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David Stodder

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It's All in the Memory: New Battleground for BI and Analytics

Where is the biggest battleground today in the business intelligence and analytics software market? On the technology front, one of the main battles is in the addressable memory space of systems that feature 64-bit computing and operating system platforms. The “in-memory” revolution is upon us, and no BI or analytics vendor wants to be left out. Large memory platforms will be critical to users working with tools for big data analytics, data discovery, data visualization, and more.

While the development of large-memory computing is not really new, it took a while for the software industry to adapt to 64-bit hardware processing and operating system platforms. Throw in the difficult learning curve for creating software to work with parallel processing, and it’s easy to see why the move from older systems has taken time. When large memory and parallel processing platforms were exotic, the slow pace of adaptation might have been acceptable. Now, with mainstream systems offering up to a terabyte of addressable memory, organizations can’t wait to try them out for BI and analytics.

Traditionally, designers of these systems have had to adjust to the limits of the I/O bottleneck. The preprocessing and design work for indexing and aggregating data has been necessary because of the performance constraints involved in getting data from disk through the I/O bottleneck. If large memory systems can ease or eliminate that constraint for the majority of users’ analysis needs, then the boundaries for analytics applications can be pushed out.

Users can perform “data discovery,” asking questions that lead to more questions, without as much concern for what this iterative, ad hoc style of investigation might mean to overall performance. Unlike with BI reports that simply update standard views of data, users can engage in exploratory data inquiries without knowing exactly where they will end up. Large-memory systems can offer volumes of detailed data on systems deployed closer to users. With the right tools, line-of-business (LOB) decision makers can dive into the data to test predictive models and perform fine-grained analysis on their own rather than wait for IT’s specialized business analysts and statisticians to do it for them.

Data discovery vendors such as QlikTech, Tableau, and TIBCO Spotfire have prospered by jumping first to seize market opportunities. However, the biggest coming battle may be between SAP and Oracle. Earlier this year, SAP introduced HANA, which competes with Oracle’s Exadata by offering in-memory analytics along with traditional disk-based storage in an appliance. Oracle has been readying a response, which will most likely come at Oracle Open World in early October and be aimed at taking in-memory capabilities for BI and analytics further. In the coming year, Oracle and SAP will battle to show which vendor is better at using analytics to increase the business value of ERP investments. In-memory capabilities will make it easier for these and other vendors to deploy rich analytics for ERP that are tailored to vertical industry and LOB requirements.

Large memory is not the whole story when it comes to the future of BI and analytics. However, it is a technology trend that users will notice firsthand through deeper, more visual, and more timely data analysis.

 

Posted by David Stodder on September 15, 2011


Comments

Thu, Sep 15, 2011 Doug

I like technical innovation, and especially like new software that allows us to generate faster analytic insights. What I'd like to see from these vendors are examples where new, specific, and actionable marketing insights are discovered by using these new tools. Insights that could not have been found efficiently before. Working from a sample of data (requiring neither in-memory tools nor much of the new buzz 'big data') is one insight-generating standard they have to beat. Do we really need to pay the extra cost for having all the info sitting in RAM from all the data at once? That's what I want to know. In fact, seems to me many industries have not even used the old techniques to their fullest when generating analytic learning! It doesn't even have to be dollars. Let me see what new insights were found that got missed before. We'll turn those into dollars! I do applaud the closing direction taken by your article. Looking forward to seeing "which vendor is better at using analytics to increase the business value of ERP investments".

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