The Innovation Game: What Next for Analytics?

Thursday 7 July 2016
Reading Time: 2 minutes

We are now living in a time of great uncertainty. Previously inconceivable ideas have come to fruition, leaving the general public with an unclear future for generations to come.

That’s right – Ikea will soon be selling flat-pack bikes.

If self-driving cars on the road sounded dangerous, imagine the horrifying spectacle of Aunt Mabel straddling a homemade rattling, rickety, Allen-key inspired two-wheeler down the high street.

Perhaps this is a pointless product, a leap into the world of unnecessary DIY that is one flat-pack too far. On the other hand, this is a marvellous idea that promotes environmentally friendly, user-serviceable travel at an affordable cost to the mass public.

This is exactly what product innovation should do – polarise initial opinion by ignoring convention. By challenging the norm, forward strides are made and genuine technology advancements are possible.

A prime example of successful innovation is the MacBook Air, a notebook not released to universal praise back in 2008 due to Apple cutting down the number of ports and omitting the optical drive. Three years later it accounted for almost one-third of Apple notebook sales. The market responded quickly, with online software distribution allowing users to continue installing programs while no longer having to rely on relatively slow physical media. This allowed the size of notebooks across the industry to decrease dramatically since the original launch, with the ease of downloading software and the falling cost-per-GB of flash-based storage driving factors.

A product that innovates is one that takes a risk by driving existing ideals in a new direction, allowing a change of outlook to deliver an approach that is future-proof while meeting the existing demands of users.

For business intelligence, the need to innovate has never been greater, as user interaction with consumable internet services continues to leave an unprecedented amount of data in its wake.

The figures are astonishing, with 40,000 Google search queries per second, 300 hours of video uploaded to YouTube every minute and 500,000,000 tweets sent every day.

By shifting away from the traditional tools that have slowly evolved over time, not only can this data be collected and stored but leveraged in future data analysis.

Innovations in analytic solutions allow the previously inaccessible unstructured data, such as tweets and videos, to not only be individually analysed but analysed alongside structured data to gleam greater insight from a much wider spectrum of records, regardless of origin. This breakthrough has far-reaching ramifications for industries that can innovate based on better understanding and utilisation of customer sentiment analysis.

To combat the surge of data available, innovations in self-service analytics allow user-friendly access to the powerful reporting and dashboarding tools that were previously only available to those well-versed in the analytic field. Now, through the power of search, users are able to quickly navigate through a huge amount of data through natural language queries with the simplicity of an internet search engine.

For end users, this game-changing technology implementation ensures that a new generation of analysists will not need vast statistical knowledge to perform complex analysis. Instead, it will be completed by a wider range of personnel, empowering future workforces to pro-actively drive business change.

As data continues to be collected in both structured and unstructured format, only with innovation can the big data challenges be addressed, utilised and learned from to better understand future outcomes. The end result will be an adaptive, educated and prepared workforce of data analysists who understand the bigger picture, utilising a range of innovative technologies to their advantage.

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