What Is Business Intelligence?
This is a transcript of a Webinar hosted by InetSoft entitled "Best Practices for Deploying and Using Business Intelligence Software." The speaker is Mark Flaherty, CMO at InetSoft.
Mark Flaherty (MF): Business intelligence is a term that has been around a while. It has been used to describe a segment of the software market. It has been used to describe a technology that companies use to approach certain business problems that they have.
A comprehensive definition of business intelligence is a combination of the software and the technologies that companies use, in addition to the practices, capabilities, and disciplines that businesses need to gain in order to gain a better understanding of their business through accessing and analyzing the data that exists inside and outside the enterprise that leads to better performance of the business through making faster, more accurate decisions and taking the appropriate actions. Performance management is considered a subset of business intelligence.
How do the best companies use business intelligence?
First, I’ll explain what we mean by the best companies. We mean those companies that use business intelligence the best are the ones who also have the best measures in several areas: fastest speed to accessing information, highest usage rate of BI assets, highest employee productivity, and lowest effective cost of BI applications per employee.
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Speed to accessing information means the gap of time between an event occurring and information about that event becoming available to the company so they first realize it, and second will be able to respond to it, if appropriate. The usage rate of BI assets means to what degree is a company able to deliver self-service business intelligence capabilities to non-technical employees. This can also be called the adoption rate. Employee productivity here means the time spent by people looking for information as part of their day to day activity.
Effective cost takes into account the total expenses in terms of software licensing and administrative overhead and divides that figure by the number of employee using the BI application. So the greater the adoption rate, the better this number is. And the lower the overhead costs are, whether that be hardware necessary to run the software, the software licensing itself, or the salaries associated with BI experts, if necessary, to operate the software.
What results do the best users of business intelligence achieve?
When looking at the companies that are deemed the best business intelligence users, we have found the majority of them provide near real-time access to key metrics. All of them offer self-service BI to their business users. The companies see a 10 percent improvement in employee productivity relative to the average among companies. They realize a similar percentage reduction in effect cost per employee.
How has business intelligence different now compared to 20 years ago?
Business intelligence (BI) has undergone a dramatic transformation over the past two decades, driven by advancements in technology, data accessibility, and the increasing need for real-time decision-making. Twenty years ago, BI was primarily limited to static reports, manually generated spreadsheets, and structured databases. Today, it is an ecosystem of AI-driven analytics, real-time dashboards, and self-service data tools that empower businesses to make faster and more informed decisions.
In the early 2000s, BI was heavily IT-dependent. Organizations relied on data teams to generate reports using SQL queries and enterprise data warehouses. BI tools were complex, expensive, and required dedicated teams to maintain them. Now, with the rise of cloud computing and user-friendly BI platforms, business users can access and analyze data in real time without needing deep technical expertise. Self-service BI has democratized data, making it accessible to all levels of an organization.
Another major shift is the move from historical reporting to predictive and prescriptive analytics. Two decades ago, BI was primarily used for backward-looking analysis - companies would examine past performance to make strategic decisions. Modern BI integrates machine learning (ML) and artificial intelligence (AI) to forecast trends, suggest actions, and automate decision-making. Predictive analytics helps businesses anticipate customer behavior, market shifts, and operational risks, giving them a competitive edge.
Real-time data processing is another game-changer. In the past, BI reports were static and generated periodically—often daily, weekly, or monthly. Businesses had to wait for reports to understand performance metrics, which limited their agility. Today, with the help of cloud BI platforms, IoT (Internet of Things) data, and streaming analytics, organizations can monitor key performance indicators (KPIs) in real time. This instant access to data allows businesses to respond to changes immediately rather than retrospectively.
The evolution of data storage and management has also played a crucial role in transforming BI. Twenty years ago, businesses relied on on-premise servers and relational databases, which were expensive and difficult to scale. With the advent of cloud storage solutions like Amazon S3, Google BigQuery, and Snowflake, companies can store and process massive amounts of structured and unstructured data without significant infrastructure costs. This shift has enabled businesses of all sizes to leverage BI without requiring large IT budgets.
Furthermore, BI visualization tools have become more intuitive and interactive. In the early 2000s, Excel spreadsheets and basic graphs were the primary ways of presenting business data. Now, advanced visualization tools like InetSoft, Tableau, Power BI, and Looker provide dynamic dashboards that allow users to drill down into data and explore insights interactively. These tools make complex datasets more comprehensible and enable decision-makers to derive insights quickly.
Another key difference is the integration of BI into everyday business applications. In the past, BI was a standalone function used mainly by analysts and executives. Today, BI is embedded in CRM (Customer Relationship Management) systems, ERP (Enterprise Resource Planning) software, and marketing automation tools. Employees across various departments, from sales to operations, can access relevant BI insights directly within their workflows, improving efficiency and decision-making at every level.
Lastly, the role of data governance and security in BI has evolved significantly. With the explosion of data, organizations now face challenges related to data privacy, compliance (such as GDPR and CCPA), and cybersecurity threats. Modern BI tools incorporate advanced encryption, role-based access controls, and compliance frameworks to ensure that sensitive data is protected while still being accessible to authorized users. This focus on security was far less stringent two decades ago when data was primarily stored in controlled environments.
Overall, BI has evolved from a slow, IT-driven, and retrospective reporting function into an agile, AI-enhanced, and real-time decision-making powerhouse. As businesses continue to generate vast amounts of data, the future of BI will likely focus on even greater automation, personalization, and integration with emerging technologies such as blockchain and augmented analytics.
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