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Business Intelligence (BI)

What it means, why it matters, and best practices. This guide provides definitions, examples and practical advice to help you understand the topic of business intelligence.

Business Intelligence Guide

What is business intelligence?

Business intelligence (BI) is the combination of applications, processes, and infrastructure that provide data access and analysis to improve your decisions and performance. Modern BI tools bring together data integration, data analytics and data literacy to close the gaps between data, insights and actions.

Business intelligence examples

From self-service BI and dashboards to mobile BI and conversational analytics, modern business intelligence supports a wide range of business needs and users, allowing every employee to access the insights they need, regardless of technical skill. Here are the primary business intelligence use cases and capabilities:

Self-service BI tools let users easily explore data and gain insights without waiting for data analysts.

1. Self-service Analytics

Gone are the days when business users had to wait days or weeks for data analysts to build reports. Self-service BI tools let users easily explore data and make discoveries using natural language search and interactive selections and create their own visual analytics with simple drag-and-drop tools. And AI is making these processes easier than ever.

2. Data Visualization

The ability to explore and interact with data makes it easier for users to explore, discover patterns, and gain insights from data. Interactive BI dashboards make data even more actionable, revealing the shape of the data, highlighting outliers and trends, and putting data in the ideal context to answer any question.

3. Conversational Analytics

This class of analytics lets users explore data and discover insights using natural language text chat and voice within analytics apps. AI-powered natural language capabilities allow users to ask questions in their own words and get answers presented in an easy-to-understand conversational manner. The best BI platforms let users freely explore data using text search and interactive selections to filter within charts, tables and other elements.

Embedded analytics helps people find insights within the applications they use everyday.

4. Custom & Embedded Analytics

Embedding analytics into applications like CRMs and ERPs helps people find insights and deliver value faster, right where they work. BI technology that offers open APIs and developer tools let organizations embed analytics, build custom BI apps, and create visualizations and extensions to address the ever-growing demands for insights.

Mobile business intelligence lets users create and explore analytics and collaborate using any device.

5. Mobile BI

Work happens everywhere today. And to do their best work, employees need access to business intelligence insights whenever and wherever decisions are made. Mobile business intelligence lets users create and explore analytics and collaborate using any device. The best mobile BI solutions support interactive analytics even when users are offline.

Modern BI reporting enables drag-and-drop report creation and formatting, output in popular formats and automated delivery.

6. BI Reporting

While BI reporting is one of the most traditional forms of business intelligence, it remains an essential BI capability. While many second generation BI tools don’t offer reporting, some offer highly flexible, modern reporting capabilities. This includes drag-and-drop report creation and formatting, pixel perfect output in popular formats and flexible automated delivery.

Click on this image to play the augmented analytics video.

7. Augmented Analytics

AI and machine learning are quickly becoming an essential facet of BI, quickly processing massive volumes of data to suggest relevant insights and automate processes while letting users interact conversationally. Augmented analytics complements human intelligence and increases data literacy so more users can get value from their data.

  • 8. Open APIs and extensibility

    Organizations face an ever-growing array of specific business intelligence needs and use cases. An open platform that supports the ability to create custom applications and extensions, integrate with other technologies, and embed analytics in existing business applications processes is essential to maximize the value BI.

  • 9. Collaboration and sharing

    Modern BI tools offer users the ability to easily create and share data sets, analytics, and insights across an organization. The most effective BI ecosystems offer a governed, centralized hub to facilitate this collaborative process and equip users with robust interactive data storytelling capabilities to circulate their discoveries.

  • 10. Governance, security and flexible deployment

    Today, demand for business intelligence comes from the C-suite to the edge of every organization. Reaching this scale requires governed control, trusted security and the ability to deploy and scale analytics with any combination of infrastructure from on-premise to the most modern cloud environments.

11. Data integration and management

The most successful business intelligence initiatives combine a smart analytics strategy with an effective data strategy. Raw data only gains value when it can be transformed into highly accessible, analytics-ready information. This often begins by moving source data (ERP, CRM, etc.) into a central repository like a data lake or data warehouse. Data connectors in the analytics system load data from these repositories as well as specific applications and files so it can be prepared for use. This can be slow and tedious, requiring data experts which can create bottlenecks at scale. However, innovative data replication and data migration technologies can automate the integration process. In addition, Governed data catalogs that profile and document every data source let business users easily access, create, and share data sets on their own, combining any data they need to analyze.

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How different departments use BI

Here are some key BI strategies frequently used across major departments in an organization:

  • Screenshot of a financial reporting dashboard.

    Finance

    Finance teams optimize reporting, expense management, enhance forecasting, budgeting and planning, and better manage revenue, risk and compliance
  • Executive sales dashboards share KPIs such as closed revenue, opportunity status and performance vs quota trends.

    Sales

    Sales teams discover market trends and sales opportunities, measure sales performance and forecast pipeline.
  • An operations dashboard helps you explore relationships between complex data sources to improve planning, increase performance and monitor risk.

    Supply Chain

    Supply chain management teams make better decisions in forecasting and planning, sourcing and supplier performance, and transportation.
  • An HR dashboard makes employee management more efficient by organizing data from across the organization.

    Human Resources

    HR teams increase operational efficiency by analyzing labor, benefit and recruitment data holistically across their organization.

  • IT

    IT teams optimize data governance, security and scalability while lowering costs by spotting underused systems and applications.
  • A marketing dashboard highlights KPIs and helps you visualize data to help you boost marketing ROI.

    Marketing

    Marketing teams improve ROI through better customer segmentation, targeting and campaign analysis and predicting the performance of new business initiatives.

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Benefits of business intelligence

According to BARC’s BI Survey, the top benefits of business intelligence software are faster and more accurate planning, analysis and reporting, better business decisions and improved data quality. But how do these translate to real business value? With the right business intelligence platform and strategy, here are four major ways BI can deliver value and drive digital transformation within organizations:

1. Reinvent business processes, optimizing business performance

2. Understand customers to deepen loyalty and increase lifetime value

3. Uncover unexpected new sources of revenue

4. Better balance risk with reward across business operations

What are BI best practices?

Build Data Literacy with Training and Tools

Organizations with higher data literacy rates have greater enterprise value. But only 24 percent of all workers say they can read, work with, analyze and argue with data. Close the literacy gap by giving employees access to BI tools that are easy to learn and use, build confidence, increase adoption and empower every user.

Ensure Flexible Deployment and Scalable Architecture

Scale and deploy business analytics to every corner of the organization with the help of a multi-cloud architecture. This gives users the ability to access and share data insights between on-premise and cloud deployments while keeping governance and trust standards high.

Centralize BI Governance, Security and Management

Keep data and analytics secure through a model that lets administrators set rules based on users, actions and resources. Ensure that everyone is working with the same, accurate data by implementing governed, centralized libraries of data, analytics and measures.

Extend the Value of Your BI Investment

Companies using embedded analytics are 89 percent more likely to see higher operating profits of 10 percent or more. Ensure your BI platform gives employees access to data insights in their everyday applications.
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The history and evolution of BI

The emergence of business intelligence can be traced to the decision support systems of the 1960s, evolving in three major waves of innovation over the decades. The challenge is always the same: How can businesses analyze data to make discoveries that lead to competitive edge? Each generation has come a little closer to that promise but it’s the third generation of BI we are now entering that holds the greatest potential to spread the value of BI to every business user and unlock all the value in data:

1st Generation BI – Centralized

In the early days of BI, if a person wanted to learn something from data, they had to submit a question to a data analyst with the skills to create a query or use a complex technology stack that analyzed multi-dimensional data sets (OLAP data cubes2). Often weeks later, they’d get a report that could be out-of-date or raised further questions. This inefficient “Ask > Wait > Answer” cycle limited BI’s value, while reaching only 25% of business users with static information.

2nd Generation BI – Decentralized:

The next wave of BI, pioneered by Qlik, introduced user-driven BI. This replaced the complex technical stack with more agile methods to prepare and load data, and intuitive ways to visualize and explore that data. Business analysts could create analytics apps for key processes, delivering interactive BI dashboards to everyday users. Eventually even more lightweight data visualization tools came to market, and while BI could now reach between 25-50% of employees, most of these visualization tools lacked governance. Because these tools focus on content authoring, low data literacy rates limited user adoption and led to the use of untrustworthy data sources.

3rd Generation BI – Democratized:

BI is entering its next phase, driven by new approaches to how we manage data, deploy analytics and improve data literacy. Trusted data is accessible to all users through governed data catalogs. Augmented analytics accelerates discoveries and increases data literacy by suggesting insights, automating processes, and providing conversational, natural language interaction. And embedded analytics brings BI to the applications and processes that people and machines (IoT analytics) interact with daily. These innovations are enabling organizations to reach the 50-75% of employees not yet using BI, offering a huge potential increase in value from data.

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References

  1. “Analytics and Business Intelligence (ABI)” Gartner Glossary, Gartner Inc., retrieved 20 Aug. 2019
  2. “OLAP Cube.” Wikipedia, Wikimedia Foundation, 18 Sept. 2019