Kuala Lumpur, Feb 21 2013
Queenie Wong, Practice Lead, Information Management, SAS, queenie.wong@sas.com
Damian Lim, Senior Consultant, Information Management, Sales Support, SAS, damian.lim@sas.com
Michael Hawkins, Consultant, Customer Intelligence, SAS, michael.hawkins@sas.com
SAS is the pioneer of Analytics, a segment of computer science which analyses large amount of data in order to derive intelligence. Better known as Business Analytics, it was used to be called Business Intelligence. During the early days, the hoo-ha was having the ability to perform data aggregation from various sources into a single location – a process known as data warehousing; these data will subsequently be combined into 3 dimensional perspectives known as cubes which will serve the basis of analysing large amount of information from multi-angle perspectives and hence giving rise to intelligence.
Analytics today aspires to not only deriving intelligent reports, it also wants to expand users’ ability to analyse and discover more intelligence such as trends, patterns and etc.
To accomplish that, Analytics needs to upgrade itself from just having the ability to collect, integrate and process vast amount of data and to be able to produce 3 dimensional intelligent reports for users, it must also caters flexibility and visibility for users in the process of how information are being analysed by the solution. In other words, granted that the whole process of Analytics starts with 1.) data collection, follows by 2.) analysing and processing, and finally 3.) reporting; the advent of Analytics aspires to give end-users more visibility and control over the 2nd process, which is ‘analysing and processing’. This concept forms the essence of SAS Visual Analytics.
Parallel with industry trends, SAS Visual Analytics solution approaches the consumerisation of IT trend; rather than waiting for the top management to decide on benefits of using Analytics to gain business advantage or to discover business opportunities; sales, marketing and product departments of all sizes can now decide for themselves that Analytics will help with their job performance to meeting KPIs and proceed to adopt such solution based on own budgets than waiting for large-scale enterprise rollout.
“Visual Analytics can be seen as a stepping stone for enterprises towards advanced-analytics adoption,” said Queenie Wong. With the approach of Visual Analytics, users can relax domain expertise requirement.
Visual Analytics also works for SMEs. A typical deployment will only take 4 to 6 weeks comparing to the conventional approach which usually takes 6 to 8 months.
It is important to understand that Visual Analytics strives for 4 traits;
It is a full-fledge web-based solution. Its interface is powered by Flash – running on iOS platform requires a native app or usage of Google Chrome browser which supports Flash.
According to Damian Lim, SAS Visual Analytics was first launched in Mar 2012 as product offerings for big data trend. The newest version; 6.1 was released in Dec 2012. It now supports middle-scale deployments benefiting SMEs.
The solution is deployed as appliance (hardware and software in box). SMEs can opt to get started with the lowest scale of 16 cores processor server – the pricing is also based on the number of processor core.
Queenie Wong encourages SMEs to try out the solution. A demo version has been made available online since Feb 2013 where users get to experience the look-and-feel based on read-only data. Alternatively, interested parties are encouraged to contact SAS for free proof-of-concept session.
SAS Visual Analytics will bridge the gap of high requirement for domain expertise when it comes to Analytics deployment and enables enterprises and SMEs to adopt big data.
SME manufacturers should definitely adopt such solution. If you have a lot of information silos, you should definitely check it out as the costs-saving benefits are very attractive.
Moving forward, the Analytics and big data industry has now witnessed the opportunity of more affordable enterprise-standard solution for all parties. This is definitely going to encourage more big data adoption.
Personally, I am waiting for big data solutions from start-ups offering SaaS to consumers in a manner similar to Google Maps’ offering of GEO-mapping solution to all parties. It will be exciting.
Get more details.
More on SMEs.
Queenie Wong, Practice Lead, Information Management, SAS, queenie.wong@sas.com
Damian Lim, Senior Consultant, Information Management, Sales Support, SAS, damian.lim@sas.com
Michael Hawkins, Consultant, Customer Intelligence, SAS, michael.hawkins@sas.com
SAS is the pioneer of Analytics, a segment of computer science which analyses large amount of data in order to derive intelligence. Better known as Business Analytics, it was used to be called Business Intelligence. During the early days, the hoo-ha was having the ability to perform data aggregation from various sources into a single location – a process known as data warehousing; these data will subsequently be combined into 3 dimensional perspectives known as cubes which will serve the basis of analysing large amount of information from multi-angle perspectives and hence giving rise to intelligence.
Analytics today aspires to not only deriving intelligent reports, it also wants to expand users’ ability to analyse and discover more intelligence such as trends, patterns and etc.
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| (L-R:) Queenie Wong, Damian Lim, SAS Malaysia |
To accomplish that, Analytics needs to upgrade itself from just having the ability to collect, integrate and process vast amount of data and to be able to produce 3 dimensional intelligent reports for users, it must also caters flexibility and visibility for users in the process of how information are being analysed by the solution. In other words, granted that the whole process of Analytics starts with 1.) data collection, follows by 2.) analysing and processing, and finally 3.) reporting; the advent of Analytics aspires to give end-users more visibility and control over the 2nd process, which is ‘analysing and processing’. This concept forms the essence of SAS Visual Analytics.
Parallel with industry trends, SAS Visual Analytics solution approaches the consumerisation of IT trend; rather than waiting for the top management to decide on benefits of using Analytics to gain business advantage or to discover business opportunities; sales, marketing and product departments of all sizes can now decide for themselves that Analytics will help with their job performance to meeting KPIs and proceed to adopt such solution based on own budgets than waiting for large-scale enterprise rollout.
“Visual Analytics can be seen as a stepping stone for enterprises towards advanced-analytics adoption,” said Queenie Wong. With the approach of Visual Analytics, users can relax domain expertise requirement.
Visual Analytics also works for SMEs. A typical deployment will only take 4 to 6 weeks comparing to the conventional approach which usually takes 6 to 8 months.
It is important to understand that Visual Analytics strives for 4 traits;
- Mobility
- Simplified analytics
- Speed
- Self-service
It is a full-fledge web-based solution. Its interface is powered by Flash – running on iOS platform requires a native app or usage of Google Chrome browser which supports Flash.
According to Damian Lim, SAS Visual Analytics was first launched in Mar 2012 as product offerings for big data trend. The newest version; 6.1 was released in Dec 2012. It now supports middle-scale deployments benefiting SMEs.
The solution is deployed as appliance (hardware and software in box). SMEs can opt to get started with the lowest scale of 16 cores processor server – the pricing is also based on the number of processor core.
Queenie Wong encourages SMEs to try out the solution. A demo version has been made available online since Feb 2013 where users get to experience the look-and-feel based on read-only data. Alternatively, interested parties are encouraged to contact SAS for free proof-of-concept session.
SAS Visual Analytics will bridge the gap of high requirement for domain expertise when it comes to Analytics deployment and enables enterprises and SMEs to adopt big data.
SME manufacturers should definitely adopt such solution. If you have a lot of information silos, you should definitely check it out as the costs-saving benefits are very attractive.
Moving forward, the Analytics and big data industry has now witnessed the opportunity of more affordable enterprise-standard solution for all parties. This is definitely going to encourage more big data adoption.
Personally, I am waiting for big data solutions from start-ups offering SaaS to consumers in a manner similar to Google Maps’ offering of GEO-mapping solution to all parties. It will be exciting.
Get more details.
More on SMEs.
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