DB9
Refer to Figure 79, “Context Diagram: DW/BI” on page 384 of DAMA DMBOK Chapter 11: Data Warehousing and Business Intelligence and address the following:  
  • relevance of this framework to large-scale organizations 
  • the challenges a large-scale organization would face in creating data warehousing assets 
  • the potential impact on future operations
Requirements:  
  • Students must not copy and post from sources.  When referencing sources, students must rephrase all work from the authors and include in-text citations and references in APA format.  
  • Students must post their initial post by Thursday at 11:59 pm ET and have two full days of engagement (the first day of engagement must answer the initial post and then at least one more additional day of engagement with peers).  All posts must be answered by Sunday at 11:59 pm ET.  
  • The initial discussion board posts must be 300 words.  
  • Peer responses must be 150 words.   
  • The content must also not be from the textbook.  
  • Peer responses must be substantive.  
  • Build on something your classmate said.  
  • Explain why and how you see things differently.  
  • Ask a probing or clarifying question.  
  • Share an insight from having read your classmate's posting.  
  • Offer and support an opinion.  
  • Expand on your classmate's posting.  
  • Peer responses that are “a good job” or “I agree” do not count as a substantive post.

Disclaimer

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DMBoK framework can be very useful in large organization s because it will pr ovide infor mation about best practices, roles and responsibilities, deliverable s, and m etr ics. This will help the manager of the large organization be able to track progress and know who to ask. The DMBoK fram e work standardizes practices in all departments in a large organization and define s the scope of the practices and roles and responsibilities. The organization will have a functional framework that will guide the impl ementat ion of best practices in the o rganization.

Technological complexity is another challenge while impl e menting data warehousing assets because most source data is store d in more than one operating system and database technology (Moscoso-Zea et al., 2019) . There are very many tools that can b used to source data, manage quality, integrate data, and report and analyze data. It i s not work in the park selecting the tool to use to perform either task. This is usually complicated by emerging trends such as data virtualization, NoSQL, and in-memory computing among other s .

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