Tableau IPO: Let The Gold Rush Begin For Enterprise Software IPOs!

The year 2013 is going to be the year of enterprise software IPOs.  That is not a prediction but well discussed point in Silicon Valley.  Everybody believes that there is a pent-up demand from return hungry investors for the enterprise software IPOs.  Consumer software IPOs have failed to live up to their promise in the last couple of years but the enterprise software IPOs have continued to deliver (examples: WDAY, NOW, SPLK), case-in-point.   

In the last couple of days, two of my favorite companies, Marketo and Tableau have announced plans to go public.  Here are the links to Marketo's S1 and Tableau's S1.  I have had the good fortune to study, evaluate and follow both companies since 2010.  Both the companies have done very well in their respective segments, SaaS marketing automation and on-premise self-serve BI.  They have both exceeded expectations on all fronts (employees, customers, analyst  markets, competitors) after a long hard slog.  

To all my friends, colleagues, investors and readers of this blog, enterprise software is a hard slog, you are in it for a long-haul.  Tableau is a 10-year old company and Marketo is 7 years old (Source:  SEC Filings).

Valuation

Since Tableau ("DATA") has announced its plan to go IPO this year, I decided to put the striped-down version of my due-diligence, performed in early 2011, on my slide-share account.  Back then, I used relative valuation using QlikView ("QLIK") as a close proxy to put a number on Tableau.  I used PE (earnings multiple) and PS (revenue multiple) of QLIK and assessed a market value of $380million based on Tableau's 2010 revenues of $40 million (from their press release in 2011, this number has been revised down to $34million in S1, huh, strange!)

Now, if one were to use QLIK's current revenue multiple of 5.5 (Source: Yahoo Finance), Tableau could be valued between $700million (based on trailing revenue of $128million) and 1.4billion (based on  $256million in expected revenue for 2013 assuming that they grow their revenue YET AGAIN by 100% in 2013.)

I personally don't think that the street should use QLIK as a proxy instead apply Splunk's ("SPLK") lens to value Tableau.  So using SPLK's multiple of ~19.7 (Source: Yahoo Finance), Tableau will be valued at $2.5billion based on their 2012 revenues.  ServiceNow ("NOW") also has a PS multiple of ~19. 

I have strong reasons to believe that street will be valuing Tableau in this range based on a great growth story till this point and amazing opportunities ahead as we are just starting to drill the BigData mountain.  I will not be surprised to see the valuation range from $2.5billion to $5billion. Amazing!

Tableau's S1

I studied Tableau's S1 filing briefly looking for information on valuation and offering on number of shares.  Not much is disclosed there just yet.  It will likely be disclosed in the subsequent filings as they hit the roadshow to assess the demand from the institutional investors.  Just like Workday, Tableau will also have dual class shares (Class A and Class B) with different voting rights.  The Class A will be offered to investors by converting the Class B shares. 

The last internal valuation of employee options priced the stock at ~$15.  To raise $150million, Tableau will at least be putting 10 million shares of Class A on the block.  Now of course, this will change as the demand starts to build up following their road-show.  One thing is certain that the stock will be definitely priced above $15.  Now, how many points above $15, we will find out in the next few months.  

Let the mad rush begin!!!

If You are a R Developer, Then You Must Try SAP HANA for Free.

This is a guest blog from Alvaro Tejada Galindo, my colleague and fellow R and SAP HANA enthusiast.  I am thankful to Alvaro for coming and posting on "AllThingsBusinessAnalytics".

Are you an R developers? Have ever heard of SAP HANA? Would you like to test SAP HANA for free?

SAP HANA is an In-Memory Database Technology allowing developers to analyze big data in real-time.

Processes that took hours now take seconds due to SAP HANA's power to keep everything on RAM memory.

As announced in SAP Sapphire Now event in Orlando, Florida, SAP HANA is free for developers. You just need to download and install both the SAP HANA Client and the SAP HANA Studio, and create an SAP HANA Server on the Amazon Web Services as described in the following document:

Get your own SAP HANA DB server on Amazon Web Services - http://scn.sap.com/docs/DOC-28294

Why should this interest you? Easy...SAP HANA is an agent of change bringing speed to its limits and it can also be integrated with R as described in the following blog:

When SAP HANA met R - First kiss - http://scn.sap.com/community/developer-center/hana/blog/2012/05/21/when-sap-hana-met-r--first-kiss

Want to know more about SAP HANA? Read everything you need here: http://developers.sap.com

You're convinced but don't want to pay for the Amazon Web Services? No problem. Just leave a comment including your name, company and email. We will reach you and send you an Amazon Gift Card so you can get started. Of course, your feedback would be greatly appreciated. Of course, we only a limited set of gift cards, so be quick or be out.

Author Alvaro Tejada Galindo, mostly known as "Blag" is a Development Expert working for the Technology Innovation and Developer Experience team in SAP Labs.  He can be contacted at a.tejada.galindo@sap.com.

Alvaro's background in his own words: I used to be an ABAP Consultant for 11 years. I worked in implementations on Peru and Canada. I’m also a die hard developer using R, Python, Ruby, PHP, Flex and many more languages. Now, I work for SAP Labs and my main roles are evangelize SAP technologies by writing blogs, articles, helping people on the forums, attending SAP events, besides many other “Developer engagement” activities.

I maintain a blog called “Blag’s bag of rants” at blagrants.blogspot.com

Big Data, R and SAP HANA: Analyze 200 Million Data Points and Later Visualize in HTML5 Using D3 - Part II

Technologies: SAP HANA, R, HTML5, D3, JQuery and JSON

In my last blog, Big Data, R and SAP HANA: Analyze 200 Million Data Points and Later Visualize Using Google Maps, I analyzed historical airlines performance data set using R and SAP HANA and put the aggregated analysis on Google Maps.  Undoubtedly, Map is a pretty exciting canvas to view and analyze big data sets. One could draw shapes (circles, polygons) on the map under a marker pin, providing pin-point information and display aggregated information in the info-window when a marker is clicked.  So I enjoyed doing all of that, but I was craving for some old fashion bubble charts and other types of charts to provide comparative information on big data sets.  Ultimately, all big data sets get aggregated into smaller analytical sets for viewing, sharing and reporting.  An old fashioned chart is the best way to tell a visual story!

On bubble charts, one could display 4 dimensional data for comparative analysis. In this blog analysis, I used the same data-set which had 200M data points and went deeper looking at finer slices of information.  I leveraged D3, R and SAP HANA for this blog post.  Here I am publishing some of this work:  

In this first graphics, the performance of top airlines is compared for 2008.  As expected, Southwest, the largest airlines (when using total number of flights as a proxy), performed well for its size (1.2M flights, 64 destinations but average delay was ~10 mins.)  Some of the other airlines like American and Continental were the worst performers along with Skywest.  Note, I didn't remove outliers from this analysis.  Click here to interact with this example.

In the second analysis, I replaced airlines dimension with airports dimension but kept all the other dimensions the same.  To my disbelief, Newark airport is the worst performing airport when it comes to departure delays.  Chicago O'Hare, SFO and JFK follow.  Atlanta airport is the largest airport but it has the best performance. What are they doing differently at ATL?  Click here to interact with this example.

It was hell of a fun playing with D3, R and HANA, good intellectual stimulation if nothing else!  Happy Analyzing and remember possibilities are endless!

As always, my R modules are fairly simple and straightforward:
###########################################################################################  
#ETL - Read the AIRPORT Information, get major aiport informatoin extracted and upload this 
#transfromed dataset into HANA
###########################################################################################
major.airports <- data.table(read.csv("MajorAirports.csv",  header=TRUE, sep=",", stringsAsFactors=FALSE))
setkey(major.airports, iata)


all.airports <- data.table(read.csv("AllAirports.csv",  header=TRUE, sep=",", stringsAsFactors=FALSE)) 
setkey(all.airports, iata)


airports.2008.hp <- data.table(read.csv("2008.csv",  header=TRUE, sep=",", stringsAsFactors=FALSE)) 
setkey(airports.2008.hp, Origin, UniqueCarrier)


#Merge two datasets
airports.2008.hp <- major.airports[airports.2008.hp,]


###########################################################################################  
# Get airport statisitics for all airports
###########################################################################################
airports.2008.hp.summary <- airports.2008.hp[major.airports,     
    list(AvgDepDelay=round(mean(DepDelay, na.rm=TRUE), digits=2),
    TotalMiles=prettyNum(sum(Distance, na.rm=TRUE), big.mark=","),
    TotalFlights=length(Month),
    TotalDestinations=length(unique(Dest)),
    URL=paste("http://www.fly", Origin, ".com",sep="")), 
                    by=list(Origin)][order(-TotalFlights)]
setkey(airports.2008.hp.summary, Origin)
#merge two data tables
airports.2008.hp.summary <- major.airports[airports.2008.hp.summary, 
                                                     list(Airport=airport, 
                                                          AvgDepDelay, TotalMiles, TotalFlights, TotalDestinations, 
                                                          Address=paste(airport, city, state, sep=", "), 
                                                          Lat=lat, Lng=long, URL)][order(-TotalFlights)]




airports.2008.hp.summary.json <- getRowWiseJson(airports.2008.hp.summary)
writeLines(airports.2008.hp.summary.json, "airports.2008.hp.summary.json")                 
write.csv(airports.2008.hp.summary, "airports.2008.hp.summary.csv", row.names=FALSE)

Big Data, R and HANA: Analyze 200 Million Data Points and Later Visualize Using Google Maps

Technologies: SAP HANA, R, HTML5, D3, Google Maps, JQuery and JSON

For this fun exercise, I analyzed more than 200 million data points using SAP HANA and R and then brought in the aggregated results in HTML5 using D3, JSON and Google Maps APIs.  The 2008 airlines data is from the data expo and I have been using this entire data set (123 million rows and 29 columns) for quite sometime. See my other blogs

The results look beautiful:

Each airport icon is clickable and when clicked displays an info-window describing the key stats for the selected airport:

I then used D3 to display the aggregated result set in the modal window (light box):

Unfortunately, I can't provide the live example due to the restrictions put in by Google Maps APIs and I am approaching my free API limits.

Fun fact:  The Atlanta airport was the largest airport in 2008 on many dimensions: Total Flights Departed, Total Miles Flew, Total Destinations.  It also experienced lower average departure delay in 2008 than Chicago O'Hare. I always thought Chicago O'Hare is the largest US airport.

As always, I just needed 6 lines of R code including two lines of code to write data in JSON and CSV files:

################################################################################
airports.2008.hp.summary <- airports.2008.hp[major.airports,     
    list(AvgDepDelay=round(mean(DepDelay, na.rm=TRUE), digits=2),
    TotalMiles=prettyNum(sum(Distance, na.rm=TRUE), big.mark=","),
    TotalFlights=length(Month),
    TotalDestinations=length(unique(Dest)),
    URL=paste("http://www.fly", Origin, ".com",sep="")), 
                    by=list(Origin)][order(-TotalFlights)]
setkey(airports.2008.hp.summary, Origin)
#merge the two data tables
airports.2008.hp.summary <- major.airports[airports.2008.hp.summary, 
                                                     list(Airport=airport, 
                                                          AvgDepDelay, TotalMiles, TotalFlights, TotalDestinations, 
                                                          Address=paste(airport, city, state, sep=", "), 
                                                          Lat=lat, Lng=long, URL)][order(-TotalFlights)]


airports.2008.hp.summary.json <- getRowWiseJson(airports.2008.hp.summary)
writeLines(airports.2008.hp.summary.json, "airports.2008.hp.summary.json")                 
write.csv(airports.2008.hp.summary, "airports.2008.hp.summary.csv", row.names=FALSE)
##############################################################################

Happy Coding and remember the possibilities are endless!

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.

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

Real Time Business Analytics - A new study coinciding with SAP HANA's greater push?

This is a clip from a news article that I just read - 


A recent study by Oxford Economics, "Real-Time Business: Playing to Win in the New Global Marketplace" [PDF], found that 30 percent of the companies surveyed have implemented some kind of real-time IT system, and 65 percent of the remainder have plans to do so in the near future. In addition, early adopters are reporting significant benefits: On average, companies that have implemented such systems are seeing average revenue gains of 21 percent and cost reductions of 19 percent. In fact, among early adopters, 77 percent report revenue gains with even higher revenue gains in certain industries like oil and gas.


It is not a groundbreaking study and definitely a study on this topic has been done before. This is a survey based study and  focuses primarily on the advantages of real time business intelligence using customers' account. Good read. 


The study was sponsored by SAP and its release coincides with SAP's SAPPHIRE NOW event where real-time (In-Memory) business analytics received a disproportionate amount of coverage.  Here is the full press release with seven customer testimonials - 
http://www.sap.com/index.epx#/news-reader/index.epx?category=ALL&articleID=15202&page=1&pageSize=10


Enjoy!