LITTLE firms, don't be daunted by BIG data - Extracting value from an abundance of information easier than you think
It’s a scary word to those less-than-tech-savvy business owners and operators out there, and particularly for manufacturers who do most of their analysis on the shop floor.
But harnessing data and using it to streamline operations is easier than you think, according to the gurus at Deloitte.
And it’s even easier for small to medium-sized enterprises that can take advantage of an abundance of free tools to maximize budgets and productivity for little to no cost at all.
According to Rhys Morgan, senior manager in technology strategy with Deloitte’s Edmonton office, it’s called ‘the scale paradox’; the idea that being a smaller-sized firm makes it easier to invest in the power of analytics.
“What we’re finding is that the smaller organizations with the least capital are sometimes better positioned to leverage analytics as a concept,” he said.
“The reason for that is they are smaller (and) this work is less capital intensive. The bigger organizations that have made more significant investments (elsewhere) may not be as well positioned as some of the smaller organizations.”
Look at it as simply extracting information from data—data you probably already have, Morgan said.
Generally speaking, there are three sets, or streams, of data to break down: internal; external; and extended value chain.
“There’s a significant amount of data being created across the (manufacturing) sector from multiple sources,” Morgan said.
Internal data—sourcing, production logistics and sales—is very diverse, he said, but is infrequent and not the greatest quality to work from.
Extended value chain data—supplier, distributor and customer—isn’t widely used yet and therefore can be difficult to track, while external data—economic, geospacial, statistics, mobility and social media—is often traceable through free channels.
“You need to look at every single bit of data that you have, look at what the sources are, look at how you’re gathering it, look at the quality of it,” Morgan said.
Stashed in: Big Data