Instructor: David Schuff

Weekly Question #7: Complete by October 26, 2017

Leave your response as a comment on this post by the beginning of class on October 26, 2017. 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.

Here is the question:

In class we discussed how data cubes are made up of dimensions and measured facts. Give an example of a data cube that we didn’t discuss in class by describing:

  • The business question to be answered.
  • The dimensions.
  • The measured fact(s).

48 Responses to Weekly Question #7: Complete by October 26, 2017

  • To find out the average price of a stock over different periods of time on different channels, you could build a data cube. In this scenario, the dimensions would be the company that issued the stock, the time period, and the channel where the stock was listed on. Furthermore, the measured fact would be the average stock price for the cross-sections of those three dimensions.

  • You can use a data cube to organize information from contracts. For example, if you were asked “where was a certain contract signed?” you can use this data cube. This three-dimensional cube would include the dimensions of type of contract, location, and time. The measured facts of a contract would include financial amount, discount amount, and planned amount. You can use the data cube to answer many different questions including “how many contracts are there?” and “what was the overall spending?”.

  • We can use data cube to find out the the best ticket sales in Pennsylvania. In this example, the dimensions would be the performer for particular performance, the venue of the show which is the counties, and the date of the performance. The measured facts would be the amount sales in quantity and US dollar.

  • I think a common way of storing data as a datacube would be if a company was storing customer information from sales. You would store data such as: Customer ID, Customer first name, Customer last name, Product ID, Order Number, Order Date. So, if say you were asked to pull up the order history of ‘John Smith’. You could query John Smith and discover multiple features of data corresponding to the customer John Smith. The multidemensional cube allows for you to discover lots of information pertaining to what you were looking for and allow you to answer other questions you may have concerning this information.

  • Data are made up of dimensions and measured facts. An example of a data cube that we didn’t discuss in class is one that answers the question how many of each product was sold? The dimensions would include customers, products, and time. The measured facts in the cubes themselves would include sales amount and units sold.

  • On the website project I am working on, we utilize data cubes for analytics that are able to be displayed to the owners of a particular chat thread, that they can pick apart for information such as –

    when is the peak activity time of my thread/chat?
    where are my users geographically located?
    how many unique users are active making posts in a certain time frame?

    And so on. The system measures a user’s username, OS and browser, the time of their post, the content of their post, and the post’s ID, all of which can be used to track various slices of information about a user and their posting habits.

  • A data cube can be utilized for yearly sales. To expand on it, a data cube can help create trends and analyze the performance of the employees, the sales of the product, and the purchases of customers, etc. The measured fact would be the sales throughout the year, which includes its quantity and its total revenue – this helps analyze all things involved in these business transactions.

  • One way a data cube can be utilized is in a library. The dimensions of this data cube would include book names, book id’s, sections/subjects, card holder’s name, employees, and more. Some of the measured facts to come from this data could be how often someone checks out a certain genre, the average time a book is checked out, and how many active library card holders there are. This could lead to findings about book popularity, how often a library is utilized, and how many people use the library as a place to read vs. a place to check out books.

  • You can use a data cube to organize information for an airport. The dimensions would be airline name, time period, and location (city, state). The measured fact would be number of passengers. This would allow you to answer several business questions including “which airline company sold the most tickets to Chicago, IL in 2000?” This is a very detailed questioned, but the data cube can also answer more generic questions. This could help airports determine which airlines to keep or remove.

  • You could use the data cube to retrieve information from GrubHub. GrubHub is a food delivery app.
    Question: Which restaurant has Isha ordered the most frequently from in 2016?

    Dimentions: Date, Resteraunt Name, Cusotmer Name

    Measured Facts: Number of orders

    This is to determine which type of cuisine is more popular and which restaurants to add or drop.

  • We can use a data cube in the hospitality field. Hotel chains want to maximize their profits with the help of data analytics. Dimensions would include the particular hotel chain, location, date (time of year), etc. Measured facts would include length of stay and amount spent. Hotels can use the information when expanding and targeting potential customers.

  • To answer the business question of: What are the company’s current inventory levels? The dimensions to be included would be product type, warehouse locations, date products shipped, and time by month or week depending on how often the company wants to track inventory. The measured fact would be the count of how many products shipped by category by location by time.

  • You could create a data cube to find out which country had the highest amount of clothing manufactured in any year. The dimensions would be year, country and the amount of clothing manufactured. The measured fact is the numerical value of clothes produced an a respective country during a respective year.

  • One way a data cube can be utilized is to organize information for a movie theater. The dimensions of this data cube would include movie titles, movie airtime at that theater, and movie rating. The measured fact would be sales made by that movie theater. This would allow for answering several questions such as what times are popular for movie goers and does the rating of a movie affect the number of people seeing the movie in theaters.

  • Data cubes can be utilized to organize information regarding Eagles tickets sold. The dimensions of this data cube would be customer name, game date, and ticket price. The measured facts would include amount of tickets sold for each game.

  • You can use a data cube to organize information for a ride service like Uber. The dimensions would be Uber Driver’s license plate as an ID, ride length, date, pick up, and drop off locations. The measured fact would be number of rides the Uber driver gives. This would give answers to business questions including “Which Uber driver gives the most rides?” This could help Uber determine who it’s best drivers are because they are the ones who give the most rides.

  • An example we didn’t discuss in class could be an international manufacturer auctioning off an international maritime shipping contract:

    The business question to be answered is which companies are leaving out of a specific country’s port at a particular date and time, with the shipping capacity necessary at the lowest price(s).

    The dimensions could be port takeoff location, shipping container type, shipping cost charged.

    The measured fact could be the designated shipping price for the delivery.

  • You can use a data cube when, for example, someone is purchasing a Macbook Pro from Apple. The dimensions would be Product ID, Customer ID, location, date, employee ID. The measured facts would be how many Macbook Airs, Pros (newer and older) and when they were purchased. It can also have the employee that took care of that transaction, and whether or not the consumer decided to buy Apple Care.

  • Data cubes can be used to organize information for a transportation buses system. the dimensions for this data cube could be the bus number, the location and the time. the measured fact would be the number of passengers. doing so, would help the company increase their revenue by knowing the times and locations where the number of passenger increases.

  • A data cube could be used by a video game developer to gain insight into how their different products perform financially on the market. The business question they would want to answer would be what games sell the best. The dimensions of the cube could be Genre, Rating, Product, Store, Console, and Time. In this example, the measured fact would be the amount of sales so that the company could evaluate how well each product sells given different dimensions.

  • Data cubes can be used in sports analysis. For instance, in soccer, analysts can use a data cube to predict the performance of players or teams in a particular stadium at a particular stage of tournament. One such example can be the predictions of scoreline of the FIFA World Cup that is going to be held next year. The dimensions would be name of the team, stage of the game (e.g. semi-final), and name of stadium. Many sports channels use such analysis to predict the outcome of many of the FIFA World Cup 2018 games.

  • If one owns a website, he or she can use a data cube to organize visitor/user information. The dimensions would be date, time, and specific page. The measured facts would be the duration of the visit and the number of pages visited by the user. This would serve to answer the business question, “How many pages were visited by a specific user on a specific date?”.

  • We can use a data cube to find out the sales of women’s jean in New York. In this case, the three dimensional cube would include the dimensions of boroughs in New York (such as Bronx, Brooklyn, Queens, Manhattan etc), the style of jeans (low rise, high rise, bootcut, etc) and also the brand of the jean. The measured facts would be the sales quantity and the price.

  • A data cube could be used to answer a question like “which product was sold the most in October”. The dimensions would be time (daily), names of all the products sold, and the quantity sold for each product per day. The measured fact would be highest selling product for the month of October only and how much of that product was sold during that time.

  • Many networking companies need to asses the popularity of their TV shows before deciding which shows to renew for another season. Networks can use a data cube to determine the popularity of their various TV shows. The dimensions could include show title, genre, and time of year (in seasons). The measured fact would be the number of viewers and critic ratings.

  • A data cube could be used for any major company. For example Adidas could use a data cube to see if certain products they have sell better at certain times of the day at their outlets in different states. The dimensions would be the specific states, the different products being bought and the time of day that the product was purchased. The measured fact would be the amount of sales for the product(s) at different times.

  • A data cube can be very useful for sports statistics. In the question, “Which player on the Phillies hit the most home runs in June?” the dimensions would be stated as: Player, Time, and Team. The fact would be Batting Statistics with a home run column and the ability to determine who hit what, when and for who.

  • Data cubes are used to analyze data significantly faster as opposed to measuring data by hand. An example is measuring how popular a MMO game is. The dimensions would consist of the location of players, time spent, and content. The measured facts would be purchases and players online.

  • A data cube can be used to store information on music. The dimensions would include, artist, year released, and album. I’m not really sure if this is the method Apple would use to track songs sold on iTunes, but theoretically, they could easily see which songs were downloaded the most on a specific day or week. The question there would simply be ‘Which songs (or artists) are the most popular/selling the best?’ This would help them with pricing– as they know songs that they can probably charge more for songs with a greater number of downloads because they are in higher demand.

  • A data cube can be use to organize a service log/record information. The dimensions would be the technicians, the services, and time. The measured fact is the total cost of the services. This allow the store see the most common service provided and adjust inventory to reduce certain product to sit on the selves for a long period.

  • An amusement park can use a data cube to determine which kiosk sells the most soda. The dimensions of the cube would time, location and product. The measured facts would be sales and total quantity.

  • A data cube could be used to store data on Samsung’s promotion payouts. The dimensions of the cube would include promotion ID, date the promotion started, date the promotion ended, customer ID, date costumer made purchase, type of promotion applied to the costumers purchase, quantity of purchased items by costumer. The question to be asked- what costumers are to receive one or more gear 360 cameras for purchasing a galaxy note 8? Measured facts would be- total purchases of the costumer at the time the promotion was going on.

  • For stores such as Target, they can use a data cube to predict what type of products their consumers are most likely to purchase next. Through tracking the data such as: the dates in which a customer made a purchase, they type of product they they typically buy (clothing, accessories, shoes or food), and the customers name. This will help the company provide advertisements and coupons based around what the customer is most likely to be purchasing when in the store.

  • A data cube can be very useful to police officers. The data cube can answer the question which neighborhoods are the safest. The dimensions would be location, time, and date. The measured facts would be the number of reported crimes, the number of patrols, and the number of arrests.

  • We can use a data cube with the sports/hospitality field. Arenas like the Wells Fargo Center want to gather data analytics to find when the stadium can maximize their profits during sporting events and concerts. Dimensions would include day of the week, time of event, and concessions around the stadium. You can use this information to target when the stadium has more customers, and what aspects around the stadium get the most/least use. The measure would be the revenue made at each concession and ticket sales.

  • I am thinking a data cube can be used in a mall to determine which department store sells the most clothing items. The dimensions could be time, date, season, number of sales events/discounts. The measured facts could be revenue and inventory amount. I suppose this information could be useful for inventory purposes, like if Macy’s sells the most clothes during the fall, it could increase its fall clothing inventory in advance to prevent stockouts.

  • We can use a data cube to find out many things, I am going to talk about vendors at a sports game. The dimensions would be the specific vendor, date, and what type of food they are selling. The facts would be the amount of units they sell (quantity) and how much money they made (dollars). These dimensions will let us see which vendor is making the most money.

  • You might use a data cube to organize information for a singer/performer going on tour. Dimensions could include the different venues where they will perform, the time period/date of the concert, and location. The measured fact would be the number of tickets sold and would allow the artist to figure out how big their fan base is in different places.

  • A data cube in analyzing sport statistics can be an efficient way to determine performance for a particular team or player at a particular time. The dimensions would include teams/or player, game venue (home or away), and time (yearly). The facts would generate the performance of that particular player/team for the each year with a record.

  • When taking inventory in a department store, one can use a data cube. The dimensions of the cube may include item name, brand, and type. This data cube can answer the question of type of clothing by brand is selling the most units. The facts being measured would be units sold.

  • A data cube that could answer what artists sell the most concert tickets as well as which venues sell the most would have the dimensions of musical artists, venue, and date and time. This could also answer what times of the year concerts seem more popular. The measured fact would be the amount of tickets sold.

  • An online retailer such as Amazon could use data cubes in order to anticipate what products a consumer may want to buy next. The dimensions include past purchase behavior such as dates of purchases, number of orders, departments shopped in, and what other customers with similar behaviors have purchased. The measured facts could include products ordered, total dollars spent, number of times visiting the site.

  • The business question to be answered is “how fast do items sell out?” when talking about exclusive clothing brands like Supreme who thrive on basic supply and demand. The demand for them exceeds supply (Supreme keeps supply low purposely). Dimensions would be items, stock, size, color, etc. Measured facts for consumers would be sellout times of particular items, sizes, and colors.

  • A datacube would effectively store employee information. Dimensions for this cube might include employee, position, and department.

  • A data cube to analyze purchasing habits when there are sales would be useful. Dimensions include: number of items purchased, total cost, and purchase dates. The measured facts would have to be sales performance. ASOS would be a good example of a company that could use a data cube to track customer loyalty and spending habits.

  • You can use a data cube to determine which songs on Spotify were downloaded the most for a particular week. The dimensions would include the song title, the artist, and the date, The facts would determine which songs are the most popular for that certain time.

  • A data cube could answer the following question: What regional rail line had the most travelers to 30th Street Station in 2012. The dimensions of the cube could be regional rail lines, Center City stations, and months in 2012. The cube’s measurement could be the number of riders.

  • We can use data cube to calculate how many miles an Olympic athlete practice running a day. The measure is the miles that the athlete runs.

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Alter Hall 232
12:30 - 1:50 Tuesdays and Thursdays
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David Schuff (instructor):
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