Guide to Big Data for Small Businesses: Tools and Strategies

A man and a woman sit at a desk looking at data on a laptop.

Small businesses are applying big data analytics to gain insight into their customers, their operations and their competition. And doing so doesn’t require that they hire data scientists or put their workers through extensive training programs.

Big data for small business is more approachable, more affordable and more effective than ever. Any small business that isn’t taking advantage of big data analytics, or planning to do so, risks being left behind by the competition.

The tools, resources and strategies presented in this guide will help small businesses make more informed decisions, implement more effective marketing strategies and gain valuable insights into their business processes. It will dispel many of the myths surrounding big data and describe how companies of all sizes can realize the benefits of data analytics simply and affordably — no computer science degree required.

What Is Big Data?

Big data isn’t measured solely by quantity. Twin Cities Business explains that from a data-science perspective, big data consists of three V’s: volume, velocity and variety.

So the term “big data” describes the massive amount of information of all types being collected at astounding speeds. Businesses apply various analytics tools and techniques to this unending stream of data to gather intelligence that informs business decisions.

Estimates about just how much data there is in the world today vary widely. When TechJury crunched the numbers, it concluded that the most accurate assessments indicate a total of 40 trillion gigabytes or 40 zettabytes.

This compares with 1.2 zettabytes of data in the world in 2010, which matches IDC’s forecast at the time that the amount of data worldwide would double every two years through 2020. In other words, 90% of the world’s data was created in the past two years.

Perhaps the most important big data “tool” is cloud computing, which makes it possible for companies to store and analyze massive amounts of data using an on-demand model: Cloud customers pay for only the storage and processing power they need, at least in theory.

Data analysis is performed by machine learning tools that teach themselves how to identify meaningful patterns and derive valuable insights from the raw data of two general types:

  • Structured data includes transaction data housed in a conventional database.
  • Unstructured data exists outside of a traditional database and includes email, social media posts, texts, videos and audio recordings.

The challenge for businesses small and large is to sort through the sea of data that is flowing into their organizations to identify the quality and timely information they need to support their operations and decision-making. The data includes web logs, email and text files, as well as input from sensors and other data collection devices that make up the “Internet of Things.”

As ZDNet points out, small businesses may have an advantage over their larger counterparts in that it’s easier for a small company to catalog and maintain the data it receives from various sources. Gleaning business intelligence from raw data via cloud-based analytics tools is more straightforward when there is less irrelevant data to sift through and fewer data types to accommodate.

Misunderstandings surrounding big data for small businesses prevent these companies from taking advantage of the data they have on hand and use every day. The U.S. Small Business Administration’s SCORE Association reports the following:

  • While more than half of small-business owners believe data analytics is important (51%), only 45% actually use analytics.
  • The top priority of 73% of small businesses is finding new customers, followed by retaining existing customers (67%) and improving the customer experience (65%).

The tools small businesses need to attract new customers, hold on to current ones and achieve other business goals are already close at hand, as explains:

  • Email marketing analyses
  • Sales records
  • Social media feeds
  • Website statistics and analytics

How Small Businesses Use Big Data

Big data presents a tremendous opportunity for small businesses to boost sales and make their operations more efficient without extensive investments in new technologies, new hires or employee training. These are among the common applications of big data analytics for small businesses, as described by the Data-Driven Investor on Medium:

  • Automate routine tasks, an example of which is scanning and analyzing 50,000 pages of reports of various types to identify and compare those that share a specific trait, such as a reference to a specific product or competitor.
  • Gain insights into customers by using text mining and natural language processing (NLP) to convert unstructured data into structured data that can be analyzed to spot trends, patterns and connections.
  • Tap streams of data from social media that can be combined with marketing data to create personalized offers, such as discounts and promotions. This also lets companies identify and respond to negative comments on social media, forums and communities.
  • Geo-target customers by collecting location data from mobile devices to market custom products and services based on someone’s whereabouts.
  • Analyze the risks associated with specific business practices by applying data visualization tools that are powerful yet simple enough for employees to use without extensive training. 

Here’s a closer look at three ways big data benefits small businesses.

Improving Operations

Every data analytics project begins by identifying a real business problem and devising a strategy for solving that problem. Business consultant Bernard Marr states that any business process that generates data can be improved via the appropriate analysis of that data.

Nearly all the machines, vehicles and tools that businesses now use are “connected, data-enabled and constantly reporting their status to each other.” In particular, supply chain management and vehicle route optimization are made more efficient by analyzing in near-real-time the data companies collect as part of their daily operations.

In some cases, the adoption of big data analytics transforms a small company’s entire business model. For example, much of the data a small business gathers about its customers and the business itself can be monetized by providing value-added services or by selling the anonymized data to third-party brokers.

  • points out that 93% of all the data held by a company is “dark data,” defined as “information assets that companies collect, process or store but fail to put to use.”
  • The percentage of companies implementing big data analytics increased from 17% in 2015 to 59% in 2018.

An example of how small businesses apply data analytics to extract value from their unused data assets is by scanning customer support logs to determine which methods customers use to initiate contact with the company and the duration of each contact. This analysis can not only improve a company’s customer support efficiency but also provide insight into customers’ preferences and demographic characteristics.

Identifying Trends

Small businesses can use big data to identify trends and gain insights into what their customers find interesting or compelling. Using data culled from social media, browser logs and public data sets, businesses can monitor customer behaviors and market patterns, which helps them connect more effectively with their target audience.

Forbes identifies three areas in which small businesses can apply big data analytics to improve the accuracy of their marketing and industry forecasts:

  • Gain new insights into your customers: It is now possible to capture customer experiences and behaviors that are stored in and collected from a range of devices, including smartphones and tablets. By collecting and analyzing data about customers’ purchases, small businesses are able to anticipate the time, place and other circumstances most likely to lead to future purchases.
  • Improve the effectiveness of marketing campaigns: By tracking its performance data, a small business can spot activities that generate the highest sales and revenue. Identifying characteristics that a company’s best customers share allows it to target more closely the demographic groups that are most likely to respond positively to its marketing efforts.
  • Convert social media data into sales: Whenever a small business is mentioned on Facebook, Twitter, Instagram or another social media platform, data analytics are able to capture the context of the reference — whether positive, negative or neutral — and output intelligence that leads to smarter business decision-making. The more a business knows about its customers and potential customers, the more effective all of its marketing efforts will be.

The key to ensuring optimal use of big data for small businesses is knowing the right questions to ask, as explains. For example, tracking a company’s sales, customer retention and gross revenue often equates to little more than “vanity metrics,” because they do little to help the firm set and achieve its goals. More effective is seeking answers to such questions as “Which vendors are providing us with the most value?” and “Which of our product lines will benefit the most from being revamped?”

Recruiting and Managing Talent

Finding and retaining talented employees is one of the greatest challenges small businesses face. Inside Big Data explains that the goal of talent acquisition goes beyond the traditional hiring process because it emphasizes job candidates’ ability to help a company achieve specific goals. Rather than focusing on degrees or credentials, talent acquisition seeks applicants who possess the skills a business needs.

Big data improves the efficiency of the hiring, training and “offboarding” process by identifying each candidate’s unique combination of skills, training and experience and matching those characteristics to the requirements of specific open positions. In some instances, big data analytics can predict not only which applicants are most qualified but also which are most likely to accept an offer.

Strategic hiring based on big data analytics is already having a positive impact on companies of all sizes. LinkedIn’s 2018 report on Global Recruiting Trends found the following:

  • 56% of companies use big data analytics to improve employee retention.
  • 50% use it to identify skills gaps.
  • 50% use it to sweeten offers to potential new hires.
  • 46% use it to better understand what candidates are looking for.

These are the greatest barriers to the use of big data in talent acquisition, according to the LinkedIn survey:

  • Poor data quality (cited by 42% of respondents)
  • Inability to find the data they need (20%)
  • The high cost of the data (18%)
  • Lack of the knowledge required to use the data (14%)

The Future of Big Data Analytics for Small Businesses

Big data analytics has already become an integral part of business management for companies large and small, yet the nature of analytics tools and applications is rapidly evolving. Today, the primary sources of data analytics are a handful of established business intelligence vendors, as Business News Daily explains:

  • Google Analytics is a free service intended to provide small businesses with insight based on data collected from their websites, such as where customers are located, what they do on the sites, how long they spend there (bounce rate) and which site elements they engage with the most.
  • Kissmetrics is a marketing-focused service that allows companies to create and manage automated email campaigns tied to customer behavior. The company’s e-commerce offering is intended to minimize cart abandonment, increase return customers and integrate with a business’s social media efforts.
  • IBM Watson Analytics brings predictive analytics to small businesses by simplifying the process of performing advanced data mining and forecasting. The service integrates analysis of marketing, sales, finance, human resources and many other business operations in a single natural language system that helps businesses identify problems, recognize patterns and predict potential outcomes.
  • InsightSquared differs from other data analytics platforms for small business by integrating with popular business software, including SalesForce, Google Analytics and QuickBooks. The service analyzes data from customer relationship management (CRM) software to generate forecasts about sales, potential business leads and profitability.
  • ClearStory Data combines data about a business’s internal operations with public information to provide insight into business decisions. The analyses are shown in graphs, storylines and interactive visuals generated via an easy-to-use dashboard interface. recommends three data analytics services that are tailored to small businesses’ needs:

  • Clicky offers both free and premium services that give a business insight into a website’s performance in terms of how effective its marketing efforts are, where site traffic originates from and how effectively site visitors interact with its various elements.
  • Wolfram Alpha, which also comes in free and premium versions, combines advanced algorithms, an extensive knowledge base and artificial intelligence techniques to generate “expert-level” answers to questions about sales projections, financial projections, and more.
  • Microsoft Power BI creates visualizations of such business data as gross profit and revenue, inventory levels and product turnover. Its pro version costs $10 per user per month, and a free trial is available.

In the future, big data analytics for small business will become more powerful and more accessible, putting advanced business intelligence capabilities in the hands of more managers and other employees. Small Business Trends reports that self-service solutions are being combined with the improved presentation of analytics results to make it easier for non-technical users to “start asking the right questions and make decisions based on hard facts rather than speculation.”

One of the most important developments in data analytics is the continuing evolution from descriptive analytics, which simply reports on the current state of a business, to predictive analytics, which not only describes current conditions but also forecasts future performance, and ultimately prescriptive analytics, which not only predicts future events but also directs decision-makers on the optimal course of action to capitalize on those conditions.

While all three types of data analytics will play a role in future business intelligence gathering, as Forbes states, prescriptive analytics will be the key to realizing more efficient and more profitable operations.

  • Descriptive analytics entails making assumptions about how various groups of customers will respond in the future based on their past activities.
  • Predictive analytics removes many of these assumptions and provides a clearer view into how various demographic groups may respond to different offers, but the resulting analyses are still descriptive by nature.
  • Prescriptive analytics, by contrast, applies machine learning and other AI techniques to “guide” customers automatically to the product they need at the precise time they need it and at the price they are willing to pay for it. The company’s sales staff is not only instructed on what language to use in the offer but is also presented with the ideal time, situation and price to stipulate to close the deal.

While prescriptive analytics is the most powerful form of business intelligence, not all companies can justify using these big data techniques because they remain labor-intensive despite dashboard interfaces and reliance on automation. However, prescriptive analytics is a relatively new technology that will likely become more powerful and more accessible to small businesses in the near future.

Perhaps the most influential aspect of big data for small business in 2020 and beyond is how advanced data analytics will transform companies from an era of data-driven decision-making to one of data-driven cultures.

Business intelligence vendor Datapine describes a data-driven culture as one in which data is accurate, shared across departments, easy to access and inexpensive to maintain. All employees are able to apply data to their day-to-day decisions in ways that solve business problems quickly and provide the company with a competitive edge.

The ultimate benefit of big data for small business is how it transforms employees into data experts naturally and instinctively, without requiring extensive training or programming skills. If all companies — large and small — are now tech companies, it follows that all workers are now tech workers. Data analytics is the key to empowering the modern workforce, both today and tomorrow.

Additional Resources:

Accounting Today, “5 Keys to Greater Profitability Through Data Analytics”, “Small Business Must Take Advantage of the Democratization of Data”

Inside Big Data, “Accessible and Affordable — Big Data for Small Business”

Instant Tech Market News, “Big Data Analytics Tools Market Trends, Size, Share, Status, Analysis, Growth Rate, Demands, Status and Application Forecast Until 2025”

MarTech Series, “Top 3 Small Business Technology Predictions for 2020”

RS Web Solutions, “10 Great Ways to Use Big Data for Small Businesses in 2019”

TechRadar, “Why Even Small Businesses Need Big Data”


Renata Ramirez
(619) 260-4658