Data-driven decision-making is essential for success in a competitive business environment. That’s why business analytics, which comprises the tools, processes and skills used to inform business decisions, is increasingly important for businesses of any size. Business analytics can inform decision-making and actions across all functions — from sales and marketing, human resources and manufacturing to finance and executive leadership. Companies use business analytics to help cut costs, maximize profits, improve customer and employee experiences, respond to market changes and outsmart their competitors.

What Is Business Analytics?

Organizations have access to more data — and more different types of data — than ever before. Business analytics helps companies take advantage of that data when making decisions and developing strategies. With the right capabilities, organizations can analyze data ranging from customer transactions and online behavior to social media posts and sensor data. This helps them better understand what has happened, what is likely to happen, and how they might best respond to and take advantage of it. Companies use business analytics to help determine what they can do to boost revenue, increase efficiency and productivity, improve the employee experience and meet customer needs.

Business Analytics vs. Data Analytics

Data analytics is the overarching term for the process of analyzing raw data and transforming it into metrics and insights. Business analytics is a type of data analytics that focuses on the needs of businesses; it can be used to analyze both internal and external data.

Business Analytics vs. Business Intelligence

Business intelligence and business analytics are closely related. There’s a lot of overlap between the two, and the terms are sometimes used interchangeably. But there’s a key difference in emphasis. Business intelligence focuses largely on supporting day-to-day operational management of the business. It provides reports and dashboards that enable organizations to track performance against key metrics, with an emphasis on what has already happened and what is happening now. Business analytics, in contrast, is more oriented toward what might happen in the future, the potential outcomes of actions and what to do based on that knowledge. Companies tend to use business intelligence to understand their organization’s past or present performance, while business analytics offers a way to visualize and act on future trends.

Business Intelligence Business Analytics
Used to report on a company’s current or past performance Used to predict what might happen in the future and provide direction about how to respond

Business Analytics vs. Data Science

Data science is the application of machine learning, statistics and algorithms to gain insights from structured and unstructured data. Business analytics applies data science techniques to answer specific business questions and solve business problems.

Key Takeaways

  • Business analytics transforms vast amounts of data into insights, helping companies make informed decisions, and is used to predict future trends based on current and past data.
  • While data analytics deals with transforming raw data into insights, business analytics is specific to business needs, analyzing both internal and external data.
  • From pricing flights in airlines, managing patient flow in hospitals, to forecasting in finance, business analytics is employed across industries and functions.
  • To maximize the benefits of business analytics, companies should secure executive buy-in, focus on business outcomes, utilize cloud solutions, invest in data governance, and adopt a test-and-learn approach.

Business Analytics Explained

Business analytics is growing in importance largely because companies increasingly rely on technology and data to run their business — and they have more data than ever before. Business analytics transforms data into insights, so companies can use the data to drive better-informed decisions and actions. Organizations can use business analytics to describe the current state of the business, understand trends, anticipate what is likely to happen and suggest future actions. While business analytics uses advanced technologies such as data aggregation, data mining, statistical models and machine learning, user-friendly interfaces and data visualizations help employees put business analytics to use in their daily workflow.

How Is Business Analytics Used?

Organizations of every size, in every industry, use business analytics to improve operations. Business analytics can be applied to almost any function within a business. An airline may use business analytics to price flights based on demand. A hospital may turn to business analytics to optimize patient flow. A retailer may rely on business analytics to anticipate demand. Manufacturers use business analytics to monitor and manage the performance of their factories and suppliers and to anticipate customer demand. Marketers can analyze millions of customer interactions to better understand customer sentiment, intent and needs. Finance professionals utilize business analytics for forecasting and planning.

Why Is Business Analytics Important to Businesses?

Business analytics enables companies to better understand and predict performance and trends, both within their own business and across the markets in which they operate. Business analytics help organizations:

  • Create a clear picture of what’s working and what isn’t in order to improve performance.
  • Facilitate faster, more-informed decision-making.
  • Respond more quickly to events and better mitigate risks.
  • Foster change and innovation.
  • Better anticipate and plan for the unexpected.

Four Types of Business Analytics

Companies can use several types of business analytics. Each provides different insights: Some focus mainly on past and current performance, while others help analyze what might happen in the future. The four primary types are:

  1. Descriptive analytics

    Descriptive analytics is the most basic and prevalent type of business analytics. It helps companies track patterns and trends in current and past data. Examples of descriptive analytics include historical reports, key performance indicators (KPIs) and metrics.

  2. Diagnostic analytics

    A more advanced form of analytics, diagnostic analytics examines why things happened. It focuses on identifying the causes of trends and events. Diagnostic analytics often requires more extensive use of data science techniques than descriptive analytics.

  3. Predictive analytics

    As its name suggests, predictive analytics focuses on predicting what is likely to happen in the future. It estimates the likelihood and possible effect of specific outcomes, helping companies analyze the potential impact of events, trends and problems. It uses a variety of data science techniques, including forecasting, advanced statistics, pattern matching and predictive modeling.

  4. Prescriptive analytics

    This is the most advanced flavor of business analytics. It aims to answer the question of what should be done or how an organization can achieve a certain outcome. It can require very sophisticated data science techniques such as machine learning, complex event processing and neural networks.

Companies can combine multiple types of analytics to provide a more complete view of business performance and to solve problems. An organization might use descriptive analytics to look at what customers have done, diagnostic analytics to understand why, predictive analytics to forecast what they might do or want next and prescriptive analytics to determine the best way to meet their needs.

Elements of Business Analytics

Business analytics uses a variety of methods to extract insights from data and present them effectively. They include:

  • Data mining. Data mining enables organizations to uncover insights hidden in large volumes of structured and unstructured data. It highlights correlations and patterns in the data using statistics, machine learning and other techniques.

  • Text mining. This is a type of data mining used for text-based documents and communications. It can be used to extract insight from social media posts, call center transcripts, emails and more.

  • Data aggregation. Organizations often gain greater value by analyzing data that’s combined, or aggregated, from multiple sources. This can require several steps to clean data and resolve any consistencies. A marketing team, for example, may aggregate data from point-of-sale systems, online platforms and customer databases to analyze customer behavior and optimize marketing programs.

  • Forecasting. Forecasting helps organizations predict future events and their potential impact. Forecasting analyzes historical business data and external trends in order to make informed estimates about the future. It can be applied to many aspects of the business, such as forecasting financial data, customer demand and call center volumes.

  • Data visualization. Data visualizations such as charts and graphs help employees quickly understand the results of business analytics. Effective data visualizations make it easy for individuals to see patterns and trends, so they can use the information when making decisions.

Benefits of Business Analytics

When applied effectively, business analytics can be key to achieving competitive advantage in the digital age. Organizations can be overwhelmed by the sheer volume of data available. Business analytics enables companies to derive insights from the data deluge, helping decision-makers and employees make more-informed decisions so they can better meet customer demands, optimize operations, increase market share and improve profitability. Specific benefits of business analytics include:

  • Greater clarity about historical and current business performance — what has worked, what hasn’t worked and why.
  • Better understanding of customer behavior and marketplace trends.
  • Minimizing the risks to the business by predicting the potential impact of market shifts.
  • The ability to measure the effectiveness of new strategies and compare performance of business groups.

Challenges of Business Analytics

Developing effective business analytics capabilities takes some effort to assemble the right data, processes, skills and tools. It can be challenging to obtain executive buy-in, and employees must also be prepared and willing to apply business analytics in their jobs and act on the resulting insights. Dedication to the business analytics strategy is required because it may take time to realize the benefits. Here are some of the most common hurdles to developing effective business analytics capabilities:

  • Gaining executive-level support. If executives don’t understand the value of business analytics, they won’t allocate the budget and resources needed to develop effective analytics capabilities. This includes building a data-driven culture within your organization.
  • Creating a clear business analytics strategy. Without a clear idea of what the organization wants to achieve, business analytics tools may languish unused and the investment in the technology may be wasted. Identifying what the organizations key metrics are, and how they should be measured is a key part of business analytics.
  • Developing the ability to aggregate and reconcile data across different systems and sources. Data may be spread across multiple systems in a variety of inconsistent formats. Without establishing processes to extract and combine the data, the value of business analytics may be limited. Avoiding these data silos that can form in organizations allows you to get faster insight from your data, and provide more robust data.
  • Investing in the appropriate IT infrastructure and support. On-premises business analytics may require a significant hardware and software investment to build a data warehouse that stores data for analysis, as well as other supporting infrastructure. Beyond just hardware and software—hiring the right staff to support those systems can be a challenge.
  • Ensuring employee understanding and buy-in. Changing behaviors is not easy. If employees don’t get the right tools and training, and don’t understand the value of business analytics to their jobs, they may not use the technology.

Business Analytics Examples

Organizations in all industries apply analytics to improve many aspects of their business. Health-care providers use business analytics incorporated into clinical information systems to optimize patient care. Restaurants may use business analytics to plan for peak dining times. Manufacturers use business analytics to streamline inventory and logistics or anticipate shifts in demand. Typical uses of business analytics include:

  • Improving business forecasts by developing more accurate estimates of future financial performance or customer demand.
  • Optimizing product or service pricing.
  • Increasing operational efficiency by analyzing and comparing costs within different parts of the business.
  • Understanding customer behaviors and sentiment, and using those insights to develop personalized recommendations.
  • Enabling preventative maintenance to minimize downtime, based on analysis of failure rates in sources such as maintenance logs and trouble tickets.
  • Identifying and mitigating financial, supply-chain and logistics risks.

Business Analytics Best Practices

Achieving the undeniable benefits of business analytics requires more than just buying new analytics tools. Here are some best practices to help get the most value from the company’s investment in business analytics:

  • Get executive buy-in. Developing effective business analytics takes time and money. It requires support from the top to ensure an adequate budget. Executive backing can also help make sure employees commit to using the technology.
  • Focus on business requirements and outcomes. With so many business analytics tools available, it’s easy to become focused on product features rather than thinking about how well they fit the needs of the business. It’s important to define your needs and priority use cases first, then look for solutions that cost-effectively meet those needs.
  • Take advantage of the cloud. Building an on-premises business analytics system can be time-consuming and expensive. Cloud-based solutions can eliminate the need for up-front investment in hardware and software, simplify management and enable organizations to get up and running faster. Cloud solutions can also bring large enterprise solutions to small organizations at a fraction of what the on-premises cost would have been.
  • Invest in data governance. Correct and consistent data is critical to ensure that business analytics delivers accurate insights. It’s also vital to make sure personal data is handled correctly for regulatory compliance. A data catalog should be a central part of any business analytics effort.
  • Make the insights usable. Data visualization is your friend. Business analytics are most valuable when employees can quickly understand the results and take action accordingly.
  • Invest in analytics training and change management. Employees need to understand how business analytics benefit them and acquire the skills needed to make the best use of analytics.
  • Define your success metrics. Decide on the criteria for business analytics effectiveness — whether that means increased accuracy of financial forecasting, better views of business performance or faster response to changing market conditions — and assess progress over time.
  • Take a test-and-learn approach. Implementing business analytics can be an art as well as a science. Learn from your early experiences and successes to improve what works well and rethink what doesn’t.

Careers in Business Analytics

Because analytics is so important for helping organizations thrive, business analytics professionals are in high demand. A variety of career paths are possible. Some of the most in-demand professionals are:

  • Data scientists, who understand how to collect, organize and analyze data for business value. Data scientists may need specialized skills in computer science, statistics and mathematics to create analytical models and apply them to data.
  • Data analysts, who are skilled at using business analytics tools to identify relationships in data and communicate insights to business users.
  • Business analysts and consultants, who have expertise in specific industries or in functions such as marketing or manufacturing, and who understand how to apply analytics to those areas.

Many other roles within the organization may have some involvement with business analytics, including database administrators, market research analysts and other marketing specialists, and data architects.

Business Analytics Skills

Most employees don’t need specialized expertise to use business analytics tools to generate reports, charts and other useful output. However, data scientists, analysts and others closely involved with the organization’s business analytics strategy need specific skills to collect data, perform complex analysis, build dashboards or other tools for less-skilled employees, and communicate insights to others. Data scientists, for example, must understand how to gather information from different sources and apply advanced statistical techniques to analyze the data. Business analysts need a good understanding of the business, as well as the technology, so they can generate insights that meet specific business needs.

Gain Business Insights With Business Analytics

Analytics capabilities that are embedded into business applications help employees apply analytics in their daily workflow to inform decision-making and improve business performance. NetSuite SuiteAnalytics embeds real-time reporting, KPIs and dashboards into a leading enterprise resource planning (ERP) application suite to provide up-to-date, actionable insights into company performance. Analytics also helps unveil patterns in data that can predict and guide decision-makers toward the best potential outcomes.

Business analytics has become an essential requirement for remaining competitive and optimizing business performance. It enables more informed decision-making, providing actionable insights into business performance and market conditions. Analytics helps businesses not only optimize current and business performance but also navigate an uncertain future.

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Business Analytics FAQs

What are the 3 types of business analytics?

Three common types of business analytics are descriptive, predictive and prescriptive analytics. Descriptive analytics are the most widely used and basic form of analytics. They help an organization understand what has happened or what is currently happening. More advanced predictive analytics are used to determine what is likely to happen in the future. Prescriptive analytics help a company decide how it should act in order to respond to events or achieve a desired outcome. A fourth widely used type of analytics is diagnostic analytics, which is used to determine the cause of events — i.e., why something has happened.

What are examples of business analytics?

Organizations in every industry use business analytics to aid decision-making. Companies can use business analytics to optimize product pricing, increase operational efficiency, understand customer sentiment, develop preventative maintenance schedules, identify problems and mitigate risks.

What is a career in business analytics?

There’s a growing number of career paths related to business analytics. They include data scientists, who understand how to collect, organize and analyze data for business value; data analysts skilled at presenting data and insight to business users; and other roles such as business analyst, data engineer, database administrator, management consultant, systems analyst, marketing specialist, data architect and analytics manager.

What kind of jobs can you get with a business analytics degree?

Because business analytics is essential for organizations to thrive in the digital business era, business analytics skills and professionals are in high demand. People with degrees in business analytics or related areas such as data science, information management or statistics have a number of possible career paths, including data scientist, data analyst, data architect, systems analyst, marketing specialist, analytics manager, business analyst, data engineer, operations research analyst and market research analyst.