Instructor: Jing Gong

Weekly Question #6 (Due Friday, October 23)

Leave your response as a comment on this post by the beginning of class on October 23, 2015. Remember, it only needs to be three or four sentences. For these weekly questions, I’m mainly interested in your opinions, not so much particular “facts” from the class!If you sign in using your AccessNet ID and password you won’t have to fill in the name, email and captcha fields when you leave your comment.
Think through a dimensional data mart for a scenario from your own experience. What is the fact, the measures in the fact table, and the dimensions? What questions will this data mart answer?


33 Responses to Weekly Question #6 (Due Friday, October 23)

  • This is about my weight loss and weight training. I need to always regulate my monthly weight training/fitness routines. To answer my question, the fact of the dimensional data mart would be the events that tells me when and how am I going to train or adjust to my weight training.
    The dimensions would be:
    -Checking weight that tells me how much weight i have lost, and what i did to get to that.
    -Time that tells me how frequently i loss weight.
    -My record that tells me in detail how i trained. Hence, helping me create and adjust my monthly weight training, and clarifies things related to how I got to that point.

  • Ijaspid Kenna and Michael Ragozine will present on Friday.

  • One scenario from my own experience could be budgeting and spending my money. The data mart could answer where I spend my money, how much I have in my account, and If I have enough to continue spending

  • While working with the international longshoreman association I learned the about the functionality of the port and the flow of import/export.
    Fact Table: Shipping manifest Report (primary key “Manifest_ID”), Cargo_ID, Port_ID(receiving),Store_ID, Shipping_ID(origin), Time_ID,Quantity sold and the Total Price.
    Dimension: Cargo_ID(primary), cargo_name, Cargo_price, Cargo_Weight.
    Dimension: Port_ID(primary), Port_number, Port_Country, Port_address, Port_city, Port_State, Port_type
    Dimension: Store_ID(primary), Store_Address, Store_City, Store_State, Store_type
    Dimension: Shipping_ID(primary), Ship_country, Ship_address, Ship_State, Ship_type
    Dimension: Time_ID(primary), Day, Month, Year, Time_Zone

    The Star Schema will support a shipping process with goods coming in from another country and identifying what port the product is coming to. In addition it will also identify what Cargo is being received at the port. This will also tell us what location the cargo is going to be sent to upon entering the Country and what store by the information in Store_ID and pairing it with Cargo_ID information.
    The Business Process here is the flow of Cargo from one point to its destination. My Data cube will consist of Time, Cargo and Port.

  • At my part-time job, my company uses a software called Teapplix. It manages our online orders and print shipping labels. One thing we need to track is inventory, so that’s our data mart. The dimensions are products, date we received products, and location is it in our warehouse, office basement, or in our shelves room. The key fact is product ID and the numeric facts includes number unopened packages, number of opened packages ready to be shipped, number of days we holding our products. The inventory Data mart is helping us know answer questions like which producfs are selling fast? Do we need to order more products from our suppliers? Our main objective is low holding costs.

  • Class Ex. Sales transaction
    The fact is the: who, what, where, when, why, and how—the sale. The measures in the fact table are: a particular product, store, or time. The questions this data mart will answer are: What is the best selling product? Who is the best customer?

    My Ex. Selecting a university to attend
    The fact is: the university. The measures in the fact table are: my university options, location, or national ranking. The questions this data mart will answer are: What is the best university within my vicinity? Do I fulfill the qualifications for this university?

  • An example of a dimensional data mart I’ve experienced is while working at a therapist office. The fact table would be the total hours spent and total pay. The dimensions would be office, type of therapy and time. This data mart will answer the total hours and pay accumulated from one or more offices, one or more times of the year and one or more types of therapy.

  • From my educational experience, I could build a data mart to store my schools, classes, and when I attended (time). Those would be the Dimensions. The Facts would be the quantity of classes taken in a given time-period and perhaps the grade earned in the course.

    • The questions the data mart would answer are:
      How many classes have I taken at Temple University?
      At what point in time did I take the most classes?

  • For example, if I want to do sort out all the homework and exams I have finished for each course in order of the the credits I have finished out of the total credits. I would need a data mart. The fact should be the total credits and how many homework/exams I have finished, the measures should be the credit each homework is worth for each course, and the dimensions should be course, homework/exams, and credit percentage.

  • Amazon has a data mart in order to sell their products. One fact in their data mart would be a specific product. The quantity sold of a specific product would be the measured fact. One dimension would be the specific warehouse that the product is shipped from.

  • If I were to build a data mart of transactions for a retail store the fact would be Sales. The measures in the Sales would be quantity sold and price. The dimensions would be the store location, product and time purchased. This could answer questions such as when was a sale made and what product was purchased most often at a certain location, etc.

  • I interned for PwC over the summer. PwC is a professional services firm that has many clients. The measured facts to their services are the number of clients they get, the number of clients they retain, And the prices charged to each client. The dimensions to their firm are the time, location, and services offered to clients.

  • In highschool I worked at a CVS and one of my responsibilities was to monitor inventory levels. A data mart for a typical product on the shelf would include the product name, the product’s shelf location, and the product’s expiration date. This data mart could be used to determine basic information about the products in the store.

  • For lunch today I wanted pizza, a cheesesteak, a soda, and a bottle of water. I could have gone to Maxi’s, City View, Plaza, or Pazzo Pazzo for these items. These would be my store and products, and the elements would cover quantity and total amount spent at each store. This data mart would answer the question of at which restaurant I would spend the least amount of money on each product I am looking for so I could decide which place I want to go to for lunch.

  • Think through a dimensional data mart for a scenario from your own experience. What is the fact, the measures in the fact table, and the dimensions? What questions will this data mart answer?

    My scenario will be Walgreens’s pharmacy. The fact would be the number of patients and sales. The dimension will be which location, time, and date. The data mart would answer which time of year would have the most patients and sales. Also, find out which Walgreens area would have the most impact with patients and sales.

  • My dimension is based on merchandise I buy from Victoria Secret locations. The measures in the fact table consists of merchandise I purchase and the total price I spend at the stores. In addition, the dimensions consists of products,locations, and time. The products include cosmetics, PINK wear, and undergarments. Locations include Victoria Secret downtown, Victoria Secret in Cherry hill,NJ, and Victoria Secret in King of Prussia. Time varies from August 2014 to December 2014.The data mart will answer questions about which stores I purchase more merchandise from in the fall semester and/or which store I spend the most money in during the fall semester.

  • A good example from my own experience of a dimensional data mart would be one related to my grandfather’s car business of Foster and Kardane motors, a company that was in business for 55 years in Doylestown, Pennsylvania. Since this car dealerships primary revenue driver is sales, the fact for this dimensional data base can be a ‘sale’. The measures in the fact table can be the time, quantity sold, price, and margin on the sale. The dimensions for the data mart can be the model of the car, year of the car, make of the car, time of the sale according to day, month, and year, and an identifier of the customer purchasing the vehicle. This data mart would then be able to answer questions like:
    1) “What was the most popular car model sold in the year 2000?
    2) “What make of car has the highest margins on average?”
    3) “Which customer purchases the most cars”?
    4) “Who is our most profitable customer?”
    5) “What make of the car sells best in the month of January?”
    6) “Which quarter of the year has the highest sales revenue?”

  • – As an entrepreneur, I have experienced a scenario with a dimensional data mart for my landscaping/lawn mowing business. My scenario is a three dimensional cube with the Price (of each lawn cut, weedwhacking job, mulching, etc.) and Quantity (number of cuts/other jobs) as the measured facts.
    -Since each time I cut a lawn (or finish another job) it generates a transaction of profit for me, I consider a lawn cut/landscaping job a “sales transaction”. Also, since the sale occurs for a particular service, location, and time, it is considered a fact. Therefore the fact is a sale.
    – The dimensions in this case would be each service (since each lawn cut is not a “product”), time (dates) , and client location (each different property I choose to maintain).
    – This data mart will answer questions such as” Who is my most profitable customer?”, “Which services are making me the most money?”, “Which months am I making the most money?”

  • I believe a dimensional data mart is useful when attempting to answer important questions. Larger businesses tend to have millions upon millions of business related records in their data marts making it difficult to sort through this. However, a dimensional data mart can be made through Excel Pivot Tables to find the information you need in a matter of seconds.

    Example, the Temple Analytics challenge we are currently working on.

  • My experience of a data mart can be seen through fantasy football performance. Performance is the fact and describes how well each team owner in the league is doing. The measures in the fact table include record_id, points_id, and waiver_id. Record_id dimensions include win_id and loss_id. The points_id dimensions include against_id and for_id. Lastly, waiver_id dimensions include budget_id and moves_id. This data mart answers how well a fantasy team is performing by describing who has the most points? Team with the least points? How many wins and losses a team has is answered. Also, the waiver decisions made by the fantasy owner.

  • An example of a dimensional data mart is my bank account. This account holds all of my financial statements and also has multiple dimensions in the account. These multiple dimensions happen to be the different accounts in my bank. All of this combined together represents my dimensional data mart.

  • I utilized a dimensional data mart in my high school job at a local dry cleaner. The fact of the data mart was a customer interaction. The measures in the fact table were service performed, payment status, customer name, customer phone number. The dimensions were the date which the interaction occurred. The data mart answered countless questions about best customers and peak operation hours.

  • An imaginary dimensional data mart for a scenario from my own experience I thought of was a data mart for the sales I’ve made for my company as a waitress compared to my other servers sales. The fact would be my sales and their sales, it occurred on a certain day, and could be determined through how much food and beverage sales we had that day and then the specific food and beverage. The data mart could be telling what our most sold item over the period of time was or how high our sales were for a given time. Someone could use this to compare our serving sales over time.

  • One scenarios from my experience is my online transactions with shopping. The facts would be the items that I bought and the measure could be the brands. The dimensions can be the store or websites that i shop from. By gathering this information, this data mart can solve what kind of clothes or brand that i buy the most or how much i spend at a particular store.

  • One data mart from my own experience is the sales system database at Lowes that tells me the sales of certain products that we sell. The sale is the fact. The measures in the fact table are the product ID and the total price. The dimensions are the customer, store and products. One question that this data mart answers is how much money did a customer spend at a specific store?

  • The data mart that I think through are when I choose my class schedule each semester. The Fact is that I can have one specific schedule of a set time throughout the day. The measures in the fact table are what days and times my courses are and what classes I can take. The sections of classes are the dimensions of the table. And this table answers how many credits i will take and whether or not I have the ability to work part time or fill activities in on certain times of each day.

  • The dimensional data mart in my scenerio is making a sales for our sybase system at my work place over the summer though I did not know these terms. Our dimensions had different aspects such as customer, supplier, time, product type. And it would describe the fact of sales from what supplier at what time time that was used. We would also see how much inventory we had left by looking at the data mart and seeing how much quantity was used though this happens in real time.

  • When I worked at a boy scout camp I had to keep track of the students in my classes. Which section they were in and when they attended which classes. So I suppose the dimensions for the entire sumer would be weeks, sections, merit badges( i taught 2 badges, both with 2 morning and 2 afternoon sessions) and the actual facts would be a list of students who attended that particular match of section, week, and badge.

  • The facts are the data we collect, and dimensions are that we collect the data and analysis from different level and periods. From the own experience, we can gain the different data and history price from the TaoBao, and then we know which product is cheaper.

  • The fact is that the data warehouse is a repository of centralized, cleansed, standardized, transformed and integrated historical data. It is the organizational view of its business from a data perspective.

    In terms of my experience, when I select courses for next semester, I will think about choosing which professor, Major course or not, when is it (course time) and classroom, is it a lecture or small size class.

    So, the course is the fact table, and the dimensions are professor, Major course or not,course time,classroom, class size.

  • An experience that i have is in the workplace where i work. this is a organic food business where customers can subscribe to order food online. the the dimensions would be the box in which the customer subscribes to and the measure would be the food in which is ordered and the price of each products that is placed in the box. while the company gets a base income from the subscriptions it also gets money based of any changes that are made with the products inside the box.

  • I am currently working a Temple University’s International Admissions. From my experience, I think that there is a dimensional data mart for students’ standardized test scores. The fact would be the student’s score. The dimensions can be the student’s age, or country of origin or the status of the student (freshman/transfer). This data mart can answer questions like “what is the average test score for student’s from China?” or “What is the average test score for Freshman applicants?”

  • An example of an experience that would be a good example of a dimensional data mart would be the time I spent working at a property management company in Philadelphia. The table could focus on tenant demographics. The dimensions could be tenant age, income, family size and utility usage. The questions that could be answered could deal with the relationships between tenant age and income and family size and utility usage.

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