Is there such a thing as too much data? Once upon a time, that question may have seemed absurd. But with modern businesses constantly improving the capabilities in collecting information—capturing ever-more immense datasets related to everything from customer behavior to supply chain logistics—data overload has become a real problem. Raw numbers pile up in spreadsheets, dashboards fill with unread reports, and decision-makers struggle to separate valuable insights from digital clutter.
Simply put, more data does not automatically mean better decisions; it just means more complexity. The real advantage comes from knowing how to extract meaning from the noise, turning scattered information into a clear strategic direction. Business analytics (BA) has grown over the decades to help organizations better manage the torrential flow of increasingly available data.
Origin of business analytics
The concept of using data to gain a competitive edge dates back long before modern computing. In 1865, banker Sir Henry Furnese made a name for himself by systematically gathering and acting on information before his rivals. By the late 1800s, Frederick Taylor introduced scientific management, a system for analyzing labor efficiency that influenced industries worldwide. Henry Ford later refined this approach, using time-motion studies to optimize his assembly lines.
Fast forward to the mid-20th century, and the rise of computing paved the way for large-scale data storage and processing. Throughout the 1990s, the advent of data warehouses and business intelligence (BI) solutions transformed how companies approached analytics. Today, cloud computing, artificial intelligence (AI), and machine learning (ML), have helped BA evolve into an indispensable tool for businesses navigating an increasingly data-driven world.
What is business analytics vs. data analytics vs. data science?
The terms business analytics, data analytics, and data science are sometimes used interchangeably. Each discipline contributes to turning raw information into actionable insights, but they serve specific roles in how organizations process and interpret data:
- Business analytics focuses on analyzing data to solve specific business problems. It involves identifying trends, generating insights, and creating visual models—all of which can aid in promoting informed decision-making. Business analysts use structured data and tools like visual dashboards and predictive analytics to optimize operations and gain a clearer perspective on what is likely to occur.
- Data analytics is a broader field that examines data sets to uncover trends, patterns, and relationships. Unlike business analytics, it is not strictly limited to business applications. Data analytics can be used in scientific research, healthcare, and other fields to extract meaningful information from structured and unstructured data.
- Data science takes analytics a step further by incorporating advanced statistical techniques, machine learning, and custom coding. Data scientists explore complex data sets to develop predictive models and discover insights that may not otherwise be immediately obvious. Their work often informs long-term strategy and innovation.
In other words, business analytics is a specialized subset of data analytics focused on business decision-making. Data science, on the other hand, applies advanced scientific methodologies to find deeper meaning and solve broader, often more complex, problems.
Organizations generate massive amounts of data, but data alone is not enough—it’s what businesses do with it that matters. BA helps companies understand the meaning behind the information and then apply it to build effective strategies. And, in a constantly shifting business environment, this kind of data-based clarity is an absolute prerequisite to making informed, low-risk decisions.
Business analytics benefits
When implemented effectively, business analytics provides measurable advantages across departments. Below are some of the key benefits organizations can gain by leveraging BA:
- Single-window view of information
BA integrates data from multiple sources into a single source of truth, reducing the likelihood of data silos and ensuring consistency throughout the organization. Instead of navigating disconnected systems, employees and decision-makers can access comprehensive insights from a single dashboard for improved collaboration and data accuracy.
- Enhanced customer service
BA helps businesses tailor their services by analyzing customer behavior and preferences. Support teams can use these insights to anticipate issues and improve response times, creating a more personalized customer experience. This leads to stronger relationships and higher retention rates.
- Reduced risk
Organizations face risks in many forms—from market fluctuations to operational disruptions. BA helps mitigate these risks by identifying any evidence of potential problems. By leveraging predictive analytics, companies can preemptively adjust their strategies, reducing financial losses and improving resilience in the face of instabilities.
- Improved operational efficiency
BA helps organizations resolve the inefficiencies that slow down operations. Analyzing workflows and resource usage, businesses can pinpoint areas that need improvement. With these insights, they can eliminate unnecessary steps and allocate resources more effectively, leading to lower costs and increased productivity.
- More informed decision-making
With BA, decisions are based on data rather than guesswork. Organizations can more confidently choose strategies that match their business goals and market demands.
- Greater revenue
Investing in analytics has a direct impact on financial performance. Companies that effectively utilize BA often see increased profitability by locating new revenue streams, optimizing pricing strategies, and reducing operational waste.
- Effective visualization of complex data
BA tools transform raw data into visual formats (such as dashboards, charts, graphs, etc.) making it easier for non-technical users to interpret. Clear, interactive visuals allow decision-makers to quickly grasp key insights and take action based on real-time information.
Business analytics challenges
Despite its many advantages, implementing a business analytics strategy comes with some challenges. Addressing these obstacles early on helps BA deliver real business value:
- Too many data sources
With data coming from multiple systems—including IoT devices, CRM/CSM platforms, and external feeds—integrating information into a single analytics framework can be a complex task. Businesses need strong data governance policies and integration tools to effectively clean, standardize, and merge disparate data sources.
- Lack of skills
There’s a skills gap in modern information technology (IT), and often the demand for data analytics professionals outpaces supply. This can make it difficult for some businesses to find qualified talent. Companies can address this gap by investing in training and skills development initiatives for their internal employees. Additionally, working with user-friendly analytics tools and leveraging AI-automation to simplify data analysis can close this gap for non-technical staff.
- Data storage limitations
As data volumes grow, businesses must determine where and how to store this information. Traditional storage solutions may not be scalable or cost-effective. Cloud-based storage and data lakes offer flexible, scalable alternatives that accommodate large data sets while ensuring accessibility and data privacy.
Employing various technologies and methodologies makes it possible for BA to give companies a clearer understanding of what the numbers are trying to say. To do this, business analytics relies on several core capabilities:
- Data collection
Organizations gather data from multiple sources to compile a comprehensive dataset that accurately reflects business operations and market conditions.
- Data cleaning
Data cleaning processes remove duplicate entries, correct inaccuracies, and standardize formats to ensure reliability and eliminate the risk of basing decisions on flawed information.
- Data analysis
Analysts apply analytical techniques to structured data to discover trends and anomalies, uncovering hidden patterns that can drive business strategy.
- Data visualization
Effective visualization allows decision-makers to quickly grasp key findings and respond to emerging trends.
- Data management
A strong data management strategy keeps analytics systems accurate, scalable, and secure. Businesses must continuously monitor and update their data infrastructure to accommodate new sources, maintain compliance, and improve decision-making over time.
Business analytics is an ongoing process that follows a structured framework, applying the capabilities mentioned above to ensure that data is not only collected but also cleaned, analyzed, and applied. This systematic method may take slightly different forms depending on the business that is applying it, but most follow generally the same phases or steps:
1. Identifying needs and objectives
The first step in the business analytics process is understanding what problem needs to be solved. Organizations must define clear objectives—improving customer retention, optimizing operations, predicting market trends, etc. Stakeholders and analysts will also need to determine what data is available, find the gaps in that data, and establish key questions to help guide the analysis.
2. Preparing data
Raw data is rarely ready for analysis. Before any meaningful insights can be extracted, the data must be cleaned and structured, addressing any errors or missing values and standardizing formats across all sources. Analysts also play a role in this phase as they check for inconsistencies and outliers. Once the dataset is prepared, visualization techniques will help highlight important elements.
3. Analyzing data
With clean data in place, analysts search for data relationships that are influencing business outcomes. Statistical methods help identify key drivers behind trends, providing a deeper understanding of the factors at play. By examining the data from multiple perspectives, analysts can uncover insights that may not have been immediately apparent. The focus in this step is on moving past surface-level observations to extract more relevant connections.
4. Comparing predictions
Business analytics goes beyond describing past performance—it also predicts future outcomes. Using machine learning models, decision trees, and other predictive techniques, analysts test different scenarios to determine what patterns may be indicative of trends. These predictions are then compared against actual data to assess accuracy—if errors or discrepancies arise, adjustments are made to refine the model.
5. Determining the best solution
Once predictive models have been validated, businesses use them to evaluate different strategies. Decision-makers assess possible outcomes based on organizational goals and constraints. ‘What-if’ scenarios help determine which course of action will have the most favorable impact.
6. Measuring results
Data-driven decisions must be evaluated to determine their effectiveness. After implementing changes based on analytics findings, businesses monitor key performance indicators (KPIs) to measure impact. If results do not meet expectations, further analysis may be needed to refine the approach.
7. Updating the system
The business analytics process does not end once a decision is made. To maintain relevance, organizations must continuously update their analytics systems with new data and insights. This ensures that future analyses are based on the most current information available.
Business analytics is not limited to a single function—it extends across departments, helping organizations apply data-driven strategies to real-world challenges. Below are some of the most impactful ways businesses use analytics to enhance decision-making and performance:
Marketing
Marketing teams use BA to measure campaign performance and refine audience targeting. With predictive modeling, businesses can adjust strategies to maximize engagement.
Sales
BA helps sales teams understand the factors that drive conversions. Analyzing customer interactions and past transactions gives businesses clearer direction as they refine pricing models and prepare for shifts in product demand.
Human resources
HR analytics provides deeper insights into workforce trends, hiring effectiveness, and employee retention. Organizations can assess recruitment channels, evaluate performance metrics, and see the factors influencing turnover, improving human resources management and making it easier to attract and retain top talent.
Finance
Finance departments rely on BA for accurate forecasting and strategic budgeting. Identifying spending inefficiencies helps organizations allocate resources more effectively and minimize financial risk.
Manufacturing
BA improves production efficiency in manufacturing by locating bottlenecks and providing better insights into equipment performance. Manufacturers can use this data to streamline workflows for smoother operations and higher-quality output.
Logistics
Supply chain risk management may use BA to track inventory levels, predict disruptions, and enhance delivery efficiency. Basing decisions on real-time data helps minimize operational delays and reduce costs.
Workforce management
Organizations use BA to enhance workforce optimization—assess staffing needs, improve scheduling, and ensure teams have the right skills to meet business demands.
Customer interactions
BA enables businesses to refine the customer experience by seeing patterns in preferences and behavior. Monitoring customer satisfaction scores and other forms of feedback helps companies enhance their customer service strategies and strengthen brand loyalty.
Business analytics sets a path for leaders and other decision makers to get the most out of the vast quantities of data they have available, but to do so, they need the right tools. ServiceNow optimizes BA by providing a powerful platform for bringing together data, workflows, and AI-driven insights.
The Now Platform® connects systems across the organization, eliminating data silos and providing decision-makers with real-time analytics. Predictive Intelligence automates data classification, routing, and analysis, helping organizations detect trends and forecast outcomes. Solve issues quickly with AI-powered recommendations, automatically route tasks to the right teams, and track machine learning-driven improvements with interactive dashboards. Performance monitoring and cluster analysis refine predictive models over time, ensuring continuous improvement in analytics capabilities.
And now, Workflow Data Fabric takes things even further by uniting structured, unstructured, and streaming data across the enterprise—no complex integrations required. This advanced tool contextualizes incoming data to make it relevant to business operations and analytics, which means AI agents and human decision-makers can act on it faster and more accurately. Governance tools ensure data remains secure and compliant, while built-in connectors and real-time streaming support analytics at enterprise scale. Workflow Data Fabric turns your complex datasets into clear operational insights, so you can grow your business.
See how Workflow Data Fabric can improve your analytics maturity—demo ServiceNow today!