Business New Year’s Resolutions: Focus on small data, not big data

“Big Data” is the buzzword of the moment, but it’s overkill for many small businesses. Small data can be beautiful too

 
Man using a tablet to look at metrics
(Bernhard Lang/Getty)

This article is part of our series on New Year’s Resolutions for Small Business. Read them all here.

Big data is a big buzzword these days, but when it comes to most small businesses, it’s a bit of a misnomer. The term refers to the deluge of information that pours forth from various sources, measured in gigabytes and terabytes. It can be analyzed for trends, behaviours and patterns, provided the company has the expertise, the computing power and the money. That probably doesn’t describe most small businesses; they have to take a different approach to harness data.

“It’s probably not worth it for a small business to spend millions,” says Tom Peters, a partner in Deloitte Canada’s analytics practice. “Big data is almost a distraction. If we take it down to just data, now we’re talking.” Most small businesses already have a lot of data (sales history, finance records, customer information) that can be analyzed to make fact-based decisions, says Peters.

First, companies need to determine which business challenges to address, such as pricing or customer service. Then they can figure out how to use the data to find the insights they need. Companies can purchase software, such as Tableau or InsightSquared, to mine data, or hire consultants.

This exercise can reveal information such as the probability that customers will respond to a marketing campaign, the best neighbourhoods in which to open up shop or even how the temperature in a restaurant affects drink sales. Companies can also track how much it costs to service a customer, says Robert Krider, a marketing professor at Simon Fraser University’s Beedie School of Business. “If you’ve got customers that are costing you way more to service than you’re making out of them, fire them,” he says.

The key is to start small. “Think about understanding your own data and how to get value out of it first,” Peters says, “and then layer on more sophistication over time as you build more capabilities.”

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