Oz Analytics

Do you know what is bigger than Big Data?

From episode 10 of my Naked Analyst Channel on YouTube.

I think I do - and it is the ‘appification’ of analytics. What I mean by this is the reduction of a complex analytic activity such as market segmentation, down to a single button on your computer interface. Very much like the Apps on your smartphone, tablet or increasingly your desktop.

That’s what it looks like but the impacts are more profound. That’s because it makes it possible for analytics to be successfully done by people who may not understand how it works, but do understand the ‘why’ and ‘when’ they need to do it.

For example, a marketer in a company can access more sophisticated views of their campaigns without the need of a specialist analyst. Appification extends the range of analytic things that a non-specialist can do.

This appification is made possible because of three things that have emerged in recent years:

1. The rapid increase in the number and sophistication of APIs (Application Programming Interface).
2. The rise of open source analytic platforms like R (http://www.r-project.org). These platforms have created vast libraries of algorithms freely available to anyone who knows how to use the platform.
3. The shear number of people and organisations involved in creating open source analytic platforms like R.

The last enabler needs a little further explanation. R is a free software programming language and software environment for statistical computing and graphics. It contains thousands of packages (10,000?) specializing in topics like econometrics, data mining, spatial analysis, and bio-informatics. Nobody knows how many R users there are, but a reliable estimate (see http://spatial.ly/2013/06/r_activity/) puts it in the millions. Many thousands have helped R develop over the years.  I think that this sort of large-scale self-organising open source effort is beginning to teach the world how to use analytic algorithms.

The above is all supposition, but I can back this up with evidence. Here are 4 examples of algorithm markets - or at least they exhibit varying degrees of ‘algorithm marketness’.

• Dataxu - senses and reacts in real time to changing consumer behavior. Openness is at the technical core of DataXu. The DataXu platform is a flexible technology stack with open APIs that make it easy to integrate and extend functionality.
• algorithms.io - delivers machine learning for streaming data. The Algorithms.io cloud platform makes it easy to use machine learning algorithms to classify streaming data from connected devices. Turn the Internet of Things into the Internet of Action
• Snapanalytx - aim to provide predictive analytics for all and make them more accessible and affordable.
•  Algorithmia - are building a community around state-of-the-art algorithm development, where users can create, share, and build, on other algorithms, and then instantly make them available as a web service.

In conclusion, there is still one issue needing resolution before algorithm markets take-off: How will the world’s business people get data into and out of these algorithm apps? I’m not sure yet, but I think the answer will be more apps. Apps that themselves appify the transformation and loading of data into and out of algorithm apps.

Confused? Well don’t be, like most new and shiny things, what we are talking about is just the next generation of ETL - something business intelligence people like myself have been building for the last 20 or more years.

What’s old is new again? Maybe, but from my perspective the future looks exciting for analytics.

My YouTube channel contains tips from a 25 year veteran of the analytic profession.

Birth Announcement

We launch today version 1.0 of our new mobile web app. We're hoping to build a community of users that find Bridger a mind blowing new way to explore documents and web pages.

If you are a student, a knowledge worker or anyone who wants to get to the nitty gritty quickly in documents, articles etc., Bridger will change forever the way you look at documents.

• It summarizes.
• Shows you who's who and what's what in Word, txt, PDF documents or webpages, in a few moments.
• It works on your iPhone, galaxy, iPad, android tablet - anywhere you have a connection to the net. 
• Its easy to use, even my least techie friends have said that.

Just go to our website http://www.sixdegreesofdata.com from your mobile device. The application is free.

If you enjoy using it, tell your friends!

Changing Tack

My 2 regular readers (thanks mum and dad!) may have noticed that the frequency of my posts has dropped dramatically since 2012. There was a reason for this - beyond my strongly held opinion that those who have nothing sensible to say should simply shut-up. That reason was/is that I have been starting a new venture and the 'stealth mode' has lasted a lot longer than I expected.

In 2012 I suffered an information crisis and I suspected that I was not alone. The crisis arrived because I was being increasingly overwhelmed with information and most of the information swamping me was not on the web. Instead it was in the form of documents and email. I pride myself as being pretty well organised with effective email filters in place and an organised corpus of professional and personal knowledge approaching 1,000,000 objects. Mostly documents of one sort or another.

I was quietly proud of my knowledge management skills but after pride cometh the fall - and in 2012 that's what happened. Let me explain how it happened and I hope the story will resonate with you.

 As a serial entrepreneur and corporate analyst, I have a broad range of professional interests. These range from all aspects of analytics and information management, and broaden out into general management and IT. I am also a bit of a pack rat and if I come across some information that looks useful I store it. For those interested, my tools of choice are Mac OSX, HoudahSpot, DevonAgent and Devonthink.

So over the last 20 years I have built up a very useful corpus of knowledge. By 2011 however I was becoming increasingly frustrated by my inability to effectively use this corpus in my daily work. Each day I need to deal with the usually pile of emails and read/respond to/create multiple documents. This presented the following problems:

1. Identifying and dealing with those emails and documents that I had to.
2. Ignoring the rest.
3. Locating past knowledge/information that would help me to deal effectively with the day's emails and documents.

So the problems were pretty much at the core of what I do.

If only there was a way for me to more efficiently decide whether-or-not a long email or document was worth me reading. Then, when I needed to make a decision or give advice, how could I tap into my corpus of knowledge?

These are the problems solved by my new venture Six Degrees Of Data.

Six Degrees Of Data is building Apps and web services that we hope turn the web on its head. Our first product will launch later this year. It is called Bridger and is an App and Web Service that intelligently evaluates, summarizes and links your documents to trusted sources on the web.

Bridger is a research assistant for busy people suffering from information overload.

We are trying to release the value currently locked inside documents because despite the phenomenal growth of the internet, 80% of the new information created each year is still unstructured – in other words it is text, graphics and video contained in things like documents, emails, Facebook shares, web pages and tweets, etc. This unstructured information is poorly integrated with the world wide web. Even if they are published to a web site, they have remained isolated islands of information – until now.

Within the last five years, the size of the average web page has more than tripled, and the number of external objects has more than doubled. The average web page is now 1 MB and growing at over 20% each year. Today the average web page has about 10 links and 600 words. So it is reasonable to say that less than 2% of the content is hyperlinked to something else.

Your documents often contain only a single link (or less) and many more words than a web page.

If you are lucky, there is a single bridge (hyperlink) from the web to your document. Think of your documents as information that is connected to the web superhighway only via an off-ramp. You can find published documents easily enough because published documents are indexed by the likes of Google and Bing. But what you can’t do is start from your document and get easily back onto the web in an intelligent way.

That’s why we see the current web-document world as a one-way off-ramp.

Bridger builds you an internet on-ramp for all of your documents – whether they are published on the web or not. This is how we can release the untapped value of the 80%.

How do we do this? Well, it’s pretty complicated technically but the good news is that you really don’t have to know about this if you don’t want to. All you need to understand is that Bridger takes your isolated documents, emails, etc. and automatically links them to over 50 billion trusted additional sources of information on the world wide web.

Drop a document on to the Bridger app and it will instantly tell you what the document is about and give you concise profiles of all the relevant people, organizations, places, technologies and more.

Your document is magically transformed into a fully integrated web body of knowledge.

And Bridger is the first of a stream of new products we re planning. Future areas that we will concentrate on are enriching our personal corpus' of knowledge, collaboration and sharing.

So my posts may remain sporadic but I hope to share more about Bridger and our journey as Six Degrees Of Data gets up and running over the coming months.

In the meantime, if you have a passion for the Semantic Web, Linked Open Data, URI's, sparkle and tuples, then give me a call.

 

Day 4 Week 6 of the CORTEX MBAnalytics

Continuing week 6 - part of the 60 day information management refresher course from the CORTEX.

The CORTEX

Master of Business Analytics

Week 6, Day 4

Theme: Analytics In The Real World

Week 6, Thursday

This week's theme is application - not in the sense of a computer program - rather in the sense of 'use it or lose it'. This week we look at how analytics is being applied in the real world.

READ       Today's task Part 1:   Data Mining At The ATO 

20 minutes

'Data Mining in the ATO', presentation by Warwick Graco, Director Operational Analytics, Office of the Chief Knowledge Officer, ATO at a 2007 UTS Faculty of Information Technology  (FIT) Research Seminar

Today's task Part 2:  ATO No Longer Taxing 

10 minutes

'No Longer Taxing, Using SAS to make it easier to interact with the Australian Taxation Office', SAS Case Study, 2007

Key takeout: The Australian Tax Office (ATO) is by most measures a pretty serious user of analytics. So it is interesting to see how both traditional big-vendor tools like SAS and open source Rattle and R are co-existing. Perhaps it's 'horses for courses'? Unfortunately, little is publicly known about how the SAS and R communities interact at the ATO? Perhaps a reader of this blog knows more?

 
As always, comments, questions and ideas are all welcome. 

The MBAnalytics is just one part of the Business Intelligence CORTEX - a community of Business Intelligence, Data Warehousing, Analytics and Project Management professionals in Australia, New Zealand and Asia. Membership is free and the forums are very active with over 20,000 posts offering a uniquely Australasian viewpoint.

Day 4 Week 2 of the CORTEX MBAnalytics

Welcome back to our second week - part of the 60 day information management refresher course from the CORTEX.

The CORTEX

Master of Business Analytics

Week 2, Day 4

Theme: Big Data Practitioners

Week 2, Thursday

Continuing on with our exploration of big data we look at how Amazon is selling big data business services.

READ       Today's task:  Analytics in the Cloud    4Mb

30 minutes

'Analytics in the Cloud', presentation by Dr. Deepak Singh, Sr. Business

Development Manager at Amazon, January 2011.

Key takeout: Moving onto Amazon Web Services (S3, EC2, etc.) makes

available to you a toolkit of analytic services from a whole range of

vendors. This presentation outlines the basic economic business case for

cloud-based activities.

WATCH    For extra credit:  Amazon at Emetrics    2.5Mb

15 minutes

'Front Line Internet Analytics at Amazon.com', presentation at the Emetrics

Summit by Ronny Kohavi, Director of Data Mining and Personalization and

Matt Round, prior Director of Personalization at Amazon in 2004.

Key takeout: This nearly decade old explanation of Amazon's business model

and approach to experimentation shows how far Amazon has come. This begs the

question: Where are the rest of us? Are we cloudless because the fear of

failure is killing our chance of a developing culture that recognizes failure is

frequently the price of success?

 

 Comments,questions and ideas are all welcome. 

The MBAnalytics is just one part of the Business Intelligence CORTEX - a community of Business Intelligence, Data Warehousing, Analytics and Project Management professionals in Australia, New Zealand and Asia. Membership is free and the forums are very active with over 20,000 posts offering a uniquely Australasian viewpoint.

The Death of BIG Business intelligence

In the second decade of the 21st century the BI platforms from the SIMO vendors (SAP, IBM, Microsoft and Oracle) are dinosaurs.

A provocative statement - but bear with me.

Over the past few years I've blogged about a lot on the emerging trends of information management: real-time, BIG data, agile BI, actionable performance management and the like. The one thing that they have in common is that they are all big. Either big in terms of expensive or big in terms of the changes that they require within organisations.

I've tried to relate many of these trends to practical actions that (for those of us without large budgets) we can still extract value from. The context of these actions is that I have often been advising medium and large commercial businesses with existing business intelligence operations. So what I have experienced may not necessarily apply to government or small organisations.

Frequently my advice has involved technologies from one or more of the BI platforms from the SIMO vendors. SIMO and cheap are not words that go together. It's not a criticism of the platforms. They are what they are but I argue that their time has past because there are now a number of alternative approaches available that leverage newer low cost products and methods.

I have been increasingly looking to the benefits of open standards and web-based solutions to create low cost platforms without further investment in the SIMO vendor platforms. In the past I have used open source to extend SIMO platforms so that I didn't have to pay for the 'enterprise version' or 'additional module' that the SIMO vendors always seem to suggest when I want my platform to do something new.

An example of this was to extend our web-based dashboards to include sparklines¹. This type of graphic was not supported by the SIMO vendor product at the time but was easily extended using open source. It also saved the company the considerable expense of buying the enterprise dashboard for $250,000 + 20% annual support charge.

Now I have questioned the idea of using the SIMO platform at all. In the above scenario I today have invested in something like Tableau which although very definitely not based on open source code, is significantly cheaper to purchase and more adaptable to a variety of data sources and presentation types.

At this point, I will digress into more technical considerations. So if you become glassy-eyed at the mention of java, then it's best if you skip over the next couple of paragraphs and start reading about the example.

Open standards-based initiatives make our world of data so much more open than it once was. Everyone has heard of Big Data, but there are also dozens of open initiatives such as Mondrian (or more correctly: the Pentaho Analysis Services Community Edition) - an Online Analytical Processing (OLAP) server that enables business users to analyze large quantities of data in real-time.

Why do we care? Mondrian gives us an open standard way to access OLAP using Java on the client side. I'm not referring to XML/A the 'open' standard that Microsoft has been promoting for some years. XML/A is not truly open. Mondrian and Saiku should provide a true open driver capability to those of us building best-of-breed platforms. In time, I hope that market pressures will convince the SIMO vendors to build their own drivers for Mondrian.

A lot of brainpower has been invested over recent years improving database technologies so that they better support business intelligence activities. Combine this with the computing power now available on our desktops and in our data centres and clouds and I think you will find that best-of-breed technology stacks where you decide each layer will often do the job more sustainably and cheaper than an equivalent SIMO investment.

An Example

I can best explain the business logic behind my ideas by taking an example such as business MDM (master data management).

Old School

You purchase an MDM solution from the SIMO vendor you already have in-house. Let's take IBM in this case. So you will need to purchase the Websphere Business Glossary and probably the MetaData Server and Workbench. What's this going to cost? I'm guessing $1 million or more for an enterprise license once you factor in the specialist technical skills you will need to integrate it into your organisation.

IBM's Websphere Platform (courtesy IBM)

Now Websphere is a nice platform and the integration between DataStage, QualityStage, Information Analyzer and Business Glossary is a joy to have as a business manager of this stuff. I've just never had the business case to buy the entire platform. This is true of the majority of IBM platforms implemented across Asia-Pacific today. IBM is no exception and this is true of the other SIMO platforms. Budgets, capabilities and maturity levels almost always mean that only parts of a SIMO platform are implemented in an organisation. What a missed opportunity - but there is no point in crying over spilt milk. If the business case to implement entire platforms existed, then the SIMO vendors would be the first to let you know.

Microsoft is the exception to this rule, having a much cheaper licensing model. Unfortunately their MDM capability is very much a 'you build it' one, so the TCO (total cost of ownership) is still significant.

The challenge today is being smarter and more effective than past attempts. 

New School

Means that you leverage your existing investments - often those in web solutions.

For example, an MDM on-the-cheap solution would be to have your content management (CMS) platform hold critical business metadata. SharePoint is frequently the CMS of choice for large traditional corporations. Even if not already implemented, Using SharePoint to build a custom workflow and collaboration solution is pretty straightforward. So with a modest effort, the CMS can support an intranet-based community of business owners to create and maintain business metadata.

Making use of this business metadata is also possible. Continuing with the MDM scenario above, and assuming you use BusinessObjects, Cognos or similar, then you can export the business metadata easily to universes/cubes so that it is available to report writers, users and decision makers. This will drive the use of standard definitions of data and measures and make enterprise performance management easier.

We are also reaching the point where the presentation layer products (reports, dashboards and scorecards) from the SIMO vendors are now intelligently integrating back into - you guessed it - the CMS's like SharePoint. So production and consumption of business metadata is done on the most widespread platform available within the organisation.

This takes BI and IM out of the analytic ghetto and integrates it seamlessly into the corporate intranet - and beyond.

Cost? A conservative $100,000.

 Conclusions

What's the overall result? You get 50% or more of the benefits at 15% or less of the cost. Ongoing maintenance is also less. Most crucial of all is that the cheaper option is also the one most likely to be funded by an organisation. Why? Simple: it's CHEAPER.

This blog started with the statement that the SIMO vendors were dinosaurs. This is not quite true, but as my example above illustrates, it is now time to integrate your existing SIMO investments into your corporate intranet. Not by buying more SIMO products. Instead use more open technologies to integrate them into the intranet. Treat the SIMO-based data and report repositories as services to be consumed via a web front-end that does not require those extra SIMO products.

I thought about calling this approach 'Poorman's BI' and 'Lagging-edge BI' but I will stick with another term that I have used in the past: 'Slow BI'. It is slow in a good way because I have seen this approach allow an organisation to build a level of support for BI that is solid, broadbased and delivering benefits that the organisation can measure and acknowledge.

Slow BI provides an enduring organisational and technological platform that focuses limited resources on delivering practical business capabilities. These become part of the operational business and decision making processes among people that are only dimly aware of business intelligence and information management. This is an important step towards the elimination of analytic ghettos and the creation of an information workforce.

Slow BI over the long term exceeds the benefits of the SIMO platforms as they frequently struggle to achieve widespread adoption outside of the analytic ghettos. Perhaps slow-and-steady beats the big-bang?

 

 ¹ Sadly Microsoft has recently tried to patent sparklines - despite the wide acknowledgement that Edward Tufte invented and defined sparkline as a term to describe "small, high resolution graphics embedded in a context of words, numbers, images".

Crowdsourced Insight

I have just been reading David Cameron's blog where he talks about an interesting hack from India. You can read the story here but in short it is about Arun Ganesh, a student at the National Institute of Design in Bangalore, who was frustrated that the poor quality of the bus timetable for Chennai. With over 5,000 bus routes this was a real world problem for Chennai's 4 million residents. How did Arun solve this? He created his own map, crowd-sourced data from his fellow bus passengers and then published the new improved map along with an accompanying app BusRoutes.in for interactive route planning. The maps use OpenStreetMap - a free editable map of the whole world. In 3 days he had crowd-sourced enough data to kick-start everything. An impressive solution to an everyday data problem.

I know that corporate crowdsourcing for R&D is nothing new and prediction markets are gaining some traction. Take a look also at InnoCentive for some very serious idea outsourcing of industrial problems and Brand Tag for insight into what people really think of your brand.

I wonder if it is possible to apply similar ingenuity for information management problems within the corporation? What problems am I talking about? For me today in my role they include:

• Creating a single business glossary across a conglomerate of businesses
• Building a knowledge portal to collect information useful to management report writers and users
• Identifying solutions to complex analytic problems such as:
  • predicting propensities to substitute physical products with digital ones
  • measures of copyright protected revenues.

My initial thoughts are to invest in the following capabilities:

• Business Glossary - set-up an intranet portal (probably using SharePoint because that's what we use internally) to publish glossary term definitions and link these to discussion forums where anyone who is interested can discuss, provide feedback and mediate agreement on standard terms and definitions
• Knowledge Portal - another portal linked directly into our reporting platforms (SAP, Oracle, TM1, etc.) so that our:
  • Decision makers have access to use and can contribute to wiki's, FAQ's, known issues and documentation (tutorials, reporting guidelines, etc.) about our information assets and related business processes.
  • Project Staff and Analysts have access to the scope, structure and lineage of data from the report semantic layer back through the data marts and warehouses and into the source systems where data is created.
• Analytic Solutions - you guessed it: YAP (yet-another-portal) that lets people publish data sets that have either been purchased from 3rd parties (such as market research) or contain discovered insight that may be useful to others.

My hope is that these investments will deliver not just better support to our decision makers but also better engagement with the information they use. Give them a sense that they not only own their management reporting but also feel empowered to improve the information asset without relying on IT experts.

This may not be as innovative as people like Arun have achieved, but in the my corporate world these are sufficient wins. Maybe we will even create more super users in the organisation? Sounds good to me.

Looking again out into the rest of the world and taking a lateral step from directly involving your users, kaggle is a site that aims to facilitate better predictions by providing a platform for data mining, forecasting and bioinformatics competitions. As a side note, it is interesting to see that 'kagglers' favourite software is R, with SAS now a fairly distant 3rd in popularity. SPSS is even further away with only a quarter of the users that R has.

If you want to learn more about open innovation I suggest you start at Open Innovators website.

Big Data

Several month's ago a short video appeared on YouTube with an interview of LinkedIn's Chief Scientist DJ Patil. In it he discusses how 'Big Data' impacts the practise of analytics. I've only just got around to posting about it but I am doing so now because he has some insights that I agree with and would like to share as they are still relevant. 

Big data is today most often associated with the internet superstars like Google, eBay and Amazon. There are 3 other areas with lower profiles where big data is important: intelligence (spooks, the military, etc.), scientific and academic research, and the financial markets.

Big data's future is much bigger than this because more and more areas of human activity are going to be faced with vast data sets. When you hear people talking about the growth of knowledge and statements like 'if this data were printed then the stack would grow faster than NASA’s fastest rocket', you have to remember that there is a good chance that each page of new data is adding to someone's analytic data set.

I'm not quoting the guy verbatim but here's what I heard and my takeouts to his comments:

• Open source 'big data ready' technologies like Hadoop (see my earlier blog or here) have come into their own now. Look to people with these skills over those only with SQL if you are facing big data challenges.
• We have reached a tipping point in the use of open source for commercial solutions to big data problems.
• If you want good analysts then the best place is to look is in occupations where people will already have the practical skills in manipulating big data sets: scientific fields like meteorology, oceanography and the like. I agree but this is not the only place as in my experience I also need analysts that relate well to business decision makers - i.e. those people that make commercial decisions based on the analytics. This is perhaps less important in pure tech plays like LinkedIn.
• Open source will transform the practise of analytics in the next 3 - 5 years. I think it will take longer than this to really impact the more traditional industries. I'm not happy about this but I am realistic about the difficulty in convincing business leaders that open source is a superior solution to proprietary ones. The money behind the big vendors will keep them going for a number of years yet.

One potential qualifier to DJ Patil's perspective is that although he has a very impressive big data background as a mathematician, US Department of Defence analyst ('Threat Anticipation'), and former eBay Director of Strategy and Analytics, his current employer is LinkedIn.

The core of LinkedIn's big data is structured and fairly static: profiles of people. So I'm not sure how similar their big data challenges would be to, say, those faced with processing, understanding and predicting large streams of real time data from financial markets or very large sensor arrays. On the other hand, the growth of LinkedIn communities and their related activities must generate large amounts of semi-structured data.

I also have no idea what LinkedIn's own analytic goals are beyond what DJ mentions on his own profile where he says his analytics drives product features like:

• "People You May Know"
• "Who Viewed My Profile"
• "Groups You Might Like"

Maybe somebody reading this blog knows more?

The video is on YouTube and I embed it here for convenience:

Or you can download 'DJ Patil on How Big Data Impacts Analytics' directly from this blog.

A Vision Of Data Interaction

I am a long time data visualisation geek and I keep an eye on R&D in the field. A TED video of Pranav Mistry has recently impressed me with his vision for how we will be soon able to interact with data in the physical world. The video is available to watch on YouTube:

I have also kept a copy in Quicktime that you can download here (large file: 30Mb).

It's a 15 minute presentation so you'll need a little patience. It's worth it as he demonstrates his research that makes it possible to merge the virtual world into the physical. Examples shown include:

• Projecting and interacting with data on any surface in your physical environment; your hand, a wall, a blank bit of paper.
• Transferring data in your physical environment into the virtual; grabbing a printed picture of a pie chart, taking a picture just by gesturing it.
• Superimposing electronic data onto a book or newspaper that you are reading.

Pranav is a researcher in the Fluid Interfaces Group at MIT's Media Lab.

Will The Cloud Democratise BI?

I read with interest the announcement that Canadian BI vendor startup Indicee has successfully completed a second round of venture capital funding. In brief, Indicee sells a cloud solution where you upload your data and it works out automatically the relationships - freeing you to concentrate on answering your business questions. Here's a video from Indicee summarising their service:

Indicee Movie

I don't know the terms of the financing agreement but the US$6 million raised is another sign that BI in the cloud is an area that we should all keep a track of.

Why The Cloud?

Here are four reasons:

1. Indicee was founded by Mark Cunningham and Fred Tummonds both originally with Crystal Services - makers of Crystal Reports. Mark was a developer of the original Crystal Reports product - possibly the most ubiquitous BI tool outside of Excel in the market today.

2. Their early seed funding included all of the original founders of Crystal Services.

3. This second round adds Granite Ventures who have also previously invested in companies such as:

• Biz360 - Hosted Analytical Applications
• C-Decisions (dba DecisionView Software) - Analytic Software
• Crowd Science - Online Marketing Research
• Lucid Imagination, Inc. - Open Source Search
• Oversight Systems - Analytic Software
• Purisma - Customer Data Management SaaS, since sold to Dun & Bradstreet
• Siebel Systems - Analytic Software, since sold to Oracle
• Skytide - Digital Media Performance Management Software
• Vignette - Content Management Software, now a public company.

4. Their pricing model. It's cheap and offers small and medium sized enterprises (SMEs) some real analytic grunt without the hassles of installing and running the software themselves:

Indicee is certainly not alone and there are other startups taking similar but distinctly unique services to market. For example, take a look at CloudSwitch, RightNow Technologies, RightScale or Good Data. Good Data offers real OLAP capabilities and runs on Amazon Web Services. It is also backed by an impressive list of people including Marc Andreessen, Tim O’Reilly, Esther Dyson and John Landry.

Many of these startups are building their services on top of the EC2 infrastructure provided by Amazon's web services.

New Choices

As we approach the end of the naughties the BI market is offering some interesting and fundamentally new choices to us:

• BI in the cloud
• SaaS
• Open source.

... and we can now choose to buy each of these services from all the big vendors (at big cost) as well as a raft of new entrants at substantially lower cost.

With prices like those from Indicee and others, we could be seeing a massive increase in the number of organisations making active use of analytics.

My Predictions?

It is still early days for SaaS and full cloud solutions - especially for larger organisations with mission critical BI. For the big boys SaaS and cloud BI will remain a niche option for a number of years. In 2010, of these 3 alternatives, open source will have the biggest impact by far.

SMEs will perhaps be the biggest beneficiaries of these market developments in 2010. As new adopters of BI they are more able to make use of the cloud - especially if the entry cost is less than AU$100 a month to launch your first analytic service.

Stay tuned.