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Big data in the enterprise

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Big data, gathered in the right way, is helping businesses garner new, innovative insights. Andrew Liles, CTO at Tribal Worldwide explores this topic

Harnessing big data using AI is worth the effort; firms who are not embracing such technologies are already lagging behind in productivity terms and lose out on the competition

Why big data?

The latest ‘it’ thing right now is artificial intelligence (AI). Although AI has been around for decades, it’s only recently that it has progressed into mainstream consumer environments. Due to its high cost of entry, this industry has been mostly dominated by brands with deep pockets and access to massive amounts of data; that is because AI is nothing without today’s other great buzz phrase: big data.


AI with limited data is often no more than a set of rules, which will return rudimentary answers. Data is instrumental in helping AI devices learn how humans think and feel, and also allows for the automation of data analysis. Without enough data – AI’s raw material – we would see something similar to the terrible example of the “AI-powered” help that was Microsoft’s Clippy.

However, with the recent explosion of data, algorithms can now be trained to deliver a better result and help us do our jobs more efficiently.

Applications of big data and what is big data?

An example of what AI can do when powered by Big Data is Google’s ever evolving translation service. Over ten years ago, Google moved from a rules-based system to a statistical learning AI-based system – using billions of words from real conversations and text to build a more accurate translation model. However, that was just the beginning.

Now businesses in all industries are joining the likes of Google. Today many fashion retailers, such as ASOS, are offering AI-powered services to anticipate customer’s needs and provide better services.

To help ASOS’ customers express their own sense of style, they’re using AI image-recognition software like Wide Eyes, to analyse customer photos – locating items such as hats, skirts and handbags – to recommend relevant collections within their current catalogue. This near instant analysis has been made possible by training the software with thousands of images.

As demonstrated above, the user experience benefits of using Big Data to help customers describe what they want is self-evident, but that’s only the beginning. The diverse application of big data across many different industries is endless. Many brands are now even using big data to help them make better marketing decisions by creating tools like the Customer Lifetime Value models.

Using AI and big data algorithms – like Random Forest, Cosine Similarity and Deep Recurrent Neural Networks – to analyse all possible influencing factors and returning factors that will make the most impact, telling you whether or not you should spend your marketing dollars to encourage repurchase on certain customer segments.

Each of these AI applications requires a lot of data to be successful. The Big five – Google, Apple, Facebook, Amazon and Microsoft – don’t just have Big Data, but they have petabytes of data recording our every digital movements.

That being said, big data and AI are not beyond the reach of the rest of us. It’s not just sheer volume that matters, but the quality of “Big Data”. Take the datasets available via Transport for London as an example; it’s a great initiative to expose their historic journey data making beautiful visualisations like Oliver O’Brien’s Tube Heartbeat.

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