Why ‘Big Data’ Means Nothing Without ‘Little Data’


Bernard Marr

July 12, 2016

It’s easy to get caught up in the hype of big data.  Huge datasets, fast-moving analytics, complex and diverse data sources are hot right now, but it’s important to understand that big data would be nothing without the little data that goes along with it.

Little data, what I call traditional performance metrics, are key to the success of any big data project.

These KPIs are what measure the success of any given company. They might include customer retention rate, conversion rate, market share, or any of dozens of other metrics that determine how well your company is doing. Without good Key Performance Indicators (KPIs) it is impossible to have good big data initiatives.

Data, on its own, is practically useless.  It’s just a huge set of numbers with no context. Its value is only realized when it is used in conjunction with KPIs to deliver insights that improve decision making and improve performance. The KPIs are the measure of performance, so without them, anything gleaned from big data is simply knowledge without action.

For example, a retail company could use a big data initiative to develop promotional strategies based on customer preferences, trends, and customized offers. But without traditional KPIs such as revenue growth, profit margin, customer satisfaction, customer loyalty or market share, the company won’t be able to tell if the promotional strategies actually worked.

You’ve heard the old chestnut, what you measure grows?  This is true for companies of every size and in every field. Without the right metrics to measure growth, you cannot know if the decisions and initiatives you’ve made based on data analytics are having the desired effect.

The key, then, is having the right KPIs.

Think about taking a drive in your car.  Your car is outfitted with a number of dashboard displays that show KPIs about your journey and the overall function of the vehicle.  But if you’re focused on the wrong KPI, you’re in for a disaster.  For example, if you pass a sign saying “last fuel for 100 miles” and you’re paying attention to your speedometer instead of your fuel gauge, you could be in deep trouble.

In the same way, companies need to ensure that they are matching the right KPIs with their big data initiatives.

If a call center wants to improve customer satisfaction, they might choose a KPI of total call time, or total number of calls to help measure that.  But these may not be the right metrics to measure customer satisfaction; just because a customer gets off the phone quickly, doesn’t mean they’re happy with the service.

Therefore, it is important to link any big data initiative to the key strategic measures of an organization. If you are using big data to personalize your marketing, then it should translate into higher click-through rates, conversions rates and ultimately customer loyalty and net profit margins.

Unless big data is linked to small data it won’t deliver the much vaunted benefits companies are clamoring for — or at least, it won’t make them measurable.



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