All Things Analytics: BI On Demand

Showing posts with label BI On Demand. Show all posts

Tuesday, January 31, 2012

Agile BI, Simple BI, Self-Serve BI - Okay, What the Hell This Thing is?

In layman's terms, anyone, including my mom, who is suffering from information overload should be able to analyze any data using simple and easy to use data visualization tools, get insights (like growth in milk usage at our home) and then share the results with my dad who should cut feeding expensive organic milk to his two cats.

Wow, that sounds pretty simple, isn't it? Yes, and precisely for that reason IDC says that this phenomenon presents a big market opportunity:

“We are at the forefront of an evolutionary market that is fraught with opportunity for innovative tools and solutions that can help users handle the information overload plaguing every major organization around the globe.” IDC Market Analysis, Worldwide Interactive Data Visualization Tools Forecast

How big of a market opportunity? $1 Billion big by 2013 and $1.6 Billion by 2015 says Gartner. See this graphics:

So someone asked me few weeks ago, how would you define simple, self-serve BI and I gave him the following definition -

Agile BI is a simple yet power-packed solution which is easy-to-use, cost-effective and offers full 360 degree experience and above all my mom should be able to use it without bugging me...

And here is my definition of a power-packed solution:

There are ZERO products that fulfill this vision today. Products like QlikTech, Spotfire and Tableau do a pretty good job and therefore enjoy more that 70% of the market share. Where are the big guys?

"Agile" and "Big" doesn't go together I guess!

Here is how I contrasted Qlik against a large enterprise BI player:

This story is universal and gives competitive advantage to younger more agile players over their older and aging brethren because they have offered one single self-serve BI tool that could serve to many personas!

Qlik and Tableau have seen pretty solid growth over the past few years as a result of keeping their strategy simple. Here is an older blog on Qlik showing its amazing growth: http://goo.gl/cyV7a

The most recent evidence of double digit growth in the Agile BI market was seen in Tableau's 2011 earnings: (http://apandre.wordpress.com/)

sales doubled year over year to $72M in 2011
104% growth in bookings in Q4’11 and 94% growth YoY,
WW customer base grew by 40% in 2011
more than 7,000 organizations use its analytics product
big growth with customers in Europe, where base grew by 67 percent

2011 was the year of Agile (Simple) BI and the momentum is gaining further strength. Do you know now what Agile BI a.k.a Simple BI a.k.a self-serve BI is defined as?

Happy Simplifying!

Monday, January 30, 2012

Updated Sentiment Analysis and a Word Cloud for Netflix - The R Way!

The Netflix investors must be happy and cheerful as the stock is up more than 78% since the beginning of the year (YES, 78%, Source: Yahoo Finance!). I am not going to talk about what turned the stock around after a much talked/hyped about Netflix debacle of the late 2011 that earned Reed Hastings quite a few UNWANTED title and every one demanded his resignation from the top post. Not so fast, Mr. Bear! Reed Hastings must be smiling! After a stellar performance this year including carefully released stats on viewership, streaming hours as well as a solid Q4'11 earnings, Netflix is back and most importantly viewers are back!

Well, is is not coincidental that the sentiment for Netflix is also improving, 68% of the tweets now have positive sentiment. See the table below:

*Total*	*Positive*	*Negative*	*Average*	*Total*	*Sentiment*
Tweets Fetched	*Tweets*	*Tweets*	*Score*	*Tweets*	*Sentiment*
499	171	80	0.281	251	68%

*Make sure you understand and interpret this analysis correctly. This analysis is not based on NLP.

I updated the sentiment analysis that I did last year, http://goo.gl/fkfPy , (I was then just beginning to play with Twitter and Text Mining packages in R) and used advanced packages like "TM" and "WordCloud". The new analysis is based on more than 6,800 words which are most commonly prescribed in various sentiment analysis blogs/books. (Check out Hu and Liu http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html)

I came across this excellent blog by Jeffrey Bean, @JeffreyBean, (http://goo.gl/RPkFX) and his tutorial. Thank you Mr. Bean! Please follow the instructions from Bean's slides and the R code listed there as well as the R code here:

Here is the updated R code snippets -
#Populate the list of sentiment words from Hu and Liu (http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html)

huliu.pwords <- scan('opinion-lexicon/positive-words.txt', what='character', comment.char=';')

huliu.nwords <- scan('opinion-lexicon/negative-words.txt', what='character', comment.char=';')

# Add some words

huliu.nwords <- c(huliu.nwords,'wtf','wait','waiting','epicfail', 'crash', 'bug', 'bugy', 'bugs', 'slow', 'lie')

#Remove some words

huliu.nwords <- huliu.nwords[!huliu.nwords=='sap']

huliu.nwords <- huliu.nwords[!huliu.nwords=='cloud']

#which('sap' %in% huliu.nwords)

twitterTag <- "@Netflix"

# Get 1500 tweets - an individual is only allowed to get 1500 tweets

tweets <- searchTwitter(tag, n=1500)

tweets.text <- laply(tweets,function(t)t$getText())

sentimentScoreDF <- getSentimentScore(tweets.text)

sentimentScoreDF$TwitterTag <- twitterTag

# Get rid of tweets that have zero score and seperate +ve from -ve tweets

sentimentScoreDF$posTweets <- as.numeric(sentimentScoreDF$SentimentScore >=1)

sentimentScoreDF$negTweets <- as.numeric(sentimentScoreDF$SentimentScore <=-1)

#Summarize finidings

summaryDF <- ddply(sentimentScoreDF,"TwitterTag", summarise,

TotalTweetsFetched=length(SentimentScore),

PositiveTweets=sum(posTweets), NegativeTweets=sum(negTweets),

AverageScore=round(mean(SentimentScore),3))

summaryDF$TotalTweets <- summaryDF$PositiveTweets + summaryDF$NegativeTweets

#Get Sentiment Score

summaryDF$Sentiment <- round(summaryDF$PositiveTweets/summaryDF$TotalTweets, 2)

Saving the best for the last, here is a word cloud (also called tag cloud) for Netflix built in R-

I will be putting the R code up here for building a word cloud after scrubbing it.

Happy Analyzing!

Thursday, December 8, 2011

Tale of Two Companies - SFSF and RNOW - Why would anyone compare SAP-SuccessFactors deal with Oracle-RightNow deal?

First and foremost, a masterstroke from SAP, I generally don't say that but this is a very smart and timely move. Read my other blog on why this a solid grab by SAP here

Facts:

SAP is proposing to pay $3.4 B to acquire SuccessFactors(SFSF), a multiple of 10.2 on expected 2011 revenue of $332M.
Oracle paid $1.4B to acquire RightNow (RNOW), a multiple of 6.2 on expected 2011 revenue of $226M.

Since Saturday, every other person is commenting that SAP overpaid including this article in WSJ.

Now what my friends in other circuits don't do is to double click on the deal itself which I did in my previous blog on the business rationale. In this blog, I will use a set of visuals to illustrate that SFSF is a far superior pick on financials. Let's start and discuss tale of two companies:

Tale of Two Companies: SFSF is a better revenue story with CAGR more than DOUBLE than that of RNOW:

SFSF is a far better growth story than RNOW:

SFSF has far better cost structure than RNOW even though SFSF has grown revenues more than TWICE as fast:

And my last point – SFSF has better operating structure and is rapidly becoming more efficient with every dollar it spends on its operating cost:

Both the growth in revenue and 15m subscriber base across the globe has come at a cost in net income but it is very quickly turning around:

I hope that my friends can withdraw their criticism because both qualitatively and quantitatively this is an astute move from SAP. Making money from cloud apps has been tough but this is very quickly starting to change. As always, time will tell who read this right!