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Why data is increasingly vital to research and development

Sam Mayne
31/07/17
Research and development (R&D) has long been the mainstay of successful business models. This is because those companies looking to get a competitive edge on their rivals are the ones who benefit from innovation and industry-leading developments. 

In the IT sector there are two important ways in which R&D comes into effect – firstly in the collection of information (research) and secondly in its subsequent application (development). Increasing productivity in both the public and private sectors is always a key aim and is usually the focus of most R&D programmes, although this relies in no small part on the quality of data that is available. In today’s business environment it is easier than ever to amass information but the real question is how can it be best put to use?

More data can make a difference

'Big Data' doesn't really have an official definition but it is generally accepted to refer to the vast amounts of information generated by online and other types of contemporary customer interactions with businesses and other organisations. The huge depth and breadth of the information that is gathered creates many opportunities for driving research and development initiatives, provided that it can be adequately captured, stored, managed and analysed. 

With technology, it’s possible to get immediate answers from the data in order to make better business decisions and create more efficient operations. From a research point of view, it’s possible to gather more information and feedback from customers, meaning that concerns can be addressed. Products and services can be tailored to give the consumer exactly what they desire, while the increased sales that will result will have a direct positive impact on bottom line finances.

Dynamic data provides more options

The dynamic nature of the kind of data that is becoming ever-more available means that many different models can be 'road tested' in research environments in order to gain a greater oversight. This makes it possible to predict complex behaviours within systems, both in terms of quantifiable results and when taking into account human inputs. Ultimately, this provides more adaptability than it was previously ever possible to achieve.

By increasing the number of options available to those looking to drive research and development opportunities, it’s possible to streamline business processes to enhance the outcomes that are available. Not taking advantage of the swathes of new data that are readily available could therefore be classed as a fundamental error for any business looking to expand its research and development capabilities.

Read our related blog: Demonstrating value and measuring success from data science

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