Instructor: David Schuff

Weekly Question #1: Complete by September 7, 2017

Leave your response as a comment on this post by the beginning of class on September 7, 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 7-11 take individual purchases (data) to learn customer buying habits (information).

58 Responses to Weekly Question #1: Complete by September 7, 2017

  • I worked for my Dad’s roadside trucking repair company; large trucking companies/fleets would call a list of credible roadside repair companies and get quotes on how long it would take to have a mobile repairman come to the breakdown site to get the trcuk back on the road.

    One time during the summer, we compiled a list of quoted ETAs and looked at how often which ETA ranges resulted in the trucking fleet accepting the ETA and initiating business. We learned that quotes that were ‘too soon’ were often rejected, and quotes that were ‘too long’ or over about an hour and a half were also rejected.

    Given that information, we concluded that it was okay to quote a trucking fleet an ETA between an hour to an hour and a half, give or take about 20 minutes between how soon the repair technician could actually arrive on scene.

  • Last semester, I enrolled in a customer data analytics course where we used IBM SPSS and turned raw data into information nearly every class. For example, we used information from customer’s orders like products bought, amount spent, quantities bought, etc. and formed graphs and assumptions based on spending habits.

  • As a big social media nerd, its important to gather raw data to translate it into information. On twitter the physical tweets can act as the raw data, but the information you get from those tweets are the likes, retweets, and comments that are generated. The information you gather off the tweets shows major trends and the ability to gain a bigger following.

    What I have learned from all this raw data is you can have a large following, but not generate higher interaction numbers if you receive more likes than retweets. Also, you could have a smaller following, but if more people retweet than like the post and you will gain higher interaction.

    Why is this? Because when you like a tweet, all its showing is that you appreciate it and it will land on your like page and that is all. When you retweet something, not only is it going onto your account, but its being resent out for your entire twitter feed to see as well. This allows more exposure to your post and a more likelihood to have a higher interaction rate.

  • I am the Director of Communications for Temple’s Dance Marathon, HootaThon. This summer, I gathered data from all of our social media accounts such as Twitter and Instagram; it included aspects such as number of followers, impressions, reach, etc. From that, my committee and I were able to turn this data into information. We learned about the best times and days to post so that we could be recognized more as an organization. Overall, the data provided by these sites became information on how to reach a bigger, more precise audience.

  • In one of my classes in high school, my friends and I were assigned to come up with a profitable product that had our school logo on it. Initially, we had thought that selling water bottles or T-shirts with the school logo would be the most lucrative item due to the fact that we had seen many people try to sell similar items. However, in order to make sure that our thoughts were accurate we conducted a survey in which we asked 100 students to pick an item from a plethora options and the price they would be willing to pay. After collecting the raw information, we discovered that T-shirts and water bottles would not be profitable based on the responses we had gotten. In the end, we were able to come up with an item with a reasonable price based upon the information. It was essential to collect this information because we ended up preventing what could have been a disaster.

  • Last Spring, I completed a project-based internship with RSM in the marketing department. I collected data of non-profit organizations in the Greater-Philadelphia area for several months ( physical address, annual revenue, industry codes, C-Suite contacts, ect). After the data was collected, I used the software Tableau along with Excel tools to give an industry outlook report tailored to the areas of interest for RSM, such as trends in vertical industries within the non-profit space. Now, the marketing department is using the data I collected to create their own information about viable leads to accelerate growth in the mid-market non-profit space in Philly, South Jersey, and Delaware.

  • Last semester, Temple’s shuttle bus service kept track of how many students used the shuttles every hour. The shuttle service used this data to determine what hours of the day are the most in demand, as well as, the hours that are least in demand. This information was then used to modify the shuttle bus service hours for the current year, in order to better accommodate Temple commuters.

  • For a finance course, taken the last semester, we were given an in-class assignment to identify the best investable business using a set of financial statements. Through evaluation and comparison, we were able to transform these raw facts into the information needed to make the best decision.

  • During one of my Business Administration class, the instructor explains how Netflix determine which series to put on their database. Netflix use the data they receive from the user such as, which series were watched the most, which part the user skipped the most and which part is fast forward the most to decide how to make their original series to be a hit. One of the example is the the Netflix Original the “House of Cards” where Netflix utilize the data they get from the users. Most of the user like political drama and short intro credit. Both are the elements of the “House of Cards” and the series turned out to be a hit. This series shows how data is turned into information.

  • I worked as an accounting intern for a little over 6 months. In that time, I was inputting raw data such as invoices, and daily sales. As I entered these values in, they were categorized by what types of transactions they were. If there was shipping costs, or if it was a repair invoice, we recorded it as such. Each month, we took this data and turned it into reports. We were able to find out what we spent in shipping, repairs, and inventory, as well as track our sales and relate it to last year’s productivity. This enabled us to evaluate what factors may have positively or negatively affected performance. These processes were vital to the business model, and completed every month.

  • In one of my previous internships, my department headed an internal R&D document submission system which clients would use to submit and sort their documents for archival. Due to the complexity of the system, clients would often run into errors which then prompted the system to send my department an error type submission detailing the error. One of my assignments was to compile and sort the error type submissions received over the year (data) to determine which error types were the most common (information). Using that information, I then created new entries into the system’s help desk library detailing the correct way for clients to submit their various document types.

  • In high school, I was on a committee that organized after school events for a club. During each event, we would collect data on who attended. Using this data, we noted which days and which events had the most turnout or participation. We used this raw data to figure out which events to continue the following year and which ones to get rid of based on the popularity of the event. We also used the data to figure out which days of the week people were more likely to attend an event in order to plan more successful future events.

  • I had an internship this summer at a financial institution. I was assigned a case study that addressed the debate between active and passive investing. I sent out a survey with various questions to determine how much the average employee knew about investing. The two survey questions I focused on were one’s financial knowledge and risk tolerance. After acquiring all of the data, I averaged the results and compared the two. I learned that the less financial knowledge someone has the more risk tolerant they are. I proposed that the company develop a tool to help new investors learn more about the investment world which in turn would reduce losses because they would be making smarter investments.

  • I worked for a software development company as an IT intern during the summer. I observed that after providing a software to a client, the coding department would note what updates their existing clients demanded for the software in the future. They analyzed this data and tried to find out which updates do clients commonly demand after purchasing a software. With this information the company would develop those updates beforehand and automatically send them to the clients at the right time. This essentially improved customer service and client satisfaction.

  • Many of the shopping sites that I shop on take my past purchases and present similar ones to me anytime I visit this site. For example they will say” since you bought aveeno facial moisturizer you might be interested in aveeno hand lotion or cetaphil body lotion.” Also Amazon caters my home page to past products that I have bought. Also my itunes account takes the songs that I listen to a lot and then they show me songs that are in the same category and genre based on my music playlist. Just picking a song in the first place is the raw data, and then apple takes that information and then presents me with similar songs based on my music selection which is the information.

  • This summer I worked for my brother’s start up company in logistics as a third-party logistics broker (or 3PL). During this time I ran the company’s website on facebook using the “facebook business” feature to help promote the business using posts to collect data such as reach, website clicks/traffic, recurring visitors, and other raw information. This information is then used and converted into an analytical structure to assist with patterns, trends, and statistical suggestions – where then I would tailor posts based on when to post, what information to use, and when to boost the posts, etc.

  • This summer I worked for my brother’s start up company in logistics as a third-party logistics broker (or 3PL). During this time I ran the company’s website on facebook using the “facebook business” feature to help promote the business using posts to collect data such as reach, website clicks/traffic, recurring visitors, and other raw information. This information is then used and converted into an analytical structure to assist with patterns, trends, and statistical suggestions – where then I would tailor posts based on when to post, what information to use, and when to boost the posts, etc.

  • I had an internship from January 2017 to August 2017 at a medical supply company. I worked in the compliance department. There I received hundreds of orders a day for CPAP and BiPAP machines (breathing machines). After a month of working there my manager and I decided to see which referrals generated our company the most business. We took all of the orders for the week and saw which referral used our company the most. We were able to see which hospitals and sleep labs we were receiving the most business from and we had a plan. Our team decided to make sure that every single order that we received and created was 100% correct. We did this so we would not loss the referral because we analyzed how much money was being generated from their orders. Checking the orders helped us in the long run to make sure each patient was being set up correctly and the referral kept using our company.

  • In my opinion, a very interesting use of data analytics today is in professional sports. Today, every move made by every athlete is observed and recorded, showing just how effective they are in any particular situation. For example, a few friends and I occasionally play fantasy football. In fantasy football, who wins and who loses is based entirely on the performance of the players that each person has drafted to their fantasy team. Without the use of data, people would be picking players based on gut instincts or personal preferences. However, because each and every athlete today is having their performance recorded, people have access to complete, unbiased information on every player they could ask for. This allows people to make more informed, accurate decisions when drafting players to their respective teams. This of course is extremely important when money (or more importantly, pride) is on the line.

  • In my past internship at a boutique hotel in Rittenhouse, I was in charge of gathering all sales history data from multiple booking sites. I compiled all the raw data into an Excel database for simplification and analysis. From the data, I found information on the seasonal buying cycle of the hotel, most popular rooms, and most popular price points per room.

  • Wizards of the Coast, the company that makes Magic: The Gathering (one of the most popular trading card games in the world) has a lot to discuss about turning raw data into information. The head designer of Magic: The Gathering, Mark Rosewater, often discusses on his blog ( http://markrosewater.tumblr.com ) information about player demographics, psychographics, and the popularity of certain sets, which he determines through market research and raw purchasing amounts. For example, he often discusses a particular seasonal set of Magic: The Gathering cards called the Kamigawa Set, which, while popular with a certain subset of enfranchised players, he says is likely to never be returned to because it sold poorly and tested poorly with audience surveys.

  • Two summers ago, while working as a teacher assistant at a local SAT prep learning center, I worked with parents to enter their children’s information onto our database. When they purchased our summer program, I input the amount they’ve paid to date, how much they have to pay, etc into our log; 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.

  • Whenever I work at my parents donut shop, I always check to see which donut sells the most and which sells the least. By determining which sells and which doesn’t, I’m able to tell my brother which donuts to make more of and which to make less of. Doing this allows our business to keep up with the demand and not have an excess amount at the end of the day since we bake fresh. The customers are happy because they get their donut and I’m happy because we don’t have many extras once the day is over.

  • Last fall, I worked for the Fox School of Business Online & Digital Learning Department. In this position, I exported Excel files of raw data from Blackboard and Canvas applications including students’ enrollment and grades, as well as raw data from Vimeo (the site in which all the Fox Video Vault videos are hosted) including # of views, # of complete views, pauses, etc. I input this data into Tableau and produced data visualizations to determine that students who were watching the videos all the way through received better grades than those who did not. I also identified videos that many students were not watching all the way through, making them a target for improvement. Therefore, I saw raw data turned into useful information for management.

  • Last year I worked at a startup company called CauseEngine as a sales and marketing associate. Part of my role entailed finding prospects. Since this company focused on non-profits I used guidestar as a database for prospects. Guidestar is a data base of all registed nonprofits in the United States and I took this data and used it to compile lists of protential clients based on specifying a region, annual sales, overall size, ect.

  • Two semesters ago I conducted a survey for the Paley library in a course for my major (public relations). We were proposing a change in the libraries communicates to the student body and our goal was to increase the level of involvement with the library’s resources and the student body. We used raw data such as: hours spent in the library, how distracted they are in the library, how many times a week people visit the library, age, gender, major, class year. Using this data we concluded that the library ensures active promotion toward prospective and newly accepted students (freshman) over any other Temple Student. We also presented recommendations that the library could use to improve communication.

  • I saw raw data turned into information two summers ago during my mortgage clerk internship with Firstrust Bank. The raw data that was captured in this example was the information about mortgages that our bank issued in the past fifty years. Then, the head of our real estate department compiled the number of mortgages and their values, and displayed them as a time-lapse graph on a geo-political map to display trends in property values throughout the years in order to make predictions on the valuation of certain properties in the future.

  • Last year, I was a digital marketing intern for a women’s online fitness company called SerenaFit. Customers who participated in the subscription portion of SerenaFit had access to all live classes and video library of workouts. I monitored what types of video workouts received the most views and HIIT videos were the most popular and cardio-only videos were the least popular. I reported this and Serena started to publish more HIIT and less cardio-only videos to keep her subscribers as repeat customers.

  • At my summer internship in 2016, I turned raw data into information when I tabulated a survey for the opinion’s on the safety level of a particular park in the Bronx and entered in a bunch of numbers ranging from 1-5 into Excel (1 being the least safe and 5 being the most safe). This data was then turned into information and conclusions about what to do to make the park nicer, cleaner, and safer. We created a pivot-table with all the categories and data that corresponded to get a better picture of the popular opinion.

  • I work at Wegmans grocery stores as a member of the maintenance department. One of our tasks is to go around the store with a data collecting system called a “pipe” we use these “pipes” to hit safety points throughout the store; this allows us to ensure that there are no dangerous areas throughout the store. At the end of the week, all of this data is collected by the system and then a report is created detailing what areas around the store need more attention and sets goals for employees to hit in order to improve performance and safety throughout the store.

  • I was watching the Georgia Tech vs. Tennesse football game last night, and it seemed like Georgia Tech was converting on almost every 3rd down. Toward the end of the game, the commentator shared that Georgia Tech had converted on 13 of its 3rd downs. At the end of the game, I looked at each teams’ stats and saw that Georgia Tech had a 72.2% third efficiency rating. In this case, the 13 third down conversions was the data, and the 72.2% efficiency rating was the information.

  • I used this in my current job as Community Manager of a co-working space downtown. Part of my job is to learn who our current market is, and how to best reach out to future members. I use the demographics and interests of our current members in order to understand who we currently cater to. Hypothetically, when I notice that all of our community members happen to be marketing or social media companies, this allows me to try and target other types of startups. Now I can generate more accurate leads with the data I was given, and I don’t waste time. Maintaining a diversity amongst members is important for the success of the community, and the inter-community relationships.

  • Last semester, I would ride the subway about three times a week. I took notice of how many empty seats there were on each days. On very few select days, there were absolutely zero seats available. These would be days in which there were sporting events. In conclusion, using the data of the amount of empty seats on the subway, it is observant that there are less empty seats when there are sporting events.

  • I work Mathematica Policy Research, a private research firm, and a lot of our contracts/jobs are for the government. We get hired by the government to research how certain programs that are currently implemented are functioning. My job consists of surveying these program participants and collecting their answers. After the raw data has been collected, the research team turns that data into information that is then presented to the government and utilized to show how effective or ineffective the program is; or the impact that the program has on its participants.

  • Through digital media and re targeting, marketers are able to transform recent search histories into pin point targets to attract a potential consumer. For example, if you search brown boots from American Eagle, site banners and website advertisements begin to promote those brown boots. Searches made on the internet are constantly collected into data and then used to track and target based of off IP addresses to promote updated information regarding past searches.

  • I used to work at a restaurant called Hip City Veg in Rittenhouse Square. At this restaurant, we sold vegan food to customers. We had started selling a new sandwich and while I was working the register, my boss would ask me how many people were purchasing the new sandwich. I would gather data and see how many people were buying the new sandwich out of the total customers we had that day. We would turn this data into information by seeing how much more we needed to market the new sandwich or if it was doing well enough to sell itself.

  • In the summers when I was younger, I used to work in my dad’s warehouse. His non-profit organization, National Greyhound Adaption Program, received frequent donations of returned or damaged dog products from various different suppliers. My job, along with other warehouse workers was to unload the trucks that came to deliver the products, take everything of each pallet, and make a list for inventory. We recorded each product by supplier, type, and the date that we received it. While this was mainly used for inventory, at the end of each quarter, National Greyhound Adoption Program would analyze the raw data to determine which products they were getting from different suppliers, and how often they were receiving these donations. Using this information, the organization can determine what products they should request more of, what products they should request less of, and whether or not purchases of certain products need to be made.

  • I’m an accounting major and a few years ago I had what many would consider a mentorship with a Certified Public Accountant named John Byrne. I had just completed a QuickBooks class and my time spent with him was supposed to teach me how CPAs use QuickBooks in their daily work routine. If you do not know what QuickBooks is, it is an accounting software that keeps track of a business’s internal financial reporting information. Anyway, we started out just typing a bunch of numbers into the software’s various accounts, which were represented by accounting numbers initially. After entering the numbers and adjusting the accounts, Mr. Byrne showed me how to organize the accounts into reports. With a few clicks, Byrne combined all my number inputs into a balance sheet, income statement, statement of cash flows, etc. He also taught me how to transfer Excel information into QuickBooks and produce the same financial statements, a skill I found incredibly useful because I do a lot of my accounting coursework in Excel. It was absolutely brilliant!

  • At my summer internship this summer, I worked at a physical therapy clinic. The clinic would put on free “workshops” where physical therapists would give a presentation about a common problem such as knee pain, back pain, etc. The therapists would then offer a free screen to everyone who attended in an attempt to gain them as a patient. We would take the data of who attended, how many signed up for free screens, and ultimately who converted to a patient and would then present this data as valuable information as to the success of the workshops, who the best presenters/closers were on patients, and what should be done in the future regarding the workshops. It was simple data of just numbers but was turned into valuable marketing information for the firm.

  • The social security number is an example of raw data. It is used by the government to keep track of the its holders and they use it to obtain and save accurate information about the individuals; such as their incomes, criminal records and benefits. The government also uses these information to generate studies which would help in the decision making processes of improving all the different sectors.

  • Last summer I worked as a instructional assistant with special education students. Each student had a tailored instructional plan to fit their abilities and worked in certain areas in order to improve. Each student had a semester-long education goal which was divided up by categories of skills or subjects. Weekly we would test each category and input our scores into excel. Periodically we would compile this information and check how much closer (percentage wise) they were to reaching their goal. Based on this information the next semester goal could be set.

  • I saw raw data turned into information in my chemistry lab last semester as I was doing an acid-base titration lab. My lab partner and I were monitoring the pH as well as the amount of titrant was being added to the analyte. From the data points we collected, we generated a titration graph; from the graph we determined equivalence point and was able to calculate the concentration of the analyte.

  • Whenever I Google something, there is a good chance it is going to pop up as an advertisment when I’m scrolling through Facebook. Being that Google and Facebook are owned by the same company, Google collects raw data from your search history/spending habits and uses this information to create an advertisement that will grab the viewers interest. By collecting data from your search history, Google and Facebook are retargeting in hopes that a reminder will help persuade the consumer to make the purchase.

  • I have noticed Spotify music turning raw data into information in many different ways. Spotify will record what you are often listening to and suggest similar songs and artists that they think you will also enjoy. When you make a playlist of songs on Spotify, they give a list of other songs that would fit into your playlist. Also at the end of every year, Spotify sends an email to all premium users of their stats for the year so you can see your most listened to songs, artists, and genres. Last year, Spotify used their data to create billboards that revealed the most embarrassing listening habits of their users as an advertising technique.

  • While working at Wine & Spirits, I was able to see raw data turned into information. Looking at the numbers from sales, we were able to evaluate our inventory levels and determine whether we had enough bottles on the shelves/in the warehouse for restocking. An example of how our store used this information was with Jam Cellars Butter Chardonnay wine. In the beginning of the summer, customers constantly came in asking for it. Because it was so popular, whenever we would get a shipment in, it would almost immediately sell out (because they knew how hard it was to get, customers would often buy it by the case instead of getting single bottles). In evaluating the amount of sales and noting the number of customers who were trying to buy the product, management contacted the suppliers to increase the amount of Butter Chardonnay to be shipped to our store. Once this change occurred, we were able to keep the wine in stock and meet the needs of customers, instead of seeing them leaving the store empty-handed and dissatisfied.

  • During my high school years, social media got really big. Platforms such as Twitter/Instagram/Tumblr/Etc were all gaining traction. All of my friends and I had Instagram, a site where you share photos and videos. We wanted to figure out how to get the most likes and comments on our photos (back then Instagram didn’t allow you to upload videos). We figured it was mainly when people wake up (because most people check their phones when they wake up) and around bed time (anywhere from 10 pm to 1 am). We chose those times because that was when we suspected most people were on their phones which would lead to more people seeing our posts.

  • Having customers filling in a logbook; time and reason/services. Using that data, you can see when the store is busy on certain days, then you can make adjustments so that the store will flow smoothly during those hour.

  • This past summer I interned in the food and beverage department for a minor league baseball team in Troy, NY. One of my main responsibilities was to take in deliveries and manage inventory. At the end of each homestand, I would take inventory of the the food and dry stock in the facility. My boss and I would then use this data to determine what we needed to order for the next homestand based on how much product we went through and what the expected attendance would be.

  • Through the trampoline park I work at, I frequently see raw data turned into information. Every jumper must have a waiver filled out. From that waiver, we take the data such as “age” and are able to see what age groups come in most often. I use this information to create special events that target the most frequently attending age groups.

  • An example of raw data being turned into information would be that of a mechanic shop, they, for instance, have customers who come in for tune-ups. The mechanics do the tune up on the car and log the data on their computers, at this moment the computer knows who the customer is, what type of car the customer drives, and by gathering this data the mechanics then know when the next tune up should be done on the car. The mechanics then let the customer know when he/she should bring the car back for its next tune up.

  • My mom opened a nail/hair-salon this summer near Tufts University and had one of her friends create a website for her. Through Yelp Business, I was able to see how many new visitors were visiting my mom’s website daily and weekly. It’s good to keep track of how many new people visit the site because that would indicate that they are interested in coming by for appointments. I think one way that the raw data we see from the number of visitors can be used as information would be to compare the traffic during the summer versus during the school year when a lot of students come back.

  • I had a part-time job this summer at an insurance agency group. I was assigned to input customer’s basic information and their type of service with us, into our database for future reference and usage. During the off-season, when there is a low demand for auto and home insurance. With the data we have in our data base, we were able to use the information to analyze who are the customers we should push to get a better quote for them or call to sell them other services, such as life insurance or medical insurance. And for customers that already have both auto & home insurance with us, they will most likely to pick up our phone calls and interested in getting other services.

  • I see raw data turned into information almost every day when shopping online at Amazon or even just doing Google searches. After making purchases on Amazon, or even just searching for certain products, Amazon shows me a bunch of items that they recommend for me or that I might like. Most of the time as I’m scrolling and I see these recommended products for me; they actually are things that I like and would purchase. The raw data is my purchases and searches, the information they get is products that interest me. Google takes all the google searches I make and tries to have advertisements that are geared toward things they think I am interested in. In this case, the raw data is my Google searches and the information they get from it would be my interests.

  • Last semester, I worked for the Program Director of the Temple University Management Consulting Program who was working on launching the new consulting minor here at the Fox School of Business. My job involved running information sessions, helping out at SPO fairs, setting up informational tables all over campus, and the like. In doing so, I collected raw data from interested students, professors, parents, etc. On the sign up sheets, I would ask them to include basic information such as their name, year, major, email, and TUID as well as more in-depth information such as whether they are interested in the minor, certain classes, the club, or all of the above. I used this raw data to put together groups based on class year and interests so that I could send out emails and information more efficiently.

  • This summer I interned at an insurance company where we were given a project to research and formulate an opinion on whether or not the product we researched could be insurable for the first time. I was responsible for the data portion of the project, which involved sorting years worth of loss data into useful visuals to use in our presentation. A few examples of charts included Written Premium vs Losses by state, as well as the Losses of different product lines by state.

  • During the Summer of my second year I worked at a local Mexican Restaurant called Burritos. We collected data on consumers purchase, the time they were purchasing and what day they were purchasing. From this data we were able to interpret it into information and forecast future revenue, peak times and days. This helped us figure out how much supplies we needed and when to restock any goods, increasing our efficiency.

  • I worked for this non-profit organization called Philadelphia Furniture Bank, where we provide furniture for homeless people, veterans, refugees, etc. For one of the projects, I did the inventory count in the warehouse, to figure out the quantity and line of products available. With the inventory count – the raw data – we compared it to past inventory counts and analyzed certain patterns. We figured out the dynamic of supply and demand throughout the year, in which we used said information to better our transportation logistics and supply chain management.

  • In auditing class, last semester, we were assigned a company and essentially had to preform an audit for that company. While doing this task we looked at the data on their financial statements and looked at data on the average company figures within the same industry. Then we compared these figures to get information on how our company compared with the average company in its industry.

  • In 2015, I worked for an organic candy start up and my role was a sales analyst. Essentially I took raw sales numbers from department stores we sold too and tried to put a story together of why certain candy sold better in certain stores. We had a huge product offering of about 20 different candies, but what I noticed was that we really only sold about 4-5 different candy types. Based on my analyzation we actually eliminated some of our product offerings and were able to shave off some production expenses and in turn increase net income.

  • In my Customer Data Analytics class we often not only saw raw data turn into information but we also had the task in a few different instances where we had to turn that raw data into useful information. We did this through a software program called SPSS. In a beginning MIS course during my sophomore year we used Tableau to turn raw data in visual info-graphics. Everyday instances where raw data is turned into information is Twitter when they use analytics for tweets,posts and pictures.

  • In my past summer internship, my tech company VNG Corporation organized a race for every employee to run for charity and reinforce the sports spirit for the company. As one of the organizers, we tracked racers database (including personal info and running miles, speed, time) by Strava account. Then we built a platform to pull the data from Strava to our storage to keep track the results and coded it to automatically rank people’s miles and teams’ miles and display the result on a website. The website’s result kept updated the results with miles and ranking for everyone. So the raw info is the mile, time, and speed and the info we had is their rankings with miles.

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Where and when do we meet?
Alter Hall 232
12:30 - 1:50 Tuesdays and Thursdays
Office Hours
David Schuff (instructor):
2:00-3:00, Tuesdays and Thursdays
Speakman Hall 210E and email (see my site)

Lauren Soentgen (ITA):
1:00 - 2:00 Mondays
11:00-12:00 Fridays
Speakman Hall 210D (and see her site)