A couple of years ago Jack Olson spoke to the Business Intelligence SIG on his then recently published book "Data Quality, The Accuracy Dimension". I just finished reading the book and felt that it is well worth a review.
Data quality is a huge problem for Information Technology. In theory, IT systems capture all sorts of useful information that can be used to analyze the business and help make better decisions. In practice when we look at the data, quality problems mean that the information is not there. Data quality is about identifying problems with data and fixing them.
For example, the same customer may appear many different times in different forms so we cannot form an integrated view of all the business interactions with the customer. And then the address may be incomplete so we cannot mail the customer an exciting new offer that fits their profile exactly.
The book has several examples of databases with curious data. There is a HR database where the oldest employee appeared to have been born before the Civil war and the youngest employee had not yet been born. Then there is a medical database where people appeared to have operations inappropriate to their gender. There is also an auto insurance claims database with many different creative spellings for the color beige.
The book itself is divided into three sections. The first section describes the data quality problem, what data quality is and how the problem arises. The second section explains how to implement a data quality assurance program. The accent of this section is towards the processes needed to do data quality assurance, however it includes a chapter on the important topic of making the business case for data quality.
The final and longest section is a more technical look at implementing data quality, through data profiling technology. Data profiling is a set of analytic tools for analyzing data to find quality problems. In a simple case, grouping, counts and an order are enough to identify outlier data, like the multiple spellings of beige mentioned earlier. In other cases sophisticated algorithms are used to identify correlations that may indicate keys or other important facts about the data. Although this section is more technical, it is certainly not difficult to read or understand.
This is an extremely valuable book. Physically the book is smallish and unimposing. The writing style is straightforward, easy to understand. Yet the book packs a big punch. As I said before, Data Quality is a huge problem for IT. This book contains everything you need to start a data quality program. As such I think that it is essential reading for any IT person in data management, or for an IT consultant looking to expand their practice.
Although the book was published in 2003, it is just as relevant and useful now. In an era where most computer technology books are out of date by the time they are a couple of years old, this is a book that will last. I would compare it to the Ralph Kimball's "The Data Warehouse Toolkit" which is 10 years old but just a useful now as it was when it was first published. By the way, Kimball is a great fan of this book.
Thursday, October 13, 2005
Monday, October 03, 2005
Planning Ahead
As I said in my last entry we tend to build software systems for the hardware that we have now and not for the hardware that will exist when the system is mature. To get a glimpse of what is next for software systems we should look at the shape of hardware to come.
According to Moore, the semiconductor people can see 3 technology generations ahead where each generation is about 2 years. They get a doubling of density every 21 or so months. Thus it is safe to extrapolate over the next 6 years, in which time semiconductor density will multiply by a factor of at least 8. Six years ahead is a good target for the software systems that we are designing now.
Given that a current rack server or blade has 2 dual core processors and 4 Gigs of memory, the same system in 6 years time will have 32 processors and 32 Gigs of memory. As I noted previously processors will not get much faster, so extra performance comes from more processors. There is no doubt that this configuration is viable as it is the spec of a typical mid to high end server that you can buy now. A high end system will have several hundred to a thousand processors and perhaps a terabyte of main memory.
What does this mean for software? Well the obvious conclusions are 64 bits to address the large memory and concurrent programs to use all the processors. Slightly more subtle is the conclusion that all data can fit into main memory except for a small number of special cases. All data in main memory turns existing database systems on their head, so a specific conclusion is that we will see a new generation of data management systems.
According to Moore, the semiconductor people can see 3 technology generations ahead where each generation is about 2 years. They get a doubling of density every 21 or so months. Thus it is safe to extrapolate over the next 6 years, in which time semiconductor density will multiply by a factor of at least 8. Six years ahead is a good target for the software systems that we are designing now.
Given that a current rack server or blade has 2 dual core processors and 4 Gigs of memory, the same system in 6 years time will have 32 processors and 32 Gigs of memory. As I noted previously processors will not get much faster, so extra performance comes from more processors. There is no doubt that this configuration is viable as it is the spec of a typical mid to high end server that you can buy now. A high end system will have several hundred to a thousand processors and perhaps a terabyte of main memory.
What does this mean for software? Well the obvious conclusions are 64 bits to address the large memory and concurrent programs to use all the processors. Slightly more subtle is the conclusion that all data can fit into main memory except for a small number of special cases. All data in main memory turns existing database systems on their head, so a specific conclusion is that we will see a new generation of data management systems.
Saturday, October 01, 2005
Moore's Law
On Thursday, I went to the Computer History Museum celebration of the 40th anniversary of Moore's Law. The centerpiece of the event was Gordon Moore in converation with Carver Mead. David House introduced the speakers and in his introduction read some remarkably prescient passages from the 1965 paper describing applications for the microelectronics to come.
During the conversation Moore explained why he wrote the paper. At the time, integrated circuits were expensive and mainly used in military applications. Most people believed that integrated circuits were a niche products and would remain that way. Moore wanted to show that integrated circuit technology was advancing rapidly and that they were the best way of building any electronic product.
So in a sense the paper was marketing, selling the concept of integrated circuits to a sceptical audience with the goal of widening the market for their use, obviously to benefit the companies that were producing integrated circuits. At the time the idea was controversial. Even nowadays we often forget the remarkable logic of Moore's law, designing systems with the hardware that we have now rather than designing the system to exploit the hardware will be there when the system is fully realized.
A remarkable thing is that the original paper extrapolated Moore's Law out to 1975. Since then we have ridden the Law for another 30 years, and it is not going to stop any time soon. Moore told us that they have always been able to see out about 3 generations of manufacturing technology, where each generation is now about 2 years. So they can see how they are going to follow Moore's Law for at least the next 6 years.
During the conversation Moore explained why he wrote the paper. At the time, integrated circuits were expensive and mainly used in military applications. Most people believed that integrated circuits were a niche products and would remain that way. Moore wanted to show that integrated circuit technology was advancing rapidly and that they were the best way of building any electronic product.
So in a sense the paper was marketing, selling the concept of integrated circuits to a sceptical audience with the goal of widening the market for their use, obviously to benefit the companies that were producing integrated circuits. At the time the idea was controversial. Even nowadays we often forget the remarkable logic of Moore's law, designing systems with the hardware that we have now rather than designing the system to exploit the hardware will be there when the system is fully realized.
A remarkable thing is that the original paper extrapolated Moore's Law out to 1975. Since then we have ridden the Law for another 30 years, and it is not going to stop any time soon. Moore told us that they have always been able to see out about 3 generations of manufacturing technology, where each generation is now about 2 years. So they can see how they are going to follow Moore's Law for at least the next 6 years.
Sunday, September 25, 2005
PL vs GUI
Kal Krishnan gave us a couple of interesting insight at the September meeting of the SDForum Business Intelligence SIG. In his talk " explained the iSpheres Event Server, a system for real time Business Process Management (BAM) that captures, transforms and does complex processing on business events as they happen.
Two things about his presentation struck me. The first was a claim that the iSpheres Event Server could handle 470,000 events per second. It is an impressive performance even when you discount by a factor of 3 or 4 for benchmarkism. I have heard that a stock market feed can have a peak rates of 100,000 messages per second so we get some hope that the iSpheres server would be able to handle it.
The other insight was that iSpheres uses a programming language to specify the behavior of the Event Server. Kal told us that they had started out by providing a GUI with icons and drag and drop as their programming interface and this had proved to be too cumbersome for programming the number of events that an event server needs. So, a few years ago they rethought the programming interface and designed an event programming language instead.
I have remarked before on this issue. In practice, you always have to provide a programmable interface, either with a programming language or through an API. From this, it is straightforward to provide a GUI, either as a plug in to an existing development system or by automatically generating the GUI from the language or API specification.
Two things about his presentation struck me. The first was a claim that the iSpheres Event Server could handle 470,000 events per second. It is an impressive performance even when you discount by a factor of 3 or 4 for benchmarkism. I have heard that a stock market feed can have a peak rates of 100,000 messages per second so we get some hope that the iSpheres server would be able to handle it.
The other insight was that iSpheres uses a programming language to specify the behavior of the Event Server. Kal told us that they had started out by providing a GUI with icons and drag and drop as their programming interface and this had proved to be too cumbersome for programming the number of events that an event server needs. So, a few years ago they rethought the programming interface and designed an event programming language instead.
I have remarked before on this issue. In practice, you always have to provide a programmable interface, either with a programming language or through an API. From this, it is straightforward to provide a GUI, either as a plug in to an existing development system or by automatically generating the GUI from the language or API specification.
Wednesday, September 21, 2005
Processors Hit the Speed Limit
I was talking with a friend in the semiconductor business the other night. He told me that processors have hit their speed limit. Over the last 20 years, as silicon is scaled down, it has become faster, and we have benefited from faster and faster processors. But now as they continue to scale down, the silicon becomes leakier, and higher frequencies draw more power to the point of self defeat.
Of course, Moores Law continues and silicon continues to scale down, so we will continue to get more transistors on a chip, it is just that now the chips will not get faster as well as denser. I think that we all instinctively know that processors have hit a speed bump. The manufacturers no longer crow about how fast their chip goes, instead they talk about hyperthreading and dual core. So what my friend was telling me is that this is not just a speed bump, it is the speed limit. His view is that architectural improvements and throwing more transistors into the pot could give us another factor of 2 performance improvement, but that is it.
There is no big architecture breakthroughs on the horizon. The Von Neumann model has been around for almost 60 years. For the last 30 years, it has been criticized for serializing program execution, however nothing better has ever been made to work in a convincing way. Moreover, the Von Neumann model of sequential execution is embedded in the way we think about programming and a huge investment programming languages and all existing programs.
The alternative is that we write parallel programs to run on future generations of multi-core processors. The standard tool for writing parallel programs is threads. I have recently been writing threaded code and I can tell you that it is awful. Absolutely awful. I am going to write more pieces on the problems, but for now I can assure you that the problem of programming with threads is worse than the problem of programming memory allocation, something than many new programming languages have resolved by providing automatic garbage collection.
Of course, Moores Law continues and silicon continues to scale down, so we will continue to get more transistors on a chip, it is just that now the chips will not get faster as well as denser. I think that we all instinctively know that processors have hit a speed bump. The manufacturers no longer crow about how fast their chip goes, instead they talk about hyperthreading and dual core. So what my friend was telling me is that this is not just a speed bump, it is the speed limit. His view is that architectural improvements and throwing more transistors into the pot could give us another factor of 2 performance improvement, but that is it.
There is no big architecture breakthroughs on the horizon. The Von Neumann model has been around for almost 60 years. For the last 30 years, it has been criticized for serializing program execution, however nothing better has ever been made to work in a convincing way. Moreover, the Von Neumann model of sequential execution is embedded in the way we think about programming and a huge investment programming languages and all existing programs.
The alternative is that we write parallel programs to run on future generations of multi-core processors. The standard tool for writing parallel programs is threads. I have recently been writing threaded code and I can tell you that it is awful. Absolutely awful. I am going to write more pieces on the problems, but for now I can assure you that the problem of programming with threads is worse than the problem of programming memory allocation, something than many new programming languages have resolved by providing automatic garbage collection.
Monday, August 22, 2005
Data Mining Insight
Data mining is a difficult subject. On the one hand it is presented as this thing that will tell you all sorts of wonderful facts that you never knew about your data. On the other hand when you start getting into it, it is this daunting thing that is difficult to approach, seems to require a PhD in statistics to use and ends up telling you stuff that you already know, like when people buy bread at the market they are also likely to buy milk (or was that the other way round?)
At the August meeting of the SDForum Business Intelligence SIG Joerg Rathenberg, VP Marketing and Communications at KXEN gave a talk about predictive analytics, which is the latest way of saying data mining. KXEN is a young, privately held company that is devoted data mining and that is successful, while many other data mining startups have fallen by the wayside.
The kernel of KXEN's success comes from powerful robust algorithms that do not require a specialist to tweak, high performance so that you can get results quickly and finally and most importantly, ease of use. As part of his presentation Joerg ran through a couple of data mining exercises showing us how you could take a reasonable sized data set in say the form of comma separated values (CSV), and using a few clicks and a several seconds processing, generate an interesting data analysis.
For me the key insight of the evening was on how to use data mining. I had always thought of data mining as a tool of last resort, when the data is too large or complicated, and nothing else seems to work, you resort to data mining to try and find something that you cannot see with the naked eye. On the other hand Joerg suggested that data mining is the first thing that you do when presented with a new business question, or a new data set. You use data mining to for the initial analysis of the data to find out which factors in the data really affect the outcome that you are interested in. Once these factors are identified, you can build reports or OLAP cubes using these factors as dimensions to explore in depth what is going on.
Thus data mining is something that you should be doing early and often in your data exploration. Joerg called this "Exploratory Data Mining" and it certainly resonated with audience members who do data analysis for a living. KXEN has designed their software to make exploratory data mining possible and even easy, and hope that by this means it becomes accessible to the masses.
At the August meeting of the SDForum Business Intelligence SIG Joerg Rathenberg, VP Marketing and Communications at KXEN gave a talk about predictive analytics, which is the latest way of saying data mining. KXEN is a young, privately held company that is devoted data mining and that is successful, while many other data mining startups have fallen by the wayside.
The kernel of KXEN's success comes from powerful robust algorithms that do not require a specialist to tweak, high performance so that you can get results quickly and finally and most importantly, ease of use. As part of his presentation Joerg ran through a couple of data mining exercises showing us how you could take a reasonable sized data set in say the form of comma separated values (CSV), and using a few clicks and a several seconds processing, generate an interesting data analysis.
For me the key insight of the evening was on how to use data mining. I had always thought of data mining as a tool of last resort, when the data is too large or complicated, and nothing else seems to work, you resort to data mining to try and find something that you cannot see with the naked eye. On the other hand Joerg suggested that data mining is the first thing that you do when presented with a new business question, or a new data set. You use data mining to for the initial analysis of the data to find out which factors in the data really affect the outcome that you are interested in. Once these factors are identified, you can build reports or OLAP cubes using these factors as dimensions to explore in depth what is going on.
Thus data mining is something that you should be doing early and often in your data exploration. Joerg called this "Exploratory Data Mining" and it certainly resonated with audience members who do data analysis for a living. KXEN has designed their software to make exploratory data mining possible and even easy, and hope that by this means it becomes accessible to the masses.
Wednesday, August 03, 2005
Pauseless Garbage Collection
Programs generate a surprising amount of garbage, little pieces of memory that are used and then discarded. Low level programming languages like C require that the programmer manage the storage themselves, which is a surprisingly painful, time-consuming and error prone task. So these days application programs are written in languages like Java (or C#) where the system manages the storage and does the garbage collection. The result is much higher programmer productivity and much better and far more reliable programs.
The overhead of doing automatic garbage collection has always been a concern. However, another problem with automatic garbage collection is that up to now it has required that the system pauses, sometimes for a considerable length of time while parts of the garbage collector runs. A pause in a web application server stops customers from doing whatever they are trying to do. This can range from absolutely unacceptable in online stock trading to just very bad for customer satisfaction in a typical e-commerce application.
At the August meeting of the SDForum Java SIG, Cliff Click spoke on pauseless garbage collection. Cliff is part of Azul Systems, a startup that has developed an attached processor to run Java applications. As Azul Systems sells to large enterprises that run Java web applications to support their business, being able to do automatic garbage collection without pausing is an important feature.
Cliff is an engaging speaker who has spoken to the Java SIG before. Previously Cliff gave an overview of what Azul is doing. At this meeting, Cliff described the pauseless garbage collection algorithm in detail, and then went on to give us some indication of its performance. He had taken a part of the standard SPEC JBB Enterprise Java warehouse benchmark, modified it by adding a large slow-moving object cache and a much longer runtime that makes the benchmark more realistic and garbage collection more of an issue.
When the benchmark is run on an Azul system, the longest "Stop the world" pause is 25 milliseconds, whereas running the benchmark on other Java systems exhibited pauses of up to 5 seconds (yes seconds). On any platform almost all of the benchmark transactions run in under a millisecond. On the Azul system, no transaction took more than 26 milliseconds, which is very close to their maximum pause time, and well over 99% of the transactions ran in under 2 milliseconds. On the other Java systems, over half of the total transaction time could be taken up by transactions that took more than two milliseconds to complete.
While Cliff and Azul are proud of what they have done so far, they are not satisfied. So they are working on removing the last few vestiges of a pause from their system. We can expect even better performance in the future.
The overhead of doing automatic garbage collection has always been a concern. However, another problem with automatic garbage collection is that up to now it has required that the system pauses, sometimes for a considerable length of time while parts of the garbage collector runs. A pause in a web application server stops customers from doing whatever they are trying to do. This can range from absolutely unacceptable in online stock trading to just very bad for customer satisfaction in a typical e-commerce application.
At the August meeting of the SDForum Java SIG, Cliff Click spoke on pauseless garbage collection. Cliff is part of Azul Systems, a startup that has developed an attached processor to run Java applications. As Azul Systems sells to large enterprises that run Java web applications to support their business, being able to do automatic garbage collection without pausing is an important feature.
Cliff is an engaging speaker who has spoken to the Java SIG before. Previously Cliff gave an overview of what Azul is doing. At this meeting, Cliff described the pauseless garbage collection algorithm in detail, and then went on to give us some indication of its performance. He had taken a part of the standard SPEC JBB Enterprise Java warehouse benchmark, modified it by adding a large slow-moving object cache and a much longer runtime that makes the benchmark more realistic and garbage collection more of an issue.
When the benchmark is run on an Azul system, the longest "Stop the world" pause is 25 milliseconds, whereas running the benchmark on other Java systems exhibited pauses of up to 5 seconds (yes seconds). On any platform almost all of the benchmark transactions run in under a millisecond. On the Azul system, no transaction took more than 26 milliseconds, which is very close to their maximum pause time, and well over 99% of the transactions ran in under 2 milliseconds. On the other Java systems, over half of the total transaction time could be taken up by transactions that took more than two milliseconds to complete.
While Cliff and Azul are proud of what they have done so far, they are not satisfied. So they are working on removing the last few vestiges of a pause from their system. We can expect even better performance in the future.
Monday, August 01, 2005
Aspect Ratio Hell
I returned from summer vacation with a large number of pictures which I am editing. Which leads to a difficult decision. When cropping the pictures, what aspect ratio do I chose for the images? This is not a clear cut question, and any investigation of which aspect ratio to use for cropping pictures leads to much confusion.
Last year, for example I had a beautiful picture of us getting Lei-ed as we arrived in Hawaii. I cropped the picture for a 4 x 6 print (aspect ratio 1.5) and then deciding that it made such a good picture, printed it on a 5 x 7 (AR 1.4) only to discover that the printer chopped off the tops of our heads. Looking further I see that it would get other results if I had tried to print an 8.5 x 11 (AR 1.294...) or a 11 x 17 (AR 1.545...) or a 13 x 19 (AR 1.461...). Fortunately the last two choices are moot because my printer cannot handle theses sized sheets.
There is more. I take a group of pictures and put them on a DVD that we can watch on TV. 60 pictures at 6 seconds each with a sound track will make a high energy 6 minute video of our vacation. However, this creates more aspect ratio choices. Currently TV's are changing their aspect ratio from 4 x 3 (AR 1.333...) to 16 x 9 (1.777...). (And why has the convention changed to putting the larger number first?) In practice TV's are even more difficult as they naturally chop off some lines at the top and bottom of the picture so that a video of still images introduces even more uncertainty when deciding how to crop the images.
If we want to see the whole image in the video, we can look at it on a computer monitor which does not lose scan lines from the top and bottom and will scale everything to fit. But there is a problem even in the logical world of computers. Most of the standard display settings have an aspect ratio of 1.333... (800 x 600, 1024 x 768, 1600 x 1200) however the majority of computer displays sold today are LCD panels at 1280 x 1024 (AR 1.25).
More confusing yet, the display size of 1280 x 768 (AR 1.666...) is becoming popular in both laptops that can be used for watching DVD's on long flights and with LCD TV's where the aspect ratio seems to match what is shown on a 16 x 9 TV even although 1.66... is not 1.77...
There is a lot more to aspect ratio and it only gets worse. For example, the above discussion assumed that the pixels were square which they do not need to be. I have some ideas on what can be done which will have to wait until another time.
Last year, for example I had a beautiful picture of us getting Lei-ed as we arrived in Hawaii. I cropped the picture for a 4 x 6 print (aspect ratio 1.5) and then deciding that it made such a good picture, printed it on a 5 x 7 (AR 1.4) only to discover that the printer chopped off the tops of our heads. Looking further I see that it would get other results if I had tried to print an 8.5 x 11 (AR 1.294...) or a 11 x 17 (AR 1.545...) or a 13 x 19 (AR 1.461...). Fortunately the last two choices are moot because my printer cannot handle theses sized sheets.
There is more. I take a group of pictures and put them on a DVD that we can watch on TV. 60 pictures at 6 seconds each with a sound track will make a high energy 6 minute video of our vacation. However, this creates more aspect ratio choices. Currently TV's are changing their aspect ratio from 4 x 3 (AR 1.333...) to 16 x 9 (1.777...). (And why has the convention changed to putting the larger number first?) In practice TV's are even more difficult as they naturally chop off some lines at the top and bottom of the picture so that a video of still images introduces even more uncertainty when deciding how to crop the images.
If we want to see the whole image in the video, we can look at it on a computer monitor which does not lose scan lines from the top and bottom and will scale everything to fit. But there is a problem even in the logical world of computers. Most of the standard display settings have an aspect ratio of 1.333... (800 x 600, 1024 x 768, 1600 x 1200) however the majority of computer displays sold today are LCD panels at 1280 x 1024 (AR 1.25).
More confusing yet, the display size of 1280 x 768 (AR 1.666...) is becoming popular in both laptops that can be used for watching DVD's on long flights and with LCD TV's where the aspect ratio seems to match what is shown on a 16 x 9 TV even although 1.66... is not 1.77...
There is a lot more to aspect ratio and it only gets worse. For example, the above discussion assumed that the pixels were square which they do not need to be. I have some ideas on what can be done which will have to wait until another time.
Wednesday, July 27, 2005
Going off the Net
The internet is a wonderful thing, it is also a very dangerous place. Any computer system connected to the internet can and will be attacked. There is also a viral aspect to the internet, which means that while it is difficult to do with the internet and it is also difficult to do without.
At work we are building a software product that when combined with a number of other complex software systems on a cluster with a SAN will do wonderful things. However, configuring and debugging all this stuff requires a lot of privileged access.
The company has a lot of rules to protect its computer systems from attack. Part of that is denying users privileged access, which is sensible for most users. However when we want anything privileged done, we have to ask the IT guys to do it as they are the ones with the privilege, and each request takes its own time.
The test system arrived and my first thought was that we could bypass all these rules that slow progress by taking it off the net. If the test system is not connected, it is safe from attack, we do not have to follow all the rules and we can have all the privileged access we need to get things done as and when we want.
I suggested this at our project meeting and the first question was "if it is not on the net, how do I telnet to it for debugging?" I described sneakernet, the secure alternative to the internet. You burn a CD, pop it out, walk across the room, pop it into the test system and "Robert est votre oncle".
Everyone looked at me like I was mad, or maybe they thought that I was just lost in another millennia. The notion that you needed to be in the same room as the test system seemed retrograde. As did the idea that you could not be browsing Slashdot while waiting for that conditional breakpoint to pop. So we are going to have to put up with the constant battle with IT to get simple things done because now it is impossible to go off the net.
At work we are building a software product that when combined with a number of other complex software systems on a cluster with a SAN will do wonderful things. However, configuring and debugging all this stuff requires a lot of privileged access.
The company has a lot of rules to protect its computer systems from attack. Part of that is denying users privileged access, which is sensible for most users. However when we want anything privileged done, we have to ask the IT guys to do it as they are the ones with the privilege, and each request takes its own time.
The test system arrived and my first thought was that we could bypass all these rules that slow progress by taking it off the net. If the test system is not connected, it is safe from attack, we do not have to follow all the rules and we can have all the privileged access we need to get things done as and when we want.
I suggested this at our project meeting and the first question was "if it is not on the net, how do I telnet to it for debugging?" I described sneakernet, the secure alternative to the internet. You burn a CD, pop it out, walk across the room, pop it into the test system and "Robert est votre oncle".
Everyone looked at me like I was mad, or maybe they thought that I was just lost in another millennia. The notion that you needed to be in the same room as the test system seemed retrograde. As did the idea that you could not be browsing Slashdot while waiting for that conditional breakpoint to pop. So we are going to have to put up with the constant battle with IT to get simple things done because now it is impossible to go off the net.
Tuesday, July 19, 2005
Open Source BI
Open Source Business Intelligence is hot. This month the SDForum Open Source SIG hosted a talk from JasperSoft about their open source reporting software. Tonight the Business Intelligence SIG heard from Sandeep Giri about their OpenI initiative to provide an Open Source BI application.
Currently OpenI is a BI application built on Open Source components that provides visualization of data from an OLAP source such as SQL Server or Mondrian. Sandeep has plans to expand OpenI to become a fully fledged BI platform.
I had asked Sandeep to talk about why they are making their application Open Source. Sandeep explained that his company, Loyalty Matrix, provides Software as a Service (SaaS). [This means that they can use most Open Source software to provide their service.] While there are several Open Source components with a BI flavor, there was no ready to run Open Source BI application or platform.
Loyalty Matrix has had to develop their own platform using Open Source software. By giving their application back to the Open Source community they get the implementation help, support and feedback of a large user group to complement their tiny development team.
Currently OpenI is a BI application built on Open Source components that provides visualization of data from an OLAP source such as SQL Server or Mondrian. Sandeep has plans to expand OpenI to become a fully fledged BI platform.
I had asked Sandeep to talk about why they are making their application Open Source. Sandeep explained that his company, Loyalty Matrix, provides Software as a Service (SaaS). [This means that they can use most Open Source software to provide their service.] While there are several Open Source components with a BI flavor, there was no ready to run Open Source BI application or platform.
Loyalty Matrix has had to develop their own platform using Open Source software. By giving their application back to the Open Source community they get the implementation help, support and feedback of a large user group to complement their tiny development team.
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