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What Is Big Data and What Does It Mean for Your Small Business?
Small businesses collect vast amounts of information quickly. Big data can help them make sense of it all.
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The internet has been around for only three decades, but in that relatively short time it has become a crucial tool for individuals, educators, scientists, businesses and more, enabling the generation and flow of vast amounts of data. Harnessing that data and making sense of it can be challenging, particularly for small business owners who want to make informed business decisions, run predictive analytics for future sales, and enhance the customer experience.
We’ll explain these vast data sets — known familiarly as “big data” — and how small businesses can turn the information into valuable insights they can act on.
What is big data?
“Big data” refers to massive and complex datasets that traditional processing tools can’t handle. The continuous flow of information comes from sources such as social media, online transactions, smart devices and business software, generating an overwhelming volume of data at incredible speed.
“Big data refers to vast quantities of data that cannot be easily stored, managed or analyzed using common computing methods,” said Dustin Johnson, chief technology officer at Seeq Corporation. “The good news is that innovation in data storage, processing and computational power has enabled us to handle these challenges.”
Businesses use powerful tools to analyze these data sets and uncover trends, patterns and insights that help them make smarter decisions. “Modern big data tools allow us to quickly analyze the outcomes of the past and the state of the present to decide what action would be the most effective in a particular situation,” said Ivan Kot, director of customer acquisition at Itransition Group.
What are the 4 Vs of big data?
Big data is characterized by four Vs (although some sources identify as many as 10).
- Volume: Massive amounts of data are available daily, so big data tends to take up significant bandwidth and storage. Terabytes of data can traverse the internet, especially with the widespread use of broadband business internet. Global data creation is projected to grow to more than 394 zettabytes by 2028. Such vast amounts require specialized big data technology that can handle large datasets.
- Velocity: The speed at which incoming data needs to be processed is a hallmark of big data. The flow is so rapid that attempting to analyze it manually would be like trying to drink from a firehose.
- Variety: Data can be collected from many sources, such as social networks, business and consumer transactions, and the proliferation of smart devices that collect data from (often unwitting) users. Similarly, that data can come in different file formats and structures, from stringently categorized database information to real-time file transfers and communications.
- Veracity: Inaccurate data is useless and costly. According to a Forrester survey, 25 percent of respondents reported that poor data quality cost their firms more than $5 million annually, while 7 percent said losses exceeded $25 million per year. Big data best practices aim to minimize those risks.
>> Learn More: Using Big Data to Your Advantage on Social Media
How does big data work?
Think of big data as a fast-running river teeming with fish. If you’re the only one fishing and you’re equipped with only a small fishing pole, you’ll catch only a few fish — and you won’t know if you’re getting the ones you need. If you have a fleet of boats equipped with large traps, wide nets, sonar and sorting systems, however, you can efficiently gather, filter and categorize different types of fish.
Big data software works the same way. Instead of collecting random pieces of information, it uses advanced tools to capture vast amounts of data, sort it into meaningful categories and extract valuable insights.
Once collected, businesses can analyze the data with specific tools and identify trends, optimize operations and uncover new opportunities to improve efficiency and increase profits
How can small businesses use big data?
Big data can guide small businesses toward better decisions by revealing patterns in customer behavior, such as online purchasing habits, that can lead to profitable growth. “The reason that big data is so vital for businesses is that it can help to identify new growth opportunities and even new industries through examination of customer information,” said Jacek Żmudziński, SEO team lead at MakoLab and SEO lecturer at AGH University of Krakow, in Poland.
James Ford, the founder of Powerphase and AutoBead, explained that data scientists use big data to provide context by running queries that identify patterns and insights. Workflow-automation tools can then act on those insights by automating key processes.
“Traditionally, the types of technology used by those investing in big data initiatives included database types such as SQL or NoSQL, which were connected using an enterprise service bus (database and endpoint integrations) that standardized the data and allowed it to work together,” Ford said. “Large-scale data-processing solutions, such as Apache Hadoop or Databricks, enable large-scale data processing and analysis.”
Ford also emphasized the importance of cloud computing advancements that simplify big data management. Solutions such as Microsoft Azure’s Cosmos DB now allow businesses to store multiple database types in a single system. “[Teams] no longer need to invest in expensive and complicated integration systems, as all data exists in one location, separated by security policies and logic rather than APIs and distance,” Ford said.
Below are some examples of concrete ways businesses are using big data today.
Track and predict customer behavior
Big data provides valuable insights into customer behavior, such as:
- How many times a customer visits your website before making a purchase
- How long they spend comparing products
- Which items catch their attention first
That information allows you to spot where customers engage well with your content and where they tend to drop off. You can then experiment with ways to keep customers on your site and improve conversions, such as refining your marketing messages and the products you present to visitors.
Automate workflows to improve productivity
Big data insights can help you identify and automate repetitive yet necessary tasks, such as generating invoices or updating inventory, allowing your team to focus on higher-value work. Here are some examples:
- Streamlined order fulfillment: Several actions take place when a customer places an order, including verifying payment details, generating shipping labels and sending delivery notifications. Those tasks are ideal for big data–driven automation.
- Predictive analytics: Big data can forecast demand by identifying customer purchasing patterns, allowing businesses to automatically create on-demand orders for the right products at the right time.
- Advanced personalization: Val Neicu, co-founder and CEO of SmartSKN, relies on big data and automation to hyper-personalize the company’s on-demand, made-to-order products. “Imagine being able to offer 25,000-plus unique products without holding extra inventory,” Neicu said. “This not only reduces costs and eliminates overproduction, but also aligns with sustainable practices while maximizing customer satisfaction.”
The best CRM software, inventory-management platforms, ERP software and other applications can be programmed with workflow automations and even automated decision-making, using a combination of AI and big data to improve efficiency.
Assess and manage risks
Big data helps businesses evaluate and mitigate risks. Here are some key examples:
- Supplier analysis: By feeding all your supplier data into an inventory-management platform, its predictive analytics can identify which suppliers are prone to delays and which consistently deliver on time. That insight allows you to prioritize vendors that support your bottom line.
- Product forecasting: Overstocking and stock-outs are significant risks for retailers, but big data can help match demand with supply. “By using big data AI to forecast demand, we avoid overstocking ingredients and wasting resources,” Neicu said. “Our adaptive, data-driven approach not only reduces costs and waste, but also ensures businesses can meet consumer expectations in real time.”
Spot trends and opportunities
Big data can uncover trends you can turn into new opportunities for your business. If you analyze keyword search terms, for example, you may notice a growing interest in specific products. You can then prepare for a spike in demand and boost your inventory.
The following big data tools can help you track industry trends and accurately predict future top-selling items:
- Heuritech: Heuritech analyzes over 3 million social media images daily to predict what people will wear. It detects over 2,000 fashion attributes, from colors and fabrics to specific shapes and product lines.
- Trendalytics: Trendalytics uses search data, retail performance metrics and social media activity to predict trends in apparel, beauty, home goods, accessories and footwear.
- Centric: Centric (formerly StyleSage) uses competitor stock and price analyses to understand consumer preferences and spot market trends.
You can also use tools such as Ahrefs and Semrush as part of your SEO strategy to track keyword volumes over time to stay ahead of trends. [Read related article: 8 Technical SEO Tips to Increase Website Traffic and Conversions]
Track competitors’ pricing and inventory strategies
Numerous data-scraping tools can help you track competitors’ prices — or, if you’re tech-savvy, you can even build a web-scraping tool in PowerShell.
For deeper insights, analyze your rivals’ product offerings to see if your inventory is missing potentially profitable items. You can also track other businesses’ pay-per-click (PPC) advertising campaigns to see the keywords they bid for. That data can help you tweak your approach and stand out from the competition.
Consult a business lawyer if you’re unclear on data-scraping legalities. Generally, scraping publicly available product details and pricing is OK, but scraping private customer data is not. Be mindful of competition laws, data protection regulations and privacy rules to ensure compliance.
Improve hiring and HR
Many businesses use big data for human resources and recruiting new employees.
- Smart recruitment: Recruiters can access vast CV libraries on platforms such as Indeed. Sophisticated scraping tools can then find information about each candidate and augment their profiles to understand job seekers on a deeper level, simplifying the hiring process. [Read related article: Crystal Knows All: You’re Being Profiled]
- Reduced turnover rates: Big data–powered HR apps can now predict employee attrition risk — which employees are at risk of leaving and why. The tools can also analyze workplace sentiment, helping businesses improve company culture and reduce employee turnover.
- Improve employee well-being: Well-being and mental health monitoring tools can measure people’s emotional temperature by evaluating their time spent on tasks, workplace absenteeism and even signals of employee burnout. Employers can gain insights to rectify potentially unhealthy situations.
By leveraging big data from those sources, businesses can forecast workforce trends, improve retention and boost overall productivity.
Improve marketing tasks
Big data can improve marketing results in the following ways:
- Pinpoint the right content: Marketing teams use big data analytics to identify ideal promotion channels and content that will most likely resonate with their target audience.
- Analyze key metrics: Marketers can analyze campaign metrics, such as clickthrough rates (CTR) and conversions, to determine which strategies work best. They can use that data to hone their messaging and better address what their target audience wants to see.
- Campaign automation: Programmatic advertising tools automate campaigns in real time, ensuring messages reach the right audience segments while staying within budget. These tools continuously optimize ads to improve performance.
The benefits of big data
Analyzing big data helps business owners understand what’s really happening in their companies. Here are five ways big data delivers an advantage for SMBs:
- Make better decisions: Big data improves business decision-making by highlighting what’s working and what isn’t across all areas of operations.
- Spot hidden opportunities: Big data provides powerful insights into emerging trends and market gaps you may never have foreseen otherwise. With this information, you can address unmet target customer needs, think more strategically and plan for long-term growth.
- Reduce operational risks and costs: Big data can’t guarantee you’ll always make perfect decisions, but it provides crucial insights that minimize decision-making risk so you don’t have to rely on guesswork or unproven hunches.
- Pivot in real time: Small businesses can react quickly to big data insights. If you determine a product is primed to surge in popularity, for example, you can adjust inventory, marketing and pricing strategies accordingly.
How is big data stored and regulated?
Sufficient storage is required to house the vast amounts of data being collected. Some companies rely on physical storage solutions. Massive global data centers span millions of square feet and house billions of dollars in server equipment. For a small business, however, a server rack with terabytes of storage may be sufficient.
Other companies use cloud storage services provided by companies such as Google and Amazon Web Services (AWS). In both cases, data can be stored indefinitely as long as storage capacity allows.
As for big data regulation, the U.S. federal government has largely taken a hands-off approach. Instead, existing privacy laws govern its use and the corporations handling it. In the U.S., privacy laws typically focus on specific industries that manage sensitive information.
- Financial institutions that use nonpublic personal information must comply with the Gramm-Leach-Bliley Act (GLBA).
- Healthcare providers that use big data must ensure compliance with the Health Insurance Portability and Accountability Act (HIPAA).
As big data continues to expand, new legislation likely will place stricter controls on the use of private data. Some states have already begun taking action at the local level.
What are examples of businesses using big data?
Big data may seem like a nebulous concept that’s hard to visualize, but it’s used so widely in today’s highly connected world that some examples immediately come to mind.
Netflix
Netflix collects billions of data points daily. The most obvious metric is what people watch, but the streaming giant uses big data in more focused ways.
“It was recently estimated that Netflix saves $1 billion each year on retention due to its effective use of the data available to them,” Ford said. “[Netflix can determine] how many minutes a person watched before they stopped. Did they watch more than one episode? What type of content is someone most likely to binge? All these factors drive future production decisions, as well as personalized in-app experiences for users.”
New York Stock Exchange
Big data plays a crucial role in the global economy. One of the largest examples is the New York Stock Exchange (NYSE), which processes 38.3 million contracts traded daily, according to the NYSE. Handling that massive volume of transactional data requires sophisticated big data solutions that can receive, analyze and transmit information in real time.
Social media
Your social media activity is also part of big data. Your Facebook or X profile may seem like a single data point, but platforms track a much deeper level of information — likes, posts, photos and even personal data. These quantifiable data sets help companies predict what you’re likely to buy, what your interests are and even potential voting preferences.
Andrew Martins contributed to this article.
