The end of Masters - end of BI lecture!

This week is rather special - Masters degree comes to an end! Its been a great experience - rewarding simply because I have learned so much and pushed myself to limits I never really did imagine!

Well, Business Intelligence being my Major and area of interest was the most looked-forward to course (Sounds weird eh?!). University of Arizona - much known for this course and the professor, I was pretty excited to see what was in store.

Here a quick outline of what I learned in the past 4 months:

- Concepts of Data Warehouses
By far the first 4-5 lectures were the most informative and helpful. I say this because it is definitely a broad topic, not something that you can learn by yourself in a short period of time and more importantly it was taught very well

- Don't have any biases when it comes to Data Analytics! 
- Your analysis is only as good as your data! With big data comes big responsibilities!

-Importance of Data Quality Analysis

-Network Analysis - through my own project as well as other team's presentations. Although, I still think there's a long way to go for network analysis to actually look & feel right - and more on that later!

-Google Analytics

To do and not to do! Graphs & More

In this post I m planning to explore various types of charts. To be precise - when to use certain kinds of charts and what not to do

Learn About Tableau

OBIEE - Intelligent or not?

Something annoying about Business Intelligence - OBIEE

Before I give my opinion on the tool, let me give an overview of what exactly i did with OBIEE.

To begin with - We were given a Cloud Airlines Datawarehouse.

We were to perform our analysis on the various facts, dimensions and then try to answer a few questions.

Here is a sample of what the questions were like:

1.    Flight Delay Analysis: Are there any general patterns of flight delays for Cloud Airlines. Do flight delays typically occur from a set of specific departure and arrival cities/airports? In general are there certain times of the year when there are more or less delays? 

   Secondly, Aircraft Utilization Analysis: How well is each aircraft utilized? Is there any variation in aircraft utilization over time?

   Finally, Seat Utilization analysis: How well is Cloud airlines doing in terms of seat utilization across their various arrival and departure cities, airports, and over time? Are there any seasonal and or temporal variations?

Now, as you might imagine, these questions seem to be quite straightforward to think of logically. When you do actually get to the datawarehouse and try to answer these based on the facts, there comes a bit of trouble.

I had the opportunity to work with OBIEE 10 g a few weeks back and my notion that Oracle produces the crappiest of products was strengthened!

Network Analysis

Network Analysis 

In a world being dominated by Social Media, it is no surprise that the next big leap would be trying to analyze and effectively use interactions by people in the social media platform.

Network Analysis has been there for a while now, where people have tries to analyze complex sets of relationships between members of social systems. I believe with the hype around Big Data, social network analysis has gained some spotlight over the recent years.

LinkedIn introduced "InMaps", which it describes as "What if you could visualize what your network looks like?  Would your connections form clusters or groups?  Wouldn’t it be great if you could see the way all your connections are related to each other? Even be able to identify the elusive hubs between your professional worlds?" 

Here is an example of my LinkedIn profile's network visualization:

LinkedIn Maps:

Observations:
- Thick Clusters are being formed
- UoA forms a Densely Connected network
- Undirected network 

Network Visualization using Gephi:

- Gephi is another tool meant for network Analysis. As a part of a project to analyze behavior of News Agencies on Twitter- specifically Bloomberg & The Economist.

Following is the list of information I gathered on initial Analysis:

nBloomberg has double the tweets compared to The Economist
nNumber of followers increased by 1M for The Economist in the past 2 months
nMost tweeted topic with Bloomberg and Economist: Culture & Politics
nMost popular Hashtags: #Shutdown & #Syria
nGeographically significant areas for agencies

qBloomberg- USA

qThe Economist - UK

nDays during which news agency is less active

qBloombergNews: Sunday & Monday

qTheEconomist: Consistent

Network Developed:

nComparison of news agencies based on

nRate of Spread

nLife Span

nRate of spread for the top tweet is faster for Bloomberg

nThe original top tweet for Bloomberg started at Sep 27 05:01:52  and the last re tweet was done at Nov  14 06:12:23

nThe original top tweet for Economist started at Oct 3 11:43:01 and the last re tweet was done at Oct 3 23:48:09