What Is Bi? Understanding Business Intelligence Essentials

Are you seeking to understand “What Is Bi” and how it can revolutionize your business decisions? At WHAT.EDU.VN, we clarify Business Intelligence (BI) concepts, exploring its definition, applications, and benefits to empower you with data-driven strategies. Learn about BI tools and platforms, data analysis techniques, and predictive analytics to improve operations and revenue.

1. Delving Into the Definition of Business Intelligence (BI)

Business Intelligence (BI) represents a technology-driven process dedicated to data analysis, empowering executives, managers, and operational staff to make well-informed business decisions. This entails systematically collecting and preparing pertinent data for analysis. The process involves executing queries against the data and leveraging the resulting analytics to bolster both operational decision-making and strategic planning.

The overriding objective of BI initiatives lies in fostering superior business decisions that enable organizations to augment revenue, bolster operational efficiency, and secure a competitive edge against rival entities. To realize this objective, BI integrates a blend of analytics, data visualization, and reporting tools, augmented by diverse methodologies for overseeing and scrutinizing data.

BI software made its debut in the early 1990s and has since gained widespread adoption across organizations of varying scales. Over the years, the manner in which it is employed has undergone significant transformations. The advent of self-service BI tools, coupled with recent advancements in augmented analytics features powered by artificial intelligence (AI) and machine learning technologies, has enabled business users to independently analyze data, thereby reducing reliance on BI professionals for executing queries.

However, BI, data management, and IT teams continue to grapple with a range of challenges pertaining to the deployment, management, and support of BI systems. These challenges encompass obtaining approval and funding for BI programs, integrating data sets for analysis, selecting appropriate BI software, training users, and preventing self-service BI implementations from descending into chaotic environments characterized by inconsistent analytics outcomes. In numerous organizations, including those that have embraced a self-service BI model, BI teams persist in analyzing data and generating data visualizations, reports, and BI dashboards for business users.

Here at WHAT.EDU.VN we aim to provide you with the insight and advice needed to manage BI initiatives successfully.

2. Unraveling the Inner Workings of Business Intelligence

Business intelligence initiatives extract actionable insights suitable for utilization by senior executives, business managers, and operational personnel across a spectrum of use cases. For instance, BI applications generate insights pertaining to business performance, processes, and trends, thereby equipping management teams to pinpoint issues and prospective opportunities and subsequently implement remedial actions.

Business intelligence data typically resides within a data warehouse constructed for an entire organization or within smaller data marts tailored to individual departments and business units. Furthermore, data lakes predicated on big data systems are frequently employed as repositories or landing pads for BI data, particularly unstructured and semistructured data types. Data lakehouse platforms amalgamating elements of data lakes and data warehouses have also emerged.

BI data may encompass both historical and real-time data sourced from a combination of internal IT systems and external sources. Prior to its utilization in BI applications, raw data originating from diverse source systems undergoes integration, consolidation, and cleansing to ensure accuracy and consistency.

Subsequent steps in the BI process encompass:

1. Data Preparation: Organization, transformation, and modeling of data sets for analysis.
2. Analytical Querying: Performing analytical queries on the prepared data.
3. Visualization, Reporting, and Dashboard Development: Crafting data visualizations, reports, and dashboards that convey key performance indicators (KPIs) and other salient findings.
4. Dissemination of Analytics Results: Distributing analytics results to decision-makers, either directly by the BI team or through self-service BI users sharing information with business colleagues.
5. Utilization of Performance Metrics and Insights: Employing performance metrics and generated insights to guide business decisions.

BI programs may also integrate advanced analytics techniques, such as data mining, predictive analytics, text mining, and statistical analysis. Predictive modeling, facilitating what-if analysis of various business scenarios, serves as a prime illustration. However, advanced analytics projects are typically managed by separate data science teams, while BI teams oversee more straightforward querying and analysis of business data.

These five steps constitute the essential components of the BI process.

3. The Importance of Business Intelligence Explained

The principal aim of business intelligence is to enhance an organization’s business operations. Organizations that proficiently employ BI tools and techniques to analyze data derive valuable insights into their business processes and strategies. These insights can then be harnessed to optimize both strategic and tactical decision-making, thereby accelerating business growth and augmenting profits.

Absent BI, organizations may struggle to effectively leverage their data to influence decision-making. Consequently, executives and employees may rely on accumulated knowledge, past experiences, and intuition to make critical business decisions. While these methods may yield favorable outcomes, they are also susceptible to errors and missteps due to a lack of data support. BI substitutes management by gut feeling with data-driven decision-making.

4. Advantages of Integrating Business Intelligence

A well-executed BI program yields a myriad of business benefits for an organization. For instance, BI empowers C-suite executives and department managers to continuously monitor business performance, enabling swift action in response to emerging issues or opportunities. Analyzing customer data enhances the effectiveness of marketing, sales, and customer service endeavors. Bottlenecks within the supply chain, manufacturing processes, and distribution networks can be detected before they inflict financial harm. HR managers gain enhanced capabilities to monitor employee productivity, labor costs, and other workforce-related metrics.

Overall, the key benefits of BI applications for businesses encompass the capacity to:

• Expedite and enhance decision-making.
• Streamline internal business processes.
• Augment operational efficiency and productivity.
• Identify business challenges requiring attention.
• Uncover emerging business and market trends.
• Forge robust business strategies.
• Propel heightened sales and revenue streams.
• Achieve a competitive advantage over rival enterprises.

BI initiatives also furnish narrower business benefits, such as simplifying project manager efforts to track project status and facilitating organizations’ gathering of competitive intelligence on their rivals. Furthermore, BI, data management, and IT teams themselves derive advantages from business intelligence through its utilization in analyzing diverse facets of technology and analytics operations.

5. Defining a BI Platform and Its Essential Functions

BI tools evolved from antecedent decision support systems, which vendors commenced developing in the 1960s. These systems were primarily designed to channel information to senior business executives, with executive information systems constituting a predecessor to business intelligence. In contrast, contemporary BI platforms boast broader functionalities and user bases.

According to a 2024 Gartner report on BI platforms, essential capabilities beyond querying and analysis encompass data preparation, data visualization, reporting, and governance. Collaboration, automated insight generation, and data storytelling—integrating visualizations and text narratives in presentations for business users—were also highlighted. Most BI vendors now offer conversational interfaces based on generative AI (GenAI) and natural language query (NLQ) technologies to simplify data analysis tasks, as noted by the consulting and market research firm.

Besides GenAI and NLQ, the burgeoning array of augmented analytics features in BI platforms includes AI-driven technologies that assist users in identifying relevant data, preparing it for analysis, and generating visualizations. For instance, machine learning models can automate data cleansing and transformation tasks. AI can recommend appropriate charts and other graphics for visualizing data. Machine learning can also be employed to identify trends, patterns, and outliers within data sets for user analysis.

The ensuing functions represent the major business intelligence tasks supported by BI platforms:

5.1. Business Monitoring and Measurement. BI systems are extensively utilized to track KPIs and other business metrics, enabling executives to continuously monitor organizational performance. This facilitates more proactive identification and resolution of business issues compared to traditional performance reports. For example, a BI tool could reveal that product sales in a specific region are below projections, empowering managers to take corrective actions in time to meet quarterly targets.

5.2. Data Analysis. Beyond mere metric tracking, BI tools facilitate more intricate analytics applications. As previously noted, business users and BI professionals execute queries to analyze data for insights into business operations, strategies, and trends. This function is pivotal to data-driven decision-making processes within organizations.

5.3. Reporting and Information Delivery. BI outcomes must be made accessible to business users to enable their utilization. Initially, this primarily occurred through static reports, often disseminated in printed form. While reports remain prevalent, BI dashboards have gained increasing traction. They offer a more visually engaging and interactive interface, featuring embedded data visualizations and drill-down capabilities for deeper analysis. Online portals constitute another option for BI information delivery.

5.4. Predictive Analysis. Conventional BI represents a form of descriptive analytics. By scrutinizing historical data or real-time data streams, it examines past and present occurrences to enable timely responses. Conversely, predictive analytics forecasts future scenarios, such as customer responses to marketing offers. Executing predictive models and what-if queries introduces a predictive element to the BI process.

6. Types of Business Intelligence Instruments and Applications

Business intelligence encompasses a diverse range of data analysis applications tailored to address various information requirements. Most are supported by both self-service BI and traditional BI platforms. The subsequent list encapsulates the principal BI technologies available to organizations.

6.1. Ad Hoc Analysis. Also known as ad hoc querying, ad hoc analysis constitutes a foundational element of modern BI applications and a key feature of self-service BI tools. It entails formulating and executing queries to analyze specific business issues. While ad hoc queries are typically created on the fly, they often become recurring, with analytics results integrated into dashboards and reports.

6.2. Online Analytical Processing. An early BI technology, OLAP tools enable users to analyze data along multiple dimensions, rendering it particularly suited to complex queries and calculations. Previously, data had to be extracted from a data warehouse and stored in multidimensional OLAP cubes, but it’s increasingly feasible to execute OLAP analyses directly against columnar databases.

6.3. Mobile BI. Mobile business intelligence delivers BI applications and dashboards to smartphones and tablets. Often used more for viewing data than for analysis, mobile BI tools typically prioritize ease of use. For example, mobile dashboards may display only a few data visualizations and KPIs to facilitate easy viewing on a device’s screen.

6.4. Real-Time BI. In real-time BI applications, data is analyzed as it’s created, collected, and processed. This provides users with an up-to-date view of business operations, customer behavior, financial markets, and other areas of interest. The real-time analytics process often involves streaming data and supports activities such as credit scoring, stock trading, and targeted promotional offers.

6.5. Operational Intelligence. Also referred to as operational BI, OI represents a form of real-time analytics that delivers information to managers and frontline workers in business operations. OI applications are designed to aid in operational decision-making and enable faster action on issues—for example, assisting call center agents in resolving customer problems and logistics managers in easing distribution bottlenecks.

6.6. Embedded Analytics. Also referred to as embedded BI, embedded analytics integrates BI and data visualization functionality directly into business applications, such as ERP and CRM systems. This enables business users to analyze data within the applications they use to perform their jobs, rather than switching to a separate BI tool. Embedded analytics features are most commonly incorporated by application software vendors, but organizations can also include them in homegrown applications.

6.7. Open Source BI. Business intelligence software that is open source typically includes two versions: a community edition that can be used free of charge and a subscription-based commercial release with technical support provided by the vendor. Additionally, some vendors of proprietary BI tools offer free editions, primarily for individual users.

6.8. Collaborative BI. This represents more of a process than a specific technology. It involves combining BI applications and collaboration tools to enable different users to collaborate on data analysis tasks and share information. For example, users can annotate BI data and analytics results with comments, questions, and highlighting using online chat tools.

6.9. Location Intelligence. A specialized form of BI, location intelligence enables users to analyze location and geospatial data, incorporating map-based data visualization functionality. It provides insights into geographic elements in business data and operations. Potential uses include site selection for retail stores and corporate facilities, location-based marketing, and logistics management.

6.10. Customer Intelligence. Another specialized process, customer intelligence analyzes customer data to gain insights into customer behavior and preferences. Organizations use it to improve customer interactions, deliver personalized customer experiences, and optimize marketing campaigns through segmentation techniques.

6.11. Corporate Performance Management. Also a subset of BI, corporate performance management involves monitoring and analyzing financial performance based on KPIs such as revenue, profits, and operational costs. Senior executives are the primary users of CPM applications to support financial planning, budgeting, risk management, and other corporate functions.

7. Navigating Industry Applications of BI Tools

The subsequent examples illustrate how business intelligence is employed across various industries:

• Banks leverage BI to aid in assessing financial risks when deciding whether to approve mortgage and loan applications. They, along with other financial services firms, also analyze customer portfolios to inform cross-selling efforts aimed at promoting additional products.
• Insurers similarly rely on BI tools to analyze risks when considering applications for life, auto, and homeowners insurance policies. Additionally, they utilize BI to analyze policy pricing.
• Manufacturers employ BI software to support production planning, materials and supplies procurement, supply chain management, and monitoring of manufacturing operations.
• Retailers plan marketing campaigns and product promotions with the aid of BI and analytics tools, while also utilizing them in inventory management and product replenishment.
• Hotel chains employ BI tools to track room occupancy rates and adjust pricing based on booking demand, as well as to manage customer loyalty programs.
• Airlines likewise utilize BI to track ticket sales and flight occupancy, as well as for managing flight schedules, crew assignments, and food and beverage ordering.
• Transportation companies plan distribution schedules and routes using BI and analytics tools. They also employ BI to monitor gas mileage and other aspects of fleet operations.
• Hospitals utilize BI systems to analyze patient outcomes and readmission rates as part of efforts to enhance patient care. In addition, doctors use these tools to analyze clinical data and aid in diagnosing medical conditions.

8. Exploring Additional Technologies in a BI Environment

Business intelligence environments encompass more than just BI software itself. For example, a typical BI architecture also includes internal source systems and external data sources, data warehouses and other data repositories, and a variety of data processing and data management tools.

The following provides further details on these core BI components:

8.1. Data Sources. These primarily include an organization’s transaction processing and operational systems, such as ERP, CRM, finance, HR, and supply chain management applications. Market data, external customer lists, and other external data sets can also be collected in BI systems for analysis.

8.2. Data Stores. As previously mentioned, data from source systems is typically consolidated into a data warehouse for analysis, although data marts, data lakes, and data lakehouses are also utilized as repositories. Additionally, a BI architecture may include an operational data store as a staging area for data before it is transferred to a data warehouse. Metadata repositories, business glossaries, and data catalogs are related technologies that can assist users in locating relevant data sets for BI applications. Two newer ones are analytics catalogs, which store dashboards, reports, and curated data sets, and metrics stores, which are centralized repositories for business metrics to ensure consistency across an organization.

8.3. Data Integration and Data Quality Software. Extract, transform, and load tools are the primary means of integrating data for BI uses. ETL pulls together data sets from source systems in batch processes at regular intervals, then transforms the data and loads it into a data warehouse. Other integration methods include real-time data integration and data virtualization, which creates blended views of data sets without physically loading them into a data warehouse. Data profiling and cleansing are typically performed in conjunction with integration, utilizing separate data quality tools to identify and rectify data errors.

8.4. Data Preparation and Curation Tools. BI and data management teams often deploy specialized data preparation software to automate that task and the process of curating data sets for BI users. Data curation streamlines and focuses analytics work by providing data sets that are ready to use for specific BI applications.

9. Addressing Organizational Challenges in BI

One potential pitfall of BI applications is that the accelerated decision-making they facilitate could lead organizations astray if based on flawed or incomplete data and inaccurate analytics results. To mitigate this risk, key challenges in BI initiatives that must be addressed encompass a combination of data management and BI project management issues.

The following highlights some of these challenges:

• Integrating Data from Different Source Systems: Many organizations must integrate data from various sources, including a mix of cloud and on-premises systems, often a complex process.
• Identifying and Rectifying Data Quality Issues: BI applications necessitate high-quality data, but raw data frequently presents quality issues. Implementing processes to rectify data errors and prevent their recurrence is a critical aspect of BI initiatives.
• Breaking Down Data Silos: Siloed systems impede BI users’ access to relevant data and result in inconsistent analytics results. Eliminating these silos and adopting internal data standards to ensure consistency should be priorities for BI teams.
• Managing Self-Service BI Deployments: Uncontrolled use of self-service BI tools can also lead to conflicting analytics results that hinder effective decision-making.
• Avoiding Poor Data Visualization and Dashboard Design Practices: Poorly designed data visualizations and dashboards diminish BI’s effectiveness. Business users should be trained on how to create easy-to-grasp visualizations and dashboard design best practices.
• Connecting BI-Generated Insights to Business Actions: Presenting information to executives and operational workers in a useful manner can be difficult. Considerations include both how and where the information is presented to drive decisions and other actions.
• Delivering the Right Data to Business Decision-Makers: Similarly, customizing insights and information for different business users is a necessary but often time-consuming task. Providing users with superfluous data can overwhelm them with information.
• Justifying Investments in New BI Projects: Measuring the ROI of BI programs can be challenging in some organizations, making it difficult to advocate for additional investments to expand BI initiatives.

User adoption issues pose another potential obstacle to successful BI initiatives. These issues can arise from business users’ reluctance to relinquish familiar tools, particularly spreadsheets, and transition to BI software. Additionally, fostering a data-driven culture remains an ongoing challenge in some organizations.

10. How To Implement a BI Program Effectively

Establishing a business intelligence program commences with formulating a BI strategy. A well-conceived strategy lays the foundation for effective BI initiatives that yield the expected business benefits for an organization. The subsequent steps are crucial for building a BI strategy:

1. Align the Strategy to Business Goals and Related Metrics: BI initiatives should align with an organization’s goals and strategic objectives, as well as the KPIs and other metrics used to track progress toward achieving them.
2. Identify and Prioritize Planned BI Use Cases: A typical company has numerous potential BI applications. As they cannot all be funded and deployed simultaneously, prioritizing them based on business needs and expected ROI is essential.
3. Establish Robust Data Management Processes: A successful BI strategy requires solid data management to make relevant, high-quality data available for BI applications. In addition to data integration, preparation, and quality management processes, data governance is needed to ensure that BI data is trustworthy and used properly throughout an organization.
4. Select the Right BI Tools: Well-managed data alone is insufficient; BI users also require software that meets their needs and delivers the desired analytics results. In some instances, this may entail utilizing multiple BI tools to support different applications and user groups.
5. Create a BI Team: Particularly in large companies, business intelligence teams typically include various BI roles and responsibilities. This may include a BI director or manager, sometimes at the vice president level, as well as positions such as BI architect, BI project manager, BI developer, and BI analyst. Smaller organizations may need to consolidate some of these roles into single positions.

With the BI strategy and team in place, an organization can begin implementing BI applications. This involves a series of steps: creating a project plan, gathering detailed user requirements, building the underlying BI architecture, assessing organizational readiness for the project, addressing identified gaps, and then designing and deploying the BI system.

11. Emerging Trends in Business Intelligence

The most significant trend in business intelligence is the increasing integration of AI tools into BI initiatives. The proliferation of augmented analytics capabilities in BI systems now includes GenAI copilot tools that function as AI assistants for data exploration and analysis, as well as features that use GenAI to explain analytics results. Additionally, AI and machine learning algorithms can aid in BI applications supporting activities such as supply chain optimization, customer analytics, and anomaly detection for managing business risks.

Other current business intelligence trends that warrant attention include:

• Increased Focus on Governing BI Use: New regulatory requirements and the constant threat of cyberattacks are making data governance, security, and privacy even more critical concerns in BI initiatives. This has raised the stakes on the overall governance of BI environments. The analytics catalogs now supported by BI vendors are a technology that can help ensure users only access information that is relevant and appropriate to their roles.
• Emergence of New Analytics Approaches: New forms of analytics continue to emerge in connection with the BI process. For example, composable analytics supports modular development of BI applications using sets of building blocks from different tools. Decision intelligence is an emerging field that combines elements of data science, AI, and BI to accelerate decision-making and performance metric tracking. Another new approach is continuous intelligence, which aims to deliver real-time analytics and data insights on an ongoing basis.
• Low-Code and No-Code Development: Many BI vendors are adding graphical tools that enable BI applications to be developed with little or no coding. As such tools do not require advanced development skills, business users can perform at least some development work themselves.
• Efforts to Improve Data Literacy: With self-service BI broadening the use of business intelligence tools in organizations, it is critical to ensure that new users can understand and work with data. This is prompting BI teams to include data literacy skills in user training programs. BI vendors have also launched initiatives, such as the Qlik-led Data Literacy Project.
• Shift to the Cloud: BI systems were initially slow to transition to the cloud, partly because data warehouses were primarily deployed in on-premises data centers. However, cloud deployments of both data warehouses and BI tools have grown significantly. Cloud-based BI platforms, once a separate product category, are now the primary deployment choice for many organizations.

12. Prominent Business Intelligence Vendors and Market Landscape

Self-service BI and data visualization tools have become standard in modern BI software. All major BI tools now incorporate self-service features, such as visual data discovery, ad hoc querying, and augmented analytics capabilities, along with data visualization and dashboard design functions.

While full-featured platforms are the most widely used business intelligence technology, the BI market also includes other product categories. Some vendors offer tools specifically for embedded analytics uses. Some focus on dashboard and data visualization design, while others specialize in tools that support data storytelling efforts.

There are dozens of vendors overall. The following is an alphabetical list of 20 prominent BI tools.

• Alibaba Cloud Quick BI.
• Amazon QuickSight (AWS).
• Domo.
• GoodData.
• IBM Cognos Analytics.
• Incorta.
• Looker (Google).
• Microsoft Power BI.
• MicroStrategy.
• Oracle Analytics Cloud.
• Pyramid Decision Intelligence Platform.
• Qlik Sense.
• SAP Analytics Cloud.
• SAS Visual Analytics.
• Sisense.
• Spotfire.
• Tableau (Salesforce).
• Tellius.
• ThoughtSpot.
• Yellowfin (Idera Inc.).

13. Differentiating BI from Big Data Analytics

Big data analytics is another widely used data analysis process. BI and big data analytics complement each other, and big data sets can be integrated into BI processes. For example, some types of data initially stored in a data lake can be structured and moved to a data warehouse for use in BI applications. This can be done within the same platform in a data lakehouse.

Overall, however, the two processes target different types of analytics uses. BI primarily focuses on descriptive analytics and diagnostic analytics, examining what has happened previously or is happening now and why. Big data analytics, on the other hand, typically involves predictive analytics and prescriptive analytics, providing guidance on how to achieve desired business outcomes. BI can also be considered a structured approach to data analysis, while big data analytics is more exploratory and is typically conducted by data scientists and other data analysts with advanced analytics skills.

14. Frequently Asked Questions (FAQs) About Business Intelligence

Question	Answer
What are the core components of a BI system?	A BI system typically includes data sources (internal and external), data storage (data warehouses, data marts, data lakes), ETL processes for data integration, analytical tools for querying and analysis, and reporting tools for visualization and presentation.
How does BI differ from traditional reporting?	Traditional reporting typically focuses on historical data and generating static reports. BI, on the other hand, involves dynamic analysis of data, often in real-time, to identify trends, patterns, and insights that can inform decision-making. BI tools offer interactive dashboards and data visualization capabilities for deeper exploration.
What are the benefits of using BI for small businesses?	Even small businesses can benefit from BI by gaining insights into customer behavior, optimizing marketing campaigns, improving operational efficiency, and identifying new revenue opportunities. BI tools can help small businesses make data-driven decisions to compete effectively in the market.
How can I ensure data quality in my BI system?	Data quality is crucial for accurate BI analysis. Implement data governance policies, data validation rules, and data cleansing processes to ensure that data is accurate, consistent, and complete. Regularly monitor data quality metrics and address any issues promptly.
What skills are needed to work in business intelligence?	Professionals in BI typically need skills in data analysis, data modeling, SQL, programming languages (e.g., Python, R), data visualization tools (e.g., Tableau, Power BI), and business acumen. Strong communication and problem-solving skills are also essential.
Is BI suitable for all types of industries?	Yes, BI can be applied to virtually any industry. Different industries may have specific BI use cases and applications, but the fundamental principles of data analysis and decision-making remain the same. Industries such as finance, healthcare, retail, manufacturing, and transportation commonly leverage BI to improve their operations.
What are some best practices for implementing a BI strategy?	Key best practices include aligning BI initiatives with business goals, securing executive sponsorship, establishing a clear data governance framework, choosing the right BI tools for the organization’s needs, providing adequate training to users, and continuously monitoring and evaluating the effectiveness of the BI system.
What is the role of AI in business intelligence?	AI is increasingly being integrated into BI systems to automate tasks, improve accuracy, and uncover deeper insights. AI-powered BI tools can perform tasks such as data cleansing, anomaly detection, predictive modeling, and natural language processing. AI enhances the capabilities of BI and empowers users to make more informed decisions.
What are the ethical considerations in using BI?	Ethical considerations in BI include ensuring data privacy, protecting sensitive information, avoiding bias in data analysis, and using data responsibly. Transparency, accountability, and fairness are important principles to uphold when using BI to make decisions.
How do I measure the ROI of a BI investment?	Measuring the ROI of a BI investment involves identifying key performance indicators (KPIs) that are impacted by BI initiatives, tracking changes in these KPIs over time, and comparing the benefits of BI to the costs. Benefits may include increased revenue, cost savings, improved efficiency, and better decision-making.

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