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  • admin 9:52 am on June 28, 2016 Permalink
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    Business Intelligence Leaders Join with Teradata to Enhance Presto for the Enterprise 

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  • admin 9:56 am on July 9, 2015 Permalink
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    Join The Analytics Revolution! 

    Over the past decades, analytics processes have been developed and deployed in a custom, one-off, artisanal fashion. This makes sense when high value problems are being addressed, but isn’t scalable or affordable for the myriad small decisions a business needs to make every day. For analytics to reach its full potential for enterprises today, we’ll need to take analytics through a revolution that is much like the industrial revolution of a few centuries ago. We need to make analytics operational.

    The industrial revolution took manufacturing from a labor intensive, fully custom, non-scalable endeavor to one that can produce affordable, quality items at massive scale. We similarly must scale analytics far more than is being achieved today. Yes, we’ll have to give up some of the customisation and perfection that we’re used to in our analytics. However, that’s a tradeoff worth making to have analytics much more deeply and broadly deployed in an organisation. Just as we still buy custom pottery as a centerpiece, we’ll still have custom analytic processes in cases where it is warranted. However, we’ll also be able to affordably address many other issues through operational analytics.

    Join the Analytics Revolution

    Note that operational analytics in this context is not simply applying traditional batch analytics to operational problems, but something different. Let’s explore five specific features that differentiate operational analytics from traditional analytics.


    Operational analytics are embedded in the operational systems that run the business. Instead of pulling data out to a distinct analytics environment, the analytics are embedded directly within the systems where the data resides and that drive actions. With today’s technologies this is now entirely possible. By moving from offline, scheduled batch processes to on-demand, embedded processes, analytics can be executed in a fast and scalable fashion.


    Operational analytics are fully automated. This means that once a new analytics process is approved, it continues to run and make decisions until somebody explicitly turns it off. There is no batch window or set schedule. Rather, the analytics execute whenever a decision requires it. This can literally be thousands or millions of times per day in cases such as optimising the offers displayed on each web page.


    Operational analytics finally move us into the realm of prescriptive analytics. Prescriptive analytics not only predict what might happen, but also prescribe the actions needed to make it happen. Instead of simply predicting that an engine failure is imminent, operational analytics will also determine what should happen to avoid that failure. This has to be the case since human intervention can’t be required.

    Executed in “Decision Time”

    Not every analysis needs to be executed in real time. Many people get too hung up on trying to make everything as near to real time as possible. If a decision is needed in near real time, then this makes sense. However, many decisions have a latency that can be anywhere from seconds to hours to days. Operational analytics are configured so that they can be executed within the time-frame required for the decision. For example, if a business posts refunds at the end of each business day, then any analytics required to check for fraud simply need to be completed before the end of the day.

    Take Action

    This is what most distinguishes operational analytics from traditional batch analytics. No longer relegated to simply predicting what might happen or even just recommending what to do, operational analytics actually cause the recommended action to happen. An offer is extended or a warning is issued as a result of the analytic process. The process is no longer a passive one that supports a decision maker in taking action but rather it becomes the decision maker taking the action.

    It is important to note that making analytics operational doesn’t replace traditional skills and approaches, but rather takes them further. Someone will still need to identify the business need, to build and test the analytic process, and to validate that it works as expected in a traditional batch mode before turning the process on and making it operational. The analytics revolution is an evolution of analytics to another level and not something totally new.

    While examples of operational analytics are already starting to abound around us, most people aren’t even aware of how their daily lives are being impacted. That’s ok. The point isn’t for people to be aware of and appreciate the analytics, but to have them notice the improvement in their lives. It might be simple analytics that automatically change a home’s environmental settings to balance comfort and cost, sophisticated processes that re-route travelers following an adverse event, or self-driving cars that constantly analyze a wide range of data to decide how to get you to your destination safely. We are already impacted by the analytics revolution on a daily basis.

    What is your organisation doing to take analytics to the next level? Have you joined the analytics revolution yet? If not, what are you waiting for? The future is coming fast and you don’t want to miss it. So, please join me for my upcoming keynote presentation at Teradata Summit in Australia being held in Sydney and Melbourne in August! Click here to learn more.

    Bill Franks is Chief Analytics Officer for Teradata, providing insight on trends in the analytics & big data space and helping clients understand how Teradata and its analytic partners can support their efforts.  In this role, Bill also works to help determine the right strategies and positioning for Teradata in the areas of analytics & big data. Click here to read more on Bill Franks.

    The post Join The Analytics Revolution! appeared first on International Blog.

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