What Is Data Analytics?

Enable strategic data-driven decision-making for your small business.

By leveraging data analytics, small-business owners can make informed decisions based on market trends and customers’ buying patterns, thus boosting growth and satisfaction for their company. 

This guide covers everything you need to know about data analytics. We explain what it is, why it is important, best practices and strategy, the future of data analytics, and more. We also discuss software tools that can help turn your business data into actionable insights.

What is data analytics?

Data analytics is the process of examining, cleaning, transforming, and modeling data to discover useful information, inform conclusions, and support decision-making. It takes a bunch of raw data and turns it into insights that help make smarter, more accurate decisions for your small business.

Why is data analytics important?

Data analytics extracts relevant insights from large volumes of raw data to drive smarter decisions for complex business problems. Experts at Gartner [1] suggest that this type of data-driven decision-making is crucial for creating business value in the form of innovation, customer-centricity, operational efficiency, etc.

Additional Gartner research [2] highlights an opportunity to leverage data analytics for the following types of business decisions:

  • Operational decisions such as scheduling employees, purchasing products from suppliers, and managing inventory.

  • Real-time decisions that are based on factors such as live visitors to your website, social media streaming, and sales transactions. 

  • Cyclical decisions that are based on your business cycle, including economic expansions.

  • Strategic decisions related to your products or services based on your company's vision and mission.

  • Macro decisions such as new product launches, and micro decisions such as automation of regular tasks.

  • Tactical decisions such as altering your product's pricing or reorganizing a department.

What are the types of data analytics?

According to Gartner research [2], data analytics is of four types:

  1. Descriptive analytics: Descriptive analytics answers the question, "What happened?" It simply describes what has happened and doesn’t delve into the specifics of why or how it happened. A coffee shop owner, for instance, can use descriptive analytics to find out the sales from coffee compared to pastry over a given period.

  2. Diagnostic analytics: Diagnostic analytics answers the question, "Why did it happen?" It digs deeper into data to understand the root cause of a problem or trend. For example, a restaurant owner can use diagnostic analytics to understand why lunchtime sales have decreased over the past few months. They can analyze data such as customer reviews, menu changes, and staffing to identify the possible causes as well as likely solutions to the problem.

  3. Predictive analytics: Predictive analytics answers the question, "What is likely to happen?" It uses historical data to make predictions about the future. For instance, a small retailer can use predictive analytics to forecast demand for a particular product during the peak sales season. They can feed data on past sales, customer behavior, and market trends, among others, to predict how much of a particular product they should stock for the upcoming month.

  4. Prescriptive analytics: Prescriptive analytics answers the question, "What should the next step be?" This type of analytics helps identify how to best proceed in a given situation. For example, a small-business owner can use prescriptive analytics to optimize their marketing spend. They can use data collected on customer behavior and past marketing campaigns to identify the most effective marketing channels and allocate funds accordingly.

Remember, each data analytics type has its own unique purpose and can enable improved decision-making in a variety of business scenarios.

What is the difference between data analytics and business analytics?

Data analytics analyzes raw data to draw insights that help improve decision-making. Business analytics, on the other hand, uses these insights to address specific business problems and make strategic decisions. It's like looking at a recipe (data analytics) and then deciding which ingredients to use and how much (business analytics) to make the best dish possible. For example, a small online retailer can use data analytics to track website traffic and then use business analytics to optimize their online marketing strategy.

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What is data literacy, and why is it important for small businesses?

Gartner [3] defines data literacy as the ability to read, write, and communicate data in context. It requires an understanding of data sources, how they work together, and the analytical methods and techniques applied, as well as the ability to describe how the data will be used and what value it will create for your business.

Data literacy teaches you how to explain important data insights to other decision-makers in your organization. Being able to communicate using the language of data—i.e., being data literate—is a must-have skill for your company's employees, allowing them to use data effectively to drive desired outcomes.

What is data governance, and how does it help small businesses?

Data governance is everything you do to make sure your business data is safe, private, accurate, available, and usable. Gartner [4] describes it as the rules and processes your company puts in place to ensure data is used appropriately and effectively. This can include determining who has access to data, how it should be used, and what processes it should be used in.

Data governance helps eliminate data duplication, leading to effective resource management. Let's say you have a customer database with duplicate entries—many sales reps have entered the same customer's information without realizing it already exists in your system.

By implementing data governance, you can establish a process to ensure customer data is checked for duplication before being entered into your database. This not only saves time and resources but also ensures you have a more accurate picture of your customer base. A defined set of rules and regulations also helps you provide data access to the required people without compromising data sensitivity.

How can small businesses use data analytics?

Data analytics benefits small businesses in the same way it helps enterprises—better decision-making. You can leverage data analytics in the following ways for your small business:

  • Analyze customer data such as demographics (age, gender, income, etc.) to facilitate better location targeting for your digital campaigns, make distribution-related decisions, or even take strategic calls on where to set up a new plant or shop. 

  • Identify areas of product improvement by analyzing customer reviews and feedback for your products. 

  • Track sales to uncover your most and least-selling products for better catalog planning and other marketing strategies.

  • Evaluate expenses and revenues to identify opportunities for saving costs and increasing efficiency to improve your business's financial health.

  • Analyze historical sales data to predict future sales or, even better, improve it.

What are some data analytics best practices for small businesses?

Follow these best practices to ensure accurate data analysis for your small business: 

  • Start with a clear objective. Before diving into data analytics, you should have a clear understanding of what you want to achieve. Identify your business goals, and determine how data analytics can help you get there. For example, if you have an eCommerce store and want to increase conversions, then you should analyze visitor data metrics such as the types of traffic sources, user behavior on landing pages, bounce rate, and cart abandonment rate to identify patterns that might help optimize your site and boost conversions.

  • Invest in the right tools and infrastructure. Data analytics is a complicated process that takes a lot of time, so it's crucial to have the right tools and infrastructure. That means choosing the right software, hardware, and storage options to manage your data. You could, for instance, use cloud-based services such as Google Cloud or Amazon Web Services to store and analyze your data. Cloud-based tools usually come with unlimited data storage and processing capabilities. Plus, they use advanced security measures to guarantee uptime without any data loss, making them a suitable option for your data analytics needs.

  • Establish governance and data quality standards. As you collect and analyze data, it's important to establish governance and quality standards to ensure your data is accurate and trustworthy. This includes identifying who’s responsible for managing data, establishing clear data entry and processing protocols, and making sure all data is secured. To control access to sensitive data and minimize the risk of breaches, you could implement a data classification and management system, which segments data into different categories based on sensitivity levels.

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Free data analysis software

Just like a premium tool, free data analysis software also helps derive insights from your business data. However, it may have limited functionality, may work with only certain data types, or may not offer the same customer support as a paid tool. But despite these shortcomings, it’s still worth checking out if you’re on a budget.

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What is a data analytics strategy?

A data analytics strategy outlines how your company collects, stores, organizes, and uses data to inform business decisions involving various stakeholders. A good strategy helps you remain realistically goal-oriented when implementing data analytics solutions and allows for learning from mistakes while ensuring data quality and literacy within your small business.

What are the steps of the data analytics process?

Gartner [2] recommends the following steps to make your data analytics process more effective:

  • Figure out the mission and goals of your organization. 

  • Specify how data and analytics can help with those.

  • Make an action plan prioritizing the steps to take to achieve those goals.

  • Create a data and analytics strategic roadmap including the techniques mentioned earlier: descriptive, diagnostic, predictive, and prescriptive analytics. 

  • Communicate your strategy to employees and stakeholders before executing the plan.

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Data visualization software

Data visualization software helps you display large volumes of data in a presentable manner using a variety of visualization forms such as reports, bar charts, pie charts, graphs, scatter plots, tables, and timelines. This makes it easy for you and your team to identify patterns, which leads to better decision-making.

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What is the future of data analytics?

The future of data analytics looks promising, especially for small businesses. As analytics technology advances, we can expect to see more powerful AI-driven tools for data analysis and processing.

Here are five Gartner-backed [5] data analytics trends. Take note of these to prepare your small business for the future of data analytics:

  1. Consider investing in areas such as adaptive AI and machine learning applications to get past traditional data analysis approaches. Quickly adapt to new strategies and processes while maintaining stability to be ahead of your competition.

  2. Invest in data fabric, an emerging data integration and management design that ensures your business data is consistently formatted and easily accessible to every employee—whether from sales, marketing, or finance.

  3. Follow a decision-focused data analysis approach, which involves understanding factors that influence decision-making, such as market trends, customer preferences, and budget constraints. By 2023, more than 33% of enterprises are expected to follow this approach. 

  4. Don't just hire people with the right technical skills; hire professionals who also know how to use data to make tactical choices. To upgrade your existing talent pool, invest in a data literacy program that trains your employees to read, write, and communicate data in an impactful manner.

  5. Incorporate data governance within your organization. This approach requires little to no investment and focuses on improving data quality and processes, thus creating more trust in data analytics as an effective business unit.

What is advanced analytics?

Gartner’s glossary [6] defines advanced data analytics as the autonomous or semi-autonomous analysis of data using multiple sophisticated data analytics techniques, including data mining, predictive modeling, artificial intelligence, machine learning, visualization, semantic analysis, and sentiment analysis.

How helpful is AI in data analytics?

Our 2022 AI Marketing Survey* found that 56% of businesses in the U.S. use artificial intelligence and machine learning in data analytics. Yes, the number is big, and so are the reasons behind it:

  • AI can scan through your data using its algorithms to learn patterns, identify anomalies, find possible combinations, and recognize opportunities to scale your business.

  • AI can automate and expedite routine data preparation tasks, such as data cleaning and data normalization, so you can focus more on better decision-making. 

  • AI can learn common human error patterns and prevent you from repeating the analytical mistakes you made in the past.

  • AI can analyze historical trends to learn how your business typically uses data and help identify opportunities to optimize or improve existing processes.

Will AI take over data analytics?

The short answer? No. Artificial intelligence and machine learning can quickly and accurately process large amounts of data that would be impossible for a human analyst to handle alone. However, while these technologies may make certain aspects of a data analyst's job easier and more efficient, they are unlikely to completely replace humans. Human analysts still play a critical role in interpreting and making decisions based on the insights generated by artificial intelligence and machine learning algorithms.

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Data analysis software with AI/machine learning functionality

Data analysis software with artificial intelligence and machine learning capabilities can make your data analysis process much more productive. You’ll be able to discover insights faster, find new patterns, and spot meaningful relationships in the data. You can also use this tool to automate tedious tasks in your analytical process and improve workflow productivity.

What is data analytics software?

Data analytics software helps clean and process raw data or sets of data. These tools present actionable insights extracted from big data by establishing relationships between data points and identifying trends which, in turn, supports data-driven decision-making.

What are the features of data analytics software?

Look for these features when shortlisting a data analysis software solution for your small business:

  • Data discovery: Collects and stores data from multiple sources for analysis.

  • Dashboard: Effectively displays data analysis results, KPIs, or critical data set findings.

  • Data visualization: Converts complex data into easy-to-understand charts and graphs for a better presentation of data results or trends.

  • Self-service analytics: Allows users to run queries and analyze data on their own, with minimal-to-no IT support.

  • Predictive analytics: Analyzes historical data sets to predict future outcomes or trends.

What is the cost of data analytics software?

Most data analysis tools on the market are priced on a per user, per month basis and can be divided into three pricing tiers based on their starting prices. 

Price ranges*:

  • $9 - $19

  • $20 - $81

  • $82+

Note: The pricing included is for the entry-level/lowest-priced offerings found on vendor websites as of April 5, 2022. These ranges correspond to the 25th, 75th, and 100th percentiles of pricing information gathered from vendor websites of sample products.

The above list summarizes pricing for the base plans of most products. An enterprise or premium product that’s priced higher may include additional features such as data storytelling and natural language search.

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GetApp’s Category Leaders in Data Analysis

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Survey methodology

*GetApp conducted the AI Marketing Survey in July 2022 among 185 U.S. marketers to explore whether or not SMB marketers should invest in AI copywriting software by evaluating its usefulness and effectiveness in a business setting. Respondents were screened to work full-time in marketing, advertising, customer service, HR/finance, sales, or IT departments and have some level of involvement in marketing-related activities. Respondents were also required to work for companies currently using artificial intelligence or machine learning software for advertising, content, or copywriting generation.

Sources

  1. Data-driven decision-making is critical to create business value, Gartner

  2. What is data and analytics, Gartner

  3. Data literacy, Gartner glossary

  4. Data governance, Gartner glossary

  5. Data and analytics trends, Gartner

  6. Advanced data analytics, Gartner glossary