Google Visualization Tools Can Help You Build a Personal Dashboard

The Google Visualization API is a great way for people to make dashboards with slick graphics based  on data without getting into the fine print of the scripting language  itself.  It utilizes the same tools as Google itself does, and makes visualizing data using API calls to the Visualization API. Thus a real-time customizable dashboard that is publishable to the internet can be created within minutes, and more importantly insights can be much more easily drawn from graphs than from looking at rows of tables and numbers.

  1. There are 41 gadgets (including made by both Google and third-party developers ) available in the Gadget  Gallery ( https://developers.google.com/chart/interactive/docs/gadgetgallery)
  2. There are 12 kinds of charts available in the Chart Gallery (https://developers.google.com/chart/interactive/docs/gallery) .
  3. However there 26 additional charts in the charts page at https://developers.google.com/chart/interactive/docs/more_charts )

Building and embedding charts is simplified to a few steps

  • Load the AJAX API
  • Load the Visualization API and the appropriate package (like piechart or barchart from the kinds of chart)
  • Set a callback to run when the Google Visualization API is loaded
    • Within the Callback – It creates and populates a data table, instantiates the particular chart type chosen, passes in the data and draws it.
    • Create the data table with appropriately named columns and data rows.
    • Set chart options with Title, Width and Height
  • Instantiate and draw the chart, passing in some options including the name and id
  • Finally write the HTML/ Div that will hold the chart

You can simply copy and paste the code directly from https://developers.google.com/chart/interactive/docs/quick_start without getting into any details, and tweak them according to your data, chart preference and voila your web dashboard is ready!
That is the beauty of working with API- you can create and display genius ideas without messing with the scripting languages and code (too much). If you like to dive deeper into the API, you can look at the various objects at https://developers.google.com/chart/interactive/docs/reference

First launched in Mar 2008, Google Visualization API has indeed come a long way in making dashboards easier to build for people wanting to utilize advanced data visualization . It came about directly as a result of Google’s 2007 acquisition of GapMinder (of Hans Rosling fame).
As invariably and inevitably computing shifts to the cloud, visualization APIs will be very useful. Tableau Software has been a pioneer in selling data visualizing to the lucrative business intelligence and business dashboards community (you can see the Tableau Software API at http://onlinehelp.tableausoftware.com/v7.0/server/en-us/embed_api.htm ), and Google Visualization can do the same and capture business dashboard and visualization market , if there is more focus on integrating it from Google in it’s multiple and often confusing API offerings.
However as of now, this is quite simply the easiest way to create a web dashboard for your personal needs. Google guarantees 3 years of backward compatibility with this API and it is completely free.

Updated Interview Elissa Fink -VP Tableau Software

Here is an interview with Elissa Fink, VP Marketing of that new wonderful software called Tableau that makes data visualization so nice and easy to learn and work with.

Elissa Fink, VP, Marketing

Ajay-  Describe your career journey from high school to over 20 plus years in marketing. What are the various trends that you have seen come and go in marketing.

Elissa- I studied literature and linguistics in college and didn’t discover analytics until my first job selling advertising for the Wall Street Journal. Oddly enough, the study of linguistics is not that far from decision analytics: they both are about taking a structured view of information and trying to see and understand common patterns. At the Journal, I was completely captivated analyzing and comparing readership data. At the same time, the idea of using computers in marketing was becoming more common. I knew that the intersection of technology and marketing was going to radically change things – how we understand consumers, how we market and sell products, and how we engage with customers. So from that point on, I’ve always been focused on technology and marketing, whether it’s working as a marketer at technology companies or applying technology to marketing problems for other types of companies.  There have been so many interesting trends. Taking a long view, a key trend I’ve noticed is how marketers work to understand, influence and motivate consumer behavior. We’ve moved marketing from where it was primarily unpredictable, qualitative and aimed at talking to mass audiences, where the advertising agency was king. Now it’s a discipline that is more data-driven, quantitative and aimed at conversations with individuals, where the best analytics wins. As with any trend, the pendulum swings far too much to either side causing backlashes but overall, I think we are in a great place now. We are using data-driven analytics to understand consumer behavior. But pure analytics is not the be-all, end-all; good marketing has to rely on understanding human emotions, intuition and gut feel – consumers are far from rational so taking only a rational or analytical view of them will never explain everything we need to know.

Ajay- Do you think technology companies are still predominantly dominated by men . How have you seen diversity evolve over the years. What initiatives has Tableau taken for both hiring and retaining great talent.

Elissa- The thing I love about the technology industry is that its key success metrics – inventing new products that rapidly gain mass adoption in pursuit of making profit – are fairly objective. There’s little subjective nature to the counting of dollars collected selling a product and dollars spent building a product. So if a female can deliver a better product and bigger profits faster and better, then that female is going to get the resources, jobs, power and authority to do exactly that. That’s not to say that the technology industry is gender-blind, race-blind, etc. It isn’t – technology is far from perfect. For example, the industry doesn’t have enough diversity in positions of power. But I think overall, in comparison to a lot of other industries, it’s pretty darn good at giving people with great ideas the opportunities to realize their visions regardless of their backgrounds or characteristics.

At Tableau, we are very serious about bringing in and developing talented people – they are the key to our growth and success. Hiring is our #1 initiative so we’ve spent a lot of time and energy both on finding great candidates and on making Tableau a place that they want to work. This includes things like special recruiting events, employee referral programs, a flexible work environment, fun social events, and the rewards of working for a start-up. Probably our biggest advantage is the company itself – working with people you respect on amazing, cutting-edge products that delight customers and are changing the world is all too rare in the industry but a reality at Tableau. One of our senior software developers put it best when he wrote “The emphasis is on working smarter rather than longer: family and friends are why we work, not the other way around. Tableau is all about happy, energized employees executing at the highest level and delivering a highly usable, high quality, useful product to our customers.” People who want to be at a place like that should check out our openings at http://www.tableausoftware.com/jobs.

Ajay- What are most notable features in tableau’s latest edition. What are the principal software that competes with Tableau Software products and how would you say Tableau compares with them.

Elissa- Tableau 6.1 will be out in July and we are really excited about it for 3 reasons.

First, we’re introducing our mobile business intelligence capabilities. Our customers can have Tableau anywhere they need it. When someone creates an interactive dashboard or analytical application with Tableau and it’s viewed on a mobile device, an iPad in particular, the viewer will have a native, touch-optimized experience. No trying to get your fingertips to act like a mouse. And the author didn’t have to create anything special for the iPad; she just creates her analytics the usual way in Tableau. Tableau knows the dashboard is being viewed on an iPad and presents an optimized experience.

Second, we’ve take our in-memory analytics engine up yet another level. Speed and performance are faster and now people can update data incrementally rapidly. Introduced in 6.0, our data engine makes any data fast in just a few clicks. We don’t run out of memory like other applications. So if I build an incredible dashboard on my 8-gig RAM PC and you try to use it on your 2-gig RAM laptop, no problem.

And, third, we’re introducing more features for the international markets – including French and German versions of Tableau Desktop along with more international mapping options.  It’s because we are constantly innovating particularly around user experience that we can compete so well in the market despite our relatively small size. Gartner’s seminal research study about the Business Intelligence market reported a massive market shift earlier this year: for the first time, the ease-of-use of a business intelligence platform was more important than depth of functionality. In other words, functionality that lots of people can actually use is more important than having sophisticated functionality that only specialists can use. Since we focus so heavily on making easy-to-use products that help people rapidly see and understand their data, this is good news for our customers and for us.

Ajay-  Cloud computing is the next big thing with everyone having a cloud version of their software. So how would you run Cloud versions of Tableau Server (say deploying it on an Amazon Ec2  or a private cloud)

Elissa- In addition to the usual benefits espoused about Cloud computing, the thing I love best is that it makes data and information more easily accessible to more people. Easy accessibility and scalability are completely aligned with Tableau’s mission. Our free product Tableau Public and our product for commercial websites Tableau Digital are two Cloud-based products that deliver data and interactive analytics anywhere. People often talk about large business intelligence deployments as having thousands of users. With Tableau Public and Tableau Digital, we literally have millions of users. We’re serving up tens of thousands of visualizations simultaneously – talk about accessibility and scalability!  We have lots of customers connecting to databases in the Cloud and running Tableau Server in the Cloud. It’s actually not complex to set up. In fact, we focus a lot of resources on making installation and deployment easy and fast, whether it’s in the cloud, on premise or what have you. We don’t want people to have spend weeks or months on massive roll-out projects. We want it to be minutes, hours, maybe a day or 2. With the Cloud, we see that people can get started and get results faster and easier than ever before. And that’s what we’re about.

Ajay- Describe some of the latest awards that Tableau has been wining. Also how is Tableau helping universities help address the shortage of Business Intelligence and Big Data professionals.

Elissa-Tableau has been very fortunate. Lately, we’ve been acknowledged by both Gartner and IDC as the fastest growing business intelligence software vendor in the world. In addition, our customers and Tableau have won multiple distinctions including InfoWorld Technology Leadership awards, Inc 500, Deloitte Fast 500, SQL Server Magazine Editors’ Choice and Community Choice awards, Data Hero awards, CODiEs, American Business Awards among others. One area we’re very passionate about is academia, participating with professors, students and universities to help build a new generation of professionals who understand how to use data. Data analysis should not be exclusively for specialists. Everyone should be able to see and understand data, whatever their background. We come from academic roots, having been spun out of a Stanford research project. Consequently, we strongly believe in supporting universities worldwide and offer 2 academic programs. The first is Tableau For Teaching, where any professor can request free term-length licenses of Tableau for academic instruction during his or her courses. And, we offer a low-cost Student Edition of Tableau so that students can choose to use Tableau in any of their courses at any time.

Elissa Fink, VP Marketing,Tableau Software

 

Elissa Fink is Tableau Software’s Vice President of Marketing. With 20+ years helping companies improve their marketing operations through applied data analysis, Elissa has held executive positions in marketing, business strategy, product management, and product development. Prior to Tableau, Elissa was EVP Marketing at IXI Corporation, now owned by Equifax. She has also served in executive positions at Tele Atlas (acquired by TomTom), TopTier Software (acquired by SAP), and Nielsen/Claritas. Elissa also sold national advertising for the Wall Street Journal. She’s a frequent speaker and has spoken at conferences including the DMA, the NCDM, Location Intelligence, the AIR National Forum and others. Elissa is a graduate of Santa Clara University and holds an MBA in Marketing and Decision Systems from the University of Southern California.

Elissa first discovered Tableau late one afternoon at her previous company. Three hours later, she was still “at play” with her data. “After just a few minutes using the product, I was getting answers to questions that were taking my company’s programmers weeks to create. It was instantly obvious that Tableau was on a special mission with something unique to offer the world. I just had to be a part of it.”

To know more – read at http://www.tableausoftware.com/

and existing data viz at http://www.tableausoftware.com/learn/gallery

Storm seasons: measuring and tracking key indicators
What’s happening with local real estate prices?
How are sales opportunities shaping up?
Identify your best performing products
Applying user-defined parameters to provide context
Not all tech companies are rocket ships
What’s really driving the economy?
Considering factors and industry influencers
The complete orbit along the inside, or around a fixed circle
How early do you have to be at the airport?
What happens if sales grow but so does customer churn?
What are the trends for new retail locations?
How have student choices changed?
Do patients who disclose their HIV status recover better?
Closer look at where gas prices swing in areas of the U.S.
U.S. Census data shows more women of greater age
Where do students come from and how does it affect their grades?
Tracking customer service effectiveness
Comparing national and local test scores
What factors correlate with high overall satisfaction ratings?
Fund inflows largely outweighed outflows well after the bubble
Which programs are competing for federal stimulus dollars?
Oil prices and volatility
A classic candlestick chart
How do oil, gold and CPI relate to the GDP growth rate?

 

Tableau Interactive "Viz" Contest

The Las Vegas Sign.
Image via Wikipedia
One more contest- open only for US though
but the prizes are hmm okay. The catch is you have to use the software Tableau created 
not R or J or ggobi or ggplot or java

Check out http://www.tableausoftware.com/public/biz-viz-contest/?=decisionstats

Tableau Interactive “Viz” Contest

AS FEATURED AT

Win a trip to Vegas and a chance for $2,000 & an iPad2

Are you a business, finance or real estate geek? This contest is for you! In cooperation with The Economist Ideas Economy conference, the Tableau Software Interactive “Viz” Contest will focus on business, finance and real estate data… Find some data then use Tableau Public to analyze and visualize it. That’s all it takes.

What you’ll win

A 3-day trip to Las Vegas and a chance to win $2,000 & an iPad2

The winner chosen by our judges will also take away a free roundtrip ticket to attend the2011 Tableau Customer Conference. This includes 3 night’s accommodations at theEncore and a chance to compete in the Iron Viz championship with the winners of two other contests. The winner of Iron Viz will take away a new iPad2, and $2,000.

Cash for the crowd favorite

After entering you’ll receive a custom bit.ly link to your viz. Tweet, Facebook and e-mail that link to everyone you can! Whoever gets the most clicks through their link will become our Crowd Favorite and receive a $250 debit card.

Recognition from The Economist Ideas Economy

Your winning entry will be announced live on stage at The Economist Ideas Economy conference, and Tableau will issue a national press release naming the winner.

Everyone who enters gets a t-shirt!

Everyone who enters will get a very cool Tableau t-shirt. The winner will also receive increased Tableau Public limits and a free copy of Tableau Desktop (a $1999 value)!

How it works

(Click on the steps to expand and get the details.)
 Check the box to view all steps and details.

  • Step 1

    Download the FREE Tableau Public tool


  • Step 2

    Create and publish your “viz” to your blog or website


  • Step 3

    Submit your entry formFill out the entry form and submit by June 3, 2011. A panel of judges will evaluate all submissions based on overall appeal, design elements, and data analysis/findings.

Contest Rules Summary

The following contest is open to legal residents of the United Sates only. You must publish your “viz” on your blog or website to be qualified. Submission form must be submitted by June 3, 2011. Winners will be notified by June 7, 2010. Incomplete applications will not be accepted.

Please read all the rules in their entirety before entering.

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Choosing R for business – What to consider?

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Additional features in R over other analytical packages-

1) Source Code is given to ensure complete custom solution and embedding for a particular application. Open source code has an advantage that is extensively peer- reviewed in Journals and Scientific Literature.  This means bugs will found, shared and corrected transparently.

2) Wide literature of training material in the form of books is available for the R analytical platform.

3) Extensively the best data visualization tools in analytical software (apart from Tableau Software ‘s latest version). The extensive data visualization available in R is of the form a variety of customizable graphs, as well as animation. The principal reason third-party software initially started creating interfaces to R is because the graphical library of packages in R is more advanced as well as rapidly getting more features by the day.

4) Free in upfront license cost for academics and thus budget friendly for small and large analytical teams.

5) Flexible programming for your data environment. This includes having packages that ensure compatibility with Java, Python and C++.

 

6) Easy migration from other analytical platforms to R Platform. It is relatively easy for a non R platform user to migrate to R platform and there is no danger of vendor lock-in due to the GPL nature of source code and open community.

Statistics are numbers that tell (descriptive), advise ( prescriptive) or forecast (predictive). Analytics is a decision-making help tool. Analytics on which no decision is to be made or is being considered can be classified as purely statistical and non analytical. Thus ease of making a correct decision separates a good analytical platform from a not so good analytical platform. The distinction is likely to be disputed by people of either background- and business analysis requires more emphasis on how practical or actionable the results are and less emphasis on the statistical metrics in a particular data analysis task. I believe one clear reason between business analytics is different from statistical analysis is the cost of perfect information (data costs in real world) and the opportunity cost of delayed and distorted decision-making.

Specific to the following domains R has the following costs and benefits

  • Business Analytics
    • R is free per license and for download
    • It is one of the few analytical platforms that work on Mac OS
    • It’s results are credibly established in both journals like Journal of Statistical Software and in the work at LinkedIn, Google and Facebook’s analytical teams.
    • It has open source code for customization as per GPL
    • It also has a flexible option for commercial vendors like Revolution Analytics (who support 64 bit windows) as well as bigger datasets
    • It has interfaces from almost all other analytical software including SAS,SPSS, JMP, Oracle Data Mining, Rapid Miner. Existing license holders can thus invoke and use R from within these software
    • Huge library of packages for regression, time series, finance and modeling
    • High quality data visualization packages
    • Data Mining
      • R as a computing platform is better suited to the needs of data mining as it has a vast array of packages covering standard regression, decision trees, association rules, cluster analysis, machine learning, neural networks as well as exotic specialized algorithms like those based on chaos models.
      • Flexibility in tweaking a standard algorithm by seeing the source code
      • The RATTLE GUI remains the standard GUI for Data Miners using R. It was created and developed in Australia.
      • Business Dashboards and Reporting
      • Business Dashboards and Reporting are an essential piece of Business Intelligence and Decision making systems in organizations. R offers data visualization through GGPLOT, and GUI like Deducer and Red-R can help even non R users create a metrics dashboard
        • For online Dashboards- R has packages like RWeb, RServe and R Apache- which in combination with data visualization packages offer powerful dashboard capabilities.
        • R can be combined with MS Excel using the R Excel package – to enable R capabilities to be imported within Excel. Thus a MS Excel user with no knowledge of R can use the GUI within the R Excel plug-in to use powerful graphical and statistical capabilities.

Additional factors to consider in your R installation-

There are some more choices awaiting you now-
1) Licensing Choices-Academic Version or Free Version or Enterprise Version of R

2) Operating System Choices-Which Operating System to choose from? Unix, Windows or Mac OS.

3) Operating system sub choice- 32- bit or 64 bit.

4) Hardware choices-Cost -benefit trade-offs for additional hardware for R. Choices between local ,cluster and cloud computing.

5) Interface choices-Command Line versus GUI? Which GUI to choose as the default start-up option?

6) Software component choice- Which packages to install? There are almost 3000 packages, some of them are complimentary, some are dependent on each other, and almost all are free.

7) Additional Software choices- Which additional software do you need to achieve maximum accuracy, robustness and speed of computing- and how to use existing legacy software and hardware for best complementary results with R.

1) Licensing Choices-
You can choose between two kinds of R installations – one is free and open source from http://r-project.org The other R installation is commercial and is offered by many vendors including Revolution Analytics. However there are other commercial vendors too.

Commercial Vendors of R Language Products-
1) Revolution Analytics http://www.revolutionanalytics.com/
2) XL Solutions- http://www.experience-rplus.com/
3) Information Builder – Webfocus RStat -Rattle GUI http://www.informationbuilders.com/products/webfocus/PredictiveModeling.html
4) Blue Reference- Inference for R http://inferenceforr.com/default.aspx

  1. Choosing Operating System
      1. Windows

 

Windows remains the most widely used operating system on this planet. If you are experienced in Windows based computing and are active on analytical projects- it would not make sense for you to move to other operating systems. This is also based on the fact that compatibility problems are minimum for Microsoft Windows and the help is extensively documented. However there may be some R packages that would not function well under Windows- if that happens a multiple operating system is your next option.

        1. Enterprise R from Revolution Analytics- Enterprise R from Revolution Analytics has a complete R Development environment for Windows including the use of code snippets to make programming faster. Revolution is also expected to make a GUI available by 2011. Revolution Analytics claims several enhancements for it’s version of R including the use of optimized libraries for faster performance.
      1. MacOS

 

Reasons for choosing MacOS remains its considerable appeal in aesthetically designed software- but MacOS is not a standard Operating system for enterprise systems as well as statistical computing. However open source R claims to be quite optimized and it can be used for existing Mac users. However there seem to be no commercially available versions of R available as of now for this operating system.

      1. Linux

 

        1. Ubuntu
        2. Red Hat Enterprise Linux
        3. Other versions of Linux

 

Linux is considered a preferred operating system by R users due to it having the same open source credentials-much better fit for all R packages and it’s customizability for big data analytics.

Ubuntu Linux is recommended for people making the transition to Linux for the first time. Ubuntu Linux had an marketing agreement with revolution Analytics for an earlier version of Ubuntu- and many R packages can  installed in a straightforward way as Ubuntu/Debian packages are available. Red Hat Enterprise Linux is officially supported by Revolution Analytics for it’s enterprise module. Other versions of Linux popular are Open SUSE.

      1. Multiple operating systems-
        1. Virtualization vs Dual Boot-

 

You can also choose between having a VMware VM Player for a virtual partition on your computers that is dedicated to R based computing or having operating system choice at the startup or booting of your computer. A software program called wubi helps with the dual installation of Linux and Windows.

  1. 64 bit vs 32 bit – Given a choice between 32 bit versus 64 bit versions of the same operating system like Linux Ubuntu, the 64 bit version would speed up processing by an approximate factor of 2. However you need to check whether your current hardware can support 64 bit operating systems and if so- you may want to ask your Information Technology manager to upgrade atleast some operating systems in your analytics work environment to 64 bit operating systems.

 

  1. Hardware choices- At the time of writing this book, the dominant computing paradigm is workstation computing followed by server-client computing. However with the introduction of cloud computing, netbooks, tablet PCs, hardware choices are much more flexible in 2011 than just a couple of years back.

Hardware costs are a significant cost to an analytics environment and are also  remarkably depreciated over a short period of time. You may thus examine your legacy hardware, and your future analytical computing needs- and accordingly decide between the various hardware options available for R.
Unlike other analytical software which can charge by number of processors, or server pricing being higher than workstation pricing and grid computing pricing extremely high if available- R is well suited for all kinds of hardware environment with flexible costs. Given the fact that R is memory intensive (it limits the size of data analyzed to the RAM size of the machine unless special formats and /or chunking is used)- it depends on size of datasets used and number of concurrent users analyzing the dataset. Thus the defining issue is not R but size of the data being analyzed.

    1. Local Computing- This is meant to denote when the software is installed locally. For big data the data to be analyzed would be stored in the form of databases.
      1. Server version- Revolution Analytics has differential pricing for server -client versions but for the open source version it is free and the same for Server or Workstation versions.
      2. Workstation
    2. Cloud Computing- Cloud computing is defined as the delivery of data, processing, systems via remote computers. It is similar to server-client computing but the remote server (also called cloud) has flexible computing in terms of number of processors, memory, and data storage. Cloud computing in the form of public cloud enables people to do analytical tasks on massive datasets without investing in permanent hardware or software as most public clouds are priced on pay per usage. The biggest cloud computing provider is Amazon and many other vendors provide services on top of it. Google is also coming for data storage in the form of clouds (Google Storage), as well as using machine learning in the form of API (Google Prediction API)
      1. Amazon
      2. Google
      3. Cluster-Grid Computing/Parallel processing- In order to build a cluster, you would need the RMpi and the SNOW packages, among other packages that help with parallel processing.
    3. How much resources
      1. RAM-Hard Disk-Processors- for workstation computing
      2. Instances or API calls for cloud computing
  1. Interface Choices
    1. Command Line
    2. GUI
    3. Web Interfaces
  2. Software Component Choices
    1. R dependencies
    2. Packages to install
    3. Recommended Packages
  3. Additional software choices
    1. Additional legacy software
    2. Optimizing your R based computing
    3. Code Editors
      1. Code Analyzers
      2. Libraries to speed up R

citation-  R Development Core Team (2010). R: A language and environment for statistical computing. R Foundation for Statistical Computing,Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.

(Note- this is a draft in progress)

2011 Forecast-ying

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I had recently asked some friends from my Twitter lists for their take on 2011, atleast 3 of them responded back with the answer, 1 said they were still on it, and 1 claimed a recent office event.

Anyways- I take note of the view of forecasting from

http://www.uiah.fi/projekti/metodi/190.htm

The most primitive method of forecasting is guessing. The result may be rated acceptable if the person making the guess is an expert in the matter.

Ajay- people will forecast in end 2010 and 2011. many of them will get forecasts wrong, some very wrong, but by Dec 2011 most of them would be writing forecasts on 2012. almost no one will get called on by irate users-readers- (hey you got 4 out of 7 wrong last years forecast!) just wont happen. people thrive on hope. so does marketing. in 2011- and before

and some forecasts from Tom Davenport’s The International Institute for Analytics (IIA) at

http://iianalytics.com/2010/12/2011-predictions-for-the-analytics-industry/

Regulatory and privacy constraints will continue to hamper growth of marketing analytics.

(I wonder how privacy and analytics can co exist in peace forever- one view is that model building can use anonymized data suppose your IP address was anonymized using a standard secret Coco-Cola formula- then whatever model does get built would not be of concern to you individually as your privacy is protected by the anonymization formula)

Anyway- back to the question I asked-

What are the top 5 events in your industry (events as in things that occured not conferences) and what are the top 3 trends in 2011.

I define my industry as being online technology writing- research (with a heavy skew on stat computing)

My top 5 events for 2010 were-

1) Consolidation- Big 5 software providers in BI and Analytics bought more, sued more, and consolidated more.  The valuations rose. and rose. leading to even more smaller players entering. Thus consolidation proved an oxy moron as total number of influential AND disruptive players grew.

 

2) Cloudy Computing- Computing shifted from the desktop but to the mobile and more to the tablet than to the cloud. Ipad front end with Amazon Ec2 backend- yup it happened.

3) Open Source grew louder- yes it got more clients. and more revenue. did it get more market share. depends on if you define market share by revenues or by users.

Both Open Source and Closed Source had a good year- the pie grew faster and bigger so no one minded as long their slices grew bigger.

4) We didnt see that coming –

Technology continued to surprise with events (thats what we love! the surprises)

Revolution Analytics broke through R’s Big Data Barrier, Tableau Software created a big Buzz,  Wikileaks and Chinese FireWalls gave technology an entire new dimension (though not universally popular one).

people fought wars on emails and servers and social media- unfortunately the ones fighting real wars in 2009 continued to fight them in 2010 too

5) Money-

SAP,SAS,IBM,Oracle,Google,Microsoft made more money than ever before. Only Facebook got a movie named on itself. Venture Capitalists pumped in money in promising startups- really as if in a hurry to park money before tax cuts expired in some countries.

 

2011 Top Three Forecasts

1) Surprises- Expect to get surprised atleast 10 % of the time in business events. As internet grows the communication cycle shortens, the hype cycle amplifies buzz-

more unstructured data  is created (esp for marketing analytics) leading to enhanced volatility

2) Growth- Yes we predict technology will grow faster than the automobile industry. Game changers may happen in the form of Chrome OS- really its Linux guys-and customer adaptability to new USER INTERFACES. Design will matter much more in technology on your phone, on your desktop and on your internet. Packaging sells.

False Top Trend 3) I will write a book on business analytics in 2011. yes it is true and I am working with A publisher. No it is not really going to be a top 3 event for anyone except me,publisher and lucky guys who read it.

3) Creating technology and technically enabling creativity will converge at an accelerated rate. use of widgets, guis, snippets, ide will ensure creative left brains can code easier. and right brains can design faster and better due to a global supply chain of techie and artsy professionals.