Business analytics is the process of collecting and analyzing data from business operations to gain actionable insights. This information can be applied to measuring business performance, predicting outcomes and more.

The work is valuable for organizations and, as a result, related job opportunities are growing. This article will further explore business analytics so you can decide if a career in this field is right for you.

Key functions of business analytics

There are four often-used types of business analytics methodologies:

  • Descriptive analytics: Describes the data
  • Diagnostic analytics: Seeks to explain why an event happened
  • Predictive analytics: Uses existing data to project future outcomes
  • Prescriptive analytics: Helps organizations make decisions based on existing data

Joseph Ingles, assistant professor in the College of Business and Management at Lynn University, shares more insights about extracting value from data.

“We must always start by asking what we're looking to answer,” Ingles says. “Do we have the data to answer the question, or do we need to collect new data? That gives us two different processes we can use. For instance, if we already have the data, we can answer those questions relatively quickly. If not, we have to do surveys and collect data from secondary sources or wherever we can find it.”

Ingles adds that organizations often already have the data they need, as so many aspects of everyday life—from driving down the road to purchasing at a store—involve data collection.

How is business data collected?

The Cross-Industry Standard Process for Data Mining, or CRISP-DM, is a well-established process model for business analysis. It provides a structured approach to data planning, data collection, data visualization, the assessment of data mining efforts and more. CRISP-DM consists of six major phases:

  • 1. Business understanding: Establishes the project objectives and requirements from a business perspective
  • 2. Data understanding: Collects and explores the data to make sense of its properties, assess its quality and uncover initial insights
  • 3. Data preparation: Cleans and transforms the data to make it suitable for analysis
  • 4. Modeling: Applies modeling techniques to the prepared data to find patterns, relationships or predictions
  • 5. Evaluation: Assesses the model’s performance to ensure it effectively addresses the business objectives
  • 6. Deployment: Implements the final model in a real-world setting, which could involve automating it in a production environment, creating a dashboard or generating reports for stakeholders

Ingles says following a process such as CRISP-DM can take anywhere from 18-24 hours to 2-3 weeks, depending on the size and type of campaign and business strategy.

Ultimately, gathering data and building and deploying models is not a one-time effort. “It’s an interactive process that you do over and over until you make sure you have it right,” Ingles says.

Benefits of business analytics

By using business analytics, organizations can improve their planning, customer experience, risk management and more. Businesses that use analytics may potentially benefit from:

Enhanced decision-making

Businesses analyze historical and real-time data to make informed decisions and improve forecasting accuracy, enabling better strategic planning. A notable percentage of small business owners who participated in a Chase survey plan to invest in technology and data analytics. The majority of these individuals stated they would likely use data to help with competitor analysis and understanding customer buying habits.

Personalized customer experiences

The Chase survey respondents' willingness to invest in data analysis to better understand customer habits reflects businesses' ability to customize their offerings. This data allows organizations to tailor marketing strategies to specific consumer segments, which can improve customer satisfaction. This personalization can foster loyalty and enhance the overall customer experience.

A competitive advantage

Businesses can leverage artificial intelligence (AI) to anticipate market trends and respond proactively. This helps them maintain a competitive edge in rapidly changing markets. Twenty-four percent of small business owners who participated in a Truist survey plan to use AI to get actionable insights on customers and markets.

Augmented risk management

Businesses are susceptible to many risks that analytics can help identify and assess, including but not limited to:

  • Compliance risk
  • Financial risk
  • Operational risk
  • Reputational risk

According to the International Association of Business Analytics Certification (IABAC), two types of analytics can yield risk management success:

  • Predictive analytics: Analyzing historical data can help uncover future risk trends. Predictive models allow businesses to forecast future risks based on data analysis, thereby enhancing decision-making.
  • Prescriptive analytics: Simulating various risk scenarios and leveraging real-time data allows businesses to develop effective risk response strategies.

How does business analytics differ from business intelligence?

While business analytics and business intelligence are sometimes used interchangeably, business intelligence is a subset of business analytics.

Business intelligence refers to the infrastructure, processes and tools that collect, store and analyze data. It transforms raw data into actionable insights through descriptive analytics, performance benchmarking and trend analysis to support management decisions.

Indeed states business intelligence is about what's happening now, while business analytics focuses on using predictive analytics to determine what could happen in the future.

Top business analytics challenges

Factors creating challenges for business analytics include:

Artificial intelligence

While AI presents many opportunities for business analytics, integrating it effectively can be challenging. According to Ingles, AI has upended many aspects of business analytics and has led to a need for new skills.

“The skills needed for business analytics have transformed,” he says. “AI is not only changing the way we work but the very work we do. In the classroom, I’m teaching my students higher-order critical-thinking skills.”

By incorporating AI into business analytics workflows, companies can streamline processes, reduce human error, and enhance their ability to make data-driven decisions. AI tools also assist in coding and developing analytical models with greater efficiency.

However, Ingles harkens back to the DIKW Pyramid to emphasize the importance of humans in the business analytics process. Ingles uses the example of AI identifying a marketing demographic and emphasizes that human insight is needed to determine its relevancy.

Transparency and regulatory compliance

Simply assuming everybody knows their data is being collected isn’t enough, Ingles explains. It’s important to fully disclose what data is being collected and how it is used.

“Transparency in gathering data, transparency in how it’s analyzed and transparency about what you’re doing with it after it’s analyzed are of utmost importance,” he says.

Myriad federal laws, rules, regulations and guidelines about data management, usage and transparency in the United States make this more challenging. With 50 states and several territories, there are a lot of ways that data is regulated, Ingles adds. He advises organizations to lead with transparency and accountability no matter where they do business.

Data privacy and security

The global average cost of a data breach in 2024 was $4.88 million. While financially devastating for businesses, breaches also damage consumer trust. A majority of consumers around the globe say they’ve been impacted by a data breach against a company they’ve done business with. Moreover, 80% of impacted consumers say they’re likely to take their business elsewhere after a company experiences a cyberattack.

This challenge underscores the importance of ethical data handling and processing to minimize the impact of breaches—both on businesses and customers.

Demand grows for analytics professionals

Skills that students acquire while completing a business analytics degree program can be useful in professions across industries. Ingles notes that students who graduate with a data analytics degree often go on to become database administrators, data engineers or customer service analysts.

In 2023, the global big data analytics market was valued at $327.26 billion, with a projected compound annual growth rate of 14.9% through 2030. This highlights organizations' increased investments in data-related initiatives.

Using database administrators as an example, the Bureau of Labor Statistics (BLS) projects an 8% growth in job opportunities through 2033, faster than the average for all occupations. The increasing demand for valuable data across sectors is driving job growth. Continued migration to the cloud and the adoption of AI make these professionals critical, according to the BLS.

Additional reasons for job growth in data analytics include:

Desire to edge out competitors

More than half of senior IT leaders and decision-makers at U.S. organizations who participated in an Adastra survey state that they're behind competitors in the use of analytics to support business operations. Companies can benefit from adept business analytics professionals who can use data to gain insight into a competitive market.

Increasing business data complexity

As businesses add new products and services to their offerings, they collect more data about the market, the target customer and the product itself. Volume and variety are two factors that contribute to data complexity. The more data a business collects, the more complex information management becomes. This requires skilled analysts who specialize in interpreting large datasets.

Is a business analytics program right for you?

With an online bachelor's degree in data analytics, Lynn graduates could go on to help businesses make smarter decisions that drive revenue, increase business performance and enhance operational efficiency. Request more information to connect with a student success manager.