Business intelligence is a process for collecting, analyzing and distributing data that helps executives, managers and workers make more informed decisions. BI involves collecting data from internal IT systems and external sources, preparing it for analysis, running queries against that data to determine trends or patterns, and then creating visual representations of the results. BI dashboards and reports make analytics results available to business users for operational decision-making and strategic planning purposes.
Bi initiatives should ultimately drive better business decisions and enable organisations to increase revenue, improve operational efficiency, etc. Business intelligence is a set of tools and methods for gathering, analyzing, visualizing and reporting data to make more informed decisions.
How the business intelligence process works
The BI process is a five-step cycle as follows:
Data preparation, which includes organizing and modeling data for analysis;
Analyzing the prepared data in an informed way.
The distribution of key performance indicators (kpis) and other findings to business users.
Using the information to guide and influence your business decisions.
In the beginning, businesses used BI tools mostly to analyze data for internal decision-making. Business intelligence (BI) tools are becoming more accessible to executives and workers alike, thanks to self-service business intelligence platforms.
Business intelligence environments that allow users to query, visualize data and create dashboards on their own are called self-service business intelligence. Big data analytics are used in many business intelligence programs to help companies analyze large amounts of information. Companies use these tools to predict future business outcomes and make better decisions. While advanced analytics may be conducted by data scientists and statisticians on a separate team, more straightforward querying and analysis of business data is generally handled by the BI department.
Business intelligence helps you make better decisions.
Business intelligence serves to improve an organization’s business operations by providing relevant data for decision-making purposes. For companies that use business intelligence (BI) tools and techniques well, their data can reveal insights into the workings of their processes and strategies. The insights can then be used to make better business decisions that increase productivity and revenue—ultimately leading businesses to grow more quickly.
Without BI, organizations are at a disadvantage when it comes to making data-driven decisions.
In other words, instead of making business decisions based on accumulated knowledge from past experiences or intuition and instinctive feelings about a given situation. While those methods can be effective, they’re also subject to the pitfalls of what’s known as data-free decision making: massive inefficiencies and significant errors caused by incomplete or inaccurate information.
Reasons to use business intelligence
A BI program that produces business benefits is successful.
Through Business Intelligence, C-suite executives and department managers can monitor ongoing business performance so they’re able to act quickly when issues or opportunities arise.
Analyzing customer data can give marketers, salespeople and customer service representatives a better understanding of their target audience. Supply chain, manufacturing, and distribution bottlenecks can be identified in time to prevent a financial disaster. By analyzing employee productivity, labor costs and other workforce data, HR managers are able to better monitor performance.
Businesses can reap several benefits from BI applications, including:
✅Improve your decision-making and make it happen more quickly.
✅Optimize your company’s internal business processes.
✅Identify emerging business and market trends;
✅Develop strategies to strengthen your business.
✅Drive increased sales and revenues
✅Make a company more competitive over its rivals.
BI initiatives can help companies in many ways, including making it easier for project managers to track the status of business projects and for organizations to gather competitive intelligence on their rivals. Business intelligence, data management and IT teams can use their own data to analyze various aspects of analytics operations.
Business intelligence is a category of software that provides different tools to analyze data. Self-service business intelligence (BI) software and traditional BI platforms are used to create most dashboards.
The following table describes different types of business intelligence (BI) technologies that organizations can use:
Ad hoc analysis. In data analysis, ad hoc querying is one of the foundational elements that make self-service BI applications possible. Data mining is the process of writing queries to examine and understand existing data. While ad hoc queries are created on the fly, they often end up being used regularly.
Online analytical processing (OLAP).
OLAP tools allow users to analyze data from multiple perspectives – a useful feature for complex queries and calculations. In the past, analysts had to extract data from a data warehouse and store it in multidimensional OLAP cubes before they could run analyses on it. But increasingly, analysts can perform their initial analysis straight against columnar databases without having to first transfer that data into something like an OLAP cube.
Mobile BI. Mobile business intelligence is transforming the way businesses gather and analyze data—no longer requiring desktop computers to access BI applications. Although designed with usability in mind, mobile BI tools can be limiting because they don’t offer all of the features available to full-fledged desktop software.
For instance, mobile dashboards may only display two or three data visualizations and KPIs so they’re easier to view on small screens.
Real-time BI applications analyze data as it’s created, collected and processed to provide up-to-date views of business operations—for example, customer behavior or financial markets.
Real-time analytics often involves streaming data, and supports decision-making uses such as credit scoring, stock trading or targeted promotional offers.
Operational intelligence (OI). Operational BI is a form of real-time analytics that delivers information to managers and front line workers in business operations. There are several ways in which OI applications can help a company to grow. For example, they can be used by call center agents and logistics managers to resolve problems quickly and easily.
Software-as-a-service BI. SaaS BI tools encapsulate data analysis capabilities in the form of a service that’s typically priced on a subscription basis—and delivered to users by vendors who host cloud computing systems. Also known as cloud BI, SaaS has increasingly become multi-cloud—able to run on different platforms and avoid single vendor dependency.
Open source BI (OSBI).
Open source business intelligence software typically includes two versions: a community edition that can be used free of charge and a subscription-based commercial release with technical support. Some vendors of proprietary business intelligence tools offer free editions—typically, for individual users. But BI teams can also access the source code for development uses.
Business intelligence tools can be embedded directly into business applications, increasing their usefulness. Business users can analyze data within the applications that they use for their jobs.
Embedded analytics features: Most application software vendors include them in their applications, but corporate developers can also incorporate them into homegrown systems.
Business intelligence for big data
BI platforms are increasingly being used as the front-end interfaces for big data systems that combine structured, unstructured and semi structured data. Modern BI software typically offers flexible connectivity options, giving it the ability to connect with a wide range of data sources. Big data is an increasingly important part of enterprise architecture, and the tools used to analyze it—especially Business Intelligence (BI) tools—have a relatively simple user interface that makes them ideal for use in big data architectures.
BI users can use BI tools to access and analyze data in conventional databases, as well as Hadoop/Spark systems and NoSQL stores. That makes it possible for the general population to examine huge amounts of information rather than requiring data specialists.
Big data systems, on the other hand, serve as staging areas for raw data that later is filtered and refined before it’s loaded into a data warehouse for analysis by BI users.
Business intelligence trends
Business intelligence teams often include a mix of BI managers, analysts and specialists who work closely with data architects, data engineers and other types of professionals. Business analysts and other end users are also often included in the BI development process to ensure that all parties’ needs are met.
A growing number of organizations are using Agile BI and data warehousing techniques to deliver analytics faster than ever before.
BI projects can deliver new functionality to end users on an ongoing basis.
It enables companies to deploy BI features more quickly and respond as business needs or requirements change.
Other notable trends in the BI market include:
The growing number of augmented analytics technologies. More and more BI tools offer natural language querying capabilities as an alternative to writing queries in SQL or another programming language, plus AI and machine learning algorithms that help users find, understand—and even prepare data!
Low-code and no-code development platforms.
A number of BI vendors are adding graphical tools that allow for the development of BI applications without coding.
Increased use of the cloud. It was slower to adopt the cloud because data warehouses were primarily deployed in on-premises data centers. But cloud deployments of both data warehouses and BI tools are growing; Gartner Consulting recently reported that most new spending on business intelligence projects is now for cloud-based implementations..
Any effort to improve data literacy: Since self-service BI is becoming more common, it’s important to make sure new users understand the data they’re working with. To improve their results, businesses are incorporating data literacy skills in their user training programs. BI vendors have also launched initiatives such as the Qlik-led Data Literacy Project to empower users with tools for better analysis and decision making.