Assignment: ABC Vehicle Parts
Assignment: ABC Vehicle Parts – Option 2 is a work plan with a strategy for migrating a working prototype database from a source production OLTP environment, into a Data Mart star schema design…
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Assignment: ABC Vehicle Parts
Instructions
Option 2 is a work plan with a strategy for migrating a working prototype database from a source production OLTP environment, into a Data Mart star schema design. The plan will include steps to migrate non-volatile subject-oriented records into a read-only OLAP environment. Objective is to Develop a action plan for migrating historical data from an operational source into a Business Intelligence (BI) data mart to become historical records for decision making.
Signature Writing Assignment (due in Week 4):
A 500+ word plan that demonstrates results from technical research conducted about implementing a working prototype design of a database into a production environment. Assume that a working prototype has been built as a vehicle for demonstrating the design. After the work plan is accepted, scheduling the deployment will be a milestone for certifying a fully operational database (using an alternative to having any front end application) after clear test acceptance by both technical and business end users.
Yes, the design may be for the final outcome for ITM450 project. The plan must contain at least 3 references composed in APA style, with cross reference to cited quotations or paraphrased content from source. Using Owl of Purdue for proper formatting of references is suggested. Citing a professional’s expert opinion is accepted.
After composing your plan, remove all instructions on this page.
Week Three Learning Outcomes
· Define Business Intelligence (BI)
· Explain the benefit of BI
· Describe the significance of Data Warehouses
· List the four main characteristics of Online Analytical Processing (OLAP)
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Reading: Ch. 13 – Business Intelligence and Data Warehouses
The text book describes Business Intelligence (BI) outcomes for data mart. A simplifying structure for this plan is suggested by these five topics for a Wk4 Prototype Migration Activity. The bold words may be keywords for a plan sections.
A. Describe the tables in an operational environment, such as a functional application, assuming it to be running in an online transactions processing (OLTP) architecture.
B. Define your Business Intelligence (BI) Strategy for migrating history into a designed data mart to be compatible with potential OLAP usage by decision makers
C. Explain the future benefit of BI summary information to lead to improved business outcomes and profitability
D. Describe the significance of starting with a single Data Mart for building toward a repository of historical records
E. State some business rules for extracting relevant data from one or more sources, transformations for migrating historical facts into a BI environment and loading tables.
An optional plan framework is included on the next page as a starting place.
Table of Contents
Title: <Name of Solution>. 2
[A] Operational environment: 2
[B] Data Mart: 3
[C] Future Benefits: 3
[D] First Data Mart: 3
[E] Business Rule Statements: 4
[F] BI Outcomes: 4
Figure 1. Operational Application Database Design. 2
Figure 2. Business Intelligence Star Schema. 3
Figure 3. Business Intelligence Star Schema. 3
Figure 4. Data Mart outcome report or dashboard images. 4
Title: <Name of Solution>
Submitted by <your name> on Date: __/__/__
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[A] Operational environment:
Describe the nature of your prototype database design (which started in Week 1 of this course, or coming forward from previous work.)
· Who are the users?
· What product or service is being processed?
· Who is(are) the customers?
· What tables exist to represent the daily transactional activity of the business?
· What tables are descriptive of the transactions?
· When does time relevance (dates) play are part?
· What database constraints exist (on which tables)?
· What value has achieved by placing Referential Integrity into the is prototype?
A prototype operational database design, an ERD (perhaps for the selected project scenario)
Figure 1. Operational Application Database Design
[B] Data Mart:
The starting place for a first data mart is the Star Schema, including a fact and some number of dimension tables, one being a Time dimension (perhaps called a Calendar). The plan may involve adding some more test data into the operational tables, then creating queries that will extract those records, transform the records to fit the design of a fact and dimension tables. Here is a list of possible statistics to report about your solution. Remove those that do not apply. After providing answers remove the questions.
· Describe the nature of your Star schema
· Star fact contains ___<kind of data> __ Is this table contain any rows?
· Does this data match up with the source table used for initializing it?
· Earliest date in the imported and refined time dimension table? __ / ___ / ___. Latest? ___ / ___ / ___
· Earliest date for a fact records joined to a date on the time? __ / ___ / ___. Latest? ___ / ___ / ___
· Dimension tables for: <name & row count for each dimension>
o Who? (the people or the organizational units)
o Where? (locations in which OLTP operations takes place)
o What? (descriptions of content in terms of products/services created or sold)
o When? (time frame for recording business dates)
· Does each dimension table have an integer Primary Key? ___
· Does fact table have a simple or compound primary key? ____
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Insert a star schema design diagram here
Figure 2. Business Intelligence Star Schema
[C] Future Benefits:
Explain the future benefit of BI summary information to lead to improvements in business outcomes and profitability
[D] First Data Mart:
Describe the significance of starting with a single Data Mart for building toward a repository of historical records
An original star schema design… either here in [D] or above in [C]…. Keep one and discard the other, then regenerated the table of figures
Figure 3. Business Intelligence Star Schema
[E] Business Rule Statements:
State some business rules for extracting relevant data from sources, transformations for migrating historical facts into a BI environment. Which dimension table may be stable after first load, and which may change?
[F] BI Outcomes:
Describe come BI outcomes for data mart. Define the future outcome for the prototype database after it will go into production.
Perhaps an artifact that represents the possible outcome of your scenario. This might be found by a google images search and an image or two of results that vendors show, or training scenario outcomes, or perhaps an example of a real outcome.
Citation URL:
Figure 4. Data Mart outcome report or dashboard images
References: (Include a minimum of 3 sources in APA format)
Coronel, C. & Morris, S. (2017). Database systems: Design, implementation, and management (12th ed.). [Instructor Manual for Ch13_Database_Warehouse_Ed12_Review]. Retrieved from ITM450 Blackboard course Week Three Learning Activity with permission by the instructor.
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Solution
List of Figures
Figure 1: Operational Application Database Design 4
Figure 2: Business Intelligence Star Schema 5
Figure 3: Data Mart outcome report 8
[A] Operational environment:
The users of the system are older drivers, new drivers, disabled drivers, and mechanics. The system being processed involves vehicle parts and the customers are drivers. The tables that represent the daily transactional activities of the business are the time and product tables.
The descriptive tables are user and customer tables. The dates play are apart during the implementation process. The database constraints that exist are the primary key found in the sales table and foreign keys found in the customer, user, and time tables. Placing the referential integrity on this prototype has eased the process of relating the different databases.
Figure 1: Operational Application Database Design
[B] Data Mart:
The star schema contains data about users, customers, products, and time. The integral primary key is in the sales table.
Figure 2: Business Intelligence Star Schema
[C] Future Benefits:
Business Intelligence is essential for a system as it helps to organize data for easier access and analysis. It allows decision-makers to dig information more quickly, empowering them to make informed decisions (Leonardi et al., 2017). For the current vehicle parts system, there are potential benefits of business intelligence aimed at improving services.
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First, it will ensure faster data analysis. While business intelligence platforms are designed to help in data processing found on the cloud and the company servers (Coronel & Morris, 2017). The BI tools help to pull data from different sources into a warehouse and then analyze the data based on dashboards, drag and drop reports, and user queries, increasing the system’s efficiency and cutting time used for operations.
Second, it will improve business decisions. In this regard, having accurate data and improved reporting processes mainly due to its ability to have analyzed data in real-time helps in making better decisions (Leonardi et al, 2017). As a result, it will help improve customer satisfaction, increasing sales.
Third, business intelligence will improve customer experience. The system will pull data from the customer support chat sessions to the dashboard. Therefore, it can help identify opportunities that can help improve customer experience as the needs of the customer. The system will also help to reduce the number of customer support calls by approximately 40%. Lastly, business intelligence will improve organizational efficiency (Morfonios & Ioannidis, 2018). In this case, the users will access data easily, understand the view of operations and benchmark the results which enhance organizational performance.
Nonetheless, with a more detailed view of the system, it is easier to identify potential opportunities, collaborate with different experts for a better diagnosis. Focusing on data analysis will enhance the innovativeness of new operations aimed at improving the overall system’s performance leading to organizational profitability.
[D] First Data Mart:
A data mart is defined as a data warehouse subset focused on a given line of the subject area, business, or department (Song, 2018). Starting with a single data mart is essential for building a repository of historical records for various reasons. A single data mart makes data about a specific element, allowing the users to gain critical insights about a specific element making it easier to align the data with specific objectives of a business. Additionally, it allows for similar data maintenance as it addresses each element of the system, enhancing easier maintenance and less clutter of information.
Starting with a single mart also allows the data analyst to ensure all the necessary data about an element is included before moving to the next element, improving its efficiency. Moreover, starting with a single data mart ensures easier implementation as it deals with small data subsets which also takes less time to set up (Song, 2018). This implementation differs from having a data warehouse which tends to be time-consuming as it collects data from internal and external sources.
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[E] Business Rule Statements:
There are some rules implemented in extracting relevant data and transformations for migrating historical facts into a BI environment.
First, data migration is used to reveal and correct the quality issues as a way of improving data for the migration process.
Additionally, there is a need to minimize data being migrated to avoid getting overwhelmed by large amounts of data and issues that result from dealing with large amounts of data. Nonetheless, the business rules to locate existing functionality for the system to understand the need for reuse, general research, and analysis.
Mapping the business rules to the source code could help facilitate code reuse and consistency of applications.
Following extraction data and transforming it into a BI environment, the dimension tables that may be stable after the first load is the user and customer tables. However, those that will change are products and timetables.
[F] BI Outcomes:
Figure 3: Data Mart outcome report
References
- Coronel, C. & Morris, S. (2017). Database Systems: Design, implementation, and management (12th ed.). [Instructor Manual for Ch13_Database_Warehouse_Ed12_Review]. Retrieved from ITM450 Blackboard course Week Three Learning Activity with permission by the instructor.
- Leonardi, C., Sabatucci, L., Susi, A., & Zancanaro, M. (2017). Design as intercultural dialogue: Coupling human-centered design with requirement engineering methods. Human-Computer Interaction – INTERACT 2011, 485-502. https://doi.org/10.1007/978-3-642-23765-2_34
- As you continue privateEssaywriters.com has the top and most qualified writers to help with any of your assignments including how to manage stress in workplaces. All you need to do is place an order with us (Assignment: ABC Vehicle Parts).
- Morfonios, K., & Ioannidis, Y. (2018). Star schema. Encyclopedia of Database Systems, 3703-3704. https://doi.org/10.1007/978-1-4614-8265-9_888
- Song, I. (2018). Datamart. Encyclopedia of Database Systems, 793-794. https://doi.org/10.1007/978-1-4614-8265-9_883
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