10 Ways Predictive Analytics Improves Innovation - InformationWeek
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Data Management // Big Data Analytics

10 Ways Predictive Analytics Improves Innovation

From drug discovery to price optimization, across virtually every industry, more companies are using predictive analytics to increase revenue, reduce costs, and modernize the way they do business. Here are some examples.
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(Image: miradeshazer via Pixabay)

(Image: miradeshazer via Pixabay)

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LisaMorgan
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LisaMorgan,
User Rank: Moderator
3/14/2016 | 12:01:29 PM
Re: Same dangers as always
Often when I talk to people about predictive analytics, they sometimes speak about it in terms of what will happen rather than what *may* happen or will likely happen given some level of uncertainty.  While it may be possible to predict some things with impressive accuracy, it is also possible to place too much faith in Yet Another Silver Bullet, irrespective of all the details that matter.  
Whoopty
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Whoopty,
User Rank: Ninja
3/14/2016 | 8:56:15 AM
Same dangers as always
I think predictive analytics makes a lot of sense. It has huge potential to not only help businesses perform better, but also offer up some really interesting insight into how we do things as a species and on an individual level.

That said, all data, whether it's used to analyse the past, present, or future, is only as useful as the one making the interpretation. We must be wary to rely too much on raw data, as it can be misleading if you look at it looking for a particular outcome.
RebeccaH336
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RebeccaH336,
User Rank: Apprentice
3/14/2016 | 6:17:55 AM
Predictive analytics for Industrial Internet of Things
This is a great article & it's only the beginning. One of the places where the greatest gains can be made with predictive analytics is in the industrial IoT space.

Imagine the savings if companies can proactively maintain factories, rather than waiting for a breakdown or replacing parts before it is necessary on a scheduled maintenance plan. This can be done by monitoring metrics like temperature, RPM, material consuption, output, etc., learning the normal behavior, and then receiving alerts about abnormal behavior. Of course this type of analysis can't be done manually - it requires machine learning to interpret the Big Data.

The company I work for - Anodot - does real time anomaly detection and analytics and has written about predictive analytics for "Factory 4.0" recently on their blog. 
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