The desire to turn data into actionable knowledge is nothing new. In 1865, Richard Millar Devens used the term “business intelligence” to describe how one banker gained a competitive edge by gathering and acting on information before his rivals. A century later, IBM researcher Hans Peter Luhn explored how technology could automate this process. By the 1970s, Edgar Codd’s relational database model transformed how businesses stored and accessed data, paving the way for modern analytics. Over the years, various tools and other advancements further refined the process, making business intelligence more accurate and accessible. 

Today, BI extends well beyond simple reporting capabilities; companies integrate data from an ever-growing range of sources, including internet of things (IoT) devices, automated tools, and advanced analytics to gain deeper, more-relevant insights. Real-time data streams from devices, financial markets, and even social media shape business strategies in ways early adopters could never have imagined. With prescriptive and predictive analytics now part of the equation, organizations can understand what has happened in the past while also anticipating what is coming next—and make informed decisions based on knowledge.  

Business intelligence and business analytics (BA) are closely related, but can be thought of as serving individual roles in how organizations process and interpret data. BI is an umbrella term that includes BA but is primarily concerned with analyzing past and present data to assess business performance, while BA focuses on using data to predict future trends. 

More specifically, the differences between BI and BA can be described as follows: 

• Business intelligence applies descriptive and diagnostic analytics, helping organizations understand what has happened and why. It relies on data collection, storage, and visualization tools to provide a clear picture of business performance. BI answers questions such as, "How many new customers did we gain last quarter?" or "What were our top-selling products last year?" It emphasizes structured data analysis and is widely used for reporting, dashboards, and performance tracking. That said, business intelligence encompasses other specialized approaches, including business analytics. 
• Business analytics incorporates predictive and prescriptive analytics. It seeks to uncover patterns and identify correlations to accurately forecast future outcomes. Using techniques like data mining, machine learning (ML), and statistical modeling, BA helps businesses answer forward-looking questions such as, "What will customer demand look like next quarter?" or "How can we optimize pricing strategies?" 

In other words, BI is most effective at informing companies about the past and generating insights from historical data, while BA is better suited to predicting future events or outcomes. 

Data is everywhere; organizations generate massive amounts of it every day, and tools are available to help capture data from other relevant sources as well. But without a way to process and interpret that information, its true value remains untapped. BI transforms this raw data into something usable. By creating a data-driven culture, BI helps organizations go beyond guesswork, ensuring that strategies are based on concrete facts rather than assumptions. The end result? Informed strategies that match real-world conditions—even when those conditions may not be readily apparent. 

Business intelligence benefits 

On a more granular level, implementing BI provides organizations with a range of specific advantages. These include: 

• Enhanced employee satisfaction  

Providing employees with self-service access to critical business data allows them to retrieve information without relying as heavily on information technology (IT) departments. This empowers staff to make better informed decisions on their own, improving overall job satisfaction. 

• Consolidated data  

BI integrates data from multiple sources—such as CRM/CSM systems, financial records, and market research—into a single platform. This eliminates the inefficiencies of scattered information, ensuring every authorized user has easy access to accurate, consistent insights. 

• Heightened visibility  

Organizations can monitor key business functions with greater clarity, allowing managers and other leaders to track performance across departments. Instead of relying on fragmented reports, decision makers can quickly identify inefficiencies and address potential issues before they escalate. 

• Clearer reporting  

BI turns complex data into easy-to-understand dashboards and reports. Real-time metrics highlight performance, trends, and areas for improvement. 

• Optimal support for sales  

Sales teams can use BI to identify high-value prospects and track customer interactions while also accurately forecasting demand. This provides better data on which to make decisions related to sales strategies and potential revenue opportunities. 

• More actionable information  

BI tools uncover trends and correlations that might otherwise go unnoticed, giving businesses a data-backed foundation for making adjustments. 

• Improved efficiency  

Automating data collection and analysis reduces manual effort, allowing employees to focus on higher-value work. 

• Increased customer satisfaction  

Access to detailed customer data makes it possible for support teams to resolve issues more efficiently. It also provides essential details to help personalize interactions with individual clients and deliver a better overall customer experience. 

• Real-time data insights  

With BI, there is no need to wait for end-of-quarter reports. Businesses can track and respond to sales, supply chain performance, and market trends as they happen. 

• Better understanding of customers  

Top BI tools can analyze customer behaviors, preferences, and purchasing patterns, giving businesses better customer insight that can be used to improve engagement and retention. 

• Clear competitive advantage  

All of the previous points support this all-encompassing advantage: BI allows businesses to make strategic moves before their rivals, positioning themselves for long-term success.

Business intelligence challenges 

BI offers significant benefits. On the other hand, implementing and maintaining an effective BI system is not always easy. Before they can maximize the value of their business intelligence investments, many organizations will first need to address the following obstacles: 

• Contradictory conclusions  

Different teams may interpret data in conflicting ways, leading to inconsistencies that result in poor decision-making. Without standardized data governance, businesses risk acting on inaccurate insights. Establishing clear reporting guidelines and ensuring all departments use the same data sources helps eliminate these discrepancies. 

• Skills shortfall  

Not all employees have the technical expertise needed to analyze data effectively. While BI platforms have become more user-friendly in recent years, most organizations still need skilled analysts to extract deeper insights. Investing in training programs and hiring experienced data professionals can help bridge this gap. 

• Up-front costs  

The initial investment in BI can be significant—especially for companies transitioning from outdated reporting methods. Licensing fees, implementation expenses, and infrastructure upgrades may deter some businesses from adopting BI solutions. However, careful planning, phased implementation, and cloud-based BI options can help minimize these costs without negatively impacting value.

For business intelligence to operate effectively, organizations must collect large amounts of information from internal systems, external sources, and real-time data streams. Before this data can be put to use, it must be processed, structured, and analyzed. BI platforms automate much of this work, making it easier for users to access relevant insights without requiring extensive technical expertise. 

The BI process typically begins with data collection from various sources, including databases, cloud applications, and data warehouses. This data is then prepared, cleaned, and stored in a structured format, ensuring accuracy and consistency. Once processed, BI tools apply different analytical techniques to uncover patterns, track performance, and present insights in easily digestible formats (like reports and dashboards). These tools help businesses monitor key performance indicators (KPIs) and identify areas for improvement. 

To do this, BI solutions rely on several essential components: 

• Data mining  

BI platforms use data mining to uncover hidden patterns and relationships within large datasets. By applying intelligent algorithms and statistical techniques, businesses can identify trends that may not be immediately visible through more traditional forms of analysis. 

• Reporting  

BI reporting tools generate structured summaries of business data, providing decision-makers with clear insights into company performance. Reports can be automated, customized, and distributed to key stakeholders, making relevant information easily available. 

• Performance metrics and benchmarking  

Organizations use BI to track relevant metrics and compare current performance against historical data. This benchmarking helps businesses assess progress toward goals while adjusting their strategies when needed. 

• Descriptive analytics  

This type of analysis helps organizations understand what has happened in the past. By identifying trends and patterns, businesses gain insights into earlier performance, making it easier to evaluate performance. 

• Querying  

BI platforms allow users to ask specific questions about their data and receive immediate answers. Modern BI platforms may integrate artificial intelligence (AI) to further enhance this capability. 

• Statistical analysis  

Businesses use statistical techniques to explore relationships between different data points, test hypotheses, and determine the significance of trends. This deeper level of analysis helps organizations understand why certain events occurred as well as predict potential future events. 

• Data visualization  

BI tools convert raw data into visual formats such as charts, graphs, and dashboards. Visual representations make complex information easier to interpret, helping teams quickly identify essential information. 

• Visual analysis  

Unlike static reports, visual analysis makes it possible for users to interact with data dynamically. Decision-makers can adjust filters, drill down into specific metrics, and explore different scenarios in real time to uncover new insights.

Business intelligence has applications across essentially every industry. Below are some key areas where BI delivers its most measurable impact: 

• Customer service 

BI platforms consolidate customer data into a single, accessible system, giving support teams instant access to purchase history, preferences, and past interactions. This enables customer service representatives to resolve issues faster while providing personalized support leading to increased customer satisfaction. 

• Finance and banking  

Financial institutions use BI to monitor cash flow, manage risk, and evaluate market conditions. This allows banks and investment firms to enhance their financial forecasting and improve their approach to decision-making 

• Healthcare  

BI helps healthcare organizations track patient outcomes, manage hospital resources, and optimize staffing levels. Hospitals can use BI to improve overall operational efficiency, while also tracking treatment effectiveness and keeping a close eye on other important data. 

• Retail  

Retailers rely on BI to analyze sales and adjust inventory based on demand. By tracking purchasing trends, businesses can refine pricing strategies and improve stock management. 

• Sales and marketing  

BI enables sales and marketing teams to analyze customer preferences and measure campaign effectiveness. With deeper insights, businesses can target the right audiences and improve outreach efforts. 

• Security and compliance  

Organizations use BI to track compliance metrics and identify cyber-security threats. By consolidating security data, businesses can detect anomalies and respond to risks more effectively. 

• Supply chain  

BI improves supply chain management by monitoring shipments and tracking supplier performance. Businesses can use these insights to reduce delays and improve logistics coordination.

Because business intelligence is a process that involves various tasks and capabilities, it typically relies on support from specialized tools and applications. These specialized solutions vary in function, but they all play a part in helping transform raw data into meaningful insights: 

• Ad hoc analysis  

Also known as ad hoc querying, this technology allows users to create custom queries on demand to analyze specific business issues. While these queries are typically generated for immediate insights, they can easily become recurring reports integrated into BI dashboards. 

• Online analytical processing  

Online analytical processing (OLAP) enables users to examine data from different perspectives, making it well-suited for complex queries and calculations. Traditionally, OLAP required pre-aggregated data stored in specialized structures, but modern tools now allow direct analysis within databases.

• Mobile BI  

Designed for smartphones and tablets, mobile BI opens up access to dashboards and reports for users on the go. These applications focus on ease of use, often displaying only essential data visualizations for quick reference. 

• Real-time BI  

Real-time BI continuously analyzes incoming data, giving businesses up-to-the-minute insights on operations, customer behavior, and market trends. This capability supports time-sensitive activities such as fraud detection, credit scoring, and stock trading in financial use cases. 

• Operational intelligence  

A subset of BI, operational intelligence (OI) delivers insights directly to frontline employees and managers, helping them make informed operational decisions. 

• Embedded analytics  

Embedded BI integrates analytics features directly into business applications such as enterprise resource planning (ERP) or CRM systems. This allows users to analyze data without switching between different platforms. 

• Open-source BI  

Open-source BI tools offer businesses a flexible alternative to proprietary solutions. Many come in both free community editions and commercial versions with added support and functionality. 

• Collaborative BI  

This category focuses on empowering teams to coordinate more closely together on data analysis. It integrates BI tools with communication platforms, allowing users to share insights, annotate reports, and collaborate on decision-making in real time. 

• Location intelligence  

Location intelligence applies BI techniques to geographic and geospatial data. Businesses use these tools for applications such as site selection, logistics management, and location-based marketing.

Although there are many tools designed to assist with business intelligence, BI as a process is not something one can just flip a switch to activate. When done correctly, BI is the product of a structured approach consisting of several steps: 

1. Perform a feasibility study  

Organizations must first assess their existing data infrastructure and analytics needs. This involves identifying business objectives, evaluating current reporting tools, and determining gaps that a BI solution should address. This step is critical and should not be rushed—a proper feasibility study may take as much as six weeks to complete. 

2. Define requirements  

Defining functional and non-functional requirements ensures the BI system meets business needs. Stakeholders across departments should be consulted to help outline priorities, must-have features, and optional enhancements that could improve long-term usability. 

3. Select a platform  

Choosing the ‘right’ BI platform is only feasible if everyone involved knows what that term entails. Define the technology stack, data sources, and integration methods that will be used, and map any other requirements. This step also includes establishing data extraction, transformation, and loading (ETL) processes, along with ensuring data quality and developing a strategy for user adoption. 

4. Begin planning  

A detailed implementation plan is essential for managing timelines, risks, and costs. Organizations should create a roadmap outlining key deliverables, schedules, and communication strategies to ensure all teams remain aligned throughout the process. 

5. Develop the solution  

This phase involves building the BI infrastructure, including data warehouses, dashboards, and reporting tools. ETL processes are implemented to aggregate and cleanse data from various sources. Security measures and quality assurance processes are also established to maintain data integrity and system performance. 

6. Train users  

Employees need training to use BI tools effectively. Organizations should offer user manuals and hands-on workshops to help teams navigate dashboards and run reports. Adjusting workflows based on user feedback can further improve adoption and efficiency. 

7. Launch  

Before full deployment, the BI system will need to undergo user acceptance testing to validate functionality in real-world scenarios. Depending on the complexity of the rollout, organizations may opt for a phased release, gradually providing access to different user groups. 

8. Continue to support and optimize  

BI implementation does not end at launch; continuous monitoring, performance optimization, and system updates will ensure long-term success. Businesses may also enhance their BI capabilities over time by incorporating self-service analytics and automation tools.

Today’s businesses depend on a more intelligent approach to their data. ServiceNow transforms business intelligence by unifying analytics, tools, and automation together on the Now Platform^®. This centralized platform provides a single source of truth on all relevant data, integrating systems across the organization. Built-in reporting tools provide instant insights, allowing organizations to visualize data through interactive dashboards, publish reports for easy collaboration, and automate recurring analyses. Natural language queries make data exploration intuitive, enabling users to ask questions in plain language and receive answers that are both clear and reliable. And, by integrating real-time analytics and predictive intelligence, ServiceNow helps businesses anticipate trends and drive informed decision-making.

ServiceNow Workflow Data Fabric further strengthens these capabilities by making it easier to connect, understand, and act on data from any source. Virtual agents and human users gain access structured, unstructured, and streaming data without duplicating or transferring it across systems. By contextualizing data and aligning it to workflows, Workflow Data Fabric helps organizations automate complex tasks and surface insights that improve operations. 

ServiceNow empowers businesses to move beyond static reporting and adopt a dynamic, data-driven approach. Schedule a demo today and see how ServiceNow can help you elevate your BI capabilities.