Why data analytics is critical for small businesses

Today, information is king. Staying ahead of the competition requires more than hard work and dedication but making informed decisions based on solid data. Businesses, regardless of size and industry, manage large volumes of information daily, so smart analysis holds the key to their success.

With data analytics, businesses can improve efficiency and productivity, paving the way to competitive advantage and increased revenue. Although data analysis requires extra resources, the results it delivers make it a worthwhile investment. Let’s explore why data analytics is crucial for small businesses and the benefits they can gain from implementing it.

What is data analysis?

Data analysis is the science of gathering, processing, and assessing information to scrutinize a business’s present condition and identify opportunities for enhancement. Examining trends, patterns, and anomalies in the data is key to gaining insights into customer behavior, market trends, and overall business performance, which helps in making informed decisions to drive growth and increase profitability.

Data analysis makes a huge impact on companies of all sizes. For large companies and enterprises, operating with enormous amounts of diverse data, it’s the way to uncover unobvious details that shape the results of business activity. Using data analytics for small businesses is equally crucial for success driving.

Analyzing sales data helps identify top-performing products and areas for improvement, enhancing marketing, pricing, and inventory for higher sales and profits. Analysts in small and mid-sized companies use various methods for such examinations to drive success. Let’s take a closer look at them.

6 key types of data analysis techniques used by small businesses

There are various types of data analysis for small businesses tailored to their needs. Discover the five most crucial data evaluation methods vital for enhancing their economic resilience and most used by small companies:

1. Data mining (Text analysis) helps discover a pattern in large data sets using databases or data mining tools. With its help, a business data analyst can transform raw data into valuable business information. This method allows for extracting and examining data to identify  and interpret patterns and relationships to make informed business decisions. Data mining comprises such steps as data sourcing, data exploration, modeling, and deploying models.
2. Statistical analysis is the process of data collection, exploring, and presenting large amounts of data to discover patterns and trends. The statistical data analytics method comprises steps such as data description, building connections between the data and the underlying population, creating a model to summarize these connections, proof of the model’s validity, and predicting future trends.
3. Descriptive analysis aims to collect and examine historical data, process it, analyze it, and visualize it. It provides a brief summary of large data sets, giving stakeholders valuable insights into past performance. 
4. Diagnostic analysis is a form of advanced analytics that examines data or content to answer the question, “Why did it happen?” It is characterized by techniques such as drill-down, data discovery, data mining, and correlations. Diagnostic analytics uses data to determine the reasons behind trends and correlations between variables. This method is applicable to various scenarios, such as examining market demand, exploring customer data and behavior, improving company culture, etc.
5. Predictive analysis focuses on making predictions about future outcomes based on historical data, statistical modeling, data mining techniques, and machine learning. Businesses use predictive analytics to assess information that helps evaluate the company’s past behavior and predict its future, identifying future risks and opportunities based on data analysis.
6. Prescriptive analytics examines data or content to provide a recommendation on the steps a company should take to resolve an issue. This form of data analytics employs multiple data sources and techniques, including graph analysis, simulation, complex event processing, neural networks, recommendation engines, etc.

Each of these techniques empowers small businesses to make strategic decisions, optimize operations, and ultimately drive success. However, as the volume of information continues to grow exponentially, businesses must also consider using big and complex datasets in their analytical strategies. Let’s check out the nature of big data and explore how it can further transform small business operations.

Data analytics vs. Data mining

Both data analytics and data mining play essential roles in getting data insights, but they have distinct objectives and methods. Data analytics provides a comprehensive understanding of the data and supports various types of decision-making, while data mining specifically focuses on uncovering hidden patterns and relationships within data to reveal valuable, previously unknown information.

Data analytics vs. Data mining

	Data analytics	Data mining
Purpose	To examine and interpret data to gain insights and make informed decisions.	To discover hidden patterns and insights in large datasets.
Main objective	To support decision-making and problem-solving.	To extract valuable and previously unknown information from data.
Scope	Broader scope, including descriptive, diagnostic, predictive, and prescriptive analytics.	Focused on discovering patterns and relationships in data.
Techniques	Uses statistical and quantitative methods for data analysis.	Utilizes advanced algorithms and machine learning techniques.
Timeframe	Focuses on understanding past and current data.	Often aims to predict future trends and behaviors.
Application	Widely used across industries for decision-making, performance evaluation, customer behavior analysis, etc.	Applied in various fields, including marketing, finance, healthcare, fraud detection, etc.
Use case	1. Customer segmentation based on historical sales data. 2. Forecasting sales for the next quarter. - Analyzing website traffic to improve user experience.	1. Identifying fraud patterns in financial transactions. 2. Recommending personalized products to customers. 3. Predicting equipment failure in manufacturing.

How does data mining help managers?

Data mining would be most helpful to a manager that wants to uncover hidden patterns, trends, and correlations within large datasets to make data-driven decisions and gain valuable insights into their business operations. By employing data mining techniques, the manager can sift through vast amounts of information, extracting meaningful and actionable knowledge that might not be apparent through traditional analysis methods.

For instance, a retail manager interested in understanding customer behavior might utilize data mining to explore purchasing patterns, identify customer preferences, and segment their audience based on buying habits. This information could lead to targeted marketing campaigns, personalized offers, and improved inventory management, ultimately boosting sales and customer satisfaction.

Similarly, in a financial institution, data mining can be used to detect fraudulent activities by analyzing transaction data for unusual patterns or anomalies. By detecting fraudulent behavior early on, the manager can take proactive measures to safeguard their customers and the business from potential losses.

Moreover, data mining can be a valuable tool in the healthcare industry, helping a medical manager identify trends in patient outcomes, predict disease progression, or analyze treatment effectiveness based on patient data.

In essence, data mining empowers managers across various industries to make well-informed decisions, optimize processes, and gain a competitive edge by harnessing the power of data hidden within their organization’s vast information repositories.

Why is data analytics important for small businesses?

With the help of the right technology, data analytics can do its magic. Small businesses enjoy such benefits of data analytics as reduction of operational costs, business model improvement, and revenue increase. Companies that use data analytics have an advantage over their competitors by using data to provide a more personalized customer experience. Better yet, manual data analysis processes can be easily replaced with automated business solutions.

Using data wisely can help a company save money by optimizing operational processes, identifying cost inefficiencies, and making data-driven decisions. By analyzing various aspects of their business through data, businesses can pinpoint areas where resources are being underutilized or wasted, enabling them to streamline operations and reduce unnecessary expenses.

Data-driven insights can guide small businesses in making smarter investment choices, allocating resources more effectively, and negotiating better deals with suppliers. Ultimately, harnessing the power of data allows for making informed choices that lead to significant cost savings while improving overall efficiency and profitability.

Unlock the power of data analytics, a vital tool for small businesses seeking to gain valuable insights, make informed decisions, and drive growth. Explore how airSlate Academy empowers businesses with the knowledge and skills needed to harness the full potential of data analytics.

Let’s explore 6 ways you can boost your business performance with data analytics.

1. Establish a data-centric approach to business

Source: Amadeus

When companies have the ability to analyse business data and massive amounts of information associated with their business and the market in general, they can create a complete picture of the customer journey.

For instance, you’ll know how people get to know the brand, what they buy, why they do it, how they shop, at what time, from which device, why they abandon the carts, and what makes them choose a brand again and again.

With this kind of information, small businesses can change the way they interact with potential consumers, stick with the preferred communication channels, and resolve issues faster.

Data analytics provide insights that illustrate a complete business overview, eliminating the need to make decisions based only on guesswork and what others do, allowing companies to act according to their business’s and clients’ needs.

The critical point here is that such a data-centric approach relates not only to customers but to the overall performance of a business. Having data gathered and analyzed is the way to act in a data-driven way in order to streamline operations, improve HR activities, and more.

2. Add clarity to financial processes

Even small businesses can spend a fortune on advertising, but all of it may be in vain. This can happen due to limited knowledge about your target market and methods for using digital marketing tools.

Digital marketing can be a tricky thing — you have the potential to attract a lot of new customers, yet you need to know how much you are spending on advertising and what your return on investment is in the end.

With the help of data analytics and management, you can track how much money you give away to onboard new consumers and how much profit you receive from them. It is important to bear in mind that the revenue clients deliver to your small business should cover all the advertising expenses.

From a financial standpoint, data management can keep your company’s financing and reporting clean. For example, you can set up Excel integrations and get all the financial data from different tools in one place. This helps with reporting, understanding your cross-department expenses, salaries, fixing up financial processes, and more.

3. Improve inventory management

It may seem that inventory issues aren’t critical for small businesses. In fact, not having an inventory management strategy, keeping unclear inventory records, over/under ordering, and other mistakes can damage your business. You can improve all these with data analytics.

With data analytics, you can upgrade operational efficiency to avoid stockout, set proper stock levels, empower order fulfillment, set accurate product descriptions, and eliminate excessive warehouse deliveries and errors.

In this way, you collect and evaluate data to optimize your existing process and build an effective inventory management system.

You’re also encouraged to use data as a basis for predictions. For example, you can conduct stock optimization based on weather forecasts, current trends, and recent consumer queries. Instead of stocking up on too many items, you can only get the most popular things and avoid stock overload. Whenever you can track what’s happening on the market and foresee how it will change, you can massively improve inventory management and save costs.

4. How to better understand the market and competitors

If you can dive into your business data, you may be able to identify valuable insights about your competitors and their niche. There are many ways a business process analyst can understand the type of clients your competitors are targeting, what products they buy, and how much profit they earn.

Such information can be of great use for small businesses. They can adjust faster to the market’s needs, decrease costs, and gain more consumers by offering the appropriate products and services. Understanding the market’s tendencies and the moves of competitors can help small businesses beat the competition and emerge at the very top of their industry.

5. Skyrocket marketing and promotional efforts

With a better understanding of how things work on the market and how competitors win over their clients, small businesses can easily predict where the trends are heading, how much in demand certain products are, and more.

Leveraging a data-centric approach eliminates the guesswork and provides facts that you’ll be able to implement in marketing your efforts. Data analytics will give you insights into what your consumers want and allow you to track them down and promote things from their wishlist.

You’ll be able to:

• analyze website performance
• adjust your marketing strategy
• choose digital marketing tactics
• use suitable channels
• set up promo campaigns

You can measure all the data down to the minor details — how people move around the online store, how they interact with your brand, how much time they spend on certain pages, etc.

There is no need to spend money on promoting items you think people might like. Instead, you can invest in marketing products people want at this very moment and lay down a budget for the next season, already knowing what they will search for in a few months.

6. Refine customer service and the customer experience

Using business data analysis, small companies can enhance their customer experience and provide greater customer service. By knowing how people think, behave, and drive their decisions, you can change the way your company is interacting with consumers.

There are many factors that could cause people to leave — a slow website, a higher price, a lack of responsiveness from a customer support representative. Whatever it is, you can use analytics for small business to track where people fall out of the loop and make sure you are ready to catch them next time.

People tend to stick with those brands that make them feel valued. Even a small business can have many loyal consumers when the service it provides is outstanding. Based on data analysis, personalized customer service helps to engage with every client at the right time and through a proper channel, providing clients with a great experience and driving sales.

What is Big Data?

Big data refers to the vast volumes of data generated every day from various sources like social media interactions, customer transactions, and even smart devices. This information, when properly analyzed, can reveal trends, patterns, and insights that are invaluable for making strategic business decisions. For small businesses, leveraging big data can mean the difference between thriving and merely surviving.

The massive amounts of information businesses produce daily usually come in two main types:

• Structured data represents information stored in spreadsheets or SQL databases.
• Unstructured data exists in non-traditional databases, such as emails, texts, videos, and social media posts mentioning your brand.

In the past, unstructured data wasn’t easy to handle, but today’s power of machine learning and AI tools open opportunities for deep analysis and favorable use of this type of information. Like a detective uncovering hidden clues, businesses can work with these patterns, predict trends, reduce inefficiencies, and improve marketing strategies. This technology transforms all the data into a guiding compass, directing informed decisions toward success.

Big data is often associated with the “three V’s” that give a clearer understanding of the concept:

1. Volume: Unorganized data can reach terabytes or petabytes in storage size, unlike an average Word document, which is only a few dozen kilobytes.

2. Velocity: Today’s internet era produces enormous amounts of data demanding businesses to process it faster.

3. Variety: A typical business database contains a range of files, such as PDF, DOCX, HTML, etc. The file types you store, the greater the data diversity you need to process.

Key facts about Big Data you should know in 2024

Big data continues to shape the business landscape, offering unparalleled opportunities for growth and innovation. Here are some key facts and statistics published by BDAN to understand its impact and future trends in 2024:

• As of 2023, the global data volume is projected to soar to 181 zettabytes by the end of 2025.
• 95% of businesses report that handling unstructured data poses a significant challenge.
• 72% of manufacturing executives leverage advanced data analytics to enhance productivity. 
• Approximately 87.8% of companies have boosted their investment in data as per a recent Forbes survey.
• 82% of organizations globally plan to increase their investments in data modernization.

These are only a few examples of incredible big-data statistics underscoring its transformative power for modern business. Let’s dive deeper into the importance of using data analytics for small businesses and why it has become indispensable for success.

The value of Big Data analytics for small businesses

Data analytics has a huge impact on small businesses. Keep reading to learn about the key benefits of utilizing big data in your daily operations:

1. Real-time insights: Businesses can automatically incorporate new data by setting up a Big Data analysis system. Algorithms instantly analyze historical sales data, providing real-time insights. This agility allows for swift decision-making, turning your business into a dynamic and responsive entity.

2. Cost reduction and efficiency improvement: Businesses can use Big Data to identify inefficient processes and resource usage. Addressing suboptimal practices saves time and money, optimizing your company’s operations.

3. Enhanced quality control: While traditional quality control may miss specific issues, Big Data analyzes the entire production process, ensuring consistent, high-quality products. This meticulous examination boosts customer satisfaction.

4. Improved marketing results: Big Data analyzes customer purchase and return behavior, revealing preferences and buying patterns. Businesses can use this understanding to personalize marketing campaigns effectively and offer tailored promotions.

5. Better customer experience: Seamless integration of better marketing, streamlined processes, and high-quality products results in an exceptional customer experience. Reduced friction fosters customer loyalty, creates brand advocates, encourages repeat purchases, and bolsters overall sales.

6. Competitive edge: Big Data delivers faster and more accurate information, giving businesses a competitive advantage. Swift market entry, increased profit margins, and efficient resource utilization outpace competitors. Even if competitors also adopt big data, staying informed helps you maintain market share and competitiveness.

Utilizing data analytics for small business efficiency 

We’ve discovered the nature of data analytics and its undeniable influence on commercial success, and it’s time to consider how small businesses can implement its techniques to optimize their operations.

Small business analytics has been steadily increasing over the past few years. According to the SCORE Association, in 2020, only 45% of small businesses were actually using analytics, while 51% of respondents considered data analytics important. Furthermore, 73% of small businesses prioritized finding new customers, while customer retention (67%) and customer experience improvement (65%) were named less important.

Data analytics methods for small businesses

Data analytics are pivotal for business growth. Selecting the right analysis methods to leverage information wisely is crucial. Small businesses, with fewer resources than market leaders, must choose techniques that best fit their needs. Here are key data analysis methods for businesses aiming to expand:

• Text analysis or data mining focuses on uncovering hidden patterns and relationships within data to reveal valuable, previously unknown information
• Diagnostic analytics examines data to understand why things happened, what caused specific trends, and the correlation between variables.
• Predictive analytics aims to answer the question, “What will happen?” based on historical data and the one provided by machine learning to make forecasts of potential risks and growing opportunities.
• Prescriptive analysis introduces the solutions to what should be done with AI and ML (machine learning) analyzing patterns in large datasets. It helps forecast the outcomes and recommends a plan of action for its achievement.

Top use cases for implementing data analytics for small businesses

Customer behavior analysis

Small businesses can utilize big data for better customer insights, scrutinizing purchase history and browsing patterns to segment groups by preferences, demographics, and behavior. Tailoring product recommendations and marketing campaigns accordingly boost engagement, conversions, and customer satisfaction and retention.

Marketing optimization

Small businesses can leverage big data to enhance their marketing strategies in various ways. Through attribution modeling, they gain insights into customer interactions on their websites and conversion timings. This data empowers them to allocate budgets effectively across diverse channels and campaigns.

Financial data analysis

Small businesses can benefit from big data, enhancing their financial performance. It enables them to manage their cash flow thanks to deep and accurate examination of incomes and expenses, which helps identify and minimize possible financial risks.

Supply chain optimization

Small businesses can leverage big data to assess supplier performance regarding lead times, quality, and reliability for informed decisions on supplier selection, negotiations, and contract renewals. This data can also pinpoint cost-saving opportunities and optimize the supply chain.

Customer service enhancement

Leveraging data analytics can significantly enhance a small business’s customer service. Automated solutions, like chatbots fueled by big data, can manage simple queries, allowing human representatives to focus on intricate matters. Analyzing data from various sources, such as social media or customer feedback, can help businesses effectively tackle common challenges, thus enriching the overall customer experience.

Implementing big data analytics techniques helps small businesses adjust their pricing strategies in real-time to remain competitive, reduce costs, and maximize profits. Data analysis is the key to better understanding customers and tailoring marketing messaging to resonate better with the audience, enhancing customer retention. It helps companies streamline their processes, optimize workflows, and allocate resources more effectively.

How can small businesses get started with data analytics?

We’ve explored big datasets and the crucial role of data analytics for small businesses. Now, let’s find out how small companies can get started with data analytics.

Here’s what you should do to start your data analytics journey and implement its techniques into your day-to-day operations:

1. Define your big data strategy

Determine what you want to achieve and what information you will need to conduct your research. Set clear goals for your analytical efforts – to increase sales, improve customer satisfaction, reduce costs, or generally optimize your processes. Create a plan to effortlessly acquire high-quality new data.

2. Identify your big data sources

Leverage the information you already have in your CRM and utilize email marketing reports, sales receipts, social media analytics, the Internet of Things, website analytics, publicly available data, etc.

3. Ask insightful questions to get accurate data

In addition to standard metrics, ask about vendor value and product line enhancements, check the details, and examine the consequences of the latest operations to gain the desired insights.

4. Create a system for your big data

Decide the optimal approach for storing big data—whether onsite, in the cloud, within data lakes, or utilizing software solutions for streamlined access, management, and storage.

5. Get an expert assistance

Managing massive arrays of information can be challenging. Although you can learn to conduct an analysis on your own, a professional data analyst or consultant will help you better understand what lies behind your patterns and why and provide actionable recommendations for your business. In addition, they can help you set up a more efficient data collection process and ensure accurate data entry.

5. Use third-party solutions for small business data analysis

Professional software that streamlines data processing is crucial when dealing with massive datasets. Depending on your goals, you may use one or several tools to make a more in-depth examination of your activity and get a more holistic assessment.

Top small business data analytics tools 

1. Google Analytics

Google Analytics is a great starting point for small businesses looking to understand their online presence. With Google Analytics, you can track the number of visitors to your website, where they are coming from, and what pages they visit. This information is crucial for tracking conversions and understanding which marketing efforts are working and which need improvement.

2. SAS Viya

The SAS Viya software suite enables mining, managing, retrieving, and transforming data from diverse web, social media, and marketing analytics sources. This software furnishes customer intelligence to small and midsize businesses through statistical analysis.

3. Kissmetrics

Kissmetrics is one of the main person-based data analytics tools created to help businesses improve their marketing ROI by identifying customer behaviors. It helps get better insights to understand, segment, and engage customers effectively.

4. airSlate WorkFlow

airSlate WorkFlow is a no-code solution designed to automate document workflows and integrate systems effortlessly. It offers pre-programmed bots to handle document-related workflows and process analytics tools to evaluate each workflow’s efficiency.  With airSlate WorkFlow, small businesses can create and complete documents, integrate data systems, and automate processes, all within a single platform.

5. InsightSquared

This platform seamlessly integrates with business software like Salesforce, Google Analytics, QuickBooks, Zendesk, and others. It automates data collection and provides valuable marketing, staffing, and sales analytics insights.

6. Watson Analytics

IBM’s Watson Analytics empowers small businesses with cutting-edge predictive analytics, eliminating the necessity for data science skills. By merging data from various departments, the platform identifies patterns and detects potential issues.

The future of Big Data analytics

The future of data analytics is bright, with advancements in AI and machine learning set to unlock even more potential for analyzing various data types, such as videos, images, and sounds, to enhance sales and service. For small businesses, this means more accessible and affordable analytics tools that can provide deeper insights.

Leveraging these technologies will enable small businesses to stay competitive and continue to grow, reducing data storage and analytical costs. However, As big data usage rises, data becomes a target for competitors and hackers, necessitating stronger cybersecurity measures.

Final thoughts

Data analytics is no longer a luxury reserved for large corporations. Small businesses can now harness the power of data to make informed decisions, understand their customer data, optimize operations, and gain a competitive edge. By getting started with data analytics and utilizing the right tools, small businesses can unlock a world of opportunities.

And if you want to optimize your everyday processes by means of automation and analytics, airSlate WorkFlow is the perfect solution. Sign up and start your journey to business efficiency now!

Small business data analytics FAQs