Business data analytics: What it is, how it’s used & 6 proven techniques for success
Every business, big or small, deals with large amounts of data every day. Did you know that 64% of organizations manage at least one petabyte of data? That’s enough to store 500 billion pages of standard text or stream HD video for 13 years straight.
The real value of this data lies in how businesses use it. Business data analytics helps turn raw information into actionable insights. It boosts efficiency, improves productivity, and opens doors to greater success and revenue. While it does require some investment, the payoff is worth it.
In this blog, we’ll dive into why business data analytics is a game-changer for smaller organizations and the benefits it brings to the table.
What is data analytics?
Data analytics is the process of collecting, processing, and examining data to get valuable insights. It involves identifying trends, spotting patterns, and detecting anomalies within a specific field or industry. Think of it as a way to make sense of the data businesses generate daily, turning it into actionable knowledge.
Business data analytics involves analyzing raw data to gain insights into customer behavior, market trends, and overall business performance. For example, it can reveal why certain products are more popular, which marketing strategies resonate most with customers, or how operations can be optimized.
The goal of data analytics is to help businesses make better decisions. Instead of relying on guesses or instincts, companies can use data to guide their strategies. This leads to smarter investments, streamlined operations, and, ultimately, increased profitability.
Whether it’s predicting customer preferences or identifying new market opportunities, data analytics is at the heart of modern business success. It’s no longer just a tool for large corporations—businesses of all sizes can benefit from data-driven decision-making.
What are the 5 steps in data analytics?
To dive into business data analytics, organizations can follow a five-step framework:
1. Identify the data you need for analysis. It’s important to consider how this data is grouped—by age, gender, income, etc. Your data might be numerical, categorized, or grouped in other ways that align with your business objectives.
2. Collect the data across multiple sources. Data can come from various sources like online tools, customer interactions, sensors, cameras, or employee input. The aim is to gather as much relevant information as possible to fuel your analysis.
3. Organize and store your data conveniently. Whether it’s using spreadsheets or specialized software for statistical analysis, properly structured and categorized data can streamline the entire analysis process.
4. Clean the data for reliability. Before jumping into analysis, check your data for errors, get rid of duplicate entries, and fill in gaps. Cleaning your data is important because inaccurate or incomplete data can lead to misleading insights and poor business decisions.
5. Visualize and share Insights. Once you’ve analyzed the data, the final step is to present the results. Use data visualization tools like charts, graphs, and dashboards to make complex insights more accessible. Clear visualizations help communicate key findings to stakeholders and ensure that insights lead to actionable decisions.
6 key types of business data analytics
Small businesses have several data analysis methods at their disposal, each designed to meet their unique needs. Here are six key techniques that help enhance their economic resilience:
1. Data mining (Text analytics) involves uncovering patterns in large datasets using tools and databases. It helps turn raw data into valuable business insights. By extracting and analyzing data, businesses can identify patterns and relationships to make informed decisions. The process includes steps like data sourcing, exploration, modeling, and deploying models.
2. Statistical analytics focuses on collecting, exploring, and presenting data to find patterns and trends. It includes describing data, connecting it to the broader population, creating models to summarize these connections, validating models, and predicting future trends.
3. Descriptive analytics aims to collect and analyze historical data to provide a summary of past performance. It helps stakeholders gain insights into what has happened in the business, offering a clear picture of past trends.
4. Diagnostic analytics is an advanced method that focuses on the reasons for data to look a particular way. It uses techniques like drill-down, data discovery, and correlations to find the reasons behind trends. It’s useful for understanding market demand, customer behavior, and improving company culture.
5. Predictive analytics aims to predict future outcomes based on historical data and techniques like statistical modeling and machine learning. It helps businesses evaluate past behavior to forecast future risks and opportunities.
6. Prescriptive analytics provides recommendations on what steps to take to solve a problem. It uses various data sources and methods, including simulation and neural networks, to guide decision-making.
Business data analytics use cases and examples
1. Customer behavior analysis
Business data analytics allows brands to better understand their customers’ preferences and habits and, subsequently, craft effective marketing strategies.
For example, Shopify, a popular e-commerce platform, provides small businesses with built-in analytics tools. Merchants can use customer segmentation to offer personalized discounts, resulting in higher engagement and repeat purchases.
2. Marketing optimization
Data analytics helps identify which channels and campaigns yield the best results to optimize marketing strategy for brands. For instance, Mailchimp enables users to track the success of email campaigns through metrics like open rates, click-through rates (CTR), and conversion data.
3. Financial data analysis
Using data analytics tools, businesses can analyze income and expenses to better manage cash flow and reduce risks. Financial management software like QuickBooks offers businesses tools to track expenses and revenues. For instance, a bakery chain might use QuickBooks to identify that ingredient costs peak in December and negotiate bulk discounts with suppliers in advance.
4. Supply chain optimization
Data analytics enables businesses to monitor supplier performance, streamline inventory, and optimize supply chain operations. For instance, Etsy sellers often use tools like Craftybase to analyze inventory trends. One handmade jewelry shop on Etsy noticed delays in material shipments from a specific supplier and switched to a more reliable vendor, improving production times and customer satisfaction.
5. Customer service enhancement
Data analytics enhances customer service by helping businesses address common issues and streamline support operations. Tools like AI chatbots can handle repetitive inquiries, leaving human agents to focus on more complex cases. For example, Freshworks provides AI-driven customer support tools for small businesses. A family-owned bookstore using Freshworks might analyze chat data to discover frequent questions about order tracking. They then implement a self-service portal to reduce customer wait times and improve satisfaction.
Data analytics techniques
Data analytics might sound technical, but it’s essentially about using smart methods to uncover insights from information. Here are some of the most popular techniques, explained in simple terms with real-life examples to show how businesses are putting them to good use.
1. Regression analysis
Regression analysis looks at the relationship between different variables to see how one affects the other—for instance, the impact of price on sales, etc.
Airbnb uses regression analysis to figure out what factors—like location, property size, and amenities—impact how much guests are willing to pay per night. This helps hosts set competitive prices and boost bookings.
2. Factor analysis
Factor analysis is about simplifying a big pile of data into smaller, more manageable groups to uncover hidden patterns or trends.
Spotify uses factor analysis to understand music preferences of their users. By breaking down listening habits into patterns, Spotify creates personalized playlists like “Discover Weekly,” helping users discover songs they’ll love.
3. Cohort analysis
Cohort analysis splits data into smaller groups, or “cohorts,” based on shared characteristics, like when users signed up or what product they purchased. This helps businesses understand how different groups behave over time.
Netflix uses cohort analysis to track how different groups of subscribers engage with their platform. For instance, they might study how long users who joined in January stick around compared to those who joined in June. These insights help improve retention and tailor content strategies.
4. Monte Carlo simulations
The Monte Carlo simulations technique might sound fancy, but it’s just about running lots of “what-if” scenarios to predict the likelihood of different outcomes.
General Motors (GM) uses Monte Carlo simulations to test vehicle safety. By modeling thousands of potential crash scenarios, they can improve car designs to make them safer for drivers.
5. Time series analysis
Time series analysis looks at data over time to spot trends and make predictions, like forecasting next month’s sales or planning for seasonal demand.
Amazon uses time series analysis to predict what products will be in demand. By analyzing past sales trends, they manage inventory efficiently, ensuring customers get what they need when they need it.
Business data analytics tools
Small businesses don’t just need data; they need actionable insights. With the right analytics tools, you can uncover customer behaviors, refine marketing efforts, and make smarter decisions to grow your business.
1. Google Analytics
Google Analytics is a crucial tool for businesses to track and analyze website traffic, providing valuable insights into user behavior and marketing performance. GA is used by 71% of small businesses under 50 employees, 6% of large businesses over 1000 employees, and 23% of medium-sized businesses.
How it works: Google Analytics helps you track what visitors do on your website or app. It shows where they come from, which pages they visit, and how they interact with your content. This makes it easier for businesses to understand customer journeys and figure out which marketing efforts are working.
Cost: There’s a free version that’s great for small businesses. For larger companies needing more advanced features, Google Analytics 360 costs $50,000 per year and includes tools like unsampled data and enterprise integrations.
Pros: It integrates with many other tools, provides real-time insights, and offers detailed reports to help businesses make data-driven decisions.
Cons: Beginners might find it hard to learn, and there are some concerns about privacy since it uses cookies to track user activity.
What makes it special: Google Analytics is one of the most widely used tools worldwide. It’s a trusted solution for businesses to monitor and improve their online presence.
2. SAS Viya
SAS Viya isn’t just for data experts—it’s designed for businesses that want to enhance their insights. Recent research shows that business analysts and other non-technical staff can perform 86% of data life cycle tasks using Viya, compared to 56% in commercial settings and 47% in non-commercial settings.
How it works: SAS Viya is a tool that helps process and analyze large sets of data. It has an easy-to-use interface and combines advanced analytics with machine learning.
Cost: The pricing for SAS Viya is customized based on what each user needs, usually aiming at medium to large enterprises.
Pros: It is very powerful for predictive modeling and data mining and offers flexible options for deployment.
Cons: It can be expensive and complex for smaller businesses to use.
What makes it special: SAS Viya stands out because it can integrate machine learning and AI to provide deep insights.
3. Kissmetrics
Kissmetrics is a powerful tool for tracking individual customer behaviors, helping businesses understand their users’ actions and improve engagement. By offering detailed insights into customer journeys and marketing effectiveness, it enables companies to enhance customer retention and boost ROI.
How it works: Kissmetrics is a tool that helps businesses understand how their customers behave. It allows companies to track what users do, group them into segments, and communicate with them effectively.
Cost: The service starts at $299 per month for smaller teams, with prices increasing for larger companies.
Pros: Kissmetrics excels at tracking the entire customer journey and helps improve the return on investment (ROI) from marketing efforts.
Cons: It offers fewer integrations with other tools compared to its competitors and can be expensive for smaller businesses.
What makes it special: Kissmetrics uses a person-based tracking method, providing businesses with detailed insights into each customer’s journey.
4. airSlate WorkFlow
airSlate WorkFlow is like the Swiss Army knife of workflow automation. From automating document approvals to integrating multiple systems, it takes repetitive tasks off your plate.
How it works: This no-code tool helps you automate document workflows and connect different business processes. It also provides analytics tools to track how well your workflows are performing.
Cost: airSlate WorkFlow offers a Free plan with 10 credits/mo. The Growth plan starts at $399/mo, and there are custom pricing options available for larger enterprise needs.
Pros: It’s user-friendly and perfect for teams of all sizes, offering pre-set bots that boost process efficiency.
Cons: Advanced features may require technical expertise.
What makes it special: It stands out because it uniquely combines document automation and integration in one platform.
5. InsightSquared
InsightSquared centralizes all your data, providing real-time insights that you can actually use. Whether you need sales forecasts or want to spot customer support trends, this tool helps you make smarter decisions.
How it works: InsightSquared pulls data from CRM systems like Salesforce and connects with other business tools to offer advanced sales analytics.
Cost: Pricing is tailored based on the size of your team and your specific needs.
Pros: It has strong integration abilities and provides actionable insights for sales teams.
Cons: It can be expensive and has a limited focus outside of sales analytics.
What makes it special: Its deep integration with Salesforce and real-time analytics make it a must-have for businesses driven by sales.
The future of business data analytics
Business data analytics is an essential tool for companies of all sizes. It helps businesses make smarter decisions, streamline operations, and boost growth. Tools like Google Analytics and Kissmetrics let businesses understand their customers better by tracking their behavior and preferences, while platforms like airSlate WorkFlow and SAS Viya help improve efficiency and reduce risks.
These insights help businesses fine-tune their marketing efforts, optimize processes, and predict future trends. Simply put, using data analytics isn’t just a smart choice—it’s crucial for staying competitive and achieving long-term success in today’s fast-paced world. By embracing these tools, businesses can unlock new opportunities and thrive.
FAQs
1. What is meant by business data analytics?
Business data analytics refers to the process of collecting, analyzing, and interpreting data to uncover trends, patterns, and insights that help businesses make informed decisions. By leveraging analytics tools, businesses can track customer behavior, optimize marketing efforts, and improve overall performance. Data analytics empowers companies to optimize their strategies, reduce risks, and stay competitive in a data-driven world.
2. What does a business data analyst do?
A business data analyst is responsible for collecting, processing, and analyzing data to provide insights that help businesses make informed decisions. They use various tools and techniques to interpret complex data, identify trends, and present findings in a clear, actionable way. This role involves tasks like creating reports, building dashboards, and working with different departments to optimize strategies based on data. By analyzing key metrics, business data analysts help organizations improve efficiency, increase profitability, and stay competitive.
3. What are the 4 types of business analytics?
Data analytics is divided into four main types. Descriptive analytics focuses on what has happened in the past. Diagnostic analytics digs into why something occurred. Predictive analytics looks ahead, forecasting future outcomes. Finally, prescriptive analytics recommends the best actions to take moving forward.
4. Is data analysis a well-paid job?
Yes, data analysis is generally a well-paid job. According to ZipRecruiter, the average salary for data analysts in the U.S. is around $94,537 per year, with more experienced professionals earning upwards of $120,000 annually. The demand for skilled data analysts continues to rise as businesses increasingly rely on data to drive decisions. Specializations in areas like machine learning or data science can lead to even higher salaries. However, salary figures can vary based on location, industry, and level of experience .
5. Is a bachelor’s degree in business analytics worth it?
Yes, a bachelor’s degree in business analytics is worth it, as it offers strong job prospects and competitive salaries. The demand for data analysts is growing rapidly, with positions in this field expected to increase by 11% through 2033 (U.S. Bureau of Labor Statistics). Graduates can expect salaries starting from $60,000, depending on experience and location. The skills gained in this degree are highly valued across industries, making it a versatile and future-proof investment .
6. What is the difference between data analytics & business analytics? Which one is better for a non-technical background?
Data analytics focuses on processing raw data to extract insights, while business analytics applies those insights to improve business decisions and outcomes. For someone without a technical background, business analytics is typically the better choice, as it focuses more on practical, strategic applications rather than technical data manipulation. It is more focused on strategic applications, such as improving business processes, marketing strategies, and customer experiences, rather than deep technical data manipulation.
