The value of small business data and its untapped potential to drive and benefit from AI development
Executive Summary
Small and medium-sized businesses (SMBs) generate vast amounts of precise, customer-specific, and diverse data. However, SMBs lack the resources and expertise to draw valuable insights from datasets or use artificial intelligence (AI) tools to improve processes or applications. Addressing the challenge surrounding SMB data presents an opportunity to drive AI development and create incredible value for SMBs.
Strength of the Data
The scale of the data
Small and medium-sized businesses (SMBs) account for 99.9% of all U.S.-based businesses or 33.2 million small businesses. They are the cornerstone for economic mobility, employing 46% of the U.S. private sector workforce and driving 43.5% of gross domestic product (GDP). To put this into perspective, SMBs contributed over $17.7 trillion to the economy.
Accelerated by the rapid technological advancement and the COVID-19 pandemic, SMBs increased technology adoption as a core component of facilitating their business, with over 80% of businesses claiming tech was their "lifeline" during the pandemic. The U.S. Chamber of Commerce reports that 95% of small business owners today use at least one type of technology platform in their business.
The most recent data published in 2016 by the International Data Group (IDG) suggests that the average amount of data managed by each SMB is 47.81 terabytes - the average full-length HD film is ~5GB, implying that each SMB has enough data to store 4,700 movies, in 2016. With the adoption of cloud storage and the digitization of businesses, it is reasonable to infer that SMBs are storing significantly higher datasets than possible seven years ago.
The characteristics of the data
SMBs are often a melting pot for business functions, including sales, marketing, HR, operations, logistics, customer service, finance, and others, resulting in a more eclectic data environment than what may exist in enterprise data, which tends to be concentrated in specific domains or business functions. Within each domain, quantitative and qualitative data points emerge that are often more granular than aggregate enterprise data. These include but are not limited to:
Customer-level transaction data: SMB point of sale and e-commerce systems capture detailed logs of each customer's purchases, from items purchased to transaction time and price. This is typically not the case with aggregated enterprise sales data
Product/service feedback data: Due to the agility of SMBs, they can collect specific product reviews, complaints, and satisfaction ratings at an individual customer level that otherwise would be a broad customer survey outputs for enterprise firms
Employee records: SMB employee data can include detailed work histories, performance reviews, compensation, and attendance details, among other data points on an individual employee basis versus high-level enterprise HR statistics
SMBs typically serve a variety of sectors, geographies, and customer segments, yielding real-world data closer to the customer than what may be true with enterprise firms. With the ubiquitous nature of SMBs in the U.S., there is an argument to be made that SMBs serve as a fundamental reflection of consumer needs and ever-evolving market dynamics.
The existing challenges of the data
However, the vastness and richness of this data do not come without its challenges. Despite the significant value that data currently brings to businesses, including improved production time, customer experiences, and decision-making, SMBs face several challenges in data collection and utilization, including:
Lack of capital and talent to support data management practices: SMBs operate on constrained budgets, and the dedicated budgets for technology do not sufficiently address the required tools and talent. Onepath conducted a survey of SMBs and found that despite 67% of businesses spending $10,000 per year or more (keep in mind the typical range for SMBs on analytics tooling is $10,000 - $100,000 per year), they are not reaping the full benefit of their data. Additionally, the survey concluded that ⅕ businesses do not have the talent to create the systems they require [Onepath]
Lack of data education: Amazon Web Services (AWS) surveyed SMBs and found that more than ½ do not have the knowledge or the experience to leverage data. They lack understanding across the data workflow, from collection and management to the appropriate tooling to generate insights. Additionally, 53% of businesses do not know what insights their data could bring, and 52% don't understand the ROI that could be generated by analytical tools [AWS]
Additionally, due to the fragmented nature of SMBs, datasets are often siloed, and only less than ½ (42%) of businesses are directly investing in data to support their businesses despite 90% citing they are seeking to improve their data management approaches [AWS]
Implications of Collecting & Processing The Data
SMBs are often overlooked as a source of valuable data. However, in many cases, SMBs do not fully tap into the insights their data can provide due to constraints around resources, expertise, and technical capabilities. Most SMBs do not have data scientists on staff or the capabilities to collect, structure, store, and analyze significant volumes of granular data, and this often leaves significant opportunities to optimize operations, reduce costs, and improve their offerings.
Centralizing, standardizing, and democratizing access to SMB data would present unique opportunities for more small businesses to take advantage of analytics and AI to improve their businesses and competitiveness in the market. SMB data aligns well with the need for robust and reliable AI development in several ways:
SMB data can provide the labeled, structured, sector-specific data ideal for training AI systems to handle specialized real-world tasks
Broadly sampling SMB datasets can lead to more representative AI that better reflects diverse use cases, avoiding bias
Tailored AI tools focused on automation and decision-making in areas like customer service, operations, accounting, and others, powered by SMB data, can enable growth for SMBs themselves
The Unlock of Value for AI
Small and medium-sized businesses (SMBs) generate diverse types of data across many areas of their operations. This variety and detail of SMB data represents a valuable opportunity to build tailored solutions that drive tremendous results for small businesses. SMBs need to begin to evaluate and strategize how to leverage their data sets to drive their businesses, and it does not have to start with a highly technical team. Here are some opportunities that could bring immediate efficiencies and value for small businesses:
Customer service automation: SMBs can leverage chatbots trained on historical customer service logs and dialogue data to provide faster, more personalized customer support at lower costs and mitigate any capacity constraints
Sales demand forecasting: SMBs can leverage historical sales data, customer profiles, and ancillary data points like local events data to train AI algorithms that can better predict demand fluctuations and optimize inventory and staffing
Personalized marketing: SMBs can leverage individual customer transaction history and larger scope data points, such as regional demographics, to train AI systems to create effective digital marketing campaigns
Accounting and invoicing automation: Accounting and invoicing data can be used to train AI systems to automate tedious, time-consuming bookkeeping tasks
A broad sampling of SMB data can lead to more robust, unbiased models that reflect diverse real-world use cases. With appropriate data systems, adopting AI tools in several business areas, such as customer services, sales forecasting, accounting, and marketing powered by SMB data, can enable growth and competitiveness for SMBs.
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