Democratization of Business Analytics Dashboards

I am super impressed with the following visual dashboard from IPL T20 tournament - IPL 2013 in Numbers.  For those of you not so familiar with cricket or IPL, IPL is the biggest, the most extravagant and the most lucrative cricket tournament in the world.  I like the way IPL is bringing sports analytics to the common masses.

What is impressive is that each metric (runs, wickets, or tweets) is live so these numbers get updated automatically, pretty cool for IPL and cricket fans.  Also, each metric is clickable so one can drill down to his or her heart's content.  This is a common roll-up analysis but the visualization and the real time updates make this dashboard pretty appealing.  IPL team, thanks for not putting any dials on this dashboard (LOL).

I have been influencing and now building analytics products that power these sports and various other dashboards/reports for many years.  The most fascinating thing is that these dashboards (or lets call it analytics in general) are reaching the masses like never before.  Everyone has heard of terms like democratization of data and humanization of analytics.  This is it!  The data revolution is underway.  

Now, there are many new frontiers to go after and the existing ones need to be reinvented.  Yes, the analytics market is ready for massive disruption.  This is what keeps me excited about Business Analytics space.

Happy Analyzing and Happy Friday!

Financial Markets and President Elect: Do Financial Markets Favor A Republican Over A Democrat?

US financial markets favor a republican president over a democratic president. Has this sentiment stood the test of time?  Do financial markets care whether the president elect is a democrat or a republican?  How have financial markets behaved in the past after the announcement of next US president?  And finally, can one spot a pattern in the performance of financial markets based on the president's party affiliation?  More specifically, did financial markets fare better under a republican president or under a democratic president?

To answer all these questions, I turned to history and generated the historical performance of S&P 500 since 1952.  I also had to turn to Wikipedia to get a list of presidents and their party affiliation.  Between 1952 and 2012, US has elected 16 presidents with republican presidents outnumbering their democratic counterparts by 2 in occupying the white house (see table below):

To understand whether financial markets favored a republican president over a democratic president, I generated 1-day, 1-week, 4-week, 12-week, 52-week and the presidency term ("term") returns since the election date (see table above.)  Looking at the one day return, there was no clear indication whether markets favored one party or the other.  Financial markets welcomed Ronald Reagan, a republican, by sending the S&P 500 up 1.77% which is the highest one-day return among all the 16 presidential events.  Markets also cheered the reelection of Bill Clinton with a one-day return of 1.46% after the announcement of president elect.

Source: AllThingsAnalytics

With one-day returns of -5.27% and -2.37% in 2008 and 2012 respectively, President Obama is not much favored by financial markets.  Now, one can argue that October 2008 was a terrible period for anyone to be elected as the president because of the ongoing crash in financial markets that led to the great recession (see side chart.)  Nonetheless, markets also didn't like Obama's reelection (S&P 500 was down 2.37% following the election day) which leads to a status quo in Washington.  Combine that with all the ongoing macro concerns including the Euro debt crisis and already unraveling fiscal cliff, investors have become very jittery in the past couple of days.

Now, to overcome the short-term bias in financial market's reaction, let's review other period's returns (see table above.)  There are plenty of interesting observations one can make.  For example, under both of Clinton's (Democrat) presidencies, financial markets boomed with returns of 56% and 111% over the next 200 weeks since the election day.  Eisenhower's (Republican) presidency came second with returns of 95% and 19%.  Reagan era followed by Bush Sr's term also produced hefty gains for investors with returns of 28%, 56% and 49% under their terms.  Again, there is no clear indication whether financial markets favored one party over the other during a president's term in the office but financial markets definitely fared well under a republican president prior to 2000.

Bush Jr. (Republican) inherited dot-com crash, oversaw the biggest expansion in US public debt (see chart below) and observed the epic housing crisis of 2007-2008.  Financial markets yielded returns of -22% and 13% during Bush's two terms presidency, pretty poor for a republican president who unleashed all the expansionary polices on US economy.  Under Bush's 8 year presidency, US public debt doubled from $5.6 trillion to $10 trillion.  Obama added almost the same level of debt in just 4 years and took US public debt from $10 trillion to $14.2 trillion by the end of 2011.

Source: AllThingsAnalytics

From Wikipedia, click to expand

Are we living in times which have no historical precedence?  It took 20 years for US public debt to rise from $1 trillion level to $5.5 trillion level (see side chart).  It then just took 11 short years for US public debt to rise to $14 trillion level.  From year 1980 to 2000, S&P 500 appreciated by 1276% (from 105 at the start of 1980 to 1455 at the start of 2000). Astonishing rise!!!   Also astonishing is the fact that since 2000 till date, S&P 500 has been down -5%.  Has the mammoth economic expansion of 1980s and 1990s run its course and now debt is the only route left to sustain US economy.  Let's leave this discussion for another blog.


Financial markets care less which party's candidate is elected for the white house and focus more on the economic policies that president will enact.  All the rhetoric and party ideology does take a toll on financial markets though as evident in financial markets' immediate reaction similar to the one we are observing right now.  Hopefully, the congress and the president will put the rhetoric aside and break the impasse on the already unraveling fiscal cliff.

This blog has benefited from discussions with Jens Doerpmund, Ryan Leask and Rajani Aswani on this topic.

Disclaimer:  All numbers are approximate and the underlying analysis is preliminary.  This blog is not intended for offering any investment advice.

Besides Facebook's Botched IPO, IPO Market Returns 20% in 2012

Facebook (Ticker: FB) is down ~47% since its IPO in May.  Now, it is not the most botched IPO ever unfortunately as the infamous record belongs to BATS Exchange (Ticker: BATS) which operates an alternate stock exchange to NYSE and NASDAQ.  (Read the Business Insider story here: 8 Unforgettable IPO Disasters)

Also, FB is not the worst performing IPO either.  Groupon (Ticker: GRPN) and Zynga (Ticker: ZNGA, proudly led by Mark Pincus), are down 77% and 74% respectively since their IPO.  In comparison, FB has done ok, it could be worst but a rapid strategy shift by FB including the emphasis on mobile and a decision to allow e-commerce transactions (Facebook Gifts) on Facebook have provided some kind of a floor under its stock.  Here is a chart comparing the three (not-so) darlings of the Web 2.0.

Anyhow, below is a table of the best IPOs for this year.  Guidewire (Ticker: GWRE) and Demandware (Ticker: DWRE) are the two cloud technology companies in the list that have done very well returning 137% and 108% till date.

IPO Top Performers (YTD)

Company		Offer Date	Under	Industry	Deal Size (mm)	Offer Price	First Day Close	Closing Price	First Day Return	Total Return
Supernus Pharmac		4/30/12	Citi	Health Care	$50	$5.00	$5.37	$12.77	7.4 %	155.4 %
Nationstar Mortg		3/7/12	Merrill	Financial	$233	$14.00	$14.20	$33.29	1.4 %	137.8 %
Guidewire Softwa		1/24/12	JPM	Technology	$115	$13.00	$17.12	$30.84	31.7 %	137.2 %
Annies		3/27/12	CS	Consumer	$95	$19.00	$35.92	$44.87	89.1 %	136.2 %
Demandware		3/14/12	GS	Technology	$88	$16.00	$23.59	$33.31	47.4 %	108.2 %

Palo Alto Network (Ticker: PANW) is up 16% since IPO with returns of 48% over its IPO price of $42.  Splunk (Ticker: SPLK) is down about 10% since IPO but still giving returns of 90% over its IPO price of $17.  Both these companies didn't make the cut in the table above.

Here is a list of the worst performing IPOs till date.  If one were to change the time period from YTD to 12-months, Zynga shows up in the list, no surprise there.  Social gaming is a fast changing environment and ZNGA faces crisis in confidence with so many departures.

IPO Worst Performers (YTD)

Company		Offer Date	Under	Industry	Deal Size (mm)	Offer Price	First Day Close	Closing Price	First Day Return	Total Return
Envivio		4/24/12	GS	Technology	$70	$9.00	$8.49	$2.15	-5.7 %	-76.1 %
Audience		5/9/12	JPM	Technology	$90	$17.00	$19.10	$5.65	12.4 %	-66.8 %
CafePress		3/28/12	JPM	Technology	$86	$19.00	$19.03	$8.07	0.2 %	-57.5 %
Ceres		2/21/12	GS	Materials	$65	$13.00	$14.80	$5.77	13.8 %	-55.6 %
Renewable		1/18/12	UBS	Energy	$72	$10.00	$10.10	$5.16	1.0 %	-48.4 %

Take a closer look, FB is barely staying away from this infamous list.  On a similar note, LinkedIn (Ticker: LNKD) is up approximately 80% till date.  What a contrasting tale of the two social network companies!

So far in 2012, IPOs have resulted in 20% returns which is better than the -11% returns IPO market yielded in 2011.  Since there are about 2.5 months more to go before the curtains drop on 2012, the 2012 IPO return might beat the 25% returns the year 2010 produced.

One very encouraging signs for the IPO investors this year has been the 13% average first day pop in IPOs that is line with what IPO market observed before the great recession (~13%).  And to all the naysayers out there who claim that tech-stocks are in a bubble, take a look at the average opening day pop in 1999 (72%) and 2000 (56%) and compare it to 2012, you will hold your peace for few more years at least!






Workday (Ticker: WDAY) is on the deck for this week.  Do you due-diligence before investing.

Happy IPO Investing!
Jitender

Source: Renaissance Capital, Greenwich, CT (www.renaissancecapital.com).

Tracking SFO Airport's Performance Using R, HANA and D3

Visualize Big Data Using R, HANA, D3, JSON and HTML5/JavaScript

This is my first introduction to D3 and I am simply blown away.  Mike Bostock (@mbostock), you are genius and thanks for creating D3!  With HANA, R, D3, HTML5 and iPad, and you got yourself a KILLER combo!

I have been burning my midnight oil on piecing together my big data story using HANA, R, JSON and HTML5.  If you recall, I did a technical session on R and SAP HANA at DKOM, SAP's Development Kickoff Event last week where I showcased the supreme powers of R and HANA when analyzing 124 million records in real time.  R and SAP HANA: A Highly Potent Combo for Real Time Analytics on Big Data

Since last week, I have been looking for other creative ways to analyze and then visualize this airlines data. I am very fortunate to come across D3.  After spending couple of hours with D3, I decided to build the calendar view for the airlines data I have.  The calendar view is the first example Mike shows on his D3 page. Amazingly awesome!

I created this calendar view capturing the percent of delayed flight from SFO airports that departed daily between 2005-2008.  For this analysis, I used HANA to get the data out for SFO (out of 250 plus airports) over this 4 years period in seconds and then did all the aggregation in R including creating a JSON and .CSV file in seconds again.  Later, I moved to HTML5 and D3 to generate this beautiful calendar view showing SFO's performance.  Graphics is presented below:

As expected, December and January are two notorious months for flights delay.  Have fun with the live example hosted in the Amazon cloud..

Once again, my R code is very simple:


## Depature Delay for SF Airport
ba.hp.sfo <- ba.hp[Origin=="SFO",]


ba.hp.sfo.daily.flights <- ba.hp.sfo[,list(DailyFlights=length(DepDelay)), by=list(Year, Month, DayofMonth)][order(Year,Month,DayofMonth)]
ba.hp.sfo.daily.flights.delayed <- ba.hp.sfo[DepDelay>15,list(DelayedDailyFlights=length(DepDelay)), by=list(Year, Month, DayofMonth)][order(Year,Month,DayofMonth)]
setkey(ba.hp.sfo.daily.flights.delayed, Year, Month, DayofMonth)
response <- ba.hp.sfo.daily.flights.delayed[ba.hp.sfo.daily.flights]
response <- response[,list(Date=as.Date(paste(Year, Month, DayofMonth, sep="-"),"%Y-%m-%d"), 
                           #DailyFlights,DelayedDailyFlights,
                           PercentDelayedFlights=round((DelayedDailyFlights/DailyFlights), digits=2))]
objs <- apply(response, 1, toJSON)
res <- paste('{"dailyFlightStats": [', paste(objs, collapse=', '), ']}')
writeLines(res, "dailyFlightStatsForSFO.json")                 
write.csv(response, "dailyFlightStatsForSFO.csv", row.names=FALSE)


For D3 and HTML code, please take a look at this example from D3 website. 

Happy Analyzing and Keep That Mid Night Oil Burning!

Advanced Analytics with R and HANA at DKOM 2012 San Jose

Advanced Analytics with R and SAP HANA

R has become the open source language of choice for statistical data analysis / data mining, advanced algorithms, credit risk scoring and for other forms of predictive analytics.  R is already an in-memory based scripting language and is capable of handling big data, tens of gigabytes and hundreds of millions of rows.  And when combined with SAP's in-memory platform technology called HANA, R offers the potential to take the in-memory analytics to a whole new level.  Imagine performing advanced statistical analysis such as decision tree, game-theory, linear and multiple regressions and much more inside SAP HANA on millions of rows and turning around with critical business insights at the speed of thought. 

This is possible now with R and HANA.  This combination has the potential to completely revolutionize and advance the game of analytics in your enterprise.  This is not it yet.  Imagine taking the output from R and using the Advanced Visualization techniques available in Business Intelligence 4.0 suite based on HTML5 to create stunning visualization for today’s business users.

Just to tease you, here is a one-liner in R that processed 120 million records and brought back aggregated data under 20 seconds:

averageDelay <- dt[,list(AvgArrDelay=round(mean(ArrDelay, na.rm=TRUE), digits=2),

                  AvgDepDelay=round(mean(DepDelay, na.rm=TRUE), digits=2),

                  DistanceTravelled=sum(Distance, na.rm=TRUE),

                  FlightCount=length(Month)), 

                  by=list(UniqueCarrier, Year)][order(Year, -AvgArrDelay)][AvgArrDelay > 10 | AvgDepDelay > 10]

The machine I used for this analysis had 24 cores and 96GB of memory! More to follow over next few days.

Join me and my fellow colleagues for this session at DKOM 2012 San Jose (March 14th at 11 AM at San Jose Convention Center)

Big Four and the Battle of Sentiments - Oracle, IBM, Microsoft and SAP

In this battle of sentiments or opinions for the four software giants - Oracle, IBM, Microsoft and SAP, SAP is generating a lot of positive buzz with its message of "innovation without disruption" and leading the pack with a 95% sentiment score.

Tag	TweetsFetched	+ve Tweets	-ve Tweets	Avg.Score	Tweets	Sentiment
@IBM	198	49	45	0.081	94	52%
@Microsoft	893	307	78	0.484	385	80%
@Oracle	297	90	17	0.313	107	84%
@SAP	98	55	3	0.673	58	95%

Few days ago, I published this blog "Updated Sentiment Analysis and a Word Cloud for Netflix" and the underlying R code.  I used the same R program to compare the sentiments for the four software giants.  Now, technically speaking, IBM and Oracle are not pure software companies anymore since they both package hardware (server and storage hardware) along with the software but the rivalry between these four companies persuaded me to put a comparative analysis  here.  I originally included HP in this analysis but then dropped it as I didn't consider HP in the same league as these fours in the software category.

What surprised me the most was the lowest score IBM received, lower than Oracle!  What went wrong here?  I am also surprised to see Oracle occupying the second spot with 84% sentiment score.  So besides all the negative publicity Oracle attracts, the sentiment is overwhelmingly positive.

The one improvement I would like to make to this analysis is to get more tweets.  Twitter API restricts the number of tweets that one can fetch and doesn't allow you to fetch older tweets.  I would love to run this analysis over a year worth of tweets and also show a time series of sentiment score.  That will be fantastic!

Here are the four histograms, one each for four candidates, showing the distribution of opinion scores:

SAP

IBM

Microsoft

Oracle








Happy Analyzing!


The underlying data can be downloaded here.

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!

Geocode your data using, R, JSON and Google Maps' Geocoding API

First and foremost, I absolutely love the topic of Location Analytics (Geo-Spatial Analysis) and see tremendous business potential in not so distant future.  I would go out on a limb to predict that the Location Analytics will soon go viral in the enterprise space because it has the capability to WOW us. Look no further than your iPhone or an Android phone and count how many location aware apps you have. We all have at lease one app - Google Maps.  Mobile is one of the strongest catalyst for enterprise adoption of Location aware apps. All right, enough of business talk, let's get dirty with the code.


Over the last year and half, I have faced numerous challenges with geocoding the data that I have used to showcase my passion for location analytics.  In 2012, I decided to take thing in my control and turned to R.  Here, I am sharing a simple R script that I wrote to geo-code my data whenever I needed it, even BIG Data.


To geocode my data, I use Google's Geocoding service which returns the geocoded data in a JSON. I will recommend that you register with Google Maps API and get a key if you have large amount of data and would do repeated geo coding.

Here is function that can be called repeatedly by other functions:

getGeoCode <- function(gcStr)

{

  library("RJSONIO") #Load Library

  gcStr <- gsub(' ','%20',gcStr) #Encode URL Parameters

 #Open Connection

 connectStr <- paste('http://maps.google.com/maps/api/geocode/json?sensor=false&address=',gcStr, sep="") 

  con <- url(connectStr)

  data.json <- fromJSON(paste(readLines(con), collapse=""))

  close(con)

#Flatten the received JSON

  data.json <- unlist(data.json)

  lat <- data.json["results.geometry.location.lat"]

  lng <- data.json["results.geometry.location.lng"]

  gcodes <- c(lat, lng)

  names(gcodes) <- c("Lat", "Lng")

  return (gcodes)

}

Let's put this function to test:

geoCodes <- getGeoCode("Palo Alto,California")

> geoCodes
           Lat            Lng 
  "37.4418834" "-122.1430195" 

You can run this on the entire column of a data frame or a data table:

Here  is my sample data frame with three columns - Opposition, Ground.Country and Toss. Two of the columns, you guessed it right, need geocoding.

> head(shortDS,10)

     Opposition              Ground.Country Toss

1      Pakistan            Karachi,Pakistan  won

2      Pakistan         Faisalabad,Pakistan lost

3      Pakistan             Lahore,Pakistan  won

4      Pakistan            Sialkot,Pakistan lost

5   New Zealand    Christchurch,New Zealand lost

6   New Zealand          Napier,New Zealand  won

7   New Zealand        Auckland,New Zealand  won

8       England              Lord's,England  won

9       England          Manchester,England lost

10      England            The Oval,England  won

To geo code this, here is a simple one liner I execute:

shortDS <- with(shortDS, data.frame(Opposition, Ground.Country, Toss,

                  laply(Ground.Country, function(val){getGeoCode(val)})))

> head(shortDS, 10)
    Opposition           Ground.Country Toss  Ground.Lat  Ground.Lng
1     Pakistan         Karachi,Pakistan  won   24.893379   67.028061
2     Pakistan      Faisalabad,Pakistan lost   31.408951   73.083458
3     Pakistan          Lahore,Pakistan  won    31.54505   74.340683
4     Pakistan         Sialkot,Pakistan lost  32.4972222  74.5361111
5  New Zealand Christchurch,New Zealand lost -43.5320544 172.6362254
6  New Zealand       Napier,New Zealand  won -39.4928444 176.9120178
7  New Zealand     Auckland,New Zealand  won -36.8484597 174.7633315
8      England           Lord's,England  won     51.5294     -0.1727
9      England       Manchester,England lost   53.479251   -2.247926
10     England         The Oval,England  won   51.369037   -2.378269

Happy Demoing and Coding!

Closing the loop on Pervasive Location Analytics - an enlightening personal journey for sure!

When I started working on Google Maps deal at SAP in February of this year, I had no clue where it will end and what is next once the deal is done. I fell in love with this Location Analytics/Geo Data Visualization topic, and turned it into an opportunity to discuss this topic and also generate excitement in various different camps along the way.

Five sessions spread across three continents, 200+ attendees,1000 views and numerous downloads later, this topic became more than just a personal interest. I met great people along the way and worked with very smart and driven people to co-present from the likes of Ryan from Centigon Solutions, Nimish from FreshDirect and Brendan from ThinkSmart Technologies. (See links to slides and session evaluation below)

A proud moment arrived this morning when an alert from SlideShare popped up indicating that this topic is hot on Facebook and as a result this topic is being put on SlideShare home page. Wow!


Pervasive Location Analytics: The Next Frontier to Fall in The Enterprise Software?

Session Evaluations Results

Thank you - my next two blogs will be presenting my thoughts on Moblie Analytics and Agile BI - two topics I have spent significant amount of time from strategy, market, customer, competition and product point of view.

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! 

SuccessFactors - An amazing tech story through its financials and a solid grab by SAP!

I will take a slight detour from Analytics and talk about SAP's acquisition of  #1 cloud company SuccessFactors (SFSF). Announcement

The combination of SAP & SFSF will produce a cloud powerhouse in the cloud segment of the enterprise software market  and that is just starting to take off…

Strong business rationale:

·         Gartner - HCM to be a $10B by 2015, Talent Management alone will be a $4.5B with 75% of it coming from cloud based apps

·          SFSF is:

o    #1 HCM solution in the cloud

o   has 15m users from company of all sizes (CRM has only 3m users) in diverse 60 industries from across the globe (Example: Siemens has 450K seats)

o    60% recurring revenues from existing customers

o    90% of the growth is organic as oppose to Salesforce

o   Has just 14% overlap with SAP customers – a tremendous upside for both companies (with total addressable market of 500m employees of all SAP customers)

·         For SAP, SFSF will be a top-line acquisition with less emphasis on cost-synergies…

·         Deal will be slightly dilutive on EPS in 2012 but will be accretive in 2013 with significant upside to our revenues in 2013

Financials:

• SAP paid $3.4 B to acquire SFSF which is not profitable yet.
• SAP is paying ~10x for 2011 revenues, a multiple HP paid for Autonomy
• For SFSF, street expects $332M in 2011 revenues; SFSF had $230M YTD revenues for the first nine months with $91M coming in Q3’11
•   As of Sep, 2011, SAP had $5.2B in cash. The SFSF deal is all cash with $2B coming off SAP's own war chest and ~$1.4B of debt. 

 Taleo with 2011 expected revenues of $324M is barely profitable. Workday is on track to $320 million in billings in 2011, and is nearing profitability. Workday is preparing for an IPO.

Now let us talk about SFSF’s amazing growth over the past 9 years:

SFSF – a company which delivered a PERFECT hockey stick growth since 2002:

A revenue growth story that is enviable:

Operating structure has shown substantive improvement over the past 5 years:

Net net for SAP, a solid acquisition and timing couldn’t have been right. The ride has just begun…

Source: Company Financials and Analyst Calls