Instructor: David Schuff, Section 003

Weekly Question #1: Complete by January 26, 2017

Leave your response as a comment on this post by the beginning of class on January 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!

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Here is the question:

Give an example in your own experience where you saw raw data turned into information. For example (don’t use this one!), we discussed how convenience stores like Wawa take individual purchases (data) to learn customer buying habits (information).

55 Responses to Weekly Question #1: Complete by January 26, 2017

  • I have a project at my current job where I have to collect inventory numbers for each computer and monitor that is in my departments assigned coverage area. I then convert the numbers of inventory into a chart so we can identify which areas have aging equipment and target those areas for upgrades.

  • I had an exercise in my Macro-Econ to find out my carbon foot-print on the planet. The website ask many questions about my everyday life. At the end, the website give me a pie chart comprise my data that show me how my actions leave their carbon foot print. It also give me my rank among the people with most carbon foot print in the class.

  • Every time I “like” a post on Facebook, Facebook then uses that information to give me more relevant info. After “liking” multiple common types of posts, Facebook is able to collect the raw data and use it to create information about what kind of of topics I enjoy to see the most. My facebook page is now giving me information and “news” that I am more likely to click on and interact with.

  • A time that I have seem raw data turned into information is at the CST Advising office at Temple. When a student comes in to the office, they are asked to scan their ID. When they do this, the student’s ID number is recorded into the database. At a later time, whoever is anchorage can use the ID compilation at a later time to see how many students signed in at what time and if the same student signed in multiple times on the same day. This is useful to find out information such as if a student came in multiple times in a single day or week or when peak hours are at the office.

  • During my summer 2016 internship at The National Board of Medical Examiners (NBME), my main projects included creating a Data Cube and implementing a Client’s List, which entailed taking raw data and turning facts into meaningful elements that were incorporated into my teams daily use. While working with the Data Cube, I conduced research about international countries, regions, and markets concerning their economical, social, and cultural statistics using information available on the web, and various publications.These efforts enabled the invention of the International Data Cube, which houses international information both crucial to NBME Marketing and International Programs. Lastly, I managed data from many different resources to create the customer and prospects database from contracts and other records in International Programs.

  • A example of raw data being turned into information that I experience is also one that many people experience as well, completing a FAFSA application. In said application, I, as well as most others, need to input numbers that represent several categories of information. These types of information include Social Security numbers, tax returns, bank statements, and investments and this information is used to determine one’s eligibility of receiving financial aid.

  • When competing in the Alexion Analytics Challenge last semester, my teammates and I collected diabetes statistics (raw data) and nutrition intake (raw data) for countries and unique territories. These data on their own did not help us gain any insights into the global diabetes situation. By sorting the diabetes prevalence rate and calculating the growth of this metric over five years, we found the top 10 countries where diabetes is most widespread (information). We also tested the correlation between carbon-hydrate/ fat intake and prevalence rate in each of these countries, thereby identifying which food group to target when raising awareness about diabetes (information).

  • In my internship for Studysoup last semester, I was given the raw data of how many students each intern managed to sign up each day. It was in rows and tables – student, school, number of signups that day. I turned that raw data into information by adding it by school of the intern to see what schools were doing best, and adding up each intern’s daily results to their total to see who was overall doing the best.

  • Last semester, while doing the final project for my MIS 0855 class, my teammates and I transformed basketball statistics (raw data) and turned those into information. We collected raw data about a player’s accomplishments: field goals, two pointers, three pointers, fouls, turnovers, steals, assists, …. throughout the 2015-2016 season. Then, using John Hollinger’s equation, we used those raw data and turned those into the player efficiency rating (PER), which measures a player’s performance. By turning raw data into information, my team was able to see which player has a better performance throughout last season.

  • In my current job at Govberg Realty, I manage the digital marketing and website development work for the company. It is my responsibility to give our users the best user interface on the website – give easy access to most popular pages, make important content easily identifiable, etc. I also have to analyze which marketing methods bring the most traffic to our site – referrals from social media, email marketing, organic searches, etc. Everyday Google Analytics collects thousands of pieces of raw data from each user on each of their sessions to provide me with information to help make their user experience better and better market our properties.

  • Every time I make a purchase using my credit card, the transaction is recorded. The same step happens when I make a payment towards that card online. Furthermore, if I do not make an on-time payment or a payment that is required for the minimum payment, that information is recorded as well. All of this information is used at the end of each month to determine my credit score. Based on that credit score, a bad or a good credit score determines if lenders are more or less willing to let me spend/borrow more money in the future.

  • I believe a good example are bonus cards. They collect raw data in every transaction and they learn about the consumer habits and preferences. For example, Giant when a customer makes a purchase using his or her bonus card, raw data is collected. The data will help the company to have a better understanding of what is the consumer most likely to buy when he or she makes a purchase in the future.

  • A very simple example for me is Google.
    Google has tracked every single one of my activities from the moment I created an account to now, and this has helped learn more about than random facts. Google knows the types of stuffs I like (and I can see that from the ads they show me) and there is a setting in a Google account that lets you see what Google knows about you (such as age range, gender, and what a person likes based on search habits)

  • I see raw data turned into information every time I go on Instagram and use Amazon. If I click on a particular product’s photo or page that interests me on Instagram, my feed the following day and then on will have advertisements for those products from various companies. Also, when I order from, I get advertisements in my social media pages that are things that amazon thinks I would be interested in buying based on my past purchases. Kinda weird how it happens!

    • Leah, I noticed this happening to me frequently as well, especially with social media. If I was looking at something on amazon or various other websites, if I then log into facebook, similar products will appear on my newsfeed. On twitter, I may retweet a something from a team or sports reported and ads for ticket services and team apparel will become more prevalent on my account.

  • This past summer I interned for a chocolate manufacturing company. One of my projects was to re-develop the performance metrics used to monitor distribution operations in terms of on-time loading performance, average load time, late arrival times by drivers, etc. This essentially was to help assess our customer service levels and to improve on areas of inefficiencies.

    I ultimately created a dashboard that extracted the raw data from our TMS and WMS systems and summarized the metrics into a visual format on a daily, weekly, and monthly basis. Instead of just raw data such as driver arrival and exit time, my managers are able to see that for X week, we fulfilled customer orders on-time 97% of the time versus 93% of the time in the week prior when a forklift went down for an example. The overarching information presented in the dashboard helps managers quickly determine if performance within the time period was satisfactory and if it wasn’t they could then dig into the raw data to determine what the causes were of poor performance.

  • At my internship, I used raw data like driver age, credit score, miles per year, driving score, and if they bought a policy to see if our insurance was priced correctly. For example, if more than 50% of people over 60 bought a policy after receiving a quote, the pricing model is too generous for that age group.

  • I work in a supporting role for a class at Temple. I have to take a spreadsheet of the gradebook for the class, where every student’s “points earned” for each assignment is listed, and apply formulas to find the percentage grade on the assignment, and eventually apply a simple formula to find the students’ final grade for the class. Once calculated I use the grading guidelines as laid out in the syllabus to assign each student a letter grade for the course, and can tell how well each student did in the course, which would be difficult to tell from the raw data when presented as a large table.

  • An example of when I have seen raw data turned into information is when I search for something such as clothing on Google and Google uses that data to figure out what ads are relevant for me or what websites I might want to look at based on what I search for.

  • Most of the purchases I make using my credit card are fairly small. One time I bought something that was super expensive. The credit card company noticed that it was not something I have ever purchased before and was kind of random and flagged it. I received emails and phone calls asking me to verify the purchase and my identity. It is nice to know they are watching but it is kind of creepy too.

  • During my summer internship at an insurance company, I saw raw data about customers turned into information about the company. The raw data was information about each individual policy, such as the value of the account. This raw data would be used to show information about the company, such as total account values for all the products they sell.

  • As part of my research for a professor during the summer, I utilized Python to web scrape reviews from product and service review websites. Initially, when I ran the script and collected the product reviews, the data was unorganized and unstructured (raw data). In order to present my professor with usable data for his research, I cleaned, organized and structured the data. This data could then be used to quantify the relationship between price and quality of all consumer goods (information).

  • In my marketing research class, my team and I collected raw data through surveys. We asked Temple students their preferences on food and how much they usually spend on food per week. Our project was to create a new restaurant based on the data we collected to figure out what kind of food would sell the best and at what price. We took the raw data and turned it into information on what Temple students prefer to eat with their budget to create a new restaurant for Temple’s campus.

  • While working in retail, one part of our job is to sign people up for our Rewards program. When people comply, they jot down their name, address, phone number, etc. and later on we enter this data into our system. One of the benefits of signing up is that if they were to ever to return something without a receipt, we could find their account number based on the information they gave us and track down their receipt through our system. The raw data in this example would be what they wrote down for us to input while the information would be the list of all their transactions/receipts in our system to use for the return.

  • During my internship last summer, I managed a mail merge, and saw peoples data like their name, age, address,etc, and also the amount of money donated to Temple. This data helped formulate a letter that was able to be sent to that specific person, according to how much money was given. All of the raw data that was stored into an Excel file was transmitted into code which made a printable letter, able to be sent to the donor. This gave them the knowledge of how much that person typically donates, along with how often.

  • During my internship last semester, one of my responsibilities was to monitor Temple University Ambler’s Email Marketing campaigns. This involved reviewing email reports and evaluating important metrics such as unique clicks. The data collected allowed us to evaluate our current campaign. This data provided me with information about our viewer. Analyzing this information, I was able to understand the key words, graphics, and layout that drove the most engagement.

  • For an actuary role in a life insurance company. Based on the health condition of the insureds provide and the past data of the time customers bought policies and the time the insurance company needs to pay the benefits, actuaries can predict the likelihood of the loss from a large group of insureds for the company. Therefore actuaries are able to come up with better price for insurance premiums and prepare for future reserve.

  • The most common times I have been able to see raw data used for information is when using GPS on my phone. Just by having the location on, Google is able to tell what locations I visit and how many times I have visited that specific location, even the days of the week that I frequent to the said location. Google also uses this information to notify me of alternate routes to use to get to certain locations, traffic delays, and recommends (or advertises) other places that are similar to the locations I visit. Sometimes it will even ask me to write a review on restaurants I ate at or attractions I visited. Watching just one little source of data used for loads of greater information is both incredible and scary. They know our patterns and where we are at all times by the use of a cell phone.

  • I worked in food service at a retirement home for 5 years. During this time, I took on the responsibility of recording and updating inventory records. While I was only responsible for the data entry component, I got to see the raw data turn into information through Excel. Specifically, my manager applied a series of Excel functions and formulas to convert the raw data into usable information about weekly and monthly expenditures. My manager was able to make better purchasing decisions as a result.

  • Last semester in the introduction class to MIS 2101 my class was assigned to do the data analytics challenge for one of our learn it assignments. My group and I were given raw data of pharmacies and products in the stores that were sold. It was very unorganized data given and not yet processed yet. We took the data and started to turn it to information by determining if small pharmacies could compete with large chain companies. We finally created information with the raw data given of which top ten products sell the most for small pharmacies. That is how I experienced raw data turned into information.

  • Last semester my team and I participated in the Temple University Alexion Analytics Challenge. Our group was provided a lot of raw data from AmerisourceBergen on the topic of “How can small, independent pharmacy complete with big chains?” In order to help to find the solutions, our team looked through all the excel spreadsheets that contain transaction data such as item quantities, number of stores in the United States, etc. Without putting the raw data together, they are just simply data with no meaning. Our team read through the data and utilize Piktocharts in order to put the data together in order for them to make sense. We found out that in order for small pharmacies to do well, they need to stop selling products such as women’s contraceptive, products that do not generate much revenue. From this example, we can see that raw data does not have a meaning until we make sense of what they are useful for.

  • I see raw data turned into information when I am shopping online on various websites. After shopping online, I will receive emails and see more advertisements online for items related to what I was shopping for or purchased. It is very surprising to see how much data companies store when people are shopping online or just looking up different products.

  • I have seen raw data used in sports to create valuable insights about players and teams. For example, a basketball player records a “Field Goal” for every shot that he takes, and the location and outcome (make or miss) of the shot is kept track as well. Teams, analysts, journalists, and even fans, can have access to this information to discover important information about players and teams. Analyzing the raw shot data allows us to answer questions like “How often does Team X shoot a three pointer?” or “Where on the court does Player X take his shots the most often– or better yet– miss the most often?” When one is able to use data to answer these questions, they can better inform the way they choose to develop strategy against the competition. With the use of just simple shooting data, we can learn about the shooting habits of players and teams.

  • As a avid music listening, I’ve noticed that Apple has now updated their music app to tailor itself based on the user’s preferences. For example, when you initially open the app you’re asked a series of questions such as favorite genres and artists which helps create music playlists and mixes for the user. After using the app and liking songs, the suggestions become more custom made and specific towards the user. The app ultimately takes large amounts of data and uses it as information to make listening to music much easier to do.

  • Before coming to Temple I worked at a dry cleaners in my home town. I was able to see raw data turned into information in multiple ways. Each time a customer came in they were either dropping off clothes or picking them up. When a customer was dropping off clothes we would ask them for their telephone number which was our way of looking up our customers in our system. After that we would be linked to their account where we would start a drop off order for example, 2 pairs of pants(Beige, Blue) and 3 Laundry shirts. Although at the time this was just what it was a drop off of 5 pieces of clothing. But, over time we can see things like, what days the customer usually drops off, what day the customer usually picks up, what kind of clothes they bring in, how many clothes they bring in and how frequently they visit the store.

  • In my current job at CauseEngine, my job is to analyze excel spreadsheets and turn them into useful information. For example, there is a spreadsheet with all customer information (name, address, date that they last accessed the website, and their customer status). I then turn this data into information by creating charts that outline which customers are likely to close deals with the company and which are not. We then target those customers and try to get them to close deals. This information is very helpful for our platform because we know who to spend the most time and energy on instead of wasting time with other people.

  • Over the summer when I was interning for an employee benefits broker, I had the chance to rotate through different departments. One department that utilized raw data and turned it into information for the other departments in the company was their call center. Clients’ employee were able to call in and ask questions in regards to their current health insurance policy, or claims questions, etc. . Every time an employee called their reason for calling was recorded and how we managed to help them. Later this data was turned into information. It helped the brokers give reports to their specific clients in regards to; how many employees called from the company, what their employees were calling about to help reduce the amount of calls. Reducing calls signifies their employees understand their health benefits. Also, the information is also analyzed with all our clients to give a range from the company industry where the sat among other clients of the company in the same industry.

  • Two summers ago, while working as a teacher assistant at a local SAT prep learning center, I worked with hundreds of parents to enter their information onto our database. When they purchased our summer program, I input the amount they’ve paid, how much they have to pay, etc into our log and afterwards, we were able to jot down how well/poorly their children were doing on our practice exams. After having all that data, we were able to use such information to help accommodate each student to improve in specific areas of the SATs. I learned where certain students were struggling and where they were striving, and relayed that info to the parents, to the teachers, so that they know what to do with them.

  • I know that a lot of businesses will analyze data stored in Microsoft Excel and transform it into useful information. This process is called ETL, “Extract, Transform, Load”. Businesses will utilize the ETL strategy in order to find out what products prove most profitable next to unlikely products. For example, Baby Diapers and Beer have proven to be more profitable when paired together as opposed to apart. I know that we will learn about this process later in the class, but my experience seeing data being analyzed and transformed into valuable profit making information has been very similar.

  • During my internship at an Investment firm this summer my manager showed me how his team transformed raw data into useful information for their banking clients. The company has a large database filled with the beginning and ending market values of the portfolios they manage over periods of time. The performance reporting team uses that information to calculate the returns on these portfolios and send them back to their client provided with in-depth analysis of how they performed in any given time period.

  • While working at this past summer I saw raw data be turned into information through monitoring customer transactions and what was purchased. We would then take that data and information to send customers specifically targeted emails for products based on their sales. This helped to better our customer reorders and was very valuable to the company.

  • When I worked at the Chick-Fil-A in my home town, regional managers wanted to figure out which items were not selling as well as they could. In order to do this, they utilized POS data from our store and many others around us to find the lowest selling menu items. After this, they would design various promotions to boost the sales of particular items.

  • I like to play around with daily fantasy football sites like Draftkings during the regular season. I chart raw data on excel (fantasy points, yardage, carries, etc…) at the end of every week of the season so it leaves me with a sizeable dataset. I then convert the raw data into information by applying formulas to them. For example, if I focus on a running back who played Pittsburgh and had 10 carries for 100 yards, and a different running back that played Philadelphia and had 30 carries for 100 yards, I’m able to assume that Philadelphia has a stronger run defense than Pittsburgh. This kind of information contributes to my decisions on which players I want to play each week.

  • Last semester, In my Marketing Research course, we did a survey project on a topic of our choosing. My group chose to study the drinking habits of college aged students, specifically students of Temple University. We created a survey asking a variety of questions about their drinking habits such as when, where, what they drink, how much do they spend on alcohol, and other factors that affect their drinking. We planted the data we received into SPSS to draw conclusions about the data we received. The data we collected gave us information about the drinking habits of college aged students.

  • Over the summer I worked for a music management company and I was tasked with building a database of all the artists that worked with the company over the last 40 years. I would do research for each artist and find past tours,videos, and names of the band member. Once I found that data I combined everything and put it on that artist’s profile page which displayed all their information.

  • During my internship this past summer, I experienced raw data being put into information monthly when our error report was published and turned into productivity reports for our operations manager. The error report would gather which individuals on specific teams input information into the company’s data management tool that did not follow the tool’s rules. For each case an individual entered into the data management tool, there were 85 rules the error report would check. Each individual was able to add 200+ cases a month, so this error report was full of data that was difficult to follow. All of this data would be condensed into error rates, productivity, and efficiency percentages for our operations manager to analyze for areas of improvement.

  • When I worked at Mercedes-Benz of Princeton, I oversaw the service department staff who were receiving the vehicles to be put into service and delivered to the customer when finished. I collected raw data which consisted of each vehicle in service uniquely specified by the vehicles VIN number combined with the name of owner; along with the time and date the vehicle was brought in for repair, how long it took for the service to be completed, what severity the repair was, if the car was a comeback repair, and who the technician and service advisor was who was assigned to the specific job. Through analyzing this data and creating relatable statistics to improve the efficiency of the service jobs.

  • Every time I clicked into an item on or other shopping sites, it automatically links through the email to my social media account, and I see items pop up on facebook, instagram…etc. Also, my shopping habit is observed, and I get recommendations of what to buy next everywhere I surf.

  • Last Semester i participated the Temple Data Analytics Challenge, which accentuate on converting raw data (Statistics) into graph and eventually turn into infographics model. For the challenge, i have to use statistics to interpret the “diabetic epidemic in various continent & countries. Therefore, by using various applications, i am able to convert the raw data into different chart, graph, and in a form that allow people to understand the epidemic effortlessly. From my experience, i have to get the data (Statistics) from the medical website, such as diabetic percentage in each country, diabetic numbers in each demographics, and data of different type of diabetes. By using Tableau & Pitko chart, the raw data turns into different type of organized charts and graphs; moreover, with the art modification, the raw data transforms into infographics which appear to be more appealing and organized.

  • YouTube takes raw data, the videos I watch, and creates information about me as a viewer. They then gives me video recommendations based on the information they gathered ability my viewing habits.

  • Every time the professors of a class wants to curve the class average, they do so by taking individual test scores of students (raw data) and calculate their average. With this set of information, they can make a decision regarding the median class performance and whether they should bump up their grade or not. This has been a consistent example of raw data being converted into information in my own experience.

  • An example of when I have seen raw data turned into information was when I was working with an insurance company last year. I was in charge of taking all of the information that the brokers received from clients and inputting that information into both our system and also the government’s Affordable Care Act site. It was a very tedious job because I had to make sure all of the information was perfect and also make sure that there were no duplicates in the system. It made me realize how much work goes into building customer databases and organizing all of the information they receive.

  • My Mom works for Lori Greiner of QVC and she takes all the sales numbers of a particular item on a particular show and places it a database. This data is then compiled to show what types of products work at which seasons and time-slots on TV to better understand what is the most effective and efficient item to place on a particular show. For example the “Scrub Daddy”, an item Lori purchased on the show Shark Tank, sells best during the Spring season due to the spring cleaning fad of consumers.

  • Throughout high school I worked at a local, family owned bakery. We used Square as a our POS platform. In a business where the demand for specific items changes dramatically with the season, tracking the transactional data in Square allowed us to make extremely accurate monthly and daily predictions and adjust our inventory as such. Even in a very small business, utilizing transactional data was extremely important and effective in limiting waste, meeting customer demand, and keeping the business profitable.

  • An example of raw data being turned into information that I experience is also one that many people experience as well, Netflix online streaming. The service offers users thousands of video options and collects data on what specific users watch. This allowed Netflix to cater its experience to different demographics and individuals, creating a personal experience for users when Netflix suggests movies or TV shows. Beyond that, Netflix used market research they collected to create original content

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Where and when do we meet?
Alter Hall 232
11:00 - 12:20 Tuesdays and Thursdays
Office Hours
David Schuff (instructor):
10:00-11:00, Tuesdays and Thursdays
Speakman Hall 207G and email (see my site)

Nodir Zakhidov (ITA):
Monday: 1:00-2:00
Wednesday: 1:00-2:00
Speakman Hall 207 and email (see his site)