InetSoft Webinar: Agile Data Access

Below is the transcript of a Webinar hosted by InetSoft on the topic of Agile Data Access and Agile Business Intelligence. The presenter is Mark Flaherty, CMO at InetSoft.

Mark Flaherty: Agile business intelligence is a very important topic that lots of people are talking about now and are beginning to implement in various ways. So there is a lot of industry discussion about agile business intelligence. Often people talk about what you need behind the scenes in the infrastructure to enable maximum agility in your business intelligence and your advanced analytics initiatives.

But agile data access will be the core focus today. It is really the key enabler for agile BI. We will talk about how they complement each other, how agile data access is absolutely essential if you want to go completely agile on the BI front end with reporting, dashboards, and advanced visualization.

We will explain what you need to enable agile data access, and why should you go that route in just a moment in great detail. Well, because it speeds up development and ongoing enhancements and administration of all your BI efforts, whether they be building reports, dashboard development, or developing complex queries.

Without agile data access, you would not really be able to fully achieve the agile BI vision of basically enabling very rapid rollout of complex analytics that are tuned to the needs of each business role and user within your company. Really to enable agile BI in this way, you are supporting more agile business processes helping users to make better decisions more rapidly with analytic applications that are geared to their specific requirements.

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Read how InetSoft was rated #1 for user adoption in G2 Crowd's user survey-based index.

So really, agile data access is the key enabler to make your business processes more flexible and really to help you achieve maximum value on all of your IT investments. So fundamentally, what is agile data access? What is agile generally? Well, it's really any approach for developing applications that is incremental and fast, and thatís contrasted with the old approach of waterfall development. In other words, it is not that development approach where there are fixed stages between each of the major milestones in a development efforts.

Instead development is incremental and fast and agile. It means being collaborative and making rapid iterations. And agile is any approach that involves, in addition to incremental iterative development, involves IT and users in a collaborative iterative refinement of applications. And in this case, refinement of BI and analytic applications that are reusable in future projects.

Whereas with in-memory architecture, instead what happens is somebody takes a big extract, it could be from a data warehouse, it could also be from a source system, and that data is loaded into memory. From a user perspective, the user only interacts with a cached result set. So this is where many advanced visualization tools will take that approach where you are looking at a finished dashboard and interacting there. Some BI vendors, like InetSoft, will also use in-memory to accelerate even the traditional queries as part of the total BI platform. So the iterative process is fast.

When it comes to evaluating in-memory technologies, whether the software is 64-bit or 32-bit dictates how much of the memory can be addressed. 64-bit operating systems can address up to a full terabyte of data. So you can almost store your whole data mart or even your data warehouse in memory. Whereas 32-bit operating systems can only address 3 gigabytes of memory.

This innovation from an industry perspective is immature. There is no consistent approach, and it’s not widely adopted, yet, because not many customers have wide adoption of 64-bit. So it’s a good idea to plan for 64-bit down the road. This will be something that will be helpful for higher value and lower TCO and it’s something that benefits all users.

Read how InetSoft was rated as a top BI vendor in G2 Crowd's user survey-based index.

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