In a Big Data World, We Still Need Small Data
In a Big Data World, We Still Need Small Data

By on in Analytics

In a Big Data World, We Still Need Small Data

Big data is only getting bigger. Exponentially so. But it still hasn’t replaced the everyday hunches that fuel entrepreneurial intuition.

There’s an assumption that big data is best because it will inevitably enable a business transformation. That idea ignores a legacy of ear-to-the-ground small data gathering that continues to guide successful strategies.


So What’s the Difference, Anyway?

Given just how large a “small” dataset can be today, classifying big data versus small data rides the line between worthless buzzwords and a useful taxonomy. After all, why not talk about medium data or skinny data? I digress.

For the non-analysts in the room, big data is set apart by how it is measured and how difficult it is to visualize. Regarding measurement, big data has three defining properties colloquially termed the three V’s. The velocity with which the data is produced, the variety of data sources and formats being combined, and the overall volume (in digital storage) of the data itself. These V’s inform how complicated and expensive an analysis of a big data set will be.

Small data, by contrast, is structured and often fits into an Excel spreadsheet. It can typically analyze it at-a-glance to inform incremental improvements to your business. Qualitative data also falls into this category, which is the kind of data collection often neglected in businesses of all sizes.


Big Data Troubles

The explosion of big data in the early 2000s opened up new opportunities to target consumers and seemingly infinite ways to analyze and optimize every business process. For many, it has created a dependency on third-party data collectors and FOMO for any organization that hasn’t scaled to the point of having its own big data trove.

Sometimes bigger is better, and other times it’s just bigger.

Data quality, in general, is notoriously low, and silencing enough noise in big data to make it useful is a major investment.

Then there are the general hazards of big data. Reliance on third-party targeting risks serving ads that are too personal or ones that chase consumers so doggedly they imperil brand safety. If you’re trying to sync your data to a third party, you’re likely to find integration platforms that are often more like a marriage than SaaS.


Small Data Triumphs

Small data sets are accessible to most teams. With the help of an analyst or strategist, you can quickly unearth actionable insights.

“Small data will get teams thinking strategically about often-overlooked parts of your business. It will help everyone intuitively understand day-to-day processes which better democratizes decision-making.”

As you start to access the latent potential of your organization’s small data, you’ll discover myriad opportunities. Small data will get teams thinking strategically about often-overlooked parts of your business. It will help everyone intuitively understand day-to-day processes, which better democratizes decision-making. Collecting small data inherently leads to the adoption of new tools and practices that increase your visibility of customers, employees, and competitors.

And then there is just how exciting micro-insights can be to stumble upon. Making those unexpected realizations reminds us why we do what we do in the first place.


A New Mindset Gives the Best of Both Worlds

Rather than a “this or that” approach, consider subscribing to a philosophy of “big data projects” and “small data practices.” Big data is like finally remodeling your home office to improve productivity, while small data is your daily meditation routine. Big data gets all hands on deck to test huge hypotheses and small data lightly places your finger on the pulse.

Some big data projects you could undertake include:

  • Mining data to create personalized product or content recommendations
  • Analyzing real-time supply and demand for dynamic pricing
  • Identifying new customer personas
  • Creating a full-funnel attribution model

Remember that data itself isn’t causal. When someone sees your ad and later makes a purchase, the data will show a correlation between these events. But would they have made that purchase anyway based on some other interaction? Possibly. And that’s why you’re wise to make a habit of regular small data practices that can identify causation.

Here are a few things you can do to start a small data practice:

  • Get qualitative feedback from your community through surveys
  • Set up dashboards and automated reports so you can monitor engagement
  • A/B test to prove best practices
  • Visit competitors to compare pricing, offerings, and messaging

Collecting small data is how you uncover the stories that will inform your marketing, sales, and product development. As you look to start incorporating regular small data practices into your business, FATFREE can help you lay the strategic groundwork to make sure you not only gather the right data but know exactly what to do with it.