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).
MIS2502.001 | CRN 19561 | Fall 2018 | David Schuff
An example of a data cube that we did not discuss in class could have the customer, product, and date ordered as its dimensions. One of the many business questions that this data cube would be used to answer could be “What product(s) did a particular customer order on a specific day?”. The cube’s measured facts could be how many products were sold (count), how many customers brought a certain product (count), and the total number of products bought by all the customers over a period of time (sum).
A data cube we did not discuss in class is one that has 3 dimensions: library location, book checked out, and time spent reading. The measured facts are location and which book was checked out. The business question to be answered is which books were checked out for the longest at which locations. Time spent reading is not a measured fact because it can be different for each time the book is checked out.
The business question to be answered is “Which student from the past year had the highest test score?” The dimensions are the year, the exam, and the names of the students because they help answer the question. The measured fact is the test score since this is the data that is associated with the event.
A business question to be answered that we did not go over in class is within the context of a restaurant. The question would be, “Which customers ordered a certain dish in the months of June, July, and August.” The three dimensions would be Customer ID, the type of dish, and the time/month of the year. Instances of what could be measured through this cube are the most popular dish by amount sold, the month where the most items were purchased, and the total amount of customers within the span of the three months.
In basketball, a data cube could be created to answer “How did a particular player perform against a certain team last year?” The dimensions could be player, opponent, and season year, and the measured facts could be average points, rebounds, and assists. Therefore, you can see a summary of performance for a player against a certain team in a particular season.
In a car wash a data cube could be made to answer “Which services do customers buy based on the season?” The dimensions include the customer, season, and service type. The measured facts include which service type is most popular during which season such as during winter people tend to get a more detailed job because of the salt, etc.
The business question to be answered is What are the name of the students who has failed in a course in the past 3 years?. The dimensions are student name, courses and year. The measured facts would be how many time one student has failed in a course in the recorded 3 years. These factors will give a clear answer to the question.
The question we did not discuss in the class is “what is the average time of students spend in the class?” The dimensions are the Students, using the computer, and computer lab location from the location we can find out the building name. The measure facts are we can find out the average time of students spend in the computer lab with the data cube.
My example of a data cube can answer this business question: “Which size of the black dresses most customers bought?” The dimensions should be products, size and color. The measured fact is the number of sales. You can get the answer by slicing the data cube, then you will know which size(size dimension) of the dresses (product dimension) that is black (color dimension) has the highest number of sales (measured fact).
Consider this example of L.P. Jorgan, a well known fictitious financial institution. The business
question to be answered is, “which savings vehicle did a particular customer invest in and in which
quarter of the fiscal year”. The measured fact will be how many savings vehicles were sold. Another
measured fact will be the count of all of the popular savings investment vehicles invested during the year. The dimensions in this example are date, customer, and savings investment vehicle.
The business question to be answered is What is the most popular brand of chocolate in the past 3 year. The dimensions are chocolate brand, year, and units sold. The measured facts would be how many chocolate of a brand did the customer buy.
The business question to be answered is: What airlines did a passenger choose in the past 10 years? The dimensions are passenger ID, the year and the airline company. The measured facts could be the frequency of a passenger choose the same airline in a decade and what particular year this passenger choose the airline. We can tell which airline is popular in what period by looking at this model.
One of the many business questions that a data cube could be used to answer is, “What client is scheduled in for today, what time is the meeting and what is the case they are being represented for?”
The dimensions of this cube would be date of the meeting, time duration set for the meeting, and the case description. The measured facts would summarize the business meeting with client at hand.
A data cube for a national drugstore chain that has location, time (month), and product as dimensions and number of sales as the measured fact can answer the business question “which store location sold the most Allegra (allergy medicine) in April?”. Knowing which locations are most affected by “allergy season” (as measured by sales of allergy medicine) can be helpful in forecasting demand for that product in different regions.
The business question to be answered: “Which salesperson preformed the best over the past fiscal year?”. This data cube’s dimensions would be: Total Revenue, Salesperson Name, Volume of Units sold. Measured facts would include the overall performance of the Salesforce which indicate the most competent performer overall.
A Data Cube can be used in a restaurant’s business. A data cube can answer the question of the best meal of the night. The dimensions of this data cube would be the day, the meals, and the orders. The measured facts would be how many times the meal was ordered.
A data cube that we did not discuss in class could have the dimensions customer, type of pizza, and in the months of September to December (during football season). The business question would be “Which customers ordered a specific pizza between the months of September to December.” What can be measured is the most popular types of pizza’s by finding the amount sold for each individual pizza, the specific month in which they were sold, how many customers bought those pizzas and how many over the course of the the three months.
The business question to be answered is What is the most popular brand of chocolate in the past 3 year. The dimensions are chocolate brand, year, and units sold. The measured fact would be how many units of chocolate had been sold of this brand.
In class we discussed how data cubes are made up of dimensions and measured facts. The following is an example of a data cube that we didn’t discuss in class:
The business question to be answered is: which customers of a bank reported some type of fraud on their account for a given year?
The dimensions would be customer, fraud type, and year.
The facts that would be measured in this example include significant types of fraud that happen more frequently, how much fraud is reported by customers in a given year, and also which years have more fraud reports than others.
A business question for a hotel that could be answered with a data cube is how many times has a customer placed a reservation in the past 3 years. The dimensions would be customer, reservation, and the 3 years. The measured facts would be the total number of reservations for each customer. This data could be used to find a hotel’s most loyal customers.
The business question that a data cube could answer would be, “What brand and type of canned soup sells the most?” This could be a question groceries ask themselves when restocking or when deciding what brands of soup to carry.
The grocery store could measure the dimensions of brand, type of soup and the quarter it was sold in.
The measured fact would be the amount of of units sold per brand and per type. Another consideration would be the product placement since that could influence how much of an item is sold.
A business question that could be answered with a data cube could be ” What is our most profitable product? :.The facts that could be measured would be unit variable cost, fixed cost, and selling price, as well as units sold.
the business question is what location sells the most sweaters. The dimensions will be store 4 store locations, and total sweaters sold per location. The facts measured are number of units sold and selling price.
A business question that could be answered using a data cube is “what was the most popular flavor of coffee ordered in the past 2 years?”. The dimensions are flavor, year, and customer id. The measured fact would be number of quantity sold.
A business question that could be answered with a data cube is “Which webpage had the most views in 2018?”. The dimensions would be webpage, year, and action. The measured fact is the total number of page views.
The business question could be which basketball shoe brand in a store such as Foot Locker is the most popular over the last 5 years? The three dimensions would be shoe brand, number of units sold, and the year. The measured fact would be what brand basketball shoe has sold the most, and Foot Locker could use this information to determine the size of the supply of each brand in the future
A business question that could be answered with a date cube could be “what is our best ticket sales show?” the dimensions would be the performer for the particular performance, the date of the performance, and the time of the performance. The measured facts would be the amount sales in numbers and dollars.
A business question that could be answered by data cube would be: which brand sells the most profitable drug. The dimension would be the drug name and brand. The measurable fact would be the profit.
A data cube that we didn’t discuss in class is described:
The business question to be answered: how age and gender impact time to involve e-sport
The dimensions: catalogs of age, types of gender (Male/Female), duration
The measured fact(s). the engagement of demographic viewers.
The business question we could ask with that data cube is how much did each department from a company spend for the year just so a company can figure out what depart spends the most out of all department budgets. The dimensions that outline the solution would be departments, spending costs, and time (this could be broken up several ways).
A business question that can be answered by a data cube would be what make up brand is most popular in Sephora. The dimensions would be the brand, and the number of units sold. The measurable fact would be the revenue each brand made,
A business question that could be answered by a data cube is: Which make up brand is the most popular brand at Sephora. The dimensions would be the brand and the number of units sold. The Measurable fact would be the revenue made by each brand.
The question that could be answered by the data cube is “How many students took a specific course during a specific semester?” The dimensions would be the course name, the semester chosen, and the names of the students who took that course. The measurable fact would be the total number of students who took that course in the chosen semester.
A data cube we didn’t discuss in class could be if a baseball team wanted to see what hitters batted for the highest average in their home stadium last year. The measured facts would be hits and at-bats (computed for average). The dimensions would be player, stadium, and season year.
A data cube question that we did not discuss in class would be for example airlines. Airlines can make a cube that has the destination of different flights. Then, there are different tickets that a passenger can get like an economy, first class, business class etc.. And the time of the flights as well. After all the information airline can answer different questions. They can tell which destination has the most business class flyers. Or what time do the most business class passengers travel?
While watching the world series tonight I began thinking about which teams have won the world series in the past and when they won. In this instance a data cube could be created to answer the question “What team won the world series?” The dimensions of the cube could be team, opponent, and whatever year it was. The measured facts could be runs, hits, and errors. By looking at this cube, one could see what team won the world series in whatever year and all the stats associated with each series.
A business question not covered in class, but one that I use all the time at work is: which seat type has the most sales over the last three years? The dimensions would be kind of seat (box, mezzanine or tier 2), # of seats sold and total revenue. The measurable fact would be the sales attributed to each kind of seat.
The business question to be answered is: As a manager of a sport retailer, what is the highest selling item during each month? The dimensions would be the months, the number of items sold that month, and total reveneue. The measured facts would be the amount of items sold, which items were sold the most each month, and the total amount of these popular items sold. This way, we can determine the highest product sold each month and figure out a way to either discontinue our lowest selling product or to provide a discount on that particular item.
The business question would be which major typically gets the highest starting salary. The dimensions would be major, salary and year. This would measure what major has been getting higher salaries for those looking to pick a major solely on make more money based on recent industry demand (year dimension).
We could observe the food industry to ask any business questions that need be answered. For example, if we ran a diner, we could ask Which customers ordered a specific kind of pancakes during breakfast, lunch, and dinner? The three dimensions would be Customer ID, the specific type of pancake, and the time of the day purchased. Using this cube we could find out the most bought pancake by amount sold, the time it was bought, and which customers purchased it.
A question can be answered through a data cube would be “what is the most popular electronic device bought during the holidays?” The dimensions would be the past electronic devices bought past holidays, the year, and the brand of said electronic. The measured would be how many of those were sold during a given month.Each year companies can look at these pieces of information and determine what is going to be the most popular item sold.
A business question that was not covered in class is one that I see when I am working for RedBull: Which edition had the most sales in 2018? The dimensions would be the edition, the # of cans sold, and the year. The measurable fact would be the number of cans that were sold.
The business question I answered is: “What is the average wait time to schedule an appointment with a doctor at a practice?” The dimensions would be: date called to make an appointment, date of available appointment scheduled, doctor appointment is scheduled with. The measured facts would be date of the call, date of the appointment, and name of doctor. The cube could tell us how long a patient must wait for the their appointment with a certain doctor.
The business question to be answered is “Which artists sold the most from their new album in 2018?” The dimensions are the year, the name of the album, and the names of the artists, These dimensions help answer the question assigned. The measured fact is the amount they made for selling their album since this is the data that is associated with the event.
Septa is in desperate need of data cubes. A cube with dimensions of train, station, and time could answer a ton of questions for them in used properly. This cube could be used to calculate the average time it REALLY takes a train to make it to a station (and not just under ideal conditions) and time tables could be updated on a much more regular basis than they currently do (about once every three months is the current norm). Adding a customer influx dimension would also help them determine which trains are the busiest. Which trains need more cars? Do extra trains need to run during a certain time? Can more trains be switched to express and skip certain stops since nobody is getting on/off there during certain times of the day? The list goes on.
A data cube we did not discuss in class is “Which students are receiving higher test scores?” The demensions could be students, scores, and hours spent studying. We could sum hours spent study and average test scores to see if those who spent more hours studying had higher test scores or vice versa.
In my internship we used data cubes to keep data on our events. We wanted to know how successful our events were, and if it varied depending on date and type. We wanted to know how many people attended what type of event on what date, these were the dimensions. The measured facts were the specific events like the Ballet Event on Sep 2 had an attendence of 3o people, which we could compare to past events.
We would create a data cube to answer questions regarding restaurants. We would have the sales by month, product and quantity which will show which product is more popular in which month so we can plan which food item is more popular and which is not so we don’t overstock or understock each item since most food have a short shelf life. This can reduce spoilage and decrease expense.