You can’t have big data until you have good data

Businesses are beginning to understand the need for big data – the large and often disparate information generated by staff, systems and websites.

Across all industries, big data enables companies to analyse their information and interactions to improve customer or supply chain engagement and, of course, operational performance.

Big data in business can be gathered from financial and operational systems, social media and internet-enabled devices, including mobile. If you’re still confused about what big data is, just think of it as trend reporting on steroids.

>See also: Beneath big data: building the unbreakable

According to Gartner in 2013, 64% of companies were investing or planning to invest in big data, while Cisco has estimating that by 2017 global cloud traffic will reach 5.3 zettabytes (over a 1000 billion DVDs). There is a clear trend today that all businesses value data and are harvesting ever more of it.

Analysing data can help businesses to make smarter, more real-time decisions based on historical facts and forward-looking predictions or forecasts. So while the new generation of business users understand the value and nuances of data and want to use it to drive their decisions, the first step to achieving this is to collect the right data in the right way.

So the question quickly becomes how do businesses get it? Data and processes have to be embodied in systems. Only by having the right operational systems in place can businesses begin to ensure they are capturing the right information.

Access to high quality, plentiful data can enhance or completely transform businesses, but, for most, this will not be an overnight epiphany as disparate systems can restrict reporting and yield incoherent data.

Rather than rushing in and trying to learn big data analytics by searching through irrelevant data collected by separate IT systems, companies should prepare the ground, start organising their data – show it some respect.

Capturing data from lots of different places whether that be from emails, forms on the website and even manually can cause mistakes, so that when it comes to analysing data companies are not always analysing the correct information – it might be old data or based on false inputs.

Companies must stop measuring the wrong data, deceiving themselves about the accuracy of their data, and go back to basics.

There are many data capture solutions available on the market. For example, in the finance department, accounts processing today should include scanning paper-based invoices as standard and adding them to PDF invoices from email.

It is about using data capture to automatically pull the right information from these invoices to drive automated workflows and using collaborative tools to track and complete specific data such as customer information and payment status.

>See also: The era of big data won’t materialise without fast data

By reducing manual intervention, the invoice approval process or purchase order process can be cut to a typical two to five days. There are fewer errors and staff are freed up to work through automated data analytics instead of chasing transactional issues.

Companies need to make clean their data capture and process stages to ensure they’re generating the right information that will be beneficial to the business.

Capturing the right data means that when the business comes to analyse it in a year, or six months, it knows that it will be consistent, accurate and representative.


Sourced from Stuart Evans, CTO, Invu

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Ben Rossi

Ben was Vitesse Media's editorial director, leading content creation and editorial strategy across all Vitesse products, including its market-leading B2B and consumer magazines, websites, research and...

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