Mark Flaherty: We can take an inventory of what tools and activities, analytical activities that these the other groups of users use, the casual users and the power users. Now we didn’t talk too much about this casual user group, but they are represented by the business manager. This chart simply says that 80 percent of the time the casual users are doing very structured types of analysis that can be served through a standard report, a standard interactive report or a dashboard. And 20 percent of the time they are doing more ad hoc analytical type of work, issuing queries perhaps using that BI search tool, that’s really easy to use. Doing some analysis, dynamic filtering of data using visual discovery tools or creating plans using Excel or planning tools.
So some of the more ambitious casual users might use those analytic tools themselves, but most of them probably just ask the super user to go create an ad hoc report for them for the 20 percent of the time they need ad hoc information. In contrast, 80 percent of the time a power user’s doing all kinds of different things and need all kinds of different tools to do those different types of analytical tasks. And 20 percent of the time the user’s information is much more tailored and can be satisfied through a status report from their dashboard.
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So in summary, we have seen two waves in business intelligence, the reporting wave and analytics wave. And each of those has been broken into two waves in themselves. We focused this presentation on the first analytical wave, which is analysis of an exploration. We talked a little bit about the four types of business analyst. How they consume information, and what they do with it? How we can empower them, and the types of tools that they use to do their work.
So this is the end of part one, part two focus will a little bit more on prediction and optimization, which is the newest part of the analytical puzzle.
What about analytics in the Cloud? The question is: are you seeing lots of companies considering that option or companies out there offering that and if so, is data transfer probably large volumes of data between the Cloud of the company?
Data transfer is a problem if you are transferring large volumes of data on a daily basis, especially to the public. Clouds can get costly. It can be very time consuming because you are trying to push a lot of data over thin wire, whereas if you kept it into your own data center you wouldn’t have that bottleneck. In terms of what applications are being moved to the Cloud, all kinds are. I talked to someone yesterday who was using analytics against streaming data in the Cloud.
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Whatever you can imagine is going to be put into the Cloud. It really is a business model that makes too much sense to ignore for too many reasons. The real gating factors for Cloud-based BI environments are the data transfer issue and the security issue to some extent, but that’s kind of a red herring. It's kind of like what ecommerce was in the ‘90s when everyone thought about putting that credit card information over the Web. I think people already have got their critical data outsourced to the Cloud and you don’t know it. I may use salesforce.com or ADP for payroll, things like that. So yes, analytics is already in the Cloud.
We will go increasingly to the Cloud. We will start to see some hybrid environments where you don’t have to move the data into the Cloud. You can keep it in your data center, but if you move the analytic tools to the Cloud, they can access the data in your data center through a special interface. And some of those tools are still traditionally OLAP or ROLAP or MOLAP. I was trying to see some more, but I haven’t seen too many predictive analytics tools in the Cloud yet .They are just trying to figure it out how to do that in their database but of course those databases are moving into the Cloud, so by extension, I suppose the predictive analytics will be there too.
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I talked about the perfect storm where big data meets deep analytics. There is actually a third storm that’s mixed around in there, which is real-time data. Most companies I know are moving to operationalize their BI environment. They are moving data in at least nightly for a majority of their data. And then for various elements where it's appropriate, they update it on an intraday basis and sometimes, you know, every 15 minutes or less, depending on how critical the business process is.
How do you measure the ROI of analytics? Once you get into analytical modeling the ROI is much higher than just standard reporting or even dashboarding with KPI’s. You know, my chart shows that as you move through those waves of BI, the business value increases. It's always hard to determine the ROI of making better decisions, but when you create predictive models, they actually calculate the lift that you will get from the model compared to doing nothing or doing what you have always done. So it's little bit easier to calculate the ROI when you start moving to the analyst modeling realm.