Google Instant could kill Black-Hat SEO

Google Instant is a relatively newer feature in Google Search Engine- it suggests websites at each type of keyword rather than wait for you to type the whole keyword.

The impact on user experience is incredible- rather than search or scroll through the results- you are more likely to click on the almost one of the ten websites you would have seen by the time you finished typing- or just clicking on the relevant ad (which probably changes on the right margin as fast as the websites below)

This spells a death for all those who indulged in black hat SEO– or link building, link exchanging- as these techniques pushed up your rank in search page only incrementally and rarely to the top 2-3 for a keyword.

Remember the size of the screen is such that each Google instant snapshot basically shows you or rather makes you focus on the top ranked search (and then presumably type on to get a newer result- rather than scroll down as the case was before).

It would be interesting to see or research the effect of keywords in the auction pricing, as well as compare those keyword pricing with Maybe there should be a website api tool for advertisers -like Adwords Instant that would show them the price instantly of keywords,comparison with Bing AND the search engine results for the keyword in a visual way.

Anyways- it is a incredible innovation and it is good Google is back to the math after the flings with being “Mad Men” of advertising.

and yes- I heard there is a new movie coming- it is called “The Search Engine” 🙂

An interesting web hack is Google Images Instant at

Red Hat worth 7.8 Billion now

I was searching for a Linux install of Revolution’s latest enterprise version, but it seems version 4 will be available on Red Hat Enterprise Linux only by Decemebr 2010. Also even though Revolution once opted for co branding with Canonical’s Karmic Koala, they seem to have ignored Ubuntu from the Enterprise version of Revolution R.

Base R Revolution R Community Revolution R Enterprise
Buy Now
Target Use Open Source Product Evaluation & Simple Prototyping Business, Research & Academics
100% Compatible with R language X X X
Certified for Stability X X
Command-Line Programming X X X
Getting Started Guide X X
Performance & Scalability
Analyze larger data sets with 64-bit RAM X X
Optimized for Multi-processor workstations X X
Multi-threaded Math libraries X X
Parallel Programming (Single Workstation) X X
Out-of-the-Box Cluster-Ready X
“Big Data” Analysis
Terabyte-Class File Structures X
Specialized “Big Data” Algorithms X
Integrated Web Services
Scalable Web Services Platform X*
User Interface
Visual IDE X
Comprehensive Data Analysis GUI X*
Technical Support
Discussion Forums X X X
Online Support Mailing List Forum X
Email Support X
Phone Support X
Support for Base & Recommended R Packages X X X
Authorized Training & Consulting X
Single User X X X
Multi-User Server X X
32-bit Windows X X X
64-bit Windows X X
Mac OS X X X
Ubuntu Linux X X
Red Hat Enterprise Linux X
Cloud-Ready X

and though the page on RED HAT’s Partner page for Revolution seems old/not so updated;#productId=188

, I was still curious to see what the buzz about Red Hat is all about.

And one of the answers is Red Hat is now a 7.8 Billion Dollar Company.

Red Hat Reports Second Quarter Results

  • Revenue of $220 million, up 20% from the prior year
  • GAAP operating income up 24%, non-GAAP operating income up 25% from the prior year
  • Deferred revenue of $650 million, up 12% from the prior year

RALEIGH, NC – Sept 22, 2010 – Red Hat, Inc. (NYSE: RHT), the world’s leading provider of open source solutions, today announced financial results for its fiscal year 2011 second quarter ended August 31, 2010.

Total revenue for the quarter was $219.8 million, an increase of 20% from the year ago quarter. Subscription revenue for the quarter was $186.2 million, up 19% year-over-year.

and the stock goes zoom 48 % up for the year;NASDAQ:ORCL;NASDAQ:MSFT;NYSE:IBM&cmptdms=0;0;0;0&q=NYSE:RHT&ntsp=0

(Note to Google- please put the URL shortener on Google Finance as well)

The software is also reasonably priced starting from 80$ onwards.

Basic Subscription

Web support, 2 business day response, unlimited incidents
1 Year
Multi-OS with Basic SubscriptionWeb support, 2 business day response, unlimited incidents
1 Year
Workstation with Basic Subscription
Web support, 2 business day response, unlimited incidents
1 Year
Workstation and Multi-OS with Basic Subscription
Web support, 2 business day response, unlimited incidents
1 Year
Workstation with Standard Subscription
Business Hours phone support, web support, unlimited incidents
1 Year
Workstation and Multi-OS with Standard Subscription
Business Hours phone support, web support, unlimited incidents
1 Year
That should be a good enough case for open source as a business model.

SAS/Blades/Servers/ GPU Benchmarks

Just checked out cool new series from NVidia servers.

Now though SAS Inc/ Jim Goodnight thinks HP Blade Servers are the cool thing- the GPU takes hardware high performance computing to another level. It would be interesting to see GPU based cloud computers as well – say for the on Demand SAS (free for academics and students) but which has had some complaints of being slow.

See this for SAS and Blade Servers-

To give users hands-on experience, the program is underpinned by a virtual computing lab (VCL), a remote access service that allows users to reserve a computer configured with a desired set of applications and operating system and then access that computer over the Internet. The lab is powered by an IBM BladeCenter infrastructure, which includes more than 500 blade servers, distributed between two locations. The assignment of the blade servers can be changed to meet shifts in the balance of demand among the various groups of users. Laura Ladrie, MSA Classroom Coordinator and Technical Support Specialist, says, “The virtual computing lab chose IBM hardware because of its quality, reliability and performance. IBM hardware is also energy efficient and lends itself well to high performance/low overhead computing.

Thats interesting since IBM now competes (as owner of SPSS) and also cooperates with SAS Institute


You’re effectively turbo-charging through deployment of many processors within the blade servers?

Yes. We’ve got machines with 192 blades on them. One of them has 202 or 203 blades. We’re using Hewlett-Packard blades with 12 CP cores on each, so it’s a total 2300 CPU cores doing the computation.

Our idea was to give every one of those cores a little piece of work to do, and we came up with a solution. It involved a very small change to the algorithm we were using, and it’s just incredible how fast we can do things now.

I don’t think of it as a grid, I think of it as essentially one computer. Most people will take a blade and make a grid out of it, where everything’s a separate computer running separate jobs.

We just look at it as one big machine that has memory and processors all over the place, so it’s a totally different concept.

GPU servers can be faster than CPU servers, though , Professor G.


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Also I liked the hybrid GPU and CPU

And from a paper on comparing GPU and CPU using Benchmark tests on BLAS from a Debian- Dirk E’s excellent blog

Usage of accelerated BLAS libraries seems to shrouded in some mystery, judging from somewhat regularly recurring requests for help on lists such as r-sig-hpc(gmane version), the R list dedicated to High-Performance Computing. Yet it doesn’t have to be; installation can be really simple (on appropriate systems).

Another issue that I felt needed addressing was a comparison between the different alternatives available, quite possibly including GPU computing. So a few weeks ago I sat down and wrote a small package to run, collect, analyse and visualize some benchmarks. That package, called gcbd (more about the name below) is now onCRAN as of this morning. The package both facilitates the data collection for the paper it also contains (in the vignette form common among R packages) and provides code to analyse the data—which is also included as a SQLite database. All this is done in the Debian and Ubuntu context by transparently installing and removing suitable packages providing BLAS implementations: that we can fully automate data collection over several competing implementations via a single script (which is also included). Contributions of benchmark results is encouraged—that is the idea of the package.

And from his paper on the same-

Analysts are often eager to reap the maximum performance from their computing platforms.

A popular suggestion in recent years has been to consider optimised basic linear algebra subprograms (BLAS). Optimised BLAS libraries have been included with some (commercial) analysis platforms for a decade (Moler 2000), and have also been available for (at least some) Linux distributions for an equally long time (Maguire 1999). Setting BLAS up can be daunting: the R language and environment devotes a detailed discussion to the topic in its Installation and Administration manual (R Development Core Team 2010b, appendix A.3.1). Among the available BLAS implementations, several popular choices have emerged. Atlas (an acronym for Automatically Tuned Linear Algebra System) is popular as it has shown very good performance due to its automated and CPU-speci c tuning (Whaley and Dongarra 1999; Whaley and Petitet 2005). It is also licensed in such a way that it permits redistribution leading to fairly wide availability of Atlas.1 We deploy Atlas in both a single-threaded and a multi-threaded con guration. Another popular BLAS implementation is Goto BLAS which is named after its main developer, Kazushige Goto (Goto and Van De Geijn 2008). While `free to use’, its license does not permit redistribution putting the onus of con guration, compilation and installation on the end-user. Lastly, the Intel Math Kernel Library (MKL), a commercial product, also includes an optimised BLAS library. A recent addition to the tool chain of high-performance computing are graphical processing units (GPUs). Originally designed for optimised single-precision arithmetic to accelerate computing as performed by graphics cards, these devices are increasingly used in numerical analysis. Earlier criticism of insucient floating-point precision or severe performance penalties for double-precision calculation are being addressed by the newest models. Dependence on particular vendors remains a concern with NVidia’s CUDA toolkit (NVidia 2010) currently still the preferred development choice whereas the newer OpenCL standard (Khronos Group 2008) may become a more generic alternative that is independent of hardware vendors. Brodtkorb et al. (2010) provide an excellent recent survey. But what has been lacking is a comparison of the e ective performance of these alternatives. This paper works towards answering this question. By analysing performance across ve di erent BLAS implementations|as well as a GPU-based solution|we are able to provide a reasonably broad comparison.

Performance is measured as an end-user would experience it: we record computing times from launching commands in the interactive R environment (R Development Core Team 2010a) to their completion.


Basic Linear Algebra Subprograms (BLAS) provide an Application Programming Interface
(API) for linear algebra. For a given task such as, say, a multiplication of two conformant
matrices, an interface is described via a function declaration, in this case sgemm for single
precision and dgemm for double precision. The actual implementation becomes interchangeable
thanks to the API de nition and can be supplied by di erent approaches or algorithms. This
is one of the fundamental code design features we are using here to benchmark the di erence
in performance from di erent implementations.
A second key aspect is the di erence between static and shared linking. In static linking,
object code is taken from the underlying library and copied into the resulting executable.
This has several key implications. First, the executable becomes larger due to the copy of
the binary code. Second, it makes it marginally faster as the library code is present and
no additional look-up and subsequent redirection has to be performed. The actual amount
of this performance penalty is the subject of near-endless debate. We should also note that
this usually amounts to only a small load-time penalty combined with a function pointer
redirection|the actual computation e ort is unchanged as the actual object code is identi-
cal. Third, it makes the program more robust as fewer external dependencies are required.
However, this last point also has a downside: no changes in the underlying library will be
reected in the binary unless a new build is executed. Shared library builds, on the other
hand, result in smaller binaries that may run marginally slower|but which can make use of
di erent libraries without a rebuild.

Basic Linear Algebra Subprograms (BLAS) provide an Application Programming Interface(API) for linear algebra. For a given task such as, say, a multiplication of two conformantmatrices, an interface is described via a function declaration, in this case sgemm for singleprecision and dgemm for double precision. The actual implementation becomes interchangeablethanks to the API de nition and can be supplied by di erent approaches or algorithms. Thisis one of the fundamental code design features we are using here to benchmark the di erencein performance from di erent implementations.A second key aspect is the di erence between static and shared linking. In static linking,object code is taken from the underlying library and copied into the resulting executable.This has several key implications. First, the executable becomes larger due to the copy ofthe binary code. Second, it makes it marginally faster as the library code is present andno additional look-up and subsequent redirection has to be performed. The actual amountof this performance penalty is the subject of near-endless debate. We should also note thatthis usually amounts to only a small load-time penalty combined with a function pointerredirection|the actual computation e ort is unchanged as the actual object code is identi-cal. Third, it makes the program more robust as fewer external dependencies are required.However, this last point also has a downside: no changes in the underlying library will bereected in the binary unless a new build is executed. Shared library builds, on the otherhand, result in smaller binaries that may run marginally slower|but which can make use ofdi erent libraries without a rebuild.

And summing up,

reference BLAS to be dominated in all cases. Single-threaded Atlas BLAS improves on the reference BLAS but loses to multi-threaded BLAS. For multi-threaded BLAS we nd the Goto BLAS dominate the Intel MKL, with a single exception of the QR decomposition on the xeon-based system which may reveal an error. The development version of Atlas, when compiled in multi-threaded mode is competitive with both Goto BLAS and the MKL. GPU computing is found to be compelling only for very large matrix sizes. Our benchmarking framework in the gcbd package can be employed by others through the R packaging system which could lead to a wider set of benchmark results. These results could be helpful for next-generation systems which may need to make heuristic choices about when to compute on the CPU and when to compute on the GPU.

Source – DirkE’paper and blog

Quite appropriately-,

Hardware solutions or atleast need to be a part of Revolution Analytic’s thinking as well. SPSS does not have any choice anymore though 😉

It would be interesting to see how the new SAS Cloud Computing/ Server Farm/ Time Sharing facility is benchmarking CPU and GPU for SAS analytics performance – if being done already it would be nice to see a SUGI paper on the same at

Multi threading needs to be taken care automatically by statistical software to optimize current local computing (including for New R)

Acceptable benchmarks for testing hardware as well as software need to be reinforced and published across vendors, academics  and companies.

What do you think?

Indian Offshoring IPOs dismal performance

Using Yahoo Finance, I plotted the past three years stock price of Indian Offshores  (Genpact, Wns, Exl) and in comparison with Indian Software companies (Infosys, Wipro, TCS, Sify) and market index.

The following insights emerge-

1) Indian Software companies have constantly created wealth.

2) Indian Offshoring companies have constantly lost market value – perhaps because they were able to dump IPO prices at much higher prices by creating hype.

3) You are much better off investing in Indian stock market or a blue chip Indian software company than take part in an Indian offshorers IPO.

4) SIFY lost most value and its founder CEO is now in jail for fraud. The fraud was he added phantom employees, and phantom revenue to boost balance sheet. Auditors from PwC (were jailed) included a board member of Indian Chartered Accountants and Satyam (SIFY) had won awards for corporate governance. It makes sense to do rigorous cash flow due diligence this side of the pond.

5) I won no stock in any of this companies  (not surprisingly) but do have a portfolio of mutual funds (index).

So the next time you are promised the moon by an Indian IPO- KPO, remember to do the math 😉

Top 10 Graphical User Interfaces in Statistical Software

Here is a list of top 10 GUIs in Statistical Software. The overall criterion is based on-

  • User Friendly Nature for a New User to begin click and point and learn.
  • Cleanliness of Automated Code or Log generated.
  • Practical application in consulting and corporate world.
  • Cost and Ease of Ownership (including purchase,install,training,maintainability,renewal)
  • Aesthetics (or just plain pretty)

However this list is not in order of ranking- ( as beauty (of GUI) lies in eyes of the beholder). For a list of top 10 GUI in R language only please see –

This is only a GUI based list so it excludes notable command line or text editor submit commands based softwares which are also very powerful and user friendly.

  1. JMP –

While critics of SAS Institute often complain on the premium pricing of the basic model (especially AFTER the entry of another SAS language software WPS from – they should try out JMP from – it has a 1 month free evaluation, is much less expensive and the GUI makes it very very easy to do basic statistical analysis and testing. The learning curve is surprisingly fast to pick it up (as it should be for well designed interfaces) and it allows for very good quality output graphics as well.


The original GUI in this class of softwares- it has now expanded to a big portfolio of products. However SPSS 18 is nice with the increasing focus on Python and an early adoptee of R compatible interfaces, SPSS does offer a much affordable solution as well with a free evaluation. See especially and

the screenshot here is of SPSS Modeler

3. WPS

While it offers an alternative to Base SAS and SAS /Access software , I really like the affordability (1 Month Free Evaluation and overall lower cost especially for multiple CPU servers ), speed (on the desktop but not on the IBM OS version ) and the intuitive design as well as extensibility of the Workbench. It may look like an integrated development environment and not a proper GUI, but with all the menu features it does qualify as a GUI in my opinion. Continue reading “Top 10 Graphical User Interfaces in Statistical Software”

Interview Peter J Thomas -Award Winning BI Expert

Here is an in depth interview with Peter J Thomas, one of Europe’s top Business Intelligence expert and influential thought leaders. Peter talks about BI tools, data quality, science careers, cultural transformation and BI and the key focus areas.

I am a firm believer that the true benefits of BI are only realised when it leads to cultural transformation. -Peter James Thomas


Ajay- Describe about your early career including college to the present.

Peter –I was an all-rounder academically, but at the time that I was taking public exams in the 1980s, if you wanted to pursue a certain subject at University, you had to do related courses between the ages of 16 and 18. Because of this, I dropped things that I enjoyed such as English and ended up studying Mathematics, Further Mathematics, Chemistry and Physics. This was not because I disliked non-scientific subjects, but because I was marginally fonder of the scientific ones. In a way it is nice that my current blogging allows me to use language more.

The culmination of these studies was attending Imperial College in London to study for a BSc in Mathematics. Within the curriculum, I was more drawn to Pure Mathematics and Group Theory in particular, and so went on to take an MSc in these areas. This was an intercollegiate course and I took a unit at each of King’s College and Queen Mary College, but everything else was still based at Imperial. I was invited to stay on to do a PhD. It was even suggested that I might be able to do this in two years, given my MSc work, but I decided that a career in academia was not for me and so started looking at other options.

As sometimes happens a series of coincidences and a slice of luck meant that I joined a technology start-up, then called Cedardata, late in 1988; my first role was as a Trainee Analyst / Programmer. Cedardata was one of the first organisations to offer an Accounting system based on a relational database platform; something that was then rather novel, at least in the commercial arena. The RDBMS in question was Oracle version 5, running on VAX VMS – later DEC Ultrix and a wide variety of other UNIX flavours. Our input screens were written in SQL*Forms 2 – later Oracle Forms – and more complex processing logic and reports were in Pro*C; this was before PL/SQL. Obviously this environment meant that I had to become very conversant with SQL*Plus and C itself.

When I joined Cedardata, they had 10 employees, 3 customers and annual revenue of just £50,000 ($80,000). By the time I left the company eight years later, it had grown dramatically to having a staff of 250, over 300 clients in a wide range of industries and sales in excess of £12 million ($20 million). It had also successfully floatated on the main London Stock Exchange. When a company grows that quickly the same thing tends to happen to its employees.

Cedardata was probably the ideal environment for me at the time; an organisation that grew rapidly, offering new opportunities and challenges to its employees; that was fiercely meritocratic; and where narrow, but deep, technical expertise was encouraged to be rounded out by developing more general business acumen, a customer-focused attitude and people-management skills. I don’t think that I would have learnt as much, or progressed anything like as quickly in any other type of organisation.

It was also at Cedardata that I had my first experience of the class of applications that later became known as Business Intelligence tools. This was using BusinessObjects 3.0 to write reports, cross-tabs and graphs for a prospective client, the UK Foreign and Commonwealth Office (State Department). The approach must have worked as we beat Oracle Financials in a play-off to secure the multi-million pound account.

During my time at Cedardata, I rose to become an executive and filled a number of roles including Head of Development and also Assistant to the MD / Head of Product Strategy. Spending my formative years in an organisation where IT was the business and where the customer was King had a profound impact on me and has influenced my subsequent approach to IT / Business alignment.

Ajay- How would you convince young people to take maths and science more? What advice would you give to policy makers to promote more maths and science students?

Peter- While I have used little of my Mathematics directly in my commercial career, the approach to problem-solving that it inculcated in me has been invaluable. On arriving at University, it was something of a shock to be presented with Mathematical problems where you couldn’t simply look up the method of solution in a textbook and apply it to guarantee success. Even in my first year I had to grapple with challenges where you had no real clue where to start. Instead what worked, at least most of the time, was immersing yourself in the general literature, breaking down the problem into more manageable chunks, trying different techniques – sometimes quite recherché ones – to make progress, occasionally having an insight that provides a short-cut, but more often succeeding through dogged determination. All of that sounds awfully like the approach that has worked for me in a business context.

Having said that, I was not terribly business savvy as a student. I didn’t take Mathematics because I thought that it would lead to a career, I took it because I was fascinated by the subject. As I mentioned earlier, I enjoyed learning about a wide range of things, but Science seemed to relate to the most fundamental issues. Mathematics was both the framework that underpinned all of the Sciences and also offered its own world where astonishing an beautiful results could be found, independent of any applicability; although it has to be said that there are few braches of Mathematics that have not be applied somewhere or other.

I think you either have this appreciation of Science and Mathematics or you don’t and that this happens early on.

Certainly my interest was supported by my parents and a variety of teachers, but a lot of it arose from simply reading about Cosmology, or Vulcanism, or Palaeontology. I watched a YouTube of Steven Jay Gould recently saying that when he was a child in the 1950s all children were “in” to Dinosaurs, but that he actually got to make a career out of it. Maybe all children aren’t “in” to dinosaurs in the same way today, perhaps the mystery and sense of excitement has gone.

In the UK at least there appears to be less and less people taking Science and Mathematics. I am not sure what is behind this trend. I read pieces that suggest that Science and Maths are viewed as being “hard” subjects, and people opt for “easier” alternatives. I think creative writing is one of the hardest things to do, so I’m not sure where this perspective comes from.

Perhaps some things that don’t help are the twin images of the Scientist as a white-coated boffin and the Mathematician as a chalk-covered recluse, neither of whom have much of a grasp on the world beyond their narrow discipline. While of course there is a modicum of truth in these stereotypes, they are far from being wholly accurate in my experience.

Perhaps Science has fallen off of the pedestal that it was placed on in the 1950s and 1960s. Interest in Science had been spurred by a range of inventions that had improved people’s lives and often made the inventors a lot of money. Science was seen as the way to a better tomorrow, a view reinforced by such iconic developments as the discovery of the structure of DNA, our ever deepening insight about sub-atomic physics and the unravelling of many mysteries of the Universe. These advances in pure science were supported by feats of scientific / engineering achievement such as the Apollo space programme. The military importance of Science was also put into sharp relief by the Manhattan Project; something that also maybe sowed the seeds for later disenchantment and even fear of the area.

The inevitable fallibility of some Scientists and some scientific projects burst the bubble. High-profile problems included the Thalidomide tragedy and the outcry, however ill-informed, about genetically modified organisms. Also the poster child of the scientific / engineering community was laid low by the Challenger disaster. On top of this, living with the scientifically-created threat of mutually-assured destruction probably began to change the degree of positivity with which people viewed Science and Scientists. People arrived at the realisation that Science cannot address every problem; how much effort has gone into finding a cure for cancer for example?

In addition, in today’s highly technological world, the actual nuts and bolts of how things work are often both hidden and mysterious. While people could relatively easily understand how a steam engine works, how many have any idea about how their iPod functions? Technology has become invisible and almost unimportant, until it stops working.

I am a little wary of Governments fixing issues such as these, which are the result of major generational and cultural trends. Often state action can have unintended and perverse results. Society as a whole goes through cycles and maybe at some future point Science and Mathematics will again be viewed as interesting areas to study; I certainly hope so. Perhaps the current concerns about climate change will inspire a generation of young people to think more about technological ways to address this and interest them in pertinent Sciences such as Meteorology and Climatology.

Ajay-. How would you rate the various tools within the BI industry like in a SWOT analysis (briefly and individually)?

Peter- I am going to offer a Politician’s reply to this. The really important question in BI is not which tool is best, but how to make BI projects successful. While many an unsuccessful BI manager may blame the tool or its vendor, this is not where the real issues lie.

I firmly believe that successful BI rests on four mutually reinforcing pillars:

  • understand the questions the business needs to answer,
  • understand the data available,
  • transform the data to meet the business needs and
  • embed the use of BI in the organisation’s culture.

If you get these things right then you can be successful with almost any of the excellent BI tools available in the marketplace. If you get any one of them wrong, then using the paragon of BI tools is not going to offer you salvation.

I think about BI tools in the same way as I do the car market. Not so many years ago there were major differences between manufacturers.

The Japanese offered ultimate reliability, but maybe didn’t often engage the spirit.

The Germans prided themselves on engineering excellence, slanted either in the direction of performance or luxury, but were not quite as dependable as the Japanese.

The Italians offered out-and-out romance and theatre, with mechanical integrity an afterthought.

The French seemed to think that bizarrely shaped cars with wheels as thin as dinner plates were the way forward, but at least they were distinctive.

The Swedes majored on a mixture of safety and aerospace cachet, but sometimes struggled to shift their image of being boring.

The Americans were still in the middle of their love affair with the large and the rugged, at the expense of convenience and value-for-money.

Stereotypically, my fellow-countrymen majored on agricultural charm, or wooden-panelled nostalgia, but struggled with the demands of electronics.

Nowadays, the quality and reliability of cars are much closer to each other. Most manufacturers have products with similar features and performance and economy ratings. If we take financial issues to one side, differences are more likely to related to design, or how people perceive a brand. Today the quality of a Ford is not far behind that of a Toyota. The styling of a Honda can be as dramatic as an Alfa Romeo. Lexus and Audi are playing in areas previously the preserve of BMW and Mercedes and so on.

To me this is also where the market for BI tools is at present. It is relatively mature and the differences between product sets are less than before.

Of course this doesn’t mean that the BI field will not be shaken up by some new technology or approach (in-memory BI or SaaS come to mind). This would be the equivalent of the impact that the first hybrid cars had on the auto market.

However, from the point of view of implementations, most BI tools will do at least an adequate job and picking one should not be your primary concern in a BI project.

Ajay- SAS Institute Chief Marketing Officer, Jim Davis (interviewed with this blog) points to the superiority of business analytics rather than business intelligence as an over hyped term. What numbers, statistics and graphs would you quote rather than semantics to help re direct those perceptions?

I myself use SAS,SPSS, R and find the decision management capabilities as James Taylor calls Decision Management much better enabled than by simple ETL tools or reporting and aggregating graphs tools in many BI tools.

Peter- I have expended quite a lot of energy and hundreds of words on this subject. If people are interested in my views, which are rather different to those of Jim Davis, then I’d suggest that they read them in a series of articles starting with Business Analytics vs Business Intelligence [URL ].

I will however offer some further thoughts and to do this I’ll go back to my car industry analogy. In a world where cars are becoming more and more comparable in terms of their reliability, features, safety and economy, things like styling, brand management and marketing become more and more important.

As the true differences between BI vendors narrow, expect more noise to be made by marketing departments about how different their products are.

I have no problem in acknowledging SAS as a leader in Business Analytics, too many people I respect use their tools for me to think otherwise. However, I think a better marketing strategy for them would be to stick to the many positives of their own products. If they insist on continuing to trash competitors, then it would make sense for them to do this in a way that couldn’t be debunked by a high school student after ten seconds’ reflection.

Ajay- In your opinion what is the average RoI that a small, large medium enterprise gets by investing in a business intelligence platform. What advice would you give to such firms (separately) to help them make their minds?

Peter- The question is pretty much analogous to “What are the benefits of opening an office in China?” the answer is going to depend on what the company does; what their overall strategy is and how a China operation might complement this; whether their products and services are suitable for the Chinese market; how their costs, quality and features compare to local competitors; and whether they have cracked markets closer to home already.

To put things even more prosaically, “How long is a piece of string?”

Taking to one side the size and complexity of an organisation, BI projects come in all shapes and sizes.

Personally I have led Enterprise-wide, all-pervasive BI projects which have had a profound impact on the company. I have also seen well-managed and successful BI projects targeted on a very narrow and specific area.

The former obviously cost more than the latter, but the benefits are commensurately greater. In fact I would argue that the wider a BI project is spread, the greater its payback. Maybe lessons can be learnt and confidence built in an initial implementation to a small group, but to me the real benefit of BI is realised when it touches everything that a company does.

This is not based on a self-interested boosting of BI. To me if what we want to do is take better business decisions, then the greater number of such decisions that are impacted, the better that this is for the organisation.

Also there are some substantial up-front investments required for BI. These would include: building the BI team; establishing the warehouse and a physical architecture on which to deliver your application. If these can be leveraged more widely, then costs come down.

The same point can be made about the intellectual property that a successful BI team develops. This is one reason why I am a fan of the concept of BI Competency Centres [URL ].

I have been lucky enough to contribute to an organisation turning round from losing hundreds of millions of dollars to recording profits of twice that magnitude. When business managers cite BI as a major factor behind such a transformation, then this is clearly a technology that can be used to dramatic effect.

Nevertheless both estimating the potential impact of BI and measuring its actual effectiveness are non-trivial activities. A number of different approaches can be taken, some of which I cover in my article:

Measuring the benefits of Business Intelligence [URL ]. As ever there is no single recipe for success.

Ajay-. Which BI tool/ code are you most comfortable with and what are its salient points?

Peter –Although I have been successful with elements of the IBM-Cognos toolset and think that this has many strong points, not least being relatively user-friendly, I think I’ll go back to my earlier comments about this area being much less important than many others for the success of a BI project.

Ajay -How do you think cloud computing will change BI? What percentage of BI budgets go to data quality and what is eventual impact of data quality on results?

Peter –I think that the jury is still out on cloud computing and BI. By this I do not mean that cloud computing will not have an impact, but rather that it remains unclear what this impact will actually be.

Given the maturity of the market, my suspicion is that the BI equivalent of a Google is not going to emerge from nowhere. There are many excellent BI start-ups in this space and I have been briefed by quite a few of them.

However, I think the future of cloud computing in BI is likely to be determined by how the likes of IBM-Cognos, SAP-BusinessObjects and Oracle-Hyperion embrace the area.

Having said this, one of the interesting things in computing is how easy it is to misjudge the future and perhaps there is a potential titan of cloud BI currently gestating in the garage so beloved of IT mythology.

On data quality, I have never explicitly split out this component of a BI effort. Rather data quality has been an integral part of what we have done. Again I have taken a four-pillared approach:

  • improve how the data is entered;
  • make sure your interfaces aren’t the problem;
  • check how the data has been entered / interfaced;
  • and don’t suppress bad data in your BI.

The first pillar consists of improved validation in front-end systems – something that can be facilitated by the BI team providing master data to them – and also a focus on staff training, stressing the importance to the organisation of accurately recording certain data fields.

The second pillar is more to do with the general IT Architecture and how this relates to the Information Architecture, again master data has a role to play, but so does ensuring that the IT culture is one in which different teams collaborate well and are concerned about what happens to data when it leaves “their” systems.

The third pillar is the familiar world of after-the-fact data quality reports and auditing, something that is necessary, but not sufficient, for success in data quality.

Finally there is what I think can be one of the most important pillars; ensuring that the BI system takes a warts-and-all approach to data. This means that bad data is highlighted, rather than being suppressed. In turn this creates pressure for the problems to be addressed where they arise and creates a virtuous circle.

For those who might be interested in this area, I expand on it more in Using BI to drive improvements in data quality [URL ].

Ajay- You are well known with England’s rock climbing and boulder climbing community. A fun question- what is the similarity between a BI implementation/project and climbing a big boulder.

Peter –I would have to offer two minor clarifications.

First it is probably my partner who is better known in climbing circles, via here blog [URL ] and articles and reviews that she has written for the climbing press; though I guess I can take credit for most of the photos and videos.

Second, particularly given the fact that a lot of our climbing takes place in Wales, I should acknowledge the broader UK climbing community and also mention our most mountainous region of Scotland.

Despite what many inhabitants of Sheffield might think to the contrary, there is life beyond Stanage Edge [URL ].

I have written about the determination and perseverance that are required to get to the top of a boulder, or indeed to the top of any type of climb [URL ].

I think those same qualities are necessary for any lengthy, complex project. I am a firm believer that the true benefits of BI are only realised when it leads to cultural transformation. Certainly the discipline of change management has many parallels with rock climbing. You need a positive attitude and a strong belief in your ultimate success, despite the inevitable setbacks. If one approach doesn’t yield fruit then you need to either fine-tune or try something radically different.

I suppose a final similarity is the feeling that you get having completed a climb, particularly if it is at the limit of your ability and has taken a long time to achieve. This is one of both elation and deep satisfaction, but is quickly displaced by a desire to find the next challenge.

This is something that I have certainly experienced in business life and I think the feelings will be familiar to many readers.



Peter Thomas has led all-pervasive, Business Intelligence and Cultural Transformation projects serving the needs of 500+ users in multiple business units and service departments across 13 European and 5 Latin American countries. He has also developed Business Intelligence strategies for operations spanning four continents. His BI work has won two industry awards including “Best Enterprise BI Implementation”, from Cognos in 2006 and “Best use of IT in Insurance”, from Financial Sector Technology in 2005. Peter speaks about success factors in both Business Intelligence and the associated Change Management at seminars across both Europe and North America and writes about these areas and many other aspects of business, technology and change on his blog [URL ].

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