First, let me clarify the concept by quoting sentences from the book itself.

"Start Quote" Scientist Stuart Kauffman coined the phrase 'The Adjacent Possible' to indicate a kind of shadow future, hovering on the edges of the present state of things, a map of all the ways in which the present can reinvent itself. Yet it is not an infinite space, or a totally open playing field. What the adjacent possible tells us is that at any moment the world is capable of extraordinary change, but only certain changes can happen. The strange and beautiful truth is that its boundaries grow as you explore those boundaries. Each new combination ushers new combinations into the adjacent possible."End Quote"

Steven Johnson goes on to illustrate many examples and concludes that almost all innovations, from pre-historic life to YouTube, are based on 'The Adjacent Possible'.

Coming to Business Intelligence & Analytics, here is my view of the key trends for 2012, or in other words, 'the adjacent possible' for BI, at this juncture. The Mindmap that describes these key trends can be viewed here - BI Trends for 2012 and they are:

1) Smorgasbord of Analytical workloads
2) Self-service BI
3) Social Media Analytics
4) Mobility in BI
5) Big Data
6) BI on the Cloud
7) Advanced Analytics
8) Data Visualization

Though the list is not something that will surprise BI practitioners, each area offers immense scope for innovation. Exploring the adjacent possible in each of these areas is bound to be very interesting and if you get it right can prove to be quite rewarding too. And in the true spirit of innovation, we should let ideas get connected with one another rather than protecting them.

Wish you all a very happy new year 2012. And thanks for reading.

As a metaphor, Trim Tabs are used to denote tiny components, which nevertheless have a great impact on things that they are attached to. This blog post is about the relevance of this metaphor to BI in enterprises.

Business Intelligence practitioners acknowledge the fact that BI & Analytics in any organization is a journey, an evolution over a period of time. The canvas for BI is extensive and spans the business technology continuum.

Given that BI can add value in many areas of an organization and there are many solutions possible in each of those areas, it is important to identify the BI 'Trim Tabs', i.e. those small areas that can provide the maximum value for invested money. In one of my earlier blogs, I had talked about the concept of 'analytics anchor points' - business processes that should be considered first for optimization through analytics.

Identifying the analytics anchor points in an organization is a non-trivial exercise. In my humble opinion, such a process should start with a complete understanding of the organization's 'Business Model'. Business Model is a set of assumptions about how an organization will perform by creating value for all the players, on whom it depends, including its customers. Like all good stories, a business model relies on the basics of character, motivation and plot. For a business, the plot revolves around how will it make money. Characters are the different stakeholders (internal & external) in that business and each one of them acting in their own self-interest (motivation) makes the plot plausible and meaningful. All successful businesses have a twist in their plot (think of Google, eBay, Dell etc.) that has made them fantastically successful. You can read more about business models in the book by Joan Magretta & Nan Stone titled "What Management Is".

From BI perspective, once the practitioner understands the business model of the company, many questions gets answered:
1) What drives the company's success and how BI can help?
2) Who are the stakeholders and what information are they looking for?
3) What needs to be optimized and how it can be done?
4) What is the architectural blueprint and how will it evolve?
5) How fast should information get delivered?
6) How much data needs to be collated and how far into the past should one go?
7) What are the regulatory requirements for the company?

And many more. In essence, clarity on one aspect of the problem (business model) will go a long way in selecting the analytics anchor points (BI Trim Tabs) for that particular organization.

Thanks for reading. Please do share your thoughts.

The Wisdom of Crowds is a book written by James Surowiecki and the tag line for the title reads as "Why the many are smarter than the few and how collective wisdom shapes business, economies, societies and nations". In this book, the author argues that in any kind of problem solving / decision making scenarios, collective intelligence of a group of people always produces better decisions than an individual, even the expert, working in isolation. There are 4 factors that characterizes wise crowds - Diversity of Opinion, Independence, Decentralization and more importantly Aggregation.

Aggregating individual opinions and thoughts is a crucial factor in ensuring wisdom of crowds and such aggregation is enabled by software products that fall in the category of 'Collaborative Decision Making (CDM)' platforms and many of them have BI components embedded within them. Here are some of the products - Decision Lens, IBM Cognos, Lyza, Microsoft Sharepoint, Purus DecisionSurface, SAP Streamwork, Cogniti, Panorama Necto. Do check out these products as they offer something unique in the way Information has been delivered to business users.

Thanks for reading. Please do share your thoughts.

To me, the above story illustrates the power of collaboration and reinforces the fact that 'Wisdom of the Crowds' always produces better decisions than one taken by individuals, even by the so called experts. But then this question arises - "If Business Intelligence is all about decision making, why are BI platforms / tools not conducive for collaborative decision making?"

In my mind, the core features of a Collaborative BI environment are:

1) BI environment should have a 'Facebook' like interface that greatly simplifies interaction between users.
2) Users should be in a position to reuse analytical components developed by others.
3) Users should have the ability to communicate real-time within the context of a particular report / dashboard / scorecard
4) Users should have the ability to subscribe to analysis done by other users
5) Users should have the facility to recommend and promote useful analytical components to others
6) BI platform should have the ability to aggregate the views of multiple users with respect to analytics, so that BI gets "crowd-sourced" within the organization.

I had this 'Aha Moment' in relation to Collaborative BI when I saw a demo of the new BI product developed by Panorama software, called Necto. Panorama, company more famous for their OLAP technology that was sold to Microsoft in 1996, has developed this platform keeping collaboration as the central theme of the product. Do check out the demo of Necto at this link.

Bottomline is that Collaborative BI platforms are here to stay. In my opinion, Collaborative BI along with its Actionable & Embedded BI counterparts form the triumvirate that enables Agility in Business Intelligence for organizations. Paraphrasing the above,

Agility in BI 'Equals' Actionable (A) 'plus' Collaborative (C) 'plus' Embedded (E) BI

I will write about the other two components (Actionable and Embedded BI) of the ACE paradigm in my subsequent posts.

Thanks for reading. Please do let me know your thoughts.

The world of Business Intelligence & Analytics is complex too. I do realize that BI is nowhere as profound as some the things that Tim alludes to in his talk but having said that, I think the God complex has relevance to the world of analytics. As a BI practitioner for the last 12 years, I have numerous examples of product vendors, system integrators, consultants, etc. peddle their wares, under the guise of - "I know everything about your problems and this tool / solution will solve it".

Business Intelligence & Analytics, at a fundamental level, is all about optimizing business process in an organization. Given the complexity around business processes (even for a mid-size organization), I for one, feel that solutions have to be found only in that particular business context and not elsewhere. The practitioners have to abandon their BI 'God complex' and seek solutions only after they first understand the specific business situation at hand. Once the problem is understood, a systematic trial-and-error mechanism is to be instituted to select the best BI solution for their needs.

As a BI consultant, I try my best to work around the God complex, by following certain rules:

1) Listen intently to customers to understand their business model, strategy and specific problem / opportunity areas, before anything else.
2) Always develop 'Proof of concept' demos before narrowing down on tools / products / solutions.
3) Follow 'Wisdom of the Crowds' strategy (as opposed to the 'experts' view) by utilizing techniques like the Analytic Hierarchy Process (AHP) to drive consensus.
4) Create business process simulations that provides a dynamic view on the impact of analytics in any organization
5) Adopting Project Management techniques that allow for iterations / course change in the development cycle.

Though the specific techniques might vary, I feel that abandonment of the God complex (and the associated mental model) is the starting point for BI practitioners to seek solutions that are relevant in a particular business context rather than taking a 'templatized' view to solve problems.

Thanks for reading. Please do share your thoughts.

1) Prezi based presentation - Link here
2) BI mind-map - Link here

I took each area in the BI stack and tried to explain how traditional BI techniques are getting strengthened in certain areas and also getting morphed in different ways. I have also touched upon the emerging areas, like Mobile BI, Cloud BI etc. which are set for explosive growth in coming years.

Hope you like these artifacts. Please do let me know your feedback.

What is a Business Dataset?
Business Dataset is a self-contained collection that contains data for a particular business process or for an entity. Examples are - Point of Sales Data, Purchase orders, Customers, Products, Chart of Accounts etc. As you can see from the examples above, business datasets can be transaction oriented (business process) or master data (entities). The business dataset is processed from raw transactional data (can be present in tables of a relational data store) and can also include external data, syndicated data etc.

Why is a Business Dataset important from an analytical standpoint?
A collection of business datasets defines the analytical DNA of an organization. Each dataset represents a particular process or an entity and is typically pre-processed from raw transactional data. The datasets are combined in logically relevant scenarios and each scenario provides insights for a particular aspect of business. Quite obviously, the processing & governance of these datasets is very important and BI systems should be architected taking this into consideration. In my experience, the number of datasets in an organization can range anywhere between 25 (small firms) to 80 (large firms).

How do we organize the Business Datasets in an organization?
I propose the creation of an artifact called the 'Business Dataset Bus Matrix' (BDBM), similar to the Dimensional Bus Matrix. BDBM maps the Functional Area to the required Informational capability which is then mapped to specific business questions. Each row of BDBM contains one business question, while the master and transactional datasets are shown in the columns. A tick-mark is placed at the intersection of rows (business question) and columns (business datasets) wherever appropriate, i.e. a tick-mark signifies that a particular dataset is required to enable the informational capability.

It is important to realize that Business datasets are not intended to replace traditional data warehouses or data marts. They are infact derived from the data warehouses or any other data repositories with the sole purpose of being more amenable for analytics.

Thanks for reading. Please do let me know your thoughts.

Across the spectrum of industries there is so much talk about Analytics, Business Intelligence, Information Management, Performance Management, Big Data etc. that it is probably safe to say that we are experiencing an unprecendented demand for BI in its broadest sense. Being a first principles kind of guy, I was trying to figure out what is that one thing that can be considered as the root cause for this BI explosion. I had this eureka moment a couple of days back when I realized that the answer to that question could well be the - "Increase in Velocity of Business Decisioning".

In this context, Velocity should be construed as the tremendous speed with which businesses are trying to make decisions and also ensure that their decisions are in the right direction (i.e. positive impact and reduced risk). Sample this from Vishal Sikka's key-note address on SAP HANA during the recent SAP event at Boston.

1) Global CPG company reduced Real-time Profitability Reporting from 6 minutes to 736 ms
2) Large Manufacturing company reduced Customer Reporting from days to seconds
3) Financial services company reduced the time taken for running the Cross-Sell analytical model calculations from 45 minutes to 5 seconds
4) An Automotive company is able to perform Real-Time Analytics on 233 Million vehicles

And many more. Bottom-line is that 'Organizations have started realizing that speed of decision making is directly proportional to the magnitude of competitive advantage for their firms'. All things remaining constant, faster the decision making cycle greater is the competitive advantage. I remember reading a book called the 'Age of Speed' by Vince Poscente some years back but never realized that BI will be so intimately touched by it.

But for an organization to truly thrive, Speed by itself is just a necessary condition but not sufficient. The sufficiency condition is that the direction of decisions made needs to be constantly evaluated (Think Performance Management) - Does it create positive impact? Is there a cultural fit? Are the decisions in line with the regulatory framework? Do my customers get the intended value? etc.

So 'Speed with Direction' for which physics helps us with a nice descriptor called 'Velocity' is my answer as to the root cause of what is causing this explosion for BI & related services.

As an aside, my mind map on the different dimensions of BI
won the 3rd prize in the recently concluded competition. That mind map is an evolving artifact and so please do take a look once in a while to provide your feedback.

Thanks for reading. Have a great day!

Business Intelligence is evolving at a rapid pace on two fronts:

1) Technology improvements within the domain across the spectrum of Information Management, Reporting & Dashboarding, Analytics and Performance Management.

2) BI is increasing becoming part of a larger collaborative framework that includes other systems (BPM, ECM, SOA etc.)with the ultimate objective of improving business decisions.

I have tried creating a mind-map that illustrates the different BI dimensions. Please do take a look at the following link: BI - The Dimensions
and provide your feedback.

Before that let us take a look at this fashionable phrase called the 'New Normal' - Mr Mohammed El-Erian, CIO of Pimco, first introduced the phrase New Normal and it has since been used by many people to signify a significant shift in the way things 'were' as compared to how things 'will be' in the future. Here's an instance of its usage by Microsoft CEO Steve Ballmer in one of his periodic public e-mails.

In the context of BI, Big Data has come to include all the systems & process around data generation, collation, management, control & usage. Data generated by business transaction systems have been increasing rapidly and with the advent of social media (Facebook, Twitter, Blogs etc.), it has exploded exponentially (a little out of control, if I may add!). We are clearing entering the era of petabytes and exabytes as the 'New Normal' for data management systems.

Here goes some of the more famous very large data warehouses:
1) eBay has a 6 1/2 petabyte database running on Greenplum and a 2 1/2 petabyte enterprise data warehouse running on Teradata
2) Facebook has a 2 1/2 petabyte datawarehouse running on Hadoop/Hive
3) Walmart has a 2.5 petabytes warehouse, Bank of America has 1.5 petabytes, Dell with 1 petabyte - All running on Teradata
4) Yahoo, Fox Interactive Media, TEOCO (which runs outsourced DWs' for top US telcos) are all in the hundreds of terabytes range.

Since data management forms the core of analytical systems, it is important for BI practitioners to reset (or should I say, re-engineer) their thought process around managing data. Thinking at the scale of petabytes and beyond does alter certain preconceived notions around BI systems for many of us. For example, larger data sets require that we distribute the data among many units rather than just distributing the workload. Our notion of reliability, recoverability, consistency, scalability etc. can get turned on its head with the requirement to handle data in the petabyte and exabyte range.

Innovations will continue to happen across multiple dimensions to help tame this Big Data. Given below are some dimensions of change I could think of:

1) New data storage & manipulation techniques would continue to unfold - Ex: Hadoop, MapReduce, Columnar databases, MPP architectures etc.

2) Divide and Conquer data - Organizations will develop their business architectures around distributing data across multiple platforms (on-demand and on-premise) to make sense out of them.

3) In-Memory Analytics would help business users in analyzing large datasets rapidly - Faster and More powerful analytics with the proliferation of 64-bit processor families and In-memory based BI tools like BO Explorer, Qlikview, Microsoft PowerPivot etc.

Am sure that there are many more interesting ideas to manage and make sense out of Big Data. Please do share your thoughts.

Now, what is a non-linear system? - A non-linear system is one where the whole is not equal to sum of its parts. Let us take the example of Friction. Without friction a simple linear equation expresses the amount of energy you need to accelerate, say, a football along the ground (sounds contrived, well, it is FIFA 2010 time - Waka Waka). With friction, the relationship gets complicated, because the amount of energy changes depending on how fast the football is already moving. One cannot assign a constant importance to friction, because its magnitude depends on speed. Speed, in turn, depends on friction.

The whole body of System Dynamics developed by Professor Jay W. Forrester deals with complexity around non-linear systems. The System Dynamics Society says this and I quote - "System dynamics is a methodology for studying and managing complex feedback systems, such as one finds in business and other social systems. While the word system has been applied to all sorts of situations, feedback is the differentiating descriptor here. Feedback refers to the situation of X affecting Y and Y in turn affecting X perhaps through a chain of causes and effects. One cannot study the link between X and Y and, independently, the link between Y and X and predict how the system will behave. Only the study of the whole system as a feedback system will lead to correct results".

As BI practitioners, we are comfortable with questions around Data Management, Reporting, Dashboarding etc. but are stumped when confronted with the question of "How do the Reports, Dashboards or any analytical artifact affect the quality of business decisions"? In my mind, the missing link is the lack of understanding of business as a non-linear system. Let me provide a concrete example here.

In one of my BI consulting engagements for a large voice-based Business Process Outsourcing company, the problem was to predict the number of calls that would be received in a particular day as that number drives a lot of decisioning on the ground, viz. number of associates required, type of skills required, infrastructure, schedule for pickup and drop etc. If this problem is taken only as a predictive analytics problem, ignoring the non-linear nature of the business, the predicted value of the number of calls (using various statistical techniques and BI tools - in this case I used the Microsoft SSAS based Data Mining solution), does not provide a complete solution. When I fed the predicted number of calls to a business process simulation model (I used Powersim in this case) that captured the inter-relationships between various business processes, a much more robust solution was obtained.

The gist of what I am trying to convey is:

1) BI Practitioners would do well to understand what non-linearity is and how businesses processes are inherently non-linear in nature.

2) Goal of analytics is to really improve the quality of business decisioning. BI does not stop with just reports, cubes & dashboards.

3) Analytics in combination with Business Process simulation models (that captures the non-linear nature of business processes) can help organizations increase the quality of business decisioning.

Thanks for reading. Please do provide your feedback.

Here are some instances of such a scenario, that I have seen recently:
1) Large Healthcare information processing company uses SalesForce.com as the cloud based CRM system and needs to interact with On-premise SAP system, using Informatica as the ETL engine.
2) Leading Airline Company uses Oracle CRM On-demand and wants to exchange data with Oracle Apps which is installed in the company's data center. The enterprise standard ETL tool is SQL Server Integration Services and that needs to be leveraged for data integration.
3) A large Telecom company, which uses Jobstreet as an on-demand recruitment engine wants to integrate the recruitment data with Oracle HR Analytics (BI Apps) module installed on-premise, to develop a comprehensive BI platform for HR data.

I am very sure that there are many other scenarios where the integration between on-demand and on-premise software is required, and this trend is bound to accelerate in the future. The good news for BI practitioners is the fact that such integration (for any scenario) can be accomplished with a good understanding of web-services in the context of ETL platforms.

At a high level, the data integration architecture for the On-demand plus On-premise scenario, is as described below:

1) On-demand applications provide webservices for insert, update & delete for each entity
2) Each WSDL file has a set of methods that needs to be understood for its functionality
3) ETL tools have the capability to call webservices within its flow
4) ETL tools increasingly are providing specific integration packs with On-demand solutions that provide for an easier & more comprehensive way of integration. For example, Informatica provides a connector for SalesForce.com that just makes the integration all that more easier.

BI practitioners would do well to understand the business imperatives behind the on-demand with on-premise scenario and think through a solid ETL technology architecture to enable it.

Before signing off, I would like to introduce the EbizQ forum where I have been a forum contributor for the past few months. Given below is a forum question and my reply, that is relevant in the context of this blog post.

Thanks for reading. Please do share your thoughts.

Question:
Is There a Certain Size Business or Certain Vertical Industry for Which SaaS BI Makes Most Sense?
By ebizQ on Mar 1, 2010 at 10:05 AM

- Karthikeyan Sankaran | March 2, 2010 12:16 AM | Reply

I think SaaS BI has a role to play in organizations across industries and of various sizes. Every organization has a set of business functions (Finance, Marketing, Operations, Sales, Strategy etc.) each with its own characteristics that dictate the business decisioning requirements. The applicability of SaaS BI is more a function of these characteristics than the organization themselves. That the characteristics themselves might be dependent on the industry or size is definitely a valid argument but I will leave that for another discussion.

For example, certain business decisions require a tightly coupled information chain (from ERP to DW to Reporting) and this is best served by in-house / on-premise BI platforms. On the other side of the spectrum, certain decisions are to be taken based on a loosely coupled information chain and these decision makers would be well served by on-demand / SaaS platform.

Bottom-line, I expect, every organization, big or small, to have a mix of on-premise and on-demand BI platforms each serving a specific business community and opportunity.

Business Intelligence, arguably, is the most quantitative of areas in Information Technology. At a very basic level, BI deals with metrics collected about various business processes. The way the metrics have to be managed and manipulated depends on the mathematical content of these metrics. If that sounds too profound, well, it is intentional and I urge you to read on!

Any Data Warehouse data modeler will appreciate the fact that metrics collected in a fact table have to be understood in the context of the Fact Table grain, viz. A transaction grain fact table has metrics that are to be treated differently than the ones stored at a Periodic snapshot level or as an Accumulating snapshot. Think about Fully Additive, Semi-additive facts and you get the idea.

Similarly, a BI report developer deals with numbers on a daily basis. A good understanding of the numbers (can it be added or averaged or extrapolated) to be shown on a report, is essential to arriving at the right information content and also the correct way to visualize the numbers in question. As a simple example, read Ralph Kimball's classic article on (aren't all his articles classics!) SQL Roadblocks and Pitfalls here and we realize that to decipher an article that exposes the basic limitations of SQL in dealing with moving averages (a very common requirement in BI reporting), we need the ability to think mathematically.

Moving on to the realm of data mining, predictive analytics and its ilk, we as BI practitioners are starting to tread on areas that require a solid quantitative mindset. In one of my earlier blogs titled 'The Esoteric World of Predictive Analytics',I had argued that traditional statistics is not enough to make sense of Predictive Analytics, when it comes to modeling Human Behavioral Systems which is what BI applications are all about. More fundamentally, an understanding of probabilities, central tendencies, cause and correlation, normal distributions, regression models, design of experiments etc. is becoming very important for BI practitioners and with sites like this one - Rice Virtual Lab in Statistics, it is quite possible to get a grasp on the fundamentals in a short time-frame.

Let me close this blog with a paragraph from 'Innumeracy'. John Allen Paulos writes and I quote "In an increasingly complex world full of senseless coincidence, what's required in many situations is not more facts - we're inundated already - but a better command of known facts, and for this a course in probability is invaluable....Probability, like logic, is not just for mathematicians anymore. It permeates our lives".

BI practitioners, whose lofty ideals, relate to helping organizations make sense out of their customers' behavior, would do well to give their "Quantitative Gene" a push or shove in the right direction.

Thanks for reading. Please do share your thoughts.

A small note on those 2 "Obvious" books that I read - The first one is a classic called "Obvious Adams: The Story of a Successful Business Man" written by Robert R. Updegraff in 1916 and the second being a more contemporary one by Eliyahu M. Goldratt titled "Isn't it Obvious". Though these books talk about very different business domains (Obvious Adams is on Advertising while Goldratt's book is on Theory of Constraints as applied to the Retail industry), the central theme of these books is the fact that decision makers tend to overlook the basic principles when confronted with problems.

In my humble opinion, a simple, implementable, commonsensical approach based on fundamental principles is the need of the hour in many areas and Business Intelligence is no exception. Based on my experience, given below are some of those basic principles on Business Intelligence that the practitioner would dismiss as being too "obvious" (but hey, isn't that the intent of this post!). Let's roll...

1) Business & Business Stakeholders are the key to successful BI
2) BI should help in making decisions that support the business goals
3) BI systems should provide Hindsight, Insight and Foresight to optimize the business process
4) Quality of BI output and hence the quality of decisioning is directly dependent of the quality of data. Remember "Garbage In, Garbage Out"
5) Ensure that the business units agree on what the business really is. Educate the business about the business, if required.
6) Without strategic focus and executive sponsorship, BI projects are set for failure
7) Organizations are dynamic entities and hence ensure that BI systems can adapt to change
8 ) Enable & Empower the BI users (both operational and strategic)
9) Market the value of DW / BI platforms to the user community
10) Don't boil the ocean - There is no perfect BI system. Try building a successful one instead
11) Always build the BI system with 'detail data'. Summaries & Aggregations can follow the detail
12) Pick the technology based on Business Fitment not on Tool sophistication
13) Prototype and Visualize the end-state before embarking on major BI initiatives
14) Have the right team to sustain and grow the BI infrastructure
15) Be aware of latest developments and trends in BI

And so, here is my quick checklist on the fundamental principles around thinking, building and using BI in organizations. Please do share your thoughts. Thanks for reading!

My biggest learning from consulting assignments during 2009 is the increasing emphasis on the artistic elements of Business Intelligence. The first question asked by senior executives across organizations was, "So, how will this finally look?" before any of the other elements in the BI landscape had taken shape.

"Form" had taken center-stage or at the very least is sharing the same elevated platform as "Content". For BI practitioners like me, who have been using their left brain more than its opposite number, working through architecture, data elements, models, ETL etc. it is time to take a step back and give the right brain its due. Visual Business Intelligence, which deals with data visualization, is rapidly gaining ground and the BI practitioner would do well to pay attention to it.

Stephen Few, one of the foremost experts in this area, writes this way in the wonderful website of his and I quote:

"We are overwhelmed by information, not because there is too much, but because we don't know how to tame it. Information lies stagnant in rapidly expanding pools as our ability to collect and warehouse it increases, but our ability to make sense of and communicate it remains inert, largely without notice.

Computers speed the process of information handling, but they don't tell us what the information means or how to communicate its meaning to decision makers. These skills are not intuitive; they rely largely on analysis and presentation skills that must be learned".

So true! Yet, I do feel that visualization is not carried out with the same rigor as done for other pieces of the BI landscape, in many organizations. For example, I have seen Data Modelers, ETL architects, Reporting specialists in BI teams but haven't come across a role of "Visualization Specialist" or "BI Usability Architect" in the BI space. And I think that day is not far off!

Given below are some resources that I found extremely interesting from the Visual BI standpoint (am sure that there are many more!)

1) Stephen Few's website
2) Infosthetics
3) Gapminder
4) Dashboard Spy
5) Amazing number of BI gadgets & widgets that can be found across the web (Easiest to try out are the Google gadgets ? Load sample data in Google spreadsheets, insert gadgets like motion charts and get going!)

Going back to the title of this blog post, who knows, dashboards developed by BI visualization experts might be auctioned at Sotheby's for multi-million dollars in the future!

Wish you all a very happy new year 2010.

Thanks for reading. Please do share your thoughts.