 2011 have been described as too harsh by few Indian and US firms. As
                  per these rules, firms need to obtain written consent from customers before collecting and using their personal data.
                  This has affected US firms which outsource to Indian companies. But some companies have even embraced these harsh
                  rules, saying it will remove the fear of outsourcing to Indian companies.

               3.  What is an outsider threat? What are the sources of outsider threat?
              Ans.  Outsider threats are those that come from outside of the organisation. They can be from hacktivists, other countries,
                  white hat hackers or even your competitors.

                  Outsider threats include:
                  •  Hackers at competitions trying to expose vulnerabilities for a prize or reward.
                  •   Foreign governments trying to gain access to a defense contractor to learn about the latest military technology in
                    development.
                  •  Cybercriminals trying to access financial information for financial gain.
               4.  Explain with relevant examples why data scientists need to understand data and follow data ethics? [CBSE Handbook]
              Ans.  As data scientists who get access to a vast amount of data in their data analysis, it is rather essential for them to adhere
                  to ethical guidelines. The use of protective mechanisms and policies, to discourage the mishandling and unethical use
                  of data, should be made part of best practices.
                  Some of the negative scenarios that may arise if ethical guidelines are disrespected include:
                  a.  A few people can do an immense amount of harm:
                       Hackers worldwide are on the lookout to crack through a reputed organisation's firewalls and steal important data
                     from their servers. The stolen data is then sold out for a hefty sum.
                  b.  Lack of consent:
                       One of the leading social networking sites conducted an experiment, wherein without consent, they purposely
                     fed highly extreme point of view and particulary incendiary part of the news in their newsfeed, because they were
                     trying to elicit a reaction from the users, to see if that impacted what the users post back.
                       There must be a universal framework for what companies can and cannot do with the data they collect from
                     people, for this Data Ethics is necessary.








                                                                                        Ethics in Data Science  111
   108   109   110   111   112   113   114   115   116   117   118