Laurel Miller

http://www.excelcharts.com/blog/wp-content/uploads/2011/06/639px-K%C3%BChlschifffahrt_Bananen_Exporte_20071.png

http://www.coolinfographics.com/storage/post-images/PJ-BS591_MIDSEA_G_20140108164208.jpg?__SQUARESPACE_CACHEVERSION=1390159451168

YIKES, too much information

https://lh6.googleusercontent.com/8zxlFjd6Oja9Qke9C3UGY9NcuWjh71dUKbuQ97i76nz2IK9Hh1fP-Hl6rJ0R-STNGvOUQn552B-MQEYVOhK58jbFdHOKZW5vBHmDSfpJ2a0j9Jb3rRfT6sf8ew

http://www.theusrus.de/blog/the-good-and-the-bad-chicken-and-egg-problem/

http://exitcreative.net/blog/wp-content/uploads/2010/01/feed-report-bad-infographics.png

You would think that the size of the circle represents the proportion of %

http://www.forbes.com/sites/naomirobbins/2012/06/07/trellis-plot-alternative-to-three-dimensional-bar-charts/

https://www.google.com/search?q=visualize+data+world+bank&espv=2&biw=1920&bih=936&source=lnms&tbm=isch&sa=X&ei=QTYrVNvyKIe6yQSNvoHIBA&ved=0CAYQ_AUoAQ#facrc=_&imgdii=_&imgrc=TenGRCU8W9_0TM%253A%3B-w81LPfow1v7kM%3Bhttp%253A%252F%252Fone-org.s3.amazonaws.com%252Fus%252Fwp-content%252Fuploads%252F2014%252F05%252Fdata-visualization-gelman-1024×768.jpg%3Bhttp%253A%252F%252Fwww.one.org%252Fus%252F2014%252F05%252F20%252F12-data-visualizations-that-illustrate-povertys-biggest-challenges%252F%3B1024%3B768

http://data-visualization-software.com/wp-content/uploads/2014/02/dangers-of-data-visualization-3.png

http://gizmodo.com/8-horrible-data-visualizations-that-make-no-sense-1228022038

http://s.hswstatic.com/gif/prison-10.jpg

http://bost.ocks.org/mike/hive/

Graph shows numbers everywhere, might have been more beneficial to make 3 different ones rather than only one
http://blogs-images.forbes.com/naomirobbins/files/2012/05/3dbar_labels.png

Bad graphs examples:

http://people.stat.sfu.ca/~cschwarz/Stat-650/Notes/PDF/ChapterBadgraphs.pdf

Specifically look at page 4 & 9.

http://lilt.ilstu.edu/gmklass/pos138/datadisplay/images/dollar.gif

http://www.nytimes.com/interactive/2012/05/17/business/dealbook/how-the-facebook-offering-compares.html

This graphic makes multiple statements and comparisons with very little need for viewer action. It’s easy to see what is unique about the Facebook IPO.

http://exitcreative.net/blog/tag/bad-infographics/
The size of the bubble doesn’t correspond to the percentage number that it shows.

http://mediamatters.org/blog/2010/06/28/updated-worst-chart-ive-seen-all-day/166862

http://infosthetics.com/archives/2009/12/most_ugly_useless_infographic_the_winners.html

http://blog.visual.ly/wp-content/uploads/2013/09/3d-618×624.jpg

http://www.forbes.com/sites/naomirobbins/2012/02/16/misleading-graphs-figures-not-drawn-to-scale/

The first picture showing total medal count for each country is misleading. Two medals for Germany equates to almost 500 medals, so then wouldn’t you think four medals to equal about 1000 for Great Britain?

http://www.occupy.com/article/infographic-how-poverty-taxes-brain

This map is beautiful. It uses nearly live data. http://hint.fm/wind/

https://search.yahoo.com/search;_ylt=At2HgbrHwlfhc0D9Zibw_tWbvZx4?p=bad+infographics&toggle=1&cop=mss&ei=UTF-8&fr=yfp-t-224&fp=1

Way too much information going on at once and very confusing to even look at.

While I think there has been some breakthroughs in creative ways of representing data, I think it’s important to not get too crazy with the graphs and animations. You try to understand what the animations could mean as well as interpret the other 5 points the graph tries to convey. I prefer graphs which communicate a few data points. It seems anything more than a few and especially with animations seem to only create noise. The data might be more clearly and quickly represented and understandable in a separate graph(s).

http://www.informationisbeautiful.net/visualizations/million-lines-of-code/

While the chart presents a lot of comparison, it does a great job of presenting the sheer size and complexity of the Healthcare.gov codebase compared to other easy to visualize comparables. The combination of “honest” scaling and delineated color selections allow for a smooth flow as the chart builds to it’s ultimate conclusion. Further, the use of million line “blocks” within the bar graph similarly serves to scale the finding without cluttering the presentation.

The infographic can be found at: http://dailyinfographic.com/why-americas-healthcare-sucks-infographic

It is a good example because it presents the data in an easy-to-understand way, it enables easy comparison between countries, it includes graphics that make sense given the type of data, and it is organized. In addition, the visualization is not too wordy and does not have confusing or distracting text or graphics.

http://www.dailyinfographic.com/wp-content/uploads/2014/09/Wedding-budget-cost-of-wedding.jpg

I think this infographic is very simple and effective even though it is trying to provide lots of information. None of the information is lost as the visuals are in sync with the theme and do not obstruct the data. The money spent on each aspect of the wedding is segmented The infographic also provides details on expenses for each geographic location and highlights the cheapest and most expensive location.

http://london2012.nytimes.com/results

The New York Times has some of the best infographics I have seen. This particular one shows the number of Olympic medals won by each country over the past 29 Summer Olympic games. I particularly appreciate this graphic because it takes what could be a large, messy infographic (think crowded 3-D bar chart!) and lays it out in a clean, easy to follow manner. Each graphic is easy to read without being an information overload, with necessary details in a chart at the bottom for those who are interested. It also presents trends in countries’ winnings in a visual and obvious manner. You can easily see how countries grew and shrank in their number of medals won, and when exactly other continents (Africa and Australia) came into the picture as contenders.

If you’re interested in having your mind blown in a dialect infographic “Quiz,” try this one as well (note that it only includes American dialects):

http://www.nytimes.com/interactive/2013/12/20/sunday-review/dialect-quiz-map.html

http://www.coolinfographics.com/blog/?currentPage=5
The graphic is simple and easily understood. Shades of color represent when the vegetables are at their best. All of the information needed is clearly represented.

Identity Theft: You Should Be Worried

The presented infographic tells a story that is easy to follow. There is a good use of colors and pictures that help imagine the information that is presented. There is no visual clutter and the size of the pictures used is appropriate, the pictures that are bigger grab reader’s attention to point out the most important points of the story.

At the outset, I would highly recommend every one to check this site out http://www.statista.com/ This is a site which focuses on producing info graphics and charts with a great pool of industries and topics. I used this info graphic on my blog http://gauravvarma.com/post/78863284216/tablet-vs-pc to explain why PC’s are not going to be out of business any time soon. Let me know what you think about the blog post.
The key reason why I found this info graphic useful and impact full because of the following reasons:
1. The chart has presented three data sets (PC, Desktop , Tablet data) on a Sales VS Time series plane , yet delivered the entire information easily to comprehend
2. The chart has used contrasting colors; Two shades of Blue to depict – PC and Desktop and used Green to compare it with Tablets. The entire color scheme is aesthetically well appointed
3. Infographic has used the footer space to add the source of the data to create credibility for the information, which is very important
4. It has cleverly used asterisk to highlight to the reader important deviations / information which might effect judgement, to give clarification that the data includes hybrid devices such as Surface RT was excellent use of detail

THE LINK: http://www.purriodictableofcats.com/

Although the content is silly, this infographic does a good job of coding the cats with appropriate colors. Also it is interactive and contains only the pertinent information in the main form. As a result, even know it contains a lot of information, it is not overwhelming.

https://www.7dayshop.com/blog/wp-content/uploads/2014/09/The-Evolution-of-iOS-1-to-8_1140px.png
Infographics are interesting because they have recently grown into very long scrolls of information which start to finish can be difficult to thread all of the information together. In fact, often times they are composed of multiple distinct stories that share a common umbrella theme. What I think is effective about this infographic is the way that it sorted and organized the information to show the evolution. It reads left to right which is a comfortable way to view information. Finally the use of ink is effective because most of the phones are a deep dark black so using too much ink elsewhere could hurt the quality of the graphic.

Protect Yourself From Package Thieves {Infographic}

This infographic is tells about the package thieves. It tells a story starting from some interesting facts (for eg. in 2010, 9464 packages reported stolen), which attracts one to scroll further. The visuals add more strength to the facts. The infographic further tells why thieves steal packages, which season do they steal the most, the punishment and finally, suggests some ways to protect from package thieves.
One more little yet significant thing that caught my attention was the source cited below the infographic. It makes it very easy for the reader to glance over at the sources used to make the infographic.

Well, if anyone has interest in creating own infographic, you can make/submit your own infographic to this website!

http://www.harighotra.co.uk/my-blog/garlic

This infographic about garlic is very informative but at the same time easy to understand. The use of large font size for key important data minimizes graphic complexity and you just need to take one quick glance at the infographic to learn all about garlic! The strategic use of bolding and quotes helps us focus on the more relevant information and helps tell us the story of garlic in a very simple way.

https://images.search.yahoo.com/images/view;_ylt=AwrB8py4JDRU1zMAr42JzbkF;_ylu=X3oDMTIzZGwzMjNhBHNlYwNzcgRzbGsDaW1nBG9pZAMyMDM4NzA0OTMwNWFkNTYwZDU5OTRkMWU4MjEyMjNlMQRncG9zAzEyBGl0A2Jpbmc-?back=https%3A%2F%2Fimages.search.yahoo.com%2Fsearch%2Fimages%3Fp%3Dsports%2Binfographic%26fr%3Dyfp-t-224%26fr2%3Dpiv-web%26tab%3Dorganic%26ri%3D12&w=612&h=792&imgurl=www.stats.com%2Fimages_infographics%2FFantasyInfoGraphic.jpg&rurl=http%3A%2F%2Fwww.stats.com%2Finfographics.asp&size=128.6KB&name=embed+this+%3Cb%3Einfographic%3C%2Fb%3E&p=sports+infographic&oid=20387049305ad560d5994d1e821223e1&fr2=piv-web&fr=yfp-t-224&tt=embed+this+%3Cb%3Einfographic%3C%2Fb%3E&b=0&ni=21&no=12&ts=&tab=organic&sigr=115ecjc5l&sigb=13dbu4vb7&sigi=11on6muq4&sigt=10t8ripm9&sign=10t8ripm9&.crumb=z0KWEbWDGUS&fr=yfp-t-224&fr2=piv-web

I apologize about the lengthy URL but yahoo images was the only way to capture the fantasy sports infographic I wanted to use. This infographic gives a one page display of fantasy sports broken down into three sections. It shows which fantasy leagues are the most played, an average of how many teams each owner has, what type of single player leagues they use, and how much they spend on overall fantasy. All the information is displayed in an easily readable way for the consumer and each has a data point to show what each segment is depicting.

I like this infographic because it’s easy to follow and the colors aren’t too loud. The information it’s displaying is easy to understand and straight to the point. It doesn’t have a lot of useless graphics:

The Dangers of High Fructose Corn Syrup

http://www.stats.com/infographics_commissioned.asp
–>Strong infographic emphasizing specific and relevant stats for the ‘new’ NFL. Incorporates gender demographics, weather variables, play count statistics, and fantasy football participation. This infographic demands attention simply by the color scheme and template alone, the added value the stats provide draws a thin line between the NFL 4 years ago to now.

http://ibit.temple.edu/analytics/files/2013/11/1st-BrownFullGraphic.png

This is the infographic from last year’s big data challenge at Temple University. It is beautiful and clear to the reader. It starts from big picture and drills down to the sub segment that is lucrative.

This infographic shows a map of which NFL team every US County roots for. Surprisingly though not every single team is on the map. For example, in NY state where are the NJ Jets? I find it hard to believe they have no following or is the NY Giants following that much superior than the Jets.

http://finance.yahoo.com/news/map-shows-nfl-team-every-135324880.html

http://www.distancetomars.com/

This interactive infographic shows how far mars is and how long it would take to get there. The scale used is completely true from the size of the earth, moon, and mars to the distance and theoretical time it would take to travel. It is easy to read, but even easier to get the message through the interaction with the graphic. The “space” background draws attention to the main idea of the graphic and no ancillary noise is there to distract.

http://40.media.tumblr.com/501edea2fb93b1773882527b23aa127d/tumblr_mg4q3i3RHD1qhjscxo1_1280.png

This is a great graphic visualizing the different seeds that have won the Super Bowl over the course of the modern NFL era since the AFL-NFL merger. The graphic’s use of honest scaling makes it easy to understand the amount of teams at different seeds that have won the Super Bowl, and furthermore, the specific teams are listed within each seed’s section. Additionally, the use of a red blue scheme to differentiate AFC from NFC allows the viewer to easily compare performance of top seeds between conferences. Finally, the bottom of the graphic shows the performance of #1 seeds over the course of the modern era, which serves to further break down the performance of #1 seeds who did not make the Super Bowl, and compare how far other top seeded teams made it in the playoffs.

The infographic in this link compares and contrasts the way emails are being displayed by different mobile operating systems. This infographic is really interesting because it is easy to understand and it conveys its purpose very effectively. Besides the different color combination makes perfect sense to the viewer.

https://www.google.com/search?q=cool+infographics&espv=2&biw=1360&bih=624&source=lnms&tbm=isch&sa=X&ei=XUc0VMKZD8y3yATb64LQAw&ved=0CAYQ_AUoAQ#facrc=_&imgdii=_&imgrc=2wh7QTmPUgXKwM%253A%3B1fjuSBJ9ub6RcM%3Bhttp%253A%252F%252Fswebman.files.wordpress.com%252F2012%252F06%252Fplatforms.png%3Bhttp%253A%252F%252Fswebman.com%252F2012%252F06%252F%3B615%3B471

2014 FIFA World Cup

This gives a lot of information but in a very clear way. As we have seen in our last class, infographics become very confusing when there is a lot of information.

http://spatialsportsanalytics.blogspot.com/2012/09/spatial-analysis-of-tennis.html
This info-graphic webpage shows two visualizations of the same data. The first is pretty clean and fairly easy to decipher. But the second is an absolute mess. It is important that, when dealing with large data sets, to simplify the visual design so that viewers can be confident about the information in the depiction of the model. This is a good example of DO and DO NOT’s when it comes to data visualization!

The WSJ, in wake of the recent world cup, used this opportunity to compare countries in a bracket style challenge on “Everything Else”. The countries are grouped exactly as they were during the Brazil World Cup and you can choose different attributes to see which country, amongst WC competitors, would “win” said category. I find it to be a really interesting graphic in its familiarity, but also in its ease of use. It conveys very important information stylistically and will likely impart knowledge on the user that did not previously exist.
http://graphics.wsj.com/documents/WORLDCUPTOEE/

http://media.creativebloq.futurecdn.net/sites/creativebloq.com/files/images/2013/10/productive.jpg

This infographic displays a great array of information in a clear and easy to follow manner. The goal of the infographic is to give instructions on how to be productive in a number of areas such as tech, mind, body, schedule, food, etc. Every area is color coded and has a line that goes through a number of circles, and each one contains information. This makes it very straightforward and enables the infographic to convey a big amount of information without confusing the user.

Who keeps us safe online?

I found this inforgraphic to provide an enormous amount of interesting and thought provoking data in a well contained space. The background color scheme doesn’t get too busy, while also providing manageable snippets of information and references that you can check at your own leisure for more information. I found this to be an enormously useful resource on a thought provoking topic.

Referential Integrity: ensures that the relationship between two table remains synchronized, we cannot add values to the table containing foreign key unless there is a corresponding value in the linked table.
Should foreign key in a table have only unique values? Though it is preferred to have unique values in the foreign key as it becomes easy to map to the table it is linked to, in many to many relationships it is evident that foreign key does not only have unique values.
When is Normalization not preferred? When the databases we are dealing with are highly complex and have too many tables and the queries have too many joins then it not preferred to Normalize and also when the time is limited and when you want an application to be ready quickly, normalization is not preferred.

– Referential integrity ensures stable relationships between tables by maintaining consistency in values across linked tables.

– Foreign keys need not be unique (specifically in many-to-many relationships), although mapping can be made easier if unique values can be maintained.

– The level of complexity is the primary factor when evaluating whether normalization is preferred. Increasing complexity increases the time required for normalization. Be sure to weigh the additional investment to normalize the data against the value that data will provide if normalized.

Question: Name one key difference (there are more than one) between an ERD and a schema. For your reference, look at the Relational Modeling slides from last week.

Answer: In an ERD, each entity and its attributes and relationships are drawn out in a graphical display. In a data schema, all of those entities, attributes, and relationships are represented; however, if two entities have a many-many relationship then another table must be created in the schema using the primary keys as foreign keys in order to account for the relationship of the data. This is true for each many-many relationship in a data model.

In my position as graduate coordinator I will need to join the table of my current PhD students and the table of our departments program of study to see which students completed their preliminary exams and what the results were.

Joins need to be used when you want to retrieve information from two or more tables. Taking an example of an online bookstore, if we have two tables books and orders , we need to join both these tables to find how many orders each book has . Similar to this, using customers and orders, we can find which customer made how many orders. Using all three tables, we can also find which person bought what books.

A key difference between an ERD and a schema is an ERD is more of a visual representation of relationships within a system, while a schema can be thought of as the behind the scenes design of the system in tabular format.

I actually had a problem similar to question one during my summer internship. An organization was converting files from an FTP (file transfer protocol) server to an SFTP server (secure file transfer protocol). In order to do so, the organization had to merge data from two servers, each of which captured slightly different data. One server tracked a customer registration ID number, along with contact name and email address, while the other tracked a merchant ID number and most recent user activity and login information. In addition, some customers were located in both servers, whereas others were only in one or the other. I had to join the two tables together (unfortunately I hadn’t yet learned SQL!), by linking their ID numbers, to find out the total number of customers in the two databases combined, and their unique information.

From my understanding the key difference between an ERD and a schema is that an ERD is a diagram that outlines the structure of the database, it shows the relationships between the entities and their attributes and it is created as a very first step; whereas a schema is the actual database comprising a set of tables, a schema is created based on the outline provided by an ERD.

In my line of work, client spending information is captured when an insertion order (insertion ID) is generated at the vendor/partner level (Partner ID). Multiple vendor “buys”, typically identified by an estimate ID, roll up to a specific client (Client ID). Lastly, client activity is rolled up for internal use by account team (Dept./Employee ID). The joining of information within these tables allows the agency to analyze spending patterns across media types (i.e. TV vs. Paid Search), client types (i.e. Pharma vs. CPG) or internal departments/employees (i.e. Digital vs. Direct Response).

The most recent example I can provide to joining information in two data sets is that I am scripting a cover letter to send to my campers from last year’s Summer program. I am typing a single one page template but then need to swap out the last line with a specific anecdote tailored towards the reader. So I need to compile all the students(over 140) in one table, list their location, and then join this data set with a data set including my one line camper specific ‘hook.’ I will then send both tables, and the cover letter to an intern in my office to have them customize every letter.

In my opinion the difference between ERD and schema is that ERD is just a diagram that tells us the relationship between different entities and the attributes in each entity and how one entity is connected to another. But schema is the actual database that has the values from which we can retrieve whatever value is required by creating queries.

In some work that I did this past summer working on an electronic health record system, there were so many different tables of information that needed to constantly be joined. In fact often times these tables were stored in different databases and would be packaged to share with other practitioners looking to retrieve them. These tables could include allergies, medications, patient demographics, lab results, etc. Joining tables successfully is critical for safe and effective patient care.

During my summer internship, I researched the production costs associated with various merchandise products from a growing startup. Rather than having one excel document for each product, I compiled all their data in relation to production ranging from duty classification and percentage to the price tier ordering for each one. In this way, one can see the landed cost of each product at different quantities and how long it would take for the product to reach its destination all on one master sheet.

Q1:
In the movie database, I joined customer and rental tables in order to find out the individual rentals of customers.

In a past job, we were working to combine our databases on Refugee Resettlement Inflows in Chicago. We had one database of job places and the other with refugee demographics. We wanted to figure out if there were any correlations between country origin and job placement. Therefore, we had to combine the two databases.

Q2:
Schema is the actual database while, ERD is just a diagram showing abstract concepts.

Q1
The idea of joins allows users to retrieve information from two or multiple tables. Take for example in case of a hotel website. Imagine two tables:

Profile Table: a table which has customer profile information such as name, address, passport/nationality details and credit card

Transaction Table : a table which has customer transaction ID, date of arrival, date of departure , number of rooms, bed type preference, number of adults

When we join these two tables we get a number of important things. For example; a hotel can plan for the registration process formalities which they have to follow when letting a room to a guest who may be a foreign national to meet all government regulations

Q2
ERD to me is a visual representation/ diagram/ chart that outlines the structure of the database and shows the various connections and relationships between entities and their attributes. This is also the formative step of database design. Schema to me represents the actual database with tables and takes instructions from ERD to form its structure.

A very basic but key difference between the ERD and Relationship schema is that an ERD must necessarily identify unique entities by underlining the entity name. In the relationship schema, this need not be the case – the unique entity might or might not be differentiated(in some cases, we see that they might be written in bold or italics)

In my position in the Sport Industry Research Center, we have data on six different organizations and they are all listed in different files. We would need to join all six tables together to get overall values for the Philadelphia Youth Sport Collaborative (PYSC). Then when we would make individual reports for each organization, we need to combine all six organizations data to get an overall value of the PYSC groups to compare to each individual organization. Note that when we combine the organizations, we leave out the respective group we are making a report for so that the PYSC groups just contain the other five organizations.

At work, we book trades done in mutual funds into the accounting system. When doing so, we need to crossreference trade information with specific codes needed for the system. For example, we have a trade that has “goldman” or “goldmanXXX” as broker, which then needs to get mapped to a specific code for the system. So, we essentially build a table of trade details and then feed in broker codes by crossreferencing.

My understanding of the difference between a schema and a ERD involves the practice of actually diagramming out the information contained within the database. My understanding of the purpose of the schema is to act as a definition of the rules and information that will populate the database and govern how the information will enter the database. The ERD acts as a diagram that shows the relationships that govern the database. So an ERD is a diagram that governs the relationships, while the schema is the mathematical concept of how the database is governed.

Question: Describe a situation where you’d need to use a join to retrieve information from a set of tables?

An example of a situation requiring a join of two tables would be trying to figure out what the length of the rental is for each customer who has rented a car in a car rental company database. In this case, we would have to join the ‘Customer’ table with the ‘Rental’ question in order to answer the question via a query in MY SQL.

One situation where you would need to join tables to retrieve information would be if you wanted to match up students with teachers based on class. If you simply joined table students and teachers together, the teachers would be related to different pairings of students based on class.

Q2:

I believe the basic difference between schema and ERD is the purpose of usage. Schema is used as a pool to draw data, whereas ERD is a graphical presentation as how different entities in this data are related to one another.

In my past job I was to find relationships between customers demographic information, product attributes, and territory sales locations. First, one must join customer and product tables to determine what customers bought and how many of each product they bought. Then join the territory data to determine in which regions each customer resides and the volume of products sold in that region. This is ideal for determining sales trends and spending patterns. That way the company could better predict future sales by region per customer. There were a number of other joins involved and the more joins one adds the more in-depth ones analysis will be.

Question 1:
A situation where you would have to join tables to retrieve information would be if you wanted to know how many nurses per bed in hospitals throughout the country, specifically for hospitals with over 500 beds. You would have to join a table of hospitals throughout the country, with information on beds per hospital, and cross that with employment data on nurses per hospital. I currently am tackling this challenge as part of our Fox Management Consulting course.

2) ERD is just a representation of various entities and relationships between them. Whereas, an actual database contains schema. Moreover, ERD just shows the how different entities should be related; whereas schema contains actual tables along with relationships between them.

Question 1:
When I worked in media research, we used a join to retrieve data all the time. For example, if ESPN wanted to know how many males between the ages of 18-34 who lived in the Chicago DMA watched Sportscenter at 11:00PM every night of the week, we had to join 18-34 males with the Chicago DMA and ESPN Sportscenter at 11:00PM to come up with the number of viewers.

Team members:

1. Shriya Garvalia (Shriyagarvalia@temple.edu)
2. Anshuman Jha (Anshuman.jha@temple.edu)
3. Sindhuja Narra (Sindhuja.narra@temple.edu)
4. Karan Parikh (Karan.parikh@temple.edu)

The project we have chosen deals with inaccuracy of data in the Enterprise Resource Planning (ERP) software for ABC Ltd (named changed for security reason). During one of our team members summer internship experience, he found that ERP software is prone to errors and duplication.
In our project, we would analyse the issues with duplication and errors related to data and come up with a feasible solution.

Team members:

Shashank Agrahara Nagaraj – tuf28621@temple.edu
Patrick Burgi – tuf27756@temple.edu
Lauren Patterson – tuf02801@temple.edu
Jennifer Sargrad – tuf33273@temple.edu
Gaurav Varma – tuf29514@temple.edu

We will study how a business to consumer marketing organization could identify opportunities by combining two different databases: a transactional database of customer purchases and a profile database of customer demographics.

Team:
Alex Guilford (alex.guilford@temple.edu)
Asher Halbert (asher.halbert@temple.edu)
Mark Ramacciotti (mark.ramacciotti@temple.edu)

Topic: how NFL player conduct, in terms of arrests/ fines/ suspensions, affects ticket sales and how this can translate to a standard or protocol for reprimanding players in order to maximize ticket sales.

Team:
Greg Ebbecke – tue83901@temple.edu or greg.ebbecke@gmail.com
Ryan Lucera – ryan.lucera@gmail.com
Matt Cohan – tue87167@temple.edu

We have identified critical inconsistencies in how client and corporate information is collected at an advertising agency. This leads to ineffective collaboration on projects due to unnecessary information duplication.

Our project will focus on mapping the relationships between key stakeholders within the agency, developing standards to facilitate communication and collaboration between stakeholders and ultimately empowering stakeholder decision-making through better access to internal information.

Goda Neeharika Damera ,
Julian C. Wallace” ,
Robert Dettore ,
Nina Codeacova ,
Kartik Sridhar Rao

Use the pertinent BI techniques to analyzing existing customer and transaction data to come up with sound conclusions by aligning our strategy based on our analysis.

Team Members:
Goda Neeharika Damera – neeharika.damera@temple.edu
Julian C. Wallace – tuf20099@temple.edu
Robert Dettore – tuf31200@temple.edu
Nina Codeacova – nina.codeacova@temple.edu
Kartik Sridhar Rao – kartik.sridhar.rao@temple.edu

Use the pertinent BI techniques to analyzing existing customer and transaction data to come up with sound conclusions by aligning our strategy based on our analysis.