Data Warehousing & Casting the Nets Project - IT Computer Science Assignment Help

Assignment Task

Task 

Final Project Case: Something Fishy
The purpose of this case is for you to see the scope and flow of data warehousing in its entirety, from specifying the requirements for a data warehouse requirements, producing a dimension design, and starting with source data in a transaction processing system and going through ETL to load data into a data warehouse, using the data warehouse to demonstrate how it can be used to manage the operations of a business and for making decisions.
This case for this project is a company named Something Fishy, which is an upscale chain of seafood restaurants serving discriminating individuals who insist on the world’s finest delicacies from the sea.

Introduction
Something Fishy started in 1954 as a small restaurant located on the banks of the Woonasquatucket River at the head of Narragansett Bay in Providence, Rhode Island. During its early years the restaurant struggled to survive, as did some of their customers, because the restaurant tended to serve clams that were “artfully” harvested between storm drain overflows and red tides. In fact, the restaurant came perilously close to closing after a notorious 1972 incident in which three fish were battered.
Whether due to fewer red tides, through simple chicanery, or by sheer luck, the company avoided going under. And to everyone’s surprise, over the years it actually expanded to become a small chain of highly profitable seafood restaurants. Today, Something Fishy has established a reputation as having the best seafood anywhere, even better than Long John Silver’s. The restaurants have a great many fanatically loyal customers, with the heaviest spenders among them receiving the rank of “Whale” on their Frequent Fish diner card. Something Fishy’s management finds all this somewhat puzzling because a closely held secret is that many of the items touted on their menu as generations-old secret family recipes are really Gorton’s of Gloucester fish sticks.

Nonetheless, Something Fishy continues to reel in customers. However, calm seas do not last forever. A persistent rumor is circulating that Red Lobster is about to locate a restaurant right in the heart of Something Fishy’s territory. This has rocked Something Fishy’s boatload of managers, who had always been at sea when it came to analytics but now cling to the hope that analytics will be the key to surviving the perfect storm about to be launched by the Big Red Lobster.

A Data Warehouse for Operational Needs
Like many management teams, Something Fishy’s believes data warehouses can snag any and all types of data for any purpose they can imagine. You told them they needed to go much deeper if you were to understand their needs. The management team responded that their most urgent need is a data warehouse to enable analytics giving them the ability to track items on their menus, analyze their sales, costs, promotions, and whatever else is going on at their restaurants, and to understand something about their customers – actually, anything about them would do – since they can’t figure how people could get hooked on their good in the first place, let alone why they would keep coming in. You told them you needed more information from the company about what they wanted to get out of a data warehouse. Management discussed the performance metrics they use to run the business. 

A Data Warehouse for Decision-Making
Something Fishy needs these KPIs to run their business, but they want much more from their data warehouse. Something Fishy’s top management knows their overarching and most critical need is to improve their decision-making and are keenly aware that when it comes to analytics and datadriven decision-making, they have yet to leave dry dock. The management team has not been able to come to consensus about several decisions the company is facing. The team has been split in two on these decisions, and it has become increasingly contentious. In fact, both sides are near mutiny and executives on each side have been quietly muttering about throwing the CEO overboard. The CEO has heard these rumors and is worried about his own fate, especially because “departing” executives at Something Fishy aren’t awarded Golden Parachutes but instead are thrown Leaden Anchors. It is with some urgency that the CEO is asking you to complete the data warehouse quickly so that you can weigh in with your recommendations for the decisions the management team has foundered on. The CEO summarized the decisions and asked you to make recommends for:

1. New or old?
Many years ago, there was a great shuffling of deck chairs and most of the crew on the management team was replaced with fresh recruits. Immediately after coming on board the new crew set a new course, and since then the crew has remained the same and they have held a steady course. However, a growing faction now feels lost at sea and questions the course they have charted. This group suspects that the original restaurants, which were opened by the original management team, are somehow different than the restaurants the new team opened after taking over. This group wants to know whether there are in fact differences, which could be in terms customer mix, best-selling menu items, growth rates, costs, and of course, profitability. If there are differences, they want to know what they are and need suggestions for any actions they should take based on those differences. Of course, if there are no differences, they want to know that as well.

2. Growing gamble?
A vocal group within the team is insisting that the company needs to launch a new restaurant at a site in Central Falls. Others contend that a new restaurant will simply pull
existing customers away from other restaurants. If that were to happen, there might be only small gains in overall sales which would be more than offset by the increased costs of operating a new restaurant. Something Fishy wants to use frequent customer data from the data warehouse to understand the areas from which each restaurant draws, and how far away customers are coming from. They also want to know if customers usually visit the same restaurant again and again or tend to enjoy the ambience at a variety of Something Fishy locations.
All of this would indicate whether a new restaurant could be justified. They are asking you to find reasons why they should, or why they should not, open a new restaurant in Central Falls along with your recommendation. If it shouldn’t be Central Falls, they would like to know what other locations they might consider.

3. Promulgate promotions?
Some top managers believe that by expanding the Frequent Fish program they will gain more repeat customers for Something Fishy. Of course doing so involves costs and giving away discounts, so before they go full steam ahead on this decision, they want to know if customers who now possess the treasured Frequent Fish discount card spend more, generate higher profit, come in larger groups, visit at different times of day, or are otherwise different from those who do not.
They want your recommendations and reasons for whether they should they should expand the Frequent Fish discount program.

4. Plundering performance?
Something Fishy recognizes they have many loyal and long-time employees. To foster  such longevity among all employees, certain members of top management are recommending the company institute an employee bonus program. However, others counter that they have heard rumors that certain long-time employees are getting a little too generous with discounts and free items, and possibly even getting kickbacks for their “services” through larger gratuities. Something Fishy believes they are running a tight ship, but could there be any truth to these rumors? Should Something Fishy offer an employee bonus program, and if so, is there anything they need to be on the lookout for?

Data Source
Something Fishy put a transaction system in place several years ago, and since then has been accumulating data about meals sold in their restaurants. Their Point-of-Sale (POS) system gathers information from each check, including the menu items that are on the bill and their prices, along with discounts, taxes, tips, and the employees who served each meal. They also record frequent customer numbers for those customers lucky enough to have a Frequent Fish card. 

This first part of the final project asks you to define the data warehouse requirements and dimensional design based on the case and using what is in the readings. It is a segue to the next part of the final project because when you have well-defined requirements, designing the star schema, demonstrating how it operates, and then formulating recommendations all follow. 

Part 1: Requirements and Dimensional Design
You should first read through the case and restore the database and inspect the transaction processing data you’re working with. After that, here are the steps to follow and templates to use:
 

1. Identify and define business processes
Requirements drive design. Or should. In the case of dimension modeling, the underlying business processes that generate the data for management’s questions drive data warehouse design. Or should.
Chapter 4 of Kimball’s book describes the dimensional design process with the first step as selecting the business processes to model. Chapter 5 from the IBM Redbook describes that process in much more detail. Correspondingly, the same is the first step here, to identify the individual business processes, their granularity, and the data items they produce that will be used to populate the data warehouse. Business processes can be thought of as a series of steps that are triggered by a specific event. They can be considered as individual outcomes and thinking of their trigger events and outcomes can be helpful to identify them. Both are usually reflected in how they are named, for example, the end of each pay period might trigger an “Issue paycheck” business process, signing up for a course might trigger a “Validate student status” process, receipt of a check for a utility bill might trigger a “Record payment” process, and so on.

The case does not go into detail about a restaurant’s business processes or operations. However, it describes a familiar scenario and intentionally does not go beyond what you generally know about just be eating in them. In other words, you should assume that the restaurants described in the case operate in the same way as other restaurants you’ve been to, and the steps in a business process the same. Once you have an idea of the business processes, find the data items from the database that would be generated by that process and group them within by their level of detail. This means grouping them by granularity and thinking about “what data values do you know and when - at what point - do you know them” may be useful. Grouping data items by level of detail (granularity) help define the business process. 

2. Create Bus Matrix
With the business processes identified and defined, this step is to develop the table known variously as a “conformance matrix” or “bus matrix” such as described in Chapter 4 of the Kimball text and shown in Figure 4-10 or in the IBM Redbook and Table 5-4 “Business processes and high-level entities”.
The purpose of including data items in the first step is because thinking in terms of measures (“how much”, “how often”, etc.) within the context of dimensions (“for each”, “by what”, etc.) can be helpful here. It also might be helpful to see beyond the bus matrix by seeing a requirements table as shown in Table 5-6 “User requirements for the retail sales process” in the IBM Redbook and also Figure 18.2 of the Adamson text, “Affinities among facts provide process clues” which shows an extension of the bus matrix.

3. Define KPIs
The case describes several KPIs that need to be incorporated into the data warehouse. This step is to define those KPIs based on database provided for this case, which represents the transactional processing system for the restaurant. You should define the KPIs with table and column names with aggregate functions, similar to how they would be defined in a query. For example, if a KPI were for average class size at a university, then the definition for it might be SUM(Registration.Enrollments) / COUNT(Section.SectionID), where Enrollments would be from a Registration table and SectionID from a Section table. 

It’s important to know that design is not a linear process and is iterative by nature. Design is different than highly structured problems such as in math or statistics. It’s not realistic to expect you’ll create a perfect or “correct” design right out of the box on the first try, or even second try, etc. The important part is that you try. This is especially if you haven’t worked with concepts such as business processes before or covered them in another class. When you’ve completed these three steps, you’ll have another iteration of gathering requirements for a data warehouse and be prepared for the remainder of the final project. As before, save your work as a single .pdf file and upload it to Blackboard. 

This IT Computer Science Assignment has been solved by our IT Computer Science Expert at My Uni Paper. Our Assignment Writing Experts are efficient to provide a fresh solution to this question. We are serving more than 10000+ Students in Australia, UK & US by helping them to score HD in their academics. Our Experts are well trained to follow all marking rubrics & referencing Style. Be it a used or new solution, the quality of the work submitted by our assignment experts remains unhampered. 

You may continue to expect the same or even better quality with the used and new assignment solution files respectively. There’s one thing to be noticed that you could choose one between the two and acquire an HD either way. You could choose a new assignment solution file to get yourself an exclusive, plagiarism (with free Turn tin file), expert quality assignment or order an old solution file that was considered worthy of the highest distinction.