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!

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

Mash-up Airlines Performance Data with Historical Weather Data to Pinpoint Weather Related Delays

For this exercise, I combined following four separate blogs that I did on BigData, R and SAP HANA.  Historical airlines and weather data were used for the underlying analysis. The aggregated output of this analysis was outputted in JSON which was visualized in HTML5, D3 and Google Maps.  The previous blogs on this series are:

1. Big Data, R and HANA: Analyze 200 Million Data Points and Later Visualize in HTML5 Using D3 - Part II
2. Big Data, R and HANA: Analyze 200 Million Data Points and Later Visualize Using Google Maps
3. Getting Historical Weather Data in R and SAP HANA 
4. Tracking SFO Airport's Performance Using R, HANA and D3

In this blog, I wanted to mash-up disparate data sources in R and HANA by combining airlines data with weather data to understand the reasons behind the airport/airlines delay.  Why weather - because weather is one of the commonly cited reasons in the airlines industry for flight delays.  Fortunately, the airlines data breaks up the delay by weather, security, late aircraft etc., so weather related delays can be isolated and then the actual weather data can be mashed-up to validate the airlines' claims.  However, I will not be doing this here, I will just be displaying the mashed-up data.

I have intentionally focused on the three bay-area airports and have used last 4 years of historical data to visualize the airport's performance using a HTML5 calendar built from scratch using D3.js.  One can use all 20 years of data and for all the airports to extend this example.  I had downloaded historical weather data for the same 2005-2008 period for SFO and SJC airports as shown in my previous blog (For some strange reasons, there is no weather data for OAK, huh?).  Here is how the final result will look like in HTML5:

Click here to interact with the live example.  Hover over any cell in the live example and a tool tip with comprehensive analytics will show the break down of the performance delay for the selected cell including weather data and correct icons* - result of a mash-up.  Choose a different airport from the drop-down to change the performance calendar. 

* Weather icons are properties of Weather Underground.

As anticipated, SFO airport had more red on the calendar than SJC and OAK.  SJC definitely is the best performing airport in the bay-area.  Contrary to my expectation, weather didn't cause as much havoc on SFO as one would expect, strange?

Creating a mash-up in R for these two data-sets was super easy and a CSV output was produced to work with HTML5/D3.  Here is the R code and if it not clear from all my previous blogs: I just love data.table package.

###########################################################################################  

# Percent delayed flights from three bay area airports, a break up of the flights delay by various reasons, mash-up with weather data

###########################################################################################  

baa.hp.daily.flights <- baa.hp[,list( TotalFlights=length(DepDelay), CancelledFlights=sum(Cancelled, na.rm=TRUE)), 

                             by=list(Year, Month, DayofMonth, Origin)]

setkey(baa.hp.daily.flights,Year, Month, DayofMonth, Origin)

baa.hp.daily.flights.delayed <- baa.hp[DepDelay>15,

                                     list(DelayedFlights=length(DepDelay), 

                                      WeatherDelayed=length(WeatherDelay[WeatherDelay>0]),

                                      AvgDelayMins=round(sum(DepDelay, na.rm=TRUE)/length(DepDelay), digits=2),

                                      CarrierCaused=round(sum(CarrierDelay, na.rm=TRUE)/sum(DepDelay, na.rm=TRUE), digits=2),

                                      WeatherCaused=round(sum(WeatherDelay, na.rm=TRUE)/sum(DepDelay, na.rm=TRUE), digits=2),

                                      NASCaused=round(sum(NASDelay, na.rm=TRUE)/sum(DepDelay, na.rm=TRUE), digits=2),

                                      SecurityCaused=round(sum(SecurityDelay, na.rm=TRUE)/sum(DepDelay, na.rm=TRUE), digits=2),

                                      LateAircraftCaused=round(sum(LateAircraftDelay, na.rm=TRUE)/sum(DepDelay, na.rm=TRUE), digits=2)), by=list(Year, Month, DayofMonth, Origin)]

setkey(baa.hp.daily.flights.delayed, Year, Month, DayofMonth, Origin)

# Merge two data-tables

baa.hp.daily.flights.summary <- baa.hp.daily.flights.delayed[baa.hp.daily.flights,list(Airport=Origin,

                           TotalFlights, CancelledFlights, DelayedFlights, WeatherDelayed, 

                           PercentDelayedFlights=round(DelayedFlights/(TotalFlights-CancelledFlights), digits=2),

                           AvgDelayMins, CarrierCaused, WeatherCaused, NASCaused, SecurityCaused, LateAircraftCaused)]

setkey(baa.hp.daily.flights.summary, Year, Month, DayofMonth, Airport)

# Merge with weather data

baa.hp.daily.flights.summary.weather <-baa.weather[baa.hp.daily.flights.summary]

baa.hp.daily.flights.summary.weather$Date <- as.Date(paste(baa.hp.daily.flights.summary.weather$Year, 

                                                           baa.hp.daily.flights.summary.weather$Month, 

                                                           baa.hp.daily.flights.summary.weather$DayofMonth, 

                                                           sep="-"),"%Y-%m-%d")

# remove few columns

baa.hp.daily.flights.summary.weather <- baa.hp.daily.flights.summary.weather[, 

            which(!(colnames(baa.hp.daily.flights.summary.weather) %in% c("Year", "Month", "DayofMonth", "Origin"))), with=FALSE]

#Write the output in both JSON and CSV file formats

objs <- baa.hp.daily.flights.summary.weather[, getRowWiseJson(.SD), by=list(Airport)]

# You have now (Airportcode, JSONString), Once again, you need to attach them together.

row.json <- apply(objs, 1, function(x) paste('{\"AirportCode\":"', x[1], '","Data\":', x[2], '}', sep=""))

json.st <- paste('[', paste(row.json, collapse=', '), ']')

writeLines(json.st, "baa-2005-2008.summary.json")                 

write.csv(baa.hp.daily.flights.summary.weather, "baa-2005-2008.summary.csv", row.names=FALSE)

Happy Coding!

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!

You Get What You Pay For - Tale of Two Acquisitions - SAP-SFSF and ORCL-TLEO

Two months ago, SAP made an offer to acquire SuccessFactors("SFSF"), the leading cloud based Human Capital Management ("HCM") company for $3.4B, a multiple of 10.2 on 2011 on expected 2011 revenue of $332M.  I published the following two blogs on this development back in December:

• Tale of Two Companies - SFSF and RNOW - Why would anyone compare SAP-SuccessFactors deal with Oracle-RightNow deal?
• SuccessFactors - An amazing tech story through its financials and a solid grab by SAP!

Salesforce followed suite and acquired Rypple, a company that employs badges and achievements to imbue the employee review process with a collaborative, social media-like experience.  Financial terms were not disclosed. (Source: EnterpriseAppToday)

Oracle was long due after the RNOW acquisition and it decided to follow SAP (for the first time) and Salesforce by acquiring Taleo ("TLEO"), the #2 company in the business, for $1.9B, a multiple of 6.15 on 2011 revenues of $309M. (Source: BusinessWeek)

As usual, folks are reaching out and saying whether SAP's SFSF acquisition is expensive due to a higher multiple it paid to SFSF shareholders and whether it rushed in too early.  I don't believe that SAP's SFSF acquisition is expensive by any stretch of the imagination.  "You get what you pay for" - this notion is quite true in this case. 

The business rationale SAP announced when it made the decision to acquire SFSF was that SFSF is:

• #1 HCM solution in the cloud
• has 15m users from company of all sizes (SalesForce has only 3m users) in diverse 60 industries from across the globe (Example: Siemens has 450K seats)
• 3,500 customers in 168 countries
• 60% recurring revenues from existing customers
• 90% of the growth is organic as oppose to Salesforce
• Has just 14% overlap with SAP customers – a tremendous upside for both companies (with total addressable market of 500m employees of all SAP customers)

On the other end, this is what TLEO disclosed it has: 

• one of the world’s largest cloud deployments with nearly 16 billion transactions per year
• manages 15 percent of all hires in the US 
• has a customer base comprised of 5,000 businesses 
• its Talent Exchange boasts 240 million candidates 
• of the top 30 career sites, nearly half are powered by its technology.

(Source: Taleo)

The two companies can hardly be compared on these business metrics, so I am going to focus purely on financials.  SAP put a forward multiple of 8 on SFSF's expected 2012 revenues of $420M while Oracle is paying a forward multiple of 5 on TLEO's expected 2012 revenues of $379M.  There is this informal "rule of thumb" in place that states that one should pay a multiple of six to eight times of forward earnings for acquiring growth companies. 

Through following series of comparison charts, one could clearly see why SFSF will fetch a higher premium over TLEO.  Everything boils down to just couple of financial metrics and these metrics are: growth and operational efficiencies:

1. SFSF is a better growth story with CAGR more than DOUBLE than that of TLEO:

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

 3. SFSF has somewhat better operating structure and is rapidly becoming more efficient with every dollar it spends on its operating cost. TLEO has done a good job of keeping its cost structure the same, one must wonder, why TLEO is not becoming operationally more efficient:

4. Making money from the cloud apps has been very tough business but this is very quickly starting to change as economies of scale kick in and both companies improve their net-income. SFSF definitely has done a good job in trimming its losses: 

5. The last two charts just compare the growth in revenue for the two companies since inception:

The bottom line is that SFSF is a better growth story and is operationally more efficient than TLEO so a higher multiple for SFSF is fully justified in my opinion. 

Did you know that, Oracle paid a multiple of 10x on Endeca's 2011 revenues? It is not just other companies (SAP or HP) that pay a forward multiple of 10x.

“Though Oracle and Endeca haven't talked about the acquisition price, I reported in October that California-based Oracle had agreed to pay $1.075 billion for the company (based on a document I obtained related to the deal).” (Source: boston.com) 

Happy Browsing!

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
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!

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!

Best quotes from Forbes Article SAP-Google Maps Partnership

SAP Partnership with Google Maps Indicates a New Openness

"SAP is overcoming a legacy of an insular engineering culture that could accurately be accused of suffering from a “not invented here” complex in the past."


"The only problem with SAP’s pride in its history is that it has sometimes shut the company’s eyes to new ways of creating software. It appears that this announcement may mark a turning point to increased awareness and use of outside components. If SAP becomes truly open to using more and more outside components, and learns how to use them to create stable, reliable software, SAP could accelerate the pace of change, keeping the stable parts of its applications, but adding the best of what has newly arrived."


There is something more at stake for Google than money - it is likely that Google Maps will be adapted by Google to better meet the needs of enterprise applications.


"What’s next? What other cloud components will SAP start to incorporate? The second is: Is a bigger partnership possible? Google’s mission is “to organize the world’s information and make it universally accessible and useful.” Much of the information that runs the world is in SAP. Why aren’t Google and SAP working together to make it more universally accessible and useful?"

Pervasive Location Analytics − The Next Frontier to Fall In the Enterprise Software?

Geo-location and spatial intelligence is the strategic turning point for business analytics and BI. Location has become an essential part of the enterprise data and increasing number of enterprises have geo-coded their location data to build location-aware application to drive location-aware decisions. For example, by overlaying the foreclosure data, income data, and the data from its mortgage portfolio on a geo-chart, a regional mortgage bank assesses the risk of its mortgage portfolio and decides to take corrective actions. A fast food company uses the demographics data, the latest census data, and the historical sales of its stores to determine the location of its new restaurant. 

Slides from SAP TechEd 2011 Las Vegas - http://goo.gl/etkvI

Google Maps - SAP - The press is humming...

Thanks to my friends in marketing...

SAP Now Allows Businesses To Layer Big Data With Google Maps And Earth

Leena Rao, TechCrunch

July 27, 2011

http://techcrunch.com/2011/07/27/sap-now-allows-businesses-to-layer-big-data-with-google-maps-and-earth/

Also seen in: The Washington Post, The Wall Street Journal, NewsFactor, The Business Insider, 21^st Century Networker, Maine Social Networking, Data Centers Canada

(385 tweets)

SAP Tying Analytics to Google Maps, Earth

Chris Kanaracus, IDG News

July 27, 2011

http://news.idg.no/cw/art.cfm?id=2F653071-1A64-67EA-E4C824717009C50D

Also seen in: PCWorld, CIO, Network World, Wall Street Journal, Computerworld, PC Advisor, InfoWorld, Albuquerque Express, CFO World, ARNnet, ITworld, Good Gear Guide, Techworld, Computerworld Australia, Computerworld UK, ITBusiness.ca

(114 tweets)

Google Deal Lets SAP Customers Map Data

Doug Henschen, InformationWeek

July 27, 2011

http://www.informationweek.com/news/software/bi/231002767   

(28 tweets)

 

Google and SAP Team-Up to Help You Visualize Big Data

Klint Finley, ReadWriteWeb

July 27, 2011

http://www.readwriteweb.com/enterprise/2011/07/google-and-sap-team-up-to-help.php

(128 tweets)

SAP, Google expand collaboration partnership, eye enterprise mashups

Larry Dignan, ZDNet

July 27, 2011

http://www.zdnet.com/blog/btl/sap-google-expand-collaboration-partnership-eye-enterprise-mashups/53275

(39 tweets)

SAP and Google Maps Team Up to Visualize Corporate Data

Todd R. Weiss, CIO blog: Deciphering Enterprise Apps

July 27, 2011

http://blogs.cio.com/business-intelligence/16424/sap-and-google-maps-team-visualize-corporate-data

(3 tweets)

 

SAP Geo-tagging Business Analytics Data with Google Maps

Clint Boulton, eWEEK

July 27, 2011

http://www.eweek.com/c/a/Enterprise-Applications/SAP-Geotagging-Business-Analytics-Data-with-Google-Maps-274375/

Also seen in: eWEEK Europe

(19 tweets)

SAP Invokes Google Maps to Put Data in Context

Mike Vizard, IT Business Edge

July 27, 2011

http://www.itbusinessedge.com/cm/blogs/vizard/sap-invokes-google-maps-to-put-data-in-context/?cs=48023

(2 tweets)

 

Google Plus SAP Deepen Ties, Deliver More Enterprise Integration

Courtney Bjorlin, ASUG News

July 27, 2011

http://www.asugnews.com/2011/07/27/google-plus-sap-deepen-ties-deliver-more-enterprise-integration/

(14 tweets)

Photos: Google Maps gives SAP's business intelligence a new direction

Tim Ferguson, Silicon.com

July 27, 2011

http://www.silicon.com/technology/software/2011/07/27/photos-google-maps-gives-saps-business-intelligence-a-new-direction-39747745/

(6 tweets)

SAP and Google Put Business Analytics on the Map

Justin Kern, Information Management

July 27, 2011

http://www.information-management.com/news/business_intelligence_analytics_data_geospatial-10020840-1.html

Also seen in: Insurance Networking News

(10 tweets)

SAP and Google Partner for Geospatial Business Intelligence

Paul Shread, EnterpriseAppsToday

July 27, 2011

http://www.enterpriseappstoday.com/business-intelligence/sap-google-geospatial-business-intelligence.html

(7 tweets)

SAP, Google and geospatial analytics

Todd Morrison, SearchSAP

July 27, 2011

http://itknowledgeexchange.techtarget.com/sap-watch/sap-google-and-geospatial-analytics/

(3 tweets)

 

SAP and Google team up to mash up analytics and maps

Matt Hartley, Financial Post

July 27, 2011

http://business.financialpost.com/2011/07/27/sap-and-google-team-up-to-mash-up-analytics-and-maps/

(4 tweets)

 

The “Where” Dimension of Business Intelligence

Adrian Gonzalez, Logistics Viewpoints

July 27, 2011

http://logisticsviewpoints.com/2011/07/27/the-where-dimension-of-business-intelligence/

(15 tweets)

SAP, Google team up to tie-in business analytics to Google Map

Brad Lemaire, Proactive Investors USA & Canada

July 27, 2011

http://www.proactiveinvestors.com/companies/news/16704/sap-google-team-up-to-tie-in-business-analytics-to-google-maps-16704.html

(4 tweets)

Google and SAP Partner on Geo-Mapping

Chris Crum, WebProNews

July 27, 2011

http://www.webpronews.com/google-and-sap-partner-on-geo-mapping-2011-07

(23 tweets)

Google and SAP Team Up for Geovisualization of Business Analytics

Matt Ball, Spatial Sustain

July 27, 2011

http://www.vector1media.com/spatialsustain/google-and-sap-team-up-for-business-analytics.html

(5 tweets)

SAP Partnership With Google Allows Data Mapping on Google Maps

TechGadgetsWeb.com

July 27, 2011

http://www.techgadgetsweb.com/3412/sap-partnership-google-data-mapping-google-maps

(5 tweets)

Street Fight Daily: 07.27.11

David Hirschman, Street Fight Daily: Inside the Business of Hyperlocal

July 27, 2011

http://streetfightmag.com/2011/07/27/street-fight-daily-07-27-11/

(0 tweets)

Media and Blogger Coverage – Headlines & Links Select Global Coverage

EMEA

Germany

Geo-Dienste: SAP verbündet sich mit Google

Sibylle Gassner, Silicon.de

July 27, 2011

http://www.silicon.de/technologie/software/0,39044013,41554999,00/geo_dienste_sap_verbuendet_sich_mit_google.htm

(4 tweets)

SAP und Google wollen gemeinsam Geodaten sammeln

Jens Hartmann, Silicon.de

July 27, 2011

http://www.welt.de/wirtschaft/webwelt/article13510999/SAP-und-Google-wollen-gemeinsam-Geodaten-sammeln.html

(10 tweets)

SAP und Google rücken zusammen

Corrina Visser, Der Tagesspiegel

July 27, 2011

http://www.tagesspiegel.de/wirtschaft/sap-und-google-ruecken-zusammen/4439242.html

(1 tweet)

Netherlands

SAP, Google team up to pair enterprise apps with LBS tools

Telecompaper

July 27, 2011                 

http://www.telecompaper.com/news/sap-google-team-up-to-pair-enterprise-apps-with-lbs-tools

(2 tweets)

Switzerland

Big Data: SAP married with Google Maps and Google Earth/ Big Data: SAP verheiratet sich mit Google Maps und Google Eart

Inside-it.ch

July 27, 2011

http://www.inside-it.ch/frontend/insideit?_d=_article&site=ii&news.id=25851

(0 tweets)

UK

SAP's Google partnership adds mapping data to business intelligence

Phil Muncaster, V3.co.uk

July 27, 2011

http://www.v3.co.uk/v3-uk/news/2097376/saps-google-partnership-mapping-business-intelligence

(0 tweets)

SAP and Google plan ‘big data’ Maps

Tom Brewster, ITPro

July 27, 2011

http://www.itpro.co.uk/635227/sap-and-google-plan-big-data-maps

(13 tweets)

SAP & Google Partner For Enhanced Location-Based Analytics

ITProPortal

July 27, 2011

http://www.itproportal.com/2011/07/27/sap-google-partner-enhanced-location-based-analytics/?utm_source=dlvr.it&utm_medium=twitter&utm_campaign=itproportal

(5 tweets)

SAP sees double-digit growth in software sales

Computing.co.uk

July 27, 2011

http://www.computing.co.uk/ctg/news/2097311/sap-double-digit-growth-software-sales

(1 tweets)

APJ 

India

SAP teams-up with Google to manage big data

CIOL India

July 27, 2011

http://www.ciol.com/Storage/Data-Storage/News-Reports/SAP-teams-up-with-Google-to-manage-big-data/152768/0/

(0 tweets)