How Can Small Enterprises Benefit From Big Data?

Index

Can Small Entrepreneurs Use Big Data in Their Business?

Small Entrepreneurs & Big Data

Small Entrepreneurs & Data

Research All Entrepreneurs Can Do Themselves, With a Little Help from Excel

The Benefits Of Analyzing Big Data In Small Enterprises

Related blogs on my website

Can Small Entrepreneurs Use Big Data in Their Business?

The short answer is yes. But the how matters far more than the yes. To use Big Data effectively, an entrepreneur needs a basic understanding of research methods and must know how to explore the information already present within the business. Every company, even the smallest, already possesses Big Data. Most entrepreneurs are familiar with that drawer full of binders, contracts, old project files, outdated client addresses, notes, and paperwork that were always meant to be sorted out “during cucumber time,” as the Dutch say. All of this is data, and when it becomes messy, unused, and unstructured, it starts to resemble Big Data. Much of this material can be discarded but hidden among the clutter there is often something valuable, Big Data gold. The challenge for the entrepreneur is to identify what can be thrown away and what insights can be transformed into opportunities for improvement, innovation, or growth.

Small Entrepreneurs & Big Data

Not only do large companies have unused data lying around, but small enterprises do also as well. In Dutch terms, this includes the Midden- en Kleinbedrijf (MKB). Today, unused data is no longer limited to a physical drawer; it also sits in online storage. Many people have an email inbox with unlimited capacity and one or two cloud services where they keep old course materials, advice, photos, and documents. Most of it seems unimportant, yet it is kept “just in case,” even though it is never reviewed again. This collection of forgotten, unstructured information can be considered Big Data. Big Data typically shows the following characteristics:

Messy data: For example, a USB stick filled with files that were moved there because the computer ran out of memory. The intention was to sort it later, but “later” never arrived.
Inconsistent volume: The amount and type of data differ by source. One drawer labelled 2012 might contain photos and administration, while the 2013 drawer holds blog research, Excel course materials, and administration. The 2014 drawer might even contain advice, research, and the administration of 2015. The volume and content vary unpredictably.
Incomplete datasets: For example, an Excel course missing chapter 2 while all other chapters are present. You cannot be sure whether the dataset is whole or fragmented.
Lack of structure: Files are scattered across locations: part of the Excel course is online, part offline, and part mixed with current working files. Everything must be collected before it can be used.

This is the reality for many small entrepreneurs: data everywhere, structure nowhere. Yet within this chaos, valuable insights often hide, operational patterns, customer history, forgotten ideas, or reusable materials. That is why even small businesses can benefit from Big Data, provided they learn how to organize, evaluate, and extract meaning from what they already have.

Small Entrepreneurs & Data

Many zzp’ers and other small entrepreneurs assume that data is something only researchers or IT specialists work with. In reality, everyone deals with data every single day. Here are a few examples of everyday entrepreneurial tasks that rely on data:

Looking up a contact in an address book to send an email
Doing bookkeeping
Creating products or delivering services for clients

These activities usually do not fall under Big Data, because entrepreneurs use this information daily and keep it well‑organized. This is the type of data every entrepreneur can manage without additional expertise. Then there is another category of data, the kind that requires knowledge, structure, and analysis. This is where Big Data begins: large volumes of information, scattered across locations, inconsistent, incomplete, or unstructured. Understanding how to work with this type of data allows small entrepreneurs to uncover insights, improve operations, and make better decisions.

Research All Entrepreneurs Can Do Themselves, With a Little Help from Excel

As the end of the year approaches, preparing an annual report is a perfect example of how entrepreneurs can use their own data. Is this research? I would call it small‑scale research, because it requires basic analysis. With some practical Excel skills, every entrepreneur can do this independently. The essential Excel skills are:

Calculating in Excel: performing sums, averages, percentages, and other simple computations.
Turning figures into graphs and tables: visualizing results clearly and professionally.

Combined with a short written explanation, these figures and tables form the annual report. This report is a research outcome, a piece of data visualization that any entrepreneur with basic Excel knowledge can produce. Beyond this lies more advanced analysis. In this blog, I will focus on Big Data analysis, where larger, messier, and more complex datasets require structure, interpretation, and deeper insight.

The Benefits Of Analyzing Big Data In Small Enterprises

Analyzing Big Data is more specialized work, and it requires a foundation in statistics and analytical thinking. For small entrepreneurs, this often means examining older, previously unused data to uncover insights. Such an analysis can lead to several outcomes:

The data can be trashed, because there is nothing interesting in it.
New products can be developed based on old data.
Or the business can be improved based on past feedback from the clients.

One analysis method that entrepreneurs can perform themselves is a decision‑tree analysis. This technique helps determine whether the collected old data is suitable for developing something new, for example, an e‑course, a book, advisory services, or another product. I have dedicated an entire blog to explaining how a decision tree works, including a practical example: “Using a decision tree to determine where to go.”