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June 30, 2010
Excellent infographics
Folks.
For those of your who take interest in excellent infographics and leading edge reporting techniques.. check out
http://feltron.com/index.php
The guy takes his own life and turns it into some really engaging visualisations.
Justin
Yellowfin Business Intelligence
www.yellowfinbi.com
Posted by Justin Hewitt at 5:45 AM | Comments (0)
June 15, 2010
New Tutorial on YouTube !!
New Tutorial on YouTube !!
http://www.youtube.com/watch?v=ByExI4hkNrs&feature=channel
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Posted by Justin Hewitt at 7:15 AM | Comments (0)
June 10, 2010
Yellowfin drives used car sales in US
We find ourselves in the Rust report again !!
(Rust Report: June 11 2010)
Yellowfin drives used car sales in US
Australian business intelligence specialist Yellowfin has been selected to provide systems to ADESA, a "vehicle remarketing" company that provides a variety of vehicle auction services in the US, Canada, and Mexico. ADESA has purchased 600 licences for Yellowfin's BI systems, which will be used to provide internal reporting and analysis.
www.yellowfin.bi
Posted by Justin Hewitt at 6:30 PM | Comments (0)
CRN magazine talks to Yellowfin' CEO Glen Rabie
http://www.crn.com.au/Feature/214521,logistics-swings--roundabouts.aspx
Extract
"RFID will deliver an explosion of data" that will lead to "telco styles level of data", predicts Glen Rabie, CEO of Yellowfin, a business Intelligence solution provider. Whereas in the past the measurement of the supply chain was quite coarse, RFID will allow collection of highly detailed, nearly real-time data every fifteen minutes.
Rabie says RFID was a technology ahead of its time. "The use will be customer rather than supplier driven when customers want to know the entire life of a product. It's currently a non-event but when large logistics companies start to use it, it will become expected," he says.
"There was an awful amount of hype, more than was justifiable but the technology will mature. It was held back by the ability to manage the data volume that was being created [and analysed]. We ended up collecting all this data but couldn't process it so it was useless."
The technologies around RFID have caught up and have really matured over the last three or four years, especially with the invention of Google Maps, which companies can use to plot information collected by RFID tags.
"Now we're able to deal with the billions of rows of data and we can use GIS [geographic information systems] to map data. We used to have GIS gurus in the backrooms but we can now expose it to management," says Rabie.
Posted by Justin Hewitt at 8:15 AM | Comments (0)
Blog: 5 Reasons why In-memory analysis matters
[1] Fast BI deployment at low cost
Lets face it most organizations are not blessed with plenty of resources (time and money) for BI projects. Traditional data warehousing can be resource intensive – with a dismal ROI. What in-memory analysis allows for is rapid development and deployment of analytical datasets that can be used for reporting and analysis purposes.
The speed of development means that you can get reporting quickly into the hands of those that need it to take action – whilst the issues uncovered are still fresh and relevant for the organization. This agility means that as the organization’s competitive environment changes the reporting can quickly adapt to new needs, without the burden of high development costs and delivery cycles.
[2] Disposable BI – the art of analysis
One of the dichotomies that we are seeing in the BI space is the difference between reporting and analysis. Most vendors interchange the two – but they are very different beasts.
Reporting is an ongoing and regimented process. Performance measures are defined, targets set, then reports written to track these over time. And lets face it there can be hundreds/thousands of these in an organization. Reporting is used to drive actions within an organization. They assist people to fulfill the obligations of their role.
Analysis on the other hand is a creative exercise. Whether intended or not people who analyze data are looking to uncover new insights, discover correlations and use data to assist in strategic decision making – the what to do next, where to focus attention.
The interesting part of analysis is that in reality it does not have to be repeated ad infinitum. Once insight has been gained, and proved, that analysis can be relegated to history. A new strategy created and the organization moves on.
What this means is that most true analysis is disposable. You want to build a data set, test your theories, make decisions and then move onto the next problem at hand. In-memory analysis for this is awesome – since you do not have to build a data warehouse or invest in massive amounts of infrastructure to get your data into shape for this analysis.
[3] Proof of Concepts and Iterative / Agile Development Cycles
One of the great issues in BI is the risk of building a massive BI data warehouse before you actually write a single report. This means that business users have few examples into what they are will get from a BI project until it is delivered.
It’s really hard to change a data warehouse. It is not hard to change an in-memory database. Why? You do not have to aggregate your data (see below)
In-memory analysis means that IT can work side-by-side with the business to develop a BI solution. It is so fast that a business user can see reports and then pose more questions, get more data test reports, change logic etc etc.
This iterative and agile development means that the need for long analysis is no longer needed since the down stream risk is so low. So get out there, and start building, showing and improving continuously.
Lets face it most organizations are not blessed with plenty of resources (time and money) for BI projects. Traditional data warehousing can be resource intensive – with a dismal ROI. What in-memory analysis allows for is rapid development and deployment of analytical datasets that can be used for reporting and analysis purposes.
The speed of development means that you can get reporting quickly into the hands of those that need it to take action – whilst the issues uncovered are still fresh and relevant for the organization. This agility means that as the organization’s competitive environment changes the reporting can quickly adapt to new needs, without the burden of high development costs and delivery cycles.
[2] Disposable BI – the art of analysis
One of the dichotomies that we are seeing in the BI space is the difference between reporting and analysis. Most vendors interchange the two – but they are very different beasts.
Reporting is an ongoing and regimented process. Performance measures are defined, targets set, then reports written to track these over time. And lets face it there can be hundreds/thousands of these in an organization. Reporting is used to drive actions within an organization. They assist people to fulfill the obligations of their role.
Analysis on the other hand is a creative exercise. Whether intended or not people who analyze data are looking to uncover new insights, discover correlations and use data to assist in strategic decision making – the what to do next, where to focus attention.
The interesting part of analysis is that in reality it does not have to be repeated ad infinitum. Once insight has been gained, and proved, that analysis can be relegated to history. A new strategy created and the organization moves on.
What this means is that most true analysis is disposable. You want to build a data set, test your theories, make decisions and then move onto the next problem at hand. In-memory analysis for this is awesome – since you do not have to build a data warehouse or invest in massive amounts of infrastructure to get your data into shape for this analysis.
[3] Proof of Concepts and Iterative / Agile Development Cycles
One of the great issues in BI is the risk of building a massive BI data warehouse before you actually write a single report. This means that business users have few examples into what they are will get from a BI project until it is delivered.
It’s really hard to change a data warehouse. It is not hard to change an in-memory database. Why? You do not have to aggregate your data (see below)
In-memory analysis means that IT can work side-by-side with the business to develop a BI solution. It is so fast that a business user can see reports and then pose more questions, get more data test reports, change logic etc etc.
This iterative and agile development means that the need for long analysis is no longer needed since the down stream risk is so low. So get out there, and start building, showing and improving continuously. or Cancel
[4] Great for large data volumes
OK so time for some tech stuff. In-memory solves the problem of a slow data base response time because of large data volumes. Basically not many people have all day for a query to run – so traditionally you had to build a data warehouse to extract the data fro your source system, aggregate the data (that is roll it up into manageable volumes) then report on the aggregate. Wow – that was a lot of work just to describe. With in-memory you can be lazy. Load the lot into memory and report off that. That’s what in-memory databases are for. They love lots and lots of data.
[5] Great for complex data queries
Part 2 of the tech stuff. Apart from lots of data – traditional database designs used for your core business activities hate complex queries. Why are BI queries complex – well because they want to join multiple tables together to make sense of all the data you have collected in your CRM or ERP and view these over time.
This are not single transaction queries for which your application (and its database) have been designed. So now you have a large volume of data and a complex query – another reason you need a data warehouse – right? Wrong another key reason that in-memory matters. OK there is a trick – your data is still coming out of your transactional system but only once. Then it is loaded into your in-memory db and ready for analysis.
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Posted by Justin Hewitt at 8:15 AM | Comments (0)
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