Why data analytics should be a key part of your business strategy

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Why data analytics should be a key part of your business strategy

By Edward Gower-Isaac, Vice President, Business Process Services, Ricoh Europe

Ricoh Europe, London, 28 November 2017
– Have you ever considered data to be a source of innovation in your company?  

You may have seen in a recent paper issued by the Centre for Data Innovation, a think tank, that data innovation contributed around €300 billion (2% of GDP) to Europe’s economy in 2016. To put this in context, EU GDP growth is in total forecast to be only 2.2% for 2017.

Looking across many different sectors the report assessed how collecting, storing, and analysing large quantities of information can drive new forms of economic activity. 

In our world, what appears most relevant are the data-driven tools and applications that can help us streamline processes and become more responsive. As organisations shift to new modes of IT, being able to process and make sense of masses of information in real-time will become critical. 

Analytics highlights this – so I wanted to look at three reasons why you may want to consider building data into your business strategy. 

1) The Internet of Things (IoT) is booming

We know this of course, but the scale is still worth reflecting on.  It is predicted that IoT will hit 20 billion devices by the year 2020. And by 2021, every internet user in Western Europe is predicted to contribute to 80GB of internet traffic a month (that’s almost triple the volume of 2016!).  

This huge increase in information gives businesses an amazing opportunity to better understand our customers. Failure to do so risks the competition knowing our customers better than we do.

Forrester forecasts IoT will move to ‘business scale’ next year, along with the prediction that European legislature will publish guidelines green lighting the commercialisation of IoT data to make this even more relevant. An example of this being the use of data from a smart watch or fitness tracker to build the profile of its wearer.  

2) Respond to market changes much faster

So, we’re seeing growing volumes of data, but are they being used effectively? Huge amounts are still going unused (combined with the estimated 80% of data that already lies unstructured inside an organisation). 

If we can process that data at source rather than sending it back to a central point for analysis (as per the usual model) we increase significantly the efficiency of our operations.  This is called Edge Analytics, and expect to hear a lot more about that in the year to come. 

Because of the growth in IoT, the competitive advantage in data analytics comes from being able to act upon it as quickly as possible. It’s obviously an impossible ask for a human to process and understand all this information at source and at speed. Instead, AI can effectively do the heavy lifting by processing, sorting and automating these workflows. Use cases are still emerging, but there are already examples in Spain where one vendor is combining AI with the digitisation of healthcare records to support doctors around ‘at risk’ patients. 

3) Understand customer behaviour 

Combining the two points above will allow you to pinpoint where the business value lies from a data point at a given moment. 

This can allow an organisation to react and respond in real time, while also putting a greater focus on each individual customer. On top of that, looking at patterns of behaviour over time can then help shift your business from reactive to proactive. 

If you can predict when a customer is most open to making a purchase based on their behaviour, wouldn’t you want to be serving them with the right information at the right time?

With digital disruption rife, it’s clear that an effective data analytics model (backed up with AI and automation processes) will give businesses the clearest window into the market. By better understanding and analysing this data (at speed) you will have a solid platform for success in the digital age.

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