For the duration of this course, you will be the Chief Data Officer (CDO) of a fictional firm of your choice. You have a data enterprise that contains structured and unstructured data As the CDO, what did you decide to build, a data warehouse or a data lake? Would you build both? Why? Since you are the first CDO in company history, describe your process for developing your organization and selecting analytical tools.

For the duration of this course, you will be the Chief Data Officer (CDO) of a fictional firm of your choice. You have a data enterprise that contains structured and unstructured data As the CDO, what did you decide to build, a data warehouse or a data lake? Would you build both? Why? Since you are the first CDO in company history, describe your process for developing your organization and selecting analytical tools.

Principles of Information Systems (MindTap Course List)

12th Edition

ISBN:9781285867168

Author:Ralph Stair, George Reynolds

Publisher:Ralph Stair, George Reynolds

Chapter3: Hardware: Input, Processing, Output, And Storage Devices

Section: Chapter Questions

Problem 13DQ

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To construct a data warehouse prototype or not to develop one initially is a question your data warehousing project team is likely contemplating. In order to be ready for the business data warehouse, you'll need to learn some data warehousing basics. So, what do you think? Now, what is it that you want to say?
If your data warehousing project team is considering whether to construct a data warehouse prototype first, this is what they are discussing. In order to be ready for the enterprise-wide data warehouse, you'll need to learn some data warehousing basics. So, what are your thoughts? I want to hear what you have to say.
Your data warehousing project group may select whether or not to create a data warehouse prototype before putting it into action. The project team is especially anxious about its members' ability to learn data warehousing before beginning work on the enterprise-wide data warehouse. If you read my mind, you'd know what I'm thinking. Please be specific with your recommendations.
The data warehouse project team is contemplating whether to develop a prototype initially. Learning specific data warehousing skills prepares you for the enterprise-wide data warehouse. What's your opinion? What's your opinion?
Data warehouse prototypes are optional but highly recommended and should be decided upon by your data warehouse implementation team. Team members are especially anxious about whether or not they have the necessary data warehousing expertise to go on with implementing the enterprise-wide data warehouse. You can read my mind. Specify your recommendations and why they're the best.
This is the topic that is being discussed by the team working on the data warehousing project for your company if they are debating whether or not to construct a data warehouse prototype initially. Acquiring specialised data warehousing expertise is required in order to adequately prepare for the implementation of an enterprise-wide data warehouse. What are your thoughts on this matter? What are your thoughts on this matter?
What are some of the possible advantages that an enterprise data model may provide to Mercy? Is it now more or less critical for Mercy to have a well-designed model that specifies the organization's data needs and priorities as a result of the firm's journey into big data, or does Mercy's foray into big data make it more or less crucial for the company to have such a model?
QUESTION 2 Assume that you will be up for a promotion next month and you'd like to analyze a recently acquired database to show off your data analytic skills to the management. After using the first step of the IMPACT cycle which is identifying the goal of your data analysis, explain the steps that you would take if you have direct access to the database or information systems that holds all the data you need. You may not need to go through a formal data request process instead you can simply extract the data yourself.
What are the advantages of Mercy using an enterprise data model? Is it vital for Mercy to have a well-articulated model that specifies the organization's data needs and objectives as a result of its considerable foray into big data?
For the chosen organization, find the data needs, you may use different sources of information for the data collection such a : website of the company, publications about the company in newspapers, magazines, specialized journals, if needed you may also conduct interview with company’s representatives. After analyzing the data needs, you have to write a detailed scenario, containing at least four entities, their attributes and the relationships between them in the organization with appropriate cardinality constraints. You have to also identify at least one associative entity in the scenario. Use appropriate citations and references on the data collected from various source.
What are the responsibilities of a data scientist? If you want to be a data scientist, do you need a specific background?
What are the typical challenges and limitations associated with data warehousing projects?