What is business analytics?
Explore IBM's business analytics solution Subscribe for AI updates
Illustration with collage of pictograms of gear, robotic arm, mobile phone
What is business analytics?

Business analytics is a type of analytics that helps organizations mine, process, and visualize important business data and take advantage of patterns in their businesses that they would not see otherwise.

Business analytics is the process by which companies use data either created by their operations or publicly available data, to solve business problems, monitor their business fundamentals, identify new opportunities for growth and better serve their customers. As the saying goes, you can’t measure what you can’t see.

Business analytics involves either individual pieces of data or data sets which are either stored on-premise or in the cloud. Data sets that increase beyond a certain threshold are commonly referred to as big data, which requires significant computational power to access and analyze. Business analytics uses data exploration, data visualization, integrated dashboards and more, to allow users access to usable data and insights.

As companies increasingly digitize their businesses, business analytics is more important than ever before. Delivering advanced data analytics and AI with an integrated workflow drives organizations to implement smarter, faster and more accurate data-driven decisions.

Business analytics also delivers business optimization strategies that help organizations visualize and take advantage of patterns in their businesses that they would not see otherwise.

The world changes so fast, and organizations need to adapt quickly based on the information. Success today depends on many elements, but, primarily, organizations need access to the right data and insights fast so executives can act decisively.

Those who can make quick strategic decisions with the right information at hand often have a huge competitive advantage. With business analytics, organizations can make confident business decisions informed by real metrics and insights and take the guesswork out of decision making.

Therefore, many companies have business analysts, whose jobs depend on identifying business intelligence that can help the company make smarter and quicker decisions that produce an advantage over competitors.

Four steps to better business decisions

Explore IBM's ebook to uncover the value of integrating a business analytics solution that turns insights into action.

Related content

Read the guide for data leaders

Business analytics vs. business intelligence

Business intelligence, which has been around for many years, involves using data on hand to make important business decisions that impact the entire organization. Business intelligence is often thought of as the act of identifying and storing data so as to be used for decision making.

Business analytics1(link resides outside ibm.com) takes business intelligence a step further by using that data to ask and answer specific questions about what happened in the past that either a) may happen in the future exactly the same or b) will happen differently because of new or different contexts.

It provides a complete picture of a business, allowing organizations to explain user behavior more effectively. Not only that, but business analytics can also forecast what’s coming in the future, making predictions about changes to business results. 

Business analytics benefits data scientists and advanced data analysts to provide advanced statistical analysis. Some examples of statistical analysis include regression analysis, such as using previous sales data to estimate customer lifetime value, and cluster analysis, such as analyzing and segmenting high-usage and low-usage users in a particular area.

Business analytics solutions provide benefits for all departments, including finance, human resources, supply chain, marketing, sales, or information technology; and all industries, including healthcare, financial services and consumer goods.

Business analytics tools

Business analytics practices involve several tools that help companies make sense of the data they are collecting and use to turn that data into insights. Here are some of the most common tools, disciplines and approaches.

  • Predictive modeling: Companies will often design or develop new products, enter into new markets or otherwise explore new opportunities for which they have little prior experience or historical data to mine. This is where predictive modeling and predictive analytics shine. Predictive modeling2(link resides outside ibm.com) helps organizations avoid issues before they occur, like knowing when a vehicle or tool will break down and intervening before it occurs or knowing when changing demographics or psychographics will positively or negatively impact their product lines. 
  • Data mining: This is an extremely important component of business analytics, where mostly automated tools unearth and make sense of raw data to identify patterns, producing key insights. The growing importance of big data makes data mining, also known as knowledge discovery in data (KDD), a critical component of any modern business. Although companies often struggle with scaling their data mining activities as they seek to uncover more insights.
  • Data science: The study of how data creates business insights, incorporating elements from mathematics, statistics and computer science. With the increase in data sources and the importance of analyzing that data, data science is become one of the most important jobs in Corporate America and organizations are increasingly reliant on it to certain create actionable insights that impact business outcomes.



Business analytics types

Business analytics leverage analytics, the action of deriving insights from data, to drive increases in business performance. There are three types of valuable analytics that are often employed in business analytics situations.

Descriptive analytics

As the phrase implies, this type of analytics describes the data contained within. An example would be a pie chart that breaks down the demographics of a company’s customers. 

Predictive analytics

This form of analytics mines existing data, identifies patterns and helps companies predict what may happen in the future based on that data. It uses predictive models that data can be fed into to make hypotheses about future behaviors or outcomes. For example, an organization could make predictions about the change in coat sales if the upcoming winter season was projecting warm temperatures.

Prescriptive analytics

These analytics3(link resides outside ibm.com) help organizations make future decisions based on existing information and resources. Every business can use prescriptive analytics by using the existing data to make guesses about what will happen next. For example, marketing and sales organizations can analyze the lead success rates of recent content to determine what types of content they should prioritize in the future. Financial services firms use it for fraud detection by analyzing existing data to make real-time decisions on whether any purchase is potentially fraudulent.

Business analytics approach to data

To maximize an organization’s business analytics, it needs to clean and connect its data, create stunning data visualizations and provide insights on where a particular business is today while helping predict what will happen tomorrow. It usually involves the following components:

Data collection

First, organizations must identify all of the data they have on hand and what external data they want to incorporate to understand what opportunities for business analytics they have.

Data cleaning

Unfortunately, much of the data a company may be sitting on today is not “cleaned,” rendering it useless for real analysis unless it is addressed.

Here are some reasons why an organization’s data may need cleaning:

  • Incorrect data fields: Due to manual entry or incorrect data transfers, an organization may have bad data mixed with good data. If it has any bad data in the system, it has the potential to render the entire set meaningless.

  • Outdated data values: Certain data sets, like customer information, may need editing due to customers leaving, product lines being discontinued, or other historical data that no longer is relevant.

  • Missing data: Companies may have changed how it collects data or the data they collect, which means historic entries may be missing data that is crucial to future analysis. Companies in this situation may need to invest in either manual data entry or identify ways to use algorithms or machine learning to predict what the correct data should be.

  • Data silos: If an organization’s existing data is in multiple spreadsheets or other types of databases, it may need to merge so that it has all the data in one place. While the foundation of any business analytics approach is first-party data (e.g., data the company has collected from stakeholders and owns), they may want to append third-party data (e.g., data they’ve purchased or gleaned from other organizations) to match their data with external insights.

Data analysis

Companies now can query and quickly parse gigabytes and terabytes of data instantaneously with additional cloud computing. Data scientists can analyze data more effectively using machine learning (ML), algorithms, artificial intelligence (AI) and other technologies. Doing so can produce actionable insights based on an organization’s KPIs.

Data visualization

A company’s data is only as good as it can be understood by humans. Programs can now quickly take voluminous amounts of that analyzed data to create dashboards, visualizations and panels where the data can be stored, viewed, sorted, manipulated and sent to stakeholders. Data visualization serves several purposes for organizations, helping non-technical people understand analytics concepts, helping others see patterns in multiple data points, or demonstrating a business’s growth or decline. They can help with idea generation, idea illustration, or visual discovery. Data visualization best practices include understanding what visual best fits the data an organization is using and the key points it hopes to make, keeping the visual as clean and simple as possible, and providing the right explanations and content to ensure the audience it is shared with understands what they’re viewing.

Data management

Data management is conducted in tandem with the above, an organization that embraces business analytics must create a comprehensive strategy for maintaining its cleaned data, especially as it incorporates new data sources.

Business analytics use cases

Business analytics are useful for every type of business unit as a way to make sense of the data it has and help it generate specific insights that drive smarter decision making.

  • Financial and operational planning: Business analytics helps organizations to align financial planning and operations more seamlessly. It does this by setting rules for supply chain management, integrating data across functions, and improving demand forecasting.
  • Planning analytics: Planning analytics is an integrated business planning approach that combines uses spreadsheets and database technology to make effective business decisions about topics such as demand and lead generation, operations costs, and technology requirements. Many organizations have historically used tools like Excel for business planning, but some are transitioning to tools like IBM Planning Analytics.
  • Integrated sales and marketing planning: Every organization is sitting on historical data about their lead generation, sales conversions, and customer retention success rates. Organizations looking to create accurate revenue plans and forecasts and gain deeper visibility into their marketing and sales data using business analytics to easily allocate resources based on performance or changing demand to meet business objectives.
  • Integrated workforce performance planning: As organizations undergo digital transformation and otherwise react to changing landscapes, they need to ensure they have the right workforce with the right skills. It is especially true in a world where employees are more likely to leave a company for a new job. Workforce performance planning helps organizations understand their workforce requirements, identify and address skill gaps, and better recruit and retain talent to meet the organization's needs today and in the future.
Business analytics roles

Companies looking to harness business data will likely need to upskill existing employees or hire new employees, potentially creating new job descriptions. Data-driven organizations need employees with excellent analytical and communication skills.

Here’s the type of employees they will need to have to take advantage of the full potential of robust business analytics strategies.

  • Data scientists: These employees are usually responsible for managing the algorithms and models that power the company’s business analytics programs. Organizational data scientists either leverage open source libraries, like NTLK, for algorithms to use or build their own to conduct analysis on data. They excel at problem-solving and usually need to know several programming languages, like Python, which helps tap into access out-of-the-box machine learning algorithms, and SQL, which helps extract data from databases to feed into a model. In recent years, an increasing number of schools now offer master of science or bachelor’s degrees in data science where students engage in a degree program coursework that teaches them computer science, statistical modeling, and other mathematical applications.
  • Data engineers: They create and maintain information systems that collect data from different places that are cleaned and sorted and placed into one master database. They are often responsible for ensuring that data can be easily collected and accessed by stakeholders to provide organizations with a single-pane-of-glass view of their data operations.
  • Data analysts: Data analysts play a pivotal role in communicating insights to external and internal stakeholders. Depending on the size of the organization, they may be involved in collecting and analyzing the data sets and building the data visualizations or they may just take the work created by other data scientists and focus on building strong storytelling for the key takeaways.
Business analytics benefits

Modern organizations need to be able to make quick decisions to compete in a rapidly changing world, where new competitors spring up every year and customers’ habits are always changing. Organizations that prioritize business analytics have several advantages over competitors that do not.

  • More informed decisions: Having a flexible and expansive view of all the data an organization sits on can eliminate uncertainty and prompt an organization to take action quicker. If an organization’s data suggests that sales of a particular product line are precipitously declining, it may decide to discontinue it. If climate risk will affect the harvesting of a raw material another organization depends on, it may need to source a new material from somewhere else. It’s especially helpful when considering pricing strategies. How a company prices its goods or services is based on thousands of data points, many of which do not remain static over time. Whether a company has a fixed or dynamic pricing strategy, being able to access real-time data to make smarter short- and long-term pricing data is critical. For organizations that want to incorporate dynamic pricing, business analytics allows them to utilize thousands of data points to react to external events and trends to identify the most profitable price point as frequently as necessary.
  • Single-pane view of information: Increase collaboration between departments and line-of-business users because everyone has the same data and is talking from the same playbook. That can expose more unseen patterns, allow different departments to understand the company’s holistic approach and increase an organization’s ability to respond to changes in the marketplace.
  • Enhanced customer service: By knowing what customers want and when and how they want it, organizations will drive happier customers and, therefore, engender greater loyalty. Additionally, by being able to make smarter decisions on resource allocation or manufacturing, organizations are likely able to offer those goods or services at a more affordable price.
Business analytics products
Planning analytics IBM Planning Analytics

The flexibility of spreadsheets. Control of a database. The power of integrated business planning. Now available as a Service on AWS.

Learn more Request a demo

Business analytics IBM Cognos Analytics

AI-powered automation and insights in Cognos Analytics enable everyone in your organization to unlock the full potential of your data. 

Learn more Request a live demo

Business automation IBM Instana Observability

Detects application and business risks affecting the customer experience, enabling users to correlate application service level objectives with underlying infrastructure resourcing.

Learn more Start a free trial
Business analytics resources

Learn more about business analytics by reading these blogs and articles. 

It’s 2023… are you still planning and reporting from spreadsheets?

IBM Planning Analytics has helped support organizations across not only the office of finance – but all departments in their organization.

How IBM Planning Analytics can help fix your supply chain

A growing number of forward-looking companies are successfully navigating complexities using IBM Planning Analytics, a technology capable of supporting secure collaboration, fast automated data acquisition, and more.

What is predictive analytics?

Predictive analytics is a branch of advanced analytics that makes predictions about future outcomes using historical data combined with statistical modeling, data mining techniques and machine learning.


1 Business Intelligence vs. Business Analytics (link resides outside ibm.com), Harvard Business School
How predictive analytics can boost product development (link resides outside ibm.com), McKinsey, August 16, 2018
What is prescriptive analytics? (link resides outside ibm.com), Harvard Business School Blog, November 2, 2021