Monday, 23 July 2012 17:49

SAS pushes for 'big data' analytics


SAS is making a pitch for the business of companies with lots of data to analyse.

The SAS high performance analytics roadshow has reached Australia, with the local kick-off in Melbourne today. Company executives told a room packed with representatives of banks, telcos and other enterprises just how the SAS approach to high performance analytics could help their businesses.

David Bowie, managing director for Australia and New Zealand opened proceedings by explaining how the "data asset... needs to be captured, stored and used."

Big data is often thought to be about volume, variety and velocity, he said, but SAS adds a fourth V: value.

The company's focus is business outcomes, he said, and high performance analytics is needed to deliver value.

Commonwealth Bank, Telstra and Loyalty New Zealand are among the company's customers that have shown measurable gains from high performance analytics.

CBA is now able to carry out fraud prevention analysis in just 40ms before authorising a card transaction.

Telstra has seen a 15% improvement from campaign optimisation, and analysis that used to take three weeks to process at Loyalty New Zealand is now done in half a day.

Jim Davis, senior vice president and chief marketing officer said there was a growing awareness that data and analytics "can make or break a business."

"We're not doing BI any more," he explained. Multi-hour or overnight processes are no longer acceptable, and instead almost instantaneous analysis is needed.

SAS' approach to high-performance analytics means "you no longer need to sample large data sets" or reduce the number of variables.

Instead, massive data sets can be processed in their entirety.

This means "you no longer need OLAP - OLAP disappears," he claimed, referring to the practice of extracting 'data cubes' from operational databases for analysis, which imposes constraints on the questions that can be explored.

Mr Davis also emphasised that he wasn't talking about in-memory databases, but in-memory analytics.

In-memory database products from companies such as SAP can give quick response for BI-style queries, but not for analytics, he claimed.

SAS' approach is to spread data analysis across multiple blades, and also to deliver industry-specific solutions for a range of sectors.

The result is that an Asian bank is able to perform risk analysis in 15 minutes rather than 20 hours, and an eastern European telco can perform an analysis that used to take 10 hours in 1.5 minutes.

"It can make an enormous difference," Mr Davis said.

Oliver Schabenberger, lead architect, high performance analytics, explained that the only way to deliver the performance necessary for these types of tasks was to carry out the calculations in parallel.

SAS achieves this by using dual 8-core commodity blades with 128GB of RAM and two 600GB hard drives.

At least eight of these blades are used, with up to 16 in a chassis, and up to three chassis in a rack.

So a fully populated rack has 1536 cores, over 6TB of RAM, and over 57TB of disk space - and costs around $US500,000. Bigger systems are possible.

If that sounds expensive, consider this marketing optimisation example.

Mr Schabenberger presented a scenario where an $11 million marketing budget was to be allocated to reach 20 million customers with certain constraints (eg, no customer was to receive more than two offers, if two offers were made to the same customer they had to be of different types and delivered by different channels, and the total number of offers of each type was limited).

What would have taken six or seven hours to process on a conventional system was done in around 90 seconds, giving a $US19.2 million outcome.

But the results showed that the constraints on two of the offer types were limiting the expected return, and allowing more of those and fewer of others increased the expected outcome to $US19.5 million.

Running the analysis a second time only took another 90 seconds or so, and generated an outcome that would go a long way to paying for the analytics system, he pointed out.

It's not just about carrying out the analysis, it's also about presenting the results to other people and allowing non-specialist users to explore the data for themselves.

Peter Kokinakos, general manager, information management practice, demonstrated the SAS Visual Analytics system that helps with the process of discovery (the questions you ask once you have an answer to the first question) and sharing insights with others.

He demonstrated the software with 1.1 billion rows of data from a manufacturing company, pointing out that it was not necessary

Mr Kokinakos also noted that out that unlike the way OLAP systems work, the data hierarchy is not defined in advance and so doesn't get in the way of exploration as it can be redefined at any time.

Various graphical reports were generated with drag and drop ease - and almost instantaneous results despite the size of the database.

The software is smart enough to automatically select the appropriate chart type (eg, line graph or bar chart) for the variables selected, and data can also be presented geographically.

The visual analysis system also allows reports to be published so they are accessible on tablets.

The company currently offers an iPad client via the App Store, but plans to add support for other devices.

Mikael Hagstrom, executive vice president for Europe, Middle East, Africa and Asia Pacific, said there was a shortage of analytics skills, so it was important to provide software like this that allows many people to carry out their own analytics.

It was also important to be able to allow such self-service without needing to make copies of the data or limiting employees to predetermined subsets.

This is also an issue if you want to be able to provide access to real-time data, such as user interaction with your web site, or social data (eg, measuring the sentiments being expressed about the company).

"If we're going to make big data an asset class, we're going to need the tools to tap into them," he said.

And such tools can make a significant difference to the way organisations work.

For example, the Royal Bank of Scotland discovered it could carry out a risk assessment on prospective borrowers in less than a minute, and then realised the assessment could then be carried out by client-facing staff rather than the risk department.

This freed the risk department to spend more time on its 'real' function.

There's also the possibility of making the model available to SME customers so they can do their own 'what if' analysis (eg, the effect of interest rate changes) before approaching the bank.

The idea is that if potential borrowers understand why the bank acts as it does, their loan applications are more likely to be successful.

And that means they're more likely to take on additional staff, which will make the government happy.

Mr Hagstrom noted in closing that SAS has expanded its local operation, and skilled staff are available to run on-site workshops and similar events for customers and potential customers.

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Stephen Withers

Stephen Withers is one of Australia┬╣s most experienced IT journalists, having begun his career in the days of 8-bit 'microcomputers'. He covers the gamut from gadgets to enterprise systems. In previous lives he has been an academic, a systems programmer, an IT support manager, and an online services manager. Stephen holds an honours degree in Management Sciences and a PhD in Industrial and Business Studies.

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