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The slow evolution of predictive data analytics

David Bowie | June 5, 2017
A lack of data scientists in the market and increased complexity around analytics are holding organisations back from getting the answers they need from their information.

Stretched organisations will look for others with an ingrained analytics culture to help with the heavy lifting by offering a low cost, low risk service. The same will be true with any organisation that understands that data is probably its most important asset but doesn't have the time or capability to realise that value. They will want a cloud-based fast point of entry to analytics best practice. Think of it as analytics on-tap.

The analytics evolution is approaching the point where management is seeking pointers to competitive and other forms of business gain that will concentrate on learning - or seeking to be taught - how to define the questions that need to be asked of their data.

This will ensure that others with an agile analytics culture can be engaged to answer those questions and help them drive the desired outcomes and speed up 'time-to-value.'

This shift will start slowly, with big organisations' overloaded analyst teams needing additional resources for short term projects. Analytics-as-a-service in the cloud will also be adopted by smaller organisations leapfrogging their search for insights beyond the scale they can manage or afford for themselves.

Smaller companies in particular will want more than just the results of modelling. They will want providers to show them how revealed insights can be operationalised.

Companies adopting this approach will, in effect, be spending their time realising the value of their data, rather than suffering the chore of managing it. As service providers develop their offerings they will build suits of proven analytics models which can be fine-tuned for individual buyers' unique requirements, rather than reinventing the wheel - thereby minimising cost and speeding up results to fast track the benefits of the analytics.

Eventually, in addition to customers buying such services to overcome the shortage of employable  analysts; avoid fixed costs; enjoy the benefit of being able to lean on expert data management and analytics assistance; and accelerate the delivery of actionable insights, they will look for flexibility.

Service providers will have to expect their customers may subsequently want to transfer all or some of this outsourced work in-house. So they will need to adhere to open technology standards, and offer pricing models and agreement terms to cater to that.

Only a few years ago, many enterprises said they would never trust their data to the cloud. Now, it's common practice because it makes sense in so many different ways and is seen as secure. Similarly, we will soon wonder what we did before we could trust our analytics to outside services dedicated to that purpose - and get our analytics outcomes on-tap.

Source: CIO Australia 

 

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