The ever-growing volume of data created worldwide is staggering: 120 zettabytes today and expected to reach 181 zettabytes by the end of 2025. Companies are realizing the ability to turn their wealth of data into actionable insights is no longer a luxury; it’s a must to remain competitive. Business analytics helps them uncover the insights concealed within all those ones and zeros to better understand their operations, customers, and markets.
Although business analytics is often confused with business intelligence (BI), they are not quite the same. In this post, we’ll explain the difference between business analytics and business intelligence, talk about how business analytics works, discuss business analytics tools, and show the power of business analytics in action.
Table of Contents
What is business analytics?
Business analytics is actually a subset of a larger group of data management disciplines called business intelligence—an infrastructure that facilitates the collecting, storing, and analyzing of data from business operations. A business intelligence platform provides comprehensive business metrics in near real time so you can create performance benchmarks, spot market trends, increase compliance, and improve almost every aspect of your business.
Business analytics contributes to this effort by turning your company’s raw data into useful information for things like measuring performance, identifying trends, predicting outcomes, and more. Common methodologies used in business analytics include:
- Data mining: sorting through large amounts of data to identify patterns and trends
- Aggregation: the process of gathering and organizing data before analysis
- Forecasting: analyzing historical data to estimate future outcomes
- Predictive modeling: extracting information from data sets to identify patterns and extrapolate future trends
- Data visualization: creating visual representations of data analysis, such as charts, tables, or graphs to help communicate the findings
Before we dive deeper, it’s helpful to understand the spectrum of analytics in business:
Descriptive analytics answers the question “What happened?” It’s used to understand overall performance at an aggregate level, relying on historical data that has been combined or summarized from various sources
Diagnostic analytics also uses historical data, but addresses the question of why an occurrence or anomaly happened within your data
Predictive analytics determines what is likely to happen. It’s also based on historical data, but uses machine learning to understand patterns and trends
Prescriptive analytics merges the first three types of analytics to provide guidance toward a specific action to take, for example, proactively buying more parts for preventive maintenance
Business analytics versus business intelligence
Business Intelligence
While they’re part of the same data management discipline, business intelligence and business analytics address different questions. BI prioritizes descriptive analytics, answering the questions “what” and “how,” so you can continue doing what works and change what does not.
Business Analytics
Business analytics, on the other hand, focuses more on predictive analytics, using data mining, modeling, and machine learning to determine the likelihood of future outcomes. BA answers the question “why” so it can make informed predictions about what will happen. It lets you anticipate developments and make the adjustments necessary to succeed.
These processes work together to yield results that propel companies forward. Business analytics is an iterative process that’s improved by repeated review and testing to yield better and more targeted results.
Business analytics versus data analytics
The distinction here is more subtle, and these terms are often used interchangeably because they’re both part of business intelligence.
Data Analytics
Data analytics is a broad term for finding insights in data and can refer to any form of analysis—whether in a spreadsheet, database, or application—where the intent is to uncover trends, identify anomalies, or measure performance. Analysts who have math or IT skills can do everything from managing a database of subscribers to calculating yields for a potential investment.
Business Analytics
Business analytics focuses on the overall function and day-to-day operation of the business. Business analysts deal less with the technical aspects of analysis and more with the practical applications of data insights. Their job responsibilities might include creating a streamlined workflow or selecting the best vendors.
How does business analytics work?
To arrive at insights that help organizations make data-driven decisions, business analytics collects, processes, and analyzes vast amounts of data. The results can help companies improve their business processes, identify opportunities, and tackle challenges. The process involves several steps that typically go like this:
- Define objectives: First, get clear on what you want to accomplish or which questions you want to answer. Your objectives might involve improving efficiency, increasing sales, cutting costs, understanding customer behavior, and so forth and will vary from business to business
- Collect data: After you’ve defined your objectives, it’s time to collect relevant data—from spreadsheets, customer interactions, website traffic, social media, and more. The quality of your data is critical; it should be accurate, relevant, and comprehensive enough to address your objectives
- Clean and prepare data: Raw data can be messy and contain errors, inconsistencies, and/or missing values. That’s why, before analysis can begin, it must be cleaned and prepared. This includes removing duplicates, correcting errors, supplying missing values, and formatting the data for analysis
- Analyze the data: Once the data is cleaned and prepared, various analytical techniques are applied to uncover patterns, trends, correlations, and other insights. This could involve descriptive analytics to summarize and describe the data, diagnostic analytics to understand why certain events occurred, predictive analytics to forecast future trends or outcomes, and prescriptive analytics to recommend actions based on the analysis
- Visualize the data: Through charts, graphs, dashboards, and other graphical representations, visualizing data helps communicate insights more effectively and makes it easier for stakeholders to understand the findings
- Interpret for decision making: With the analysis complete, the results need to be understood in the context of your business objectives. This means assessing the implications of the insights and making informed decisions based on the analysis. These might involve changes to business strategies, processes, products, marketing campaigns, and so forth
- Implement and monitor: Now that you’ve made decisions based on the analysis, they need to be implemented and the outcomes monitored. This could involve tracking key performance indicators (KPIs) to measure the impact of the decisions and make adjustments as necessary
Note that business analytics is not a “one-and-done.” Rather, it’s a continuous process of collecting, analyzing, and interpreting your data to drive informed decision making and improve business performance. Business analytics requires a combination of technical skills, domain knowledge, and critical thinking.
What tools are required for business analytics?
To follow through with the above process, business analytics employs many tools and technologies for collecting, processing, analyzing, and visualizing data. They include:
• Data collection tools: These gather data from sources like databases, spreadsheets, websites, social media platforms, sensors, and more. Examples include:
— SQL (structured query language) for querying databases
— ETL (extract, transform, load) tools like Talend, Informatica, or Apache NiFi for data integration and transformation
— Web-scraping tools like BeautifulSoup or Scrapy for extracting data from websites
— APIs (application programming interfaces) for accessing data from online services and platforms
• Data storage and management tools: These store, organize, and manage large volumes of data. Examples include:
— Relational databases like MySQL, PostgreSQL, or Microsoft SQL Server
— NoSQL databases such as MongoDB, Cassandra, or Redis for handling unstructured or semi-structured data
— Data warehouses like Snowflake, Amazon Redshift, or Google BigQuery for storing and analyzing large datasets
• Data analysis tools: These are used to perform various types of data analysis, including descriptive, diagnostic, predictive, and prescriptive analytics. Examples include:
— Statistical software like R or Python with libraries such as NumPy, Pandas, and SciPy for statistical analysis and modeling
— Data mining tools such as IBM SPSS Modeler, RapidMiner, or Weka for discovering patterns and relationships in data
— Machine learning frameworks like TensorFlow, Py Torch, or scikit-learn for building and deploying predictive models
— Business intelligence platforms like Tableau, Power BI, or QlikView for creating interactive dashboards and reports
• Data visualization tools: These are used to create representations of data to facilitate understanding and decision making. Examples include:
— Tableau for creating interactive dashboards and visualizations
— Power BI for creating reports and dashboards with self-service analytics ggplot2 and matplotlib libraries in R and Python for creating customized static visualizations
— D3.js for creating custom and interactive data visualizations on the web
• Collaboration and communication tools: Used to facilitate collaboration among team members and effectively communicate insights, these tools include:
— Collaboration platforms like Slack, Microsoft Teams, or Trello for team communication and project management
— Presentation software like Google Slides or Microsoft Powerpoint for creating and sharing presentations
— Document sharing and collaboration tools like Google Drive or Microsoft Sharepoint
These are just a few of the tools commonly used in business analytics. Specific tools will vary depending on your organization’s requirements, budget, technical expertise, and your data analysis objectives.
What does business analytics in action look like?
Let's look at how powerful business analytics capabilities can help you gain valuable insights and make data-driven decisions to enhance your business strategies.
Data integration and visualization
Business analytics lets you connect and integrate data from various sources, including ERP systems, inventory databases, logistics platforms, and supplier databases. Using an intuitive interface, your supply chain team can create interactive dashboards that provide a comprehensive view of key supply chain metrics, such as inventory levels, order fulfillment, and supplier performance.
Robust visualization capabilities enable your team to transform this complex supply chain data into visually appealing charts, graphs, and maps, making it easier to identify bottlenecks, inefficiencies, and areas for improvement.
Demand and inventory analysis
Taking advantage of advanced business analytics features, your team can forecast demand based on historical data, market trends, and other relevant factors, helping optimize inventory levels and reduce stockouts. By visualizing inventory data and analyzing demand patterns, you can identify optimal inventory levels, reduce excess inventory, and improve cash flow.
And you can track and analyze supplier performance metrics, such as on-time delivery, quality, and cost, enabling better supplier management and decision making.
Data visualization capabilities enable your business to identify potential supply chain risks such as disruptions, delays, or quality issues, and develop proactive mitigation strategies. The supply chain team can perform scenario analysis to understand the potential impact of changes in demand, supplier availability, or market conditions on the supply chain, enabling better risk management and contingency planning.
Integration with real-time data sources lets you monitor supply chain performance in real time, enabling quick response to potential issues and better decision making.
Collaboration and reporting
Leveraging collaborative features, your supply chain team can share insights, collaborate on supply chain strategies, and align their efforts toward common goals. Automated reporting capabilities streamline the process of generating supply chain reports, saving time and effort.
Using a mobile app lets your supply chain team access real-time supply chain data and dashboards on the go, empowering them to make informed decisions anytime, anywhere.
Get started with business analytics
If you’ve got data—and what organization doesn’t—business analytics can arm you with insights into market trends, competitor strategies, and business performance for informed decision making. It can help you understand customer needs, preferences, and behavior so you can develop products that meet their expectations. Improved operational efficiency resulting from business analytics can boost your ROI.
Your data is a gold mine of information waiting to be deciphered. Learn more about how business analytics can help you realize insights at the speed of business.