Oz Analytics

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".

Big. Really big

"Information Management is big. Really big. You just won't believe how vastly hugely mind-bogglingly big it is. I mean, you may think it's a long way down the road to the chemist, but that's just peanuts to IM."

What Douglas Adams would have said if he was in my position.

 My role includes the words Information Manager in the title and that's where the confusion begins. Only 1 person in 100 knows what it means. A further 10 in 100 think that they know what it means - but when pressed for an explanation you will get at least 11 different interpretations.

It's hardly surprising that the wider business community is unsure about what IM is. So just to set the record straight, here's what I believe IM is:

• Data quality – to create high quality information.
• Metadata management – to clearly define all elements.
• Data integration – to bring the data together for all management processes to use.
• Analytics – processes data to reveal hidden meaning.
• Business Intelligence – delivers insight to the people and processes making decisions.
• Performance Management – uses consistent and uniform measures of core entities (departments, staff, customers, products, accounts, etc.) to optimise operations.

 

Mastering SAP BusinessObjects Conference Day 2

Day 1 of the conference was very good and the sessions I attended were packed. I expected to hear some interesting customer stories and I wasn't disappointed. What was a pleasant surprise was the information and openness of the SAP speakers.

For me, the nuggets of gold were not the headline messages of the keynote addresses by the vendor. A lot of the keynote messages are incremental and don't change much for me professionally. Too much hype for my taste - but that is to be expected from a sales pitch. Don't get me wrong, the keynoters are great people - smart, thoughtful people who deserve to be in the mentoring roles they occupy. Instead, I found value in the practical advice revealed by several speakers in the breakout sessions. I can only comment on the sessions I am chairing but I learnt some valuable knowledge about SharePoint integration and how to more elegantly scale up a reporting environment.

Maybe I prefer the tortoise to the hare these days.

I can't emphasise how valuable practical advice is in a relationship with a vendor. I work for a large company and our BI investments are not just large, the impacts of good or bad decisions I make are sizable. So straight talking from the SAP experts is important. I don't want to make this a vendor 'love fest' but credit should be given when due.

Another feature of the conference has been it's sheer size. There are around 250 - 300 people in the room. This is refreshingly large by Australian standards. Tiny by international standards but I don't know of a bigger BI event locally. By my reckoning, there is about 3,000 years of experience to tap into!

So day 2 should be good. All credit to Eventful Management for organizing the conference.

Now I'm worried - this post is too upbeat. I'll try to be my cynical 'normal' self in my next post!

It's How You Tell It

When delivering actionable analytics within an organisation I often spend some time removing commentary from management reports. I have found that the regular reporting packs produced for senior management are often drowning in long commentaries that attempt to explain the data in the packs.

This immediately gets me asking 'Why?' most of the commentary exists.

Why can't the data do the talking? What is it about the way the data is organised and presented that makes it necessary to add all of this text?

In my experience there are a number of reasons for this.

The first is poor design of the report. Not enough thought has been given to either the purpose of the report (i.e. what are the objectives? what are the decisions we want taken?) or the audience (i.e. what are the information requirements of the decisions makers? how do they prefer information to be presented?).

A second reason may be limitations in the tools being used to produce the report. Even the best business intelligence tools have their functional limitations and as the first rule of report design states:

"Within any given set of business analytic requirements there will be at least one that is impossible to deliver using the available toolset."

If you are unfamiliar with these rules, then you should know that this immediately leads to the second rule of report design:

"The amount of insight delivered by a report is halved by each undelivered business analytic requirement."

A third reason is less benign than the first two. I use the word 'spin' to describe the collection of responses that the business 'reporter' makes when they fear that the 'reportee' will:

• respond negatively to the data presented; or
• not sufficiently appreciate the 'brilliance' that was required to deliver the result.

In both cases the intent of the manager reporting the figures is to 'spin' the message given to the report reader. 9 times out of 10 the spin is intended to mislead the reader.

All of these reasons for extensive amounts of report pack commentary result in at best the report data failing to provide the insight required by the business users. At worst it misleads decision makers into making bad decisions.

I like to think that my job is to create an environment where good decisions can be made because they are based on unbiased information that fairly measures performance. In practical terms, this translates into reducing commentary by improving the quality of the automatically produced data. This sounds simple but this is deceptive. It is very challenging to achieve in practice.

Here is a little video that I have made that helps explain how identical words can be spun to justify diametrically opposed management messages. It's a bit of fun (adapted from a political campaign ad I saw some years ago) and that is why I have found it effective. Enjoy.

Download Spinning_Decisions.m4v

dataFUD

 

Fear, Uncertainty and Doubting your data is a feature of organisations who are failing to effectively manage their information resources. As a result, it is one of the main issues to address when building an information management strategy for a dataFUD organisation. How do you know if your organisation is failing? Well, if you're reading this blog then there is a good chance you already know, but just in case, here are some things to think about. Do you or others in your organisation spend a lot of time:

• Performing work incorrectly or making a wrong decision?
• Re-doing work because it was previously performed incorrectly?
• Recovering from the impact of making a wrong decision?
• Taking unnecessary time to investigate the integrity of the data before using it?
• Performing calculations or reformatting the data before it can be used?
• Hunting for additional information in order to use the data?
• Losing customers because it caused work to be performed incorrectly?
• Causing unrecoverable damage?
• Missing business opportunities?
• Miscommunication within the business or with end customers and other information stakeholders?

If any of these ring true, then there is a good chance that you need to manage information better. How will this help? I've identified 7 main benefits that an organisation can realise by managing information better. They are:

1. Improved Data Quality – Poor information management leads to poor data quality and subsequently poorer business decisions. Early indicators of the business costs of non-quality data (listed in the bullet list above) point to potentially foregoing revenue as high as 10-20% of the revenue or total budget of the organisation.
2. Elimination of Duplicate Work – Removing redundancies, reducing the need for rework and therefore reducing operational costs by repairing data quality issues that create the need for manual reconciliation and avoid futile marketing and sales expenditure. On its own, by halving the costs related to poor data quality, a 5-10% saving on operating expenses can potentially be achieved.
3. Unified Vision – or Single Source Of the Truth. Organisations that break the barriers between their silos of operations can unlock value by obtaining unified views of business-critical data like customers and products, as well as apply existing information in new value adding ways such as opportunity identification and cross selling opportunities. This will improves the ability to coordinate activities between lines of business.
4. Broader insights and Better Decisions – into data across products and business units. Decision makers gain a deeper understanding of operations and an accurate picture of past and present performance as well as predictions of future performance that shape decisions.
5. Support More Complex Operations – The ability to serve numerous users with varying requirements at a lower cost. Users include employees from different areas within the organisation, external suppliers, partners and any other party that may interact with the company.
6. Continuous workflow – Continuous workflow with timely information available. This stops people having to wait for information that they require to perform their work and make decisions. Delay can cause lost opportunities or added costs. A well designed information management capability will reduce the lead time an organisation requires to go to market with something new.
7. Compliance – Aids compliance to legislation such as Sarbanes-Oxley as knowing where to find the information required will decrease the risk of non-compliance and allow for greater responsibility.

These benefits are pretty fundamental to the competitiveness and sustainability of business in the 21st century. Just remember that although information management may often be talked about in technological terms, the true key to improving performance is found by up-skilling staff so that they are able to take advantage of your technology investments. There is a reason that almost all management and information experts are talking about competing on analytics. My favourite response? A fool with a tool is still a fool - it doesn't matter how good the data quality.

 

As always with my blogs, I may be repeating ideas from one of the many books I have read, websites I visited or methods I use. I stand on the shoulders of giants and acknowledge their original thoughts when I can. I haven't checked if I am directly quoting from them, but the Mike2.0 community has been an inspiration in recent years so you may well find their ideas mixed with mine. As always, any mistakes or controversies are my responsibility alone.

Review of Dresner's Profiles in Performance

Howard Dresner, a fellow ex-Gartner Business Intelligence and Information Management Research Director (but with a far more distinguished research record than mine!) has published his second book at the end of last year. The book is called "Profiles in Performance: Business Intelligence Journeys and the Roadmap for Change". His first book "The Performance Management Revolution: Business Results Through Insight and Action" was published back in 2007.

This book is similar in intent and size as the recently reviewed "Analytics at Work: Smarter Decisions, Better Results", the new book by Thomas Davenport, Jeanne G. Harris (both of Competing On Analytics fame) and Robert Morison. This is not a bad thing as I like both of these books and while they share a common belief (crudely put: Analytics for performance management is a good thing) both make their own unique and valuable contributions.

In Howard's case, he has created a Performance Culture Maturity Model that resonates. This model seems to me to be a much better descriptor than the widely used Information Maturity Model originally created by the now defunct Meta Group.

The bulk of The Performance Management Revolution contains four case studies on:

• Denihan Hospitality Group
• Cleveland Clinic
• Northern California Public Broadcasting
• Meuller Inc.

If the book falls short anywhere, it is here. Maybe it is my background, but these are hardly houshold names. They are also all US based and I think that this could reduce the impact the book will have on readers. Why? In my case I didn't have an existing impression of any of these companies. If the examples were, for example, Coke or Unilever, then I would have had other knowledge to draw on as I read the book - and this would have enhanced the stories.

It is the PCMM that I will benefit most from. To show how this contributes to our thinking on enterprise performance management, remember the IMM from Meta (via the MIKE2.0 website):

The PCMM captures the multifaceted nature of information and performance maturity and the fact that my organisation can be at different levels of maturity at the same time. I reproduce Howard's model here as it is published elsewhere on Google and book sale sites:

So Howard's post-Hyperion life was been productive so far. Let's hope that he continues to develop the PCMM. I for one will happily use it in my professional life.

Here are the details, and of course you can order it online:

Profiles in Performance: Business Intelligence Journeys and the Roadmap for Change (Hardcover) by Howard Dresner
174 pages
Wiley
ISBN: 978-0470408865
US$32.85

Analytics at Work

Hot off the presses jumps Analytics at Work: Smarter Decisions, Better Results, the new book by Thomas Davenport, Jeanne G. Harris (both of Competing On Analytics fame) and Robert Morison. This book is very much the sequel to Competing On Analytics and this is not a bad thing. CIO Insight magazine named Competing on Analytics one of the all-time “Top 15 Most Groundbreaking Management Books”.

What I like about the book is that it is going after the big picture where better decisions drive outstanding performance, improve competitiveness and help organisations differentiate themselves from the competition.

This time the authors have completed new research on companies in the US and Europe. They include Humana, Tesco, Air France/KLM, Best Buy, Progressive Insurance, and Hotels.com. Using these organisations as illustrations the book is intended to be an implementation-focused guide that describes a five-step model for deploying and succeeding with analytical initiatives.

In the first half of the book five factors are presented as being necessary if an organisation is to succeed at 'doing analytics'. The authors group use the acronym DELTA (the Greek letter that signifies “change” in an equation):

• D for accessible, high-quality data
• E for an enterprise orientation
• L for analytical leadership
• T for strategic targets
• A for analytical talent

This all makes sense to me and then in the second half of the book the five-step model is explained. I will not spoil the authors work by discussing that here.

To me the book is presented as a primer for doing analytics. How well does it achieve this objective? The answer is both 'very well' and 'not really'. Let me explain:

• If you are a C* level executive trying to understand why you need analytics, and you want a high-level understanding of how to achieve this, then the book delivers - and delivers well.
• If you are a Senior Manager directly responsible for making analytics happen, the book is less successful. Not through any fault of the authors. I am one of these managers and I know that analytics is hard. Even a 1,000 page tome and a dozen CD's would not be enough for that.

So I hope that the book will inspire C* level executives that are interested enough to read a 200-or-so page book on the subject. If they only read one book on the subject then this is the one I would give them. For those that can't be bothered, then I can sit waiting for the next crisis to hit and the inevitable change of management to occur!

To the author's credit, I notice scattered throughout the book discussions (on pages 14-15 for example) and insight on how the Financial Industry got risk analytics so wrong - contributing to the financial crisis of 2007. Finance's use of analytics was featured as one of the successes in Competing On Analytics.

Although a Harvard Business Press title, the book is 'tied-in' to Accenture (Harris works for them and Davenport is an Accenture Fellow) and the company has a microsite within their Institute for High Performance consulting practice. Here's a nice brochure from Accenture on Analytics At Work.

Just Tell Me What I'm Doing

In my last post I took us all in the local analytic community to task for a shared lack of ambition. Having burnt some bridges there, I thought that I'd finish the job and have a go at putting a torch to the global community at the same time. Forget about moving back to France - I've picked out a nice remote location where the 'net can't find me¹.

So enough about my future. What's my beef this time?

It's our inability to agree on a common language to hold our collective body of knowledge. What do I mean? Well let's start at the top. How do you describe your profession to others? Are you a business intelligence expert? An analytics expert? Data Warehousing? Database Marketing? Performance Manager?

I bet that if I asked 10 of us in similar functions, that we would not agree on a single description. I'm not even sure that we would all recognise that we share the same profession.

You get the picture.

Why does this matter?

This inability to agree on the most basic common language presents a serious barrier to gaining a wider appreciation of what we do. How can we expect the business community to understand us when we confuse then with a mish-mash of words seemingly designed to confuse and obfuscate?

And this problem is exacerbated as we look deeper into the language we use to describe the key concepts of our profession. Have you ever tried to create a set of standard definitions for the foundation concepts of our field of knowledge? Well good luck. I have over the last couple of years but it is not something you can do easily. There is no single source of truth for the [insert here whatever term you like to describe what you do] body of knowledge. I'm going to call this analytics.

There is no single place on the web. No professional group to codify most of the key concepts we use. Wikipedia is a good start - take a look at business intelligence:

"Business Intelligence (BI) refers to skills, processes, technologies, applications and practices used to support decision making."

I can live with that. But what about plain English definitions and examples of:

• Business Analysis Models
• Business Analytics
• Business Data Model
• Conformed dimensions
• Customer Data Integration
• Data Integration
• Data Lineage
• Data Management
• Data Mining
• Data Profiling
• Data Stewards

and the list goes on. Now I'm not advocating that we need to agree on a single definition for everything, but a definition that we can all easily access, edit and make use of would be a great start.

I don't claim to have all the answers, but I am going to try and create a solution. I will start by making a modest effort to publish definitions on this blog by assembling the best information I can glean from the web, research papers, books and presentations. I will try to acknowledge the original sources wherever I can. I will naturally be drawing deeply on the work of many past and present practitioners in the Analytic field – not least Ralph Kimball, Bill Inmon, Howard Dresner, Thomas Davenport and the rest of the BI Community. I will stand on the shoulders of giants and I'm hoping that they won't complain too much.

Into this mix I will blend my own experiences and learnings. My hope is that we will begin to have a practical set of definitions that over time will contribute to the core of our body of knowledge.

In addition to blogging individual definitions, I will collect them together on the CORTEX.

Let the arguments and the plagiarism accusations begin!

.

¹ Campbell Island - a lovely place I visited in 2008. Population 0.

A Professional Criticism

I've been back in Australia for several years now and after a period adjusting to local conditions I now feel that I have a valid perspective on the practise of analytics in Australia. Brace yourself as I do have some criticisms, but relax as well - it is not all bad news!

Let's start with the good news: Australia (and that includes the big band of Kiwi's that work here) has some real analytic punch. There are local vendors that offer innovative and world-beating features in a wide range of fields and industries. Examples include:

• Space-Time Research who have cornered the global market for analysis of public intelligence (data from National Statistics Offices like the Australian Bureau of Statistics) with their SuperSTAR suite of analytics and visualizations products.
• Wotnews have a great semantic search and aggregation technology engine for understanding unstructured data and are now building innovative products such as:
  • We Are Hunted - tracking social media and actual music downloading activity to identify popular and emerging artists. Blows the old Top 40/Billboard Charts out of the water.
  • TeamStream - a team-based media monitoring service that supports monitoring, research and distribution within organisations.
• Zap Technology - Business Intelligence packaged solutions that take the pain out of using Microsoft Dynamics and are selling well across Asia.

And not to forget New Zealand and Wherescape RED with their software for ultra-rapid data warehouse development. Users include Vodafone, the Australian Department of Defence and Westfields.

Likewise with local analytic talent. There are some very smart people I have come across. World-class analytic experts in their field. They have made my working life a joy and I consider myself lucky to be able to work with people of this calibre. I'm not going to mention any names as I don't want more competition when I next hire them - they might want to start earning what they're really worth!

Now the picture darkens as we move into the area of leadership - my backyard so to speak. To be blunt, it has been a little disappointing to see a vacuum of leadership in our field. There are lots of people who have Head of Business Intelligence or Analytics, Performance Manager, Customer Insight Manager or the like on their business cards - but on the whole (myself included) I think that we are less successful than our EU and US peers at getting our organisations to embrace widespread use of analytics. This inevitably limits our impact (and usefulness) on the performance of our employers.

I'm not saying that our efforts are all abject failures. Many are, just like in the EU and US. But there are also examples of excellence, and I believe that I have delivered a couple of them myself.

If you were to look at the ratio of successes to failures, we would be generally in line with recent overseas experiences. That's not what I'm getting at. I believe that it is our scale of ambition - or rather our lack of it - that is creating the vacuum. To put it another way: we are setting our goals too low.

Why is this? Is it that we lack imagination? Are too scared of the risk of failure? Maybe this is part of it. Australia is a big country physically but with only 20 million people living here, it contrastingly has a 'big country town' feel. A town where everyone knows everyone else. A spectacular failure here will be quickly heard of by most of your peers. In Australia the phrase 'a career limiting move' doesn't just refer to a career in your current organisation.

Perhaps this is what still marks me as an outsider - when the chips are down and I have reached too far - I still know that I can return to my old life in Europe and the States. So just in case, my address in France is Villa Parasol, Route d'Antibes, 06560 Valbonne!

Perhaps you disagree with my criticism? I'd be happy to be proven wrong! Can anyone talk about leadership successes? I'm ready to eat my hat if enough people respond that will.

The State of Analytics Across Asia

Further to my last blog, I have collected my facts and thoughts into a year-end overview of Asia in 2009. Here's a round-up of developments that I noted over the last 12 months. There were many more that you can access by using the CORTEX, but several key events are listed below.

What are common trends across the region?

• A V-shaped market recovery is predicted in the 2010 BI market.
• The leading tech companies have created analytic centres of excellence across Asia. If you engage a major vendor or consultancy then it is likely that you will be leveraging Indian or Chinese IP.
• Low-cost and innovative reporting and analytic solutions are winning worldwide accolades. Strong sales across the region will continue and price sensitive buyers in Western markets will begin to take notice.

Notable Events in 2009

January

Accenture India said it planned to hire 8,000 people by the end of 2009, which will take its total workforce in India to 50,000. “We believe that analytics is going to be an important trend that our customers are going to demand from us. We think India is going to be a great place for us. We have some core centres of excellence in the analytics space in the country,” said William D Green, chairman and CEO of the firm that employs 177,000 people globally.

April

SAP Opens Co-Innovation Lab in Bangalore. Joins existing labs in Palo Alto, California, and Tokyo, Japan. Work has concentrated on delivering workflow and mobility solutions.

May

Analytics tools are becoming more vital to outsourcers -especially quality monitoring and performance management products. Agent Performance Optimisation (APO) products have become vital as outsourcers strive to differentiate themselves competitively and cut costs for their clients.

June

Tata Consultancy Services (TCS) launched a new business intelligence solution for hospitals and health care sector. The company today said in a press release that it has developed an out of the box dashboard that will simplify projects, cut cost and dramatically shorten time to value related services to both hospital administration and patients care.

Ramco Systems in India introduced a corebanking analytics solution aimed at addressing Business Intelligence (BI) needs of banks. The analytics solution packaged and delivered with HP s Neoview Appliance box will be based on Ramco s Decisionworks software framework. 

Chinese telecom operators reportedly spent US$1.18 bln on their business and operation support systems in 2008, up 26.8 percent year-on-year and a significant portion of this was reportedly on business intelligence.

China Mobile is working on a back end system based on cloud computing technology, which it has dubbed "Big Cloud". This includes data mining systems for China Mobile's mobile search service and a business intelligence system to track user behavior.

August

IBM Analytics Centre in Beijing. IBM announced last August that it would open an advanced business analytics centre in Beijing that will serve clients including the Chinese government. This centre joins a worldwide network of others in cities such as Berlin, Tokyo, London, New York and Washington, D.C. China was the first to be based in a developing country.

The company has stated that the China centre will at first focus on products aimed at traffic, water and electrical grid management.

"A major stimulus opportunity in China, transportation infrastructure such as new high speed trains, a hugely expanded rail network and faster links between the country's major cities are in development. This center will bring together research experts, major universities, partners and clients to develop best practices for rail systems that are smarter, more efficient, greener and attractive for travelers all around the globe."

They also said that other centres are planned in China.

Activities by the centre have included:

• Building a cloud computing platform in the city of Dongying (in the Yellow River Delta) as a model for how local government and businesses can modernize in China. Chinese economic stimulus funds are fueling the project, which is expected to be duplicated in other cities. The Yellow River Delta Cloud Computing Center will serve as a central platform for developing software that automates the petroleum production supply chain. It will be used to expand e-government services in Dongying and will serve as a technology center through which Chinese software engineers will supply outsourcing services. Clients for the first of three planned phases include Sanyo and Dupont. In the second phase the Dongying City Government will use cloud software to supply government services and administer "a smart airport, smart parks, and smart railroads.". A third phase would extend into the cities health care services.
• In September it was announced that IBM, the City of Shenyang, and China's Northeastern University were embarking on a major "smart city" collaboration effort focused on enabling Shenyang to improve the lives of its citizens by meeting its environmental responsibility goals.

"The Collaboratory will focus on advancing technology solutions that: significantly reduce carbon emissions, conserve energy, manage water efficiently, effectively track food from farm to fork, enhance the intelligence of transportation and traffic systems, create environmental emergency response plans, and allow a proactive approach to environmental responsibility vs. reactive."

• It is known that local telco China Mobile Communications Corporation has been working closely with IBM's China Telecommunications Solutions Center to deploy a  business intelligence platform that supports service creation, deployment, execution, integration and management. IBM claims that China Mobile is now able to reduce time-to-market for new products, increase revenue and improve customer service with end-to-end monitoring and analysis. It is not known if the business analytics centre in Beijing is involved.

The Beijing center will employ up to 300 consultants, mathematicians and software specialists at first and recruit or retrain up to 300 more as demand grows. It is unclear if any of these are new jobs but a portion are 'reassigned' existing IBM consultants and scientists.

September

IBM Business Analytics Centre of Competency in Bangalore, India. It's focus will be on Web 2.0 Analytics, Risk and Fraud Analytics, Financial Analytics, Customer Analytics and Enterprise Business Analytics Strategy. It also appears to have more of a regional focus whereas the Beijing centre looks to be focussed exclusively on the domestic Chinese market.

IBM acquires RedPill Solutions. A privately-held company headquartered in Singapore. RedPill provides advanced customer analytics services to businesses in industries such as financial services, telecommunications, technology and hospitality. RedPill is a specialist customer management services firm with significant expertise in areas such as churn management, segment propositions, segmentation, risk management and market/opportunity assessment. RedPill has delivered engagements across in 17 different countries in Asia Pacific and the Middle East and has offices in Thailand, Singapore, Malaysia, Indonesia, India and the United Arab Emirates.

November

QlikTech named Asian Rising Star. The Business Intelligence software vendor, has been named by MIS Asia Magazine as a Rising Star in its annual Strategic 100 IT vendors. QlikTech now has 650 customers and over 50 partners in Asia Pacific.

December

Neteeza Opens in China. The company opened a Sales (they call it a Proof of Concept) Center in Beijing. Their existing presence includes Japan, Korea, Singapore and Australia.

Alterian opens a full-service operation in Singapore, expanding from their initial APAC presence in Sydney.

MAIA Intelligence wins Red Herring Top 100 Award. MAIA is an Indian startup that produces a low cost but powerful BI software suite. The company was founded in 2006, has grown rapidly with over 10,000 users of 1KEY BI products. It has also been featured in Gartner’s Hype Cycle for ICT in India 2008 & BI Trend Report 2008-09 APAC.

Tata Consultancy Services is a Gartner leader. TCS is placed in the leaders quadrant for the 'Magic Quadrant for Oracle Outsourcing, North America'.