For as high as it ranks it in magic quadrants, business intelligence (BI) market share and flat-out leadership in advanced analytics, SAS has never been a company to dwell internally on titles or pecking orders. So it was only a little surprising when we first met SASs elusive CTO Keith Collins and asked when hed earned his senior title that he replied, Um, I dont recall exactly, can we can get back to you? A natural fit for SASs homegrown culture, Collins joined the company in 1984 as a tech liaison just two years out of North Carolina State University. Rising through R&D, Collins eventually led the effort to the flagship SAS 9 platform and founded the companys CIO Consumer Advisory Board in 2004. Only this year has he been able to return to his R&D roots where he is once again living and breathing BI. A busy man with a strong outside interest in education (he sits on university and museum boards), Collins still found time to share a thought or two with DM Review Editorial Director Jim Ericson.
DMR: SAS seems to be doing a lot of new things with bundled analytics.
Keith Collins: Let me back up for a definition so you know where we are coming from. When we use the word analytics, thats a bucket for us that includes areas of classic statistics, predictive modeling, forecasting and optimization. Theyre all in the bucket Im referring to.
DMR: Okay then, lets dive into an area like forecasting where there are some new products.
KC: The whole area of forecasting is changing, for example in the retail industry, where they are ramping up technology beyond the use of POS [point-of-sale] terminals. Theyre beginning to focus on how to optimize their businesses with better-quality forecasting that deals with seasonality or requires an order of magnitude more computational power. At the same time, a lot is happening in areas of optimizing the processes and the flows. One of the things youve probably seen from us is this shift to actually provide not just tools but also packaged analytic solutions, and our move specifically into retail is a great example of that.
DMR: We know all forecasts are wrong and tend to be slow, labor-intensive and manual, so what is the qualitative advance you are bringing?
KC: One shift is the high-performance forecasting weve brought to the market, which has changed the scale of what people can accomplish with the computing resources they have available. They can forecast more frequently across a larger range of products. The other shift is putting an appropriate interface around forecasting and moving away from, I have to understand and write for an ARIMA [AutoRegressive Integrated Moving Average] model, applied to time series data to We now have enough resources to generate multiple forecasts and deliver the best one to the user. Weve shifted the paradigm away from the highly skilled technical capability required to create a forecast. Now were looking at the data, generating multiple forecasts, picking the best one and deciding whether to go with it or override it. The process of understanding the override actually helps you drive the constraints for subsequent forecasting.
DMR: When you say multiple forecasts, are you referring to scenario testing?
KC: Actually, Im referring to the high-performance engine behind the forecast server, behind the user interface people interact with. Depending on how frequently you get information or at what level of the hierarchy it resides in, you may have a lot of information about something and very little information about something else. The product runs through different analytical methods and picks the one thats most appropriate for that particular SKU or item at any level of the hierarchy. The high performance comes from the fact that it can automatically generate many different forecasts using different analytical methods and pick the best one, depending on the metrics you choose.
DMR: Youve also released a new data visualization package.
KC: Right. The core of the desktop framework is a product called JMP that is very focused on visualization and the application of analytic methods to data. Its a very powerful tool thats tied together with the SAS platform to access the data, the data integration and data quality pieces. It harnesses the data manipulation capabilities and analytical computing power were talking about for predictive analytics, statistical modeling, optimization and the rest. The connection between JMP and SAS lets you marry the best in desktop visualization with enterprise class data integration and business analytics.
DMR: Speaking of computing power, were hearing about companies starting to offload noncore infrastructure and applications.
KC: It is already changing. We have a SAS OnDemand offering, a business unit that manages that process, and it is growing year over year without us even pushing it as an objective. We call it SAS OnDemand because we dont want to get into debates about multitenant services, but its really driven by how quickly can we help a customer address time to market or skills and capability needs. We host it on our premises and, at any point in time, they can choose to bring it in house.
DMR: Some are questioning how quickly grid computing and software as a service [SaaS] will take off.
KC: What were seeing is that people are getting more comfortable with duplicating targeted data offsite where you can manage the security and privacy appropriately. And, you can leverage the ability to have systems available when you need them with an appropriate skill set that can manage your data quality, manage the analysis and delivery of information. For us its not quite SaaS because projects are unique for each customer, whether its macroeconomic analysis for a large financial institution or running regulatory reporting for a small health insurance concern.
DMR: What are the prospects for companies offloading whole data centers, and what will happen to the old data management model?
KC: Were going to see significant changes as master data management [MDM], the idea that you can federate systems and keep them in synch, matures. Youll see targeted use of data appliances such as Netezza, DATAllegro or Teradata, and I think the old drumbeat of one version of the truth all in one place is going to die off. Its going to be about managing data across a federated enterprise and some of that will be inside and some will be outside. Management of that information is going to be more important than where it lives. Youll use targeted tools, targeted applications and targeted hardware to solve some of these very large problems.
DMR: You launched an IT management system to span physical and virtual environments with VMware earlier this year.
KC: That was interesting because IT management is our oldest solution and IT resource management goes back to the mainframe days. Weve seen a resurgence in classic capacity planning and forecasting of IT resources as weve gone through data center consolidation, outsourcing of service level agreements [SLAs] and M&A activities. This is just another evolution of that product to understand not just your physical capacity and management but also the virtual systems that are coming online. You want to know how to embrace those for your classic capacity planning and forecasting.
DMR: Weve seen acceleration in virtualization with Microsoft coming out early with Hyper-V. Are you working with them as well?
KC: I expect youll see it across the board in every operating system. Its funny for me, having cut my teeth on IBMs VM/CMS in the mid-1980s; all this looks like a replay. Theyre solving the same problems. You need to be able to lock pages in memory for memory-intensive work. You need to be able to direct channel input/output [I/O] devices. This round was different, because they now depend on the hardware partners to be part of that solution, whether its Microsoft, VMware, Xen or something else.
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