It's all In the Memory: New Battleground for BI and Analytics
By Mark Dexter
28th September 2011

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.

Read at source: TDWI

Comments

Currently there are no comments. Be the first to post one!

Post Comment

*
*
*

15 years on: How times have changed…

When I incorporated KDR 15 years ago this month I had a simple mission. I wanted to run the market leading agency in its field placing contract staff with experience of working with the emerging data warehouse tool Kalido. I... Read More

The life of a Business Administration apprentice!

I started my apprenticeship with KDR in the September of 2018, when I was searching for a job I never imagined that I would find a company quite like KDR, from the moment I first stepped through the door everyone... Read More

How can data analytics impact climate change?

Every quarter we produce an e-magazine on issues and topics within the data and analytics world. In our previous issues we have looked at the impact of blockchain , the future of the data warehouse and GDPR . In our next issue... Read More

Will veganism save the planet?

Before I begin… No, I’m not a vegan. In fact, I’m no way near being a vegan! But I have always had an interest in the environment and how much impact we are having on it and running a data... Read More

Where should we send our newsletter?

Close