An organization's ability to expand does not rely solely on profits, but also on their ability to access and interpret data.
A business that does not utilize management systems or any control method does not have the means to collect and reference historical or real-time data. This means the company's expansion efforts rely on guesswork, rather than educated decisions based on trends and performance metrics.
With business intelligence, organizations can store, organize, and evaluate datasets to gain an accurate overview of their performance. This enables management to determine what operations need improvements and how to promote profitability.
What is Business Intelligence?
Business intelligence (BI) is a combination of business analytics, data mining, visualization, and infrastructure used to enhance decision-making. BI gathers and consolidates information into a comprehensive view, allowing organizations to use data to make improvements, eliminate inefficiencies, and adapt to their fluctuating market.
While BI traditionally entailed a network of manual information sharing in the 1960s, it now refers to technology-driven processes, such as analytical software.
The BI solution integrates with a company's established systems to collect data from internal and external sources. This information is then analyzed to generate reports on key performance indicators (KPIs), actionable insights, and data visualizations, such as graphs and charts.
Aside from software, BI architecture utilizes data warehouses or data marts, depending on the organization's size, to store subsets of information in a centralized area. Many systems also use what is known as data lakes to hold structured and unstructured information from internal or external sites.
BI has the capacity to handle large volumes of historical and real-time data, making it an excellent tool to define past trends, enhance tactical decision-making, and project future outcomes. However, advanced analytics often requires a collaborative effort from data scientists, predictive modelers, and analysts.
Business Intelligence Techniques
BI uses various techniques to accurately conduct analytics, detect trends, and create forecasts. The BI process is not linear, but rather cyclical, as it is performed continuously using the following functions-
Data Mining involves sorting through extensive datasets, metrics, and statistics to define trends and identify connections between variables.
Querying is a request for specific information that is located in a database.
Data Preparation is the process of aggregating and structuring data before it is analyzed.
Reporting refers to the sharing of vital information, enabling decision-makers to draw final conclusions.
Benchmarking entails comparing internal processes and measured performance to historical data to track a business's progress against competitors.
Descriptive Analytics is the reference of past data to interpret changes within a business.
Statistical Analysis is the collection of results from descriptive analytics and the application of other key metrics to define patterns.
Data Visualization illustrates sets of information in a comprehensive way, such as charts and graphs, for easy digestion.
Benefits of Business Intelligence
If used properly, BI offers businesses several benefits that help improve their internal operations. For example, many retailers use BI to evaluate customer information to enhance their marketing approaches, customer interactions, and sales. It is also used within the supply chain to improve operational efficiency and optimize production.
Regardless of an organization's industry, BI processes help-
Speed up and improve decision-making.
Enhance internal operations.
Optimize operational efficiency and productivity.
Detect and alert users of potential risks.
Define emerging trends within the business and overall market.
BI also provides more specific benefits for project managers. Teams can set parameters on the software to generate particular metrics and monitor launched projects. IT departments especially benefit from BI, as it automizes analytical operations and technology management.
Types of Business Intelligence Tools
The BI process is extensive, requiring several multi-faceted tools to perform various tasks. The most common BI technologies include-
Ad Hoc Analysis
Ad hoc analysis, also known as querying, is an essential element in modern BI solutions, especially self-service tools. Ad hoc analysis is the process of creating and launching queries to evaluate specific problems.
While this procedure is programmed to run for unexpected events, it is often used regularly to generate reports.
Online Analytical Processing
Online analytical processing (OLAP) is one of the first BI technologies, allowing users to evaluate data across multiple software layers. This helps simplify complicated queries and calculations that require several dimensions to complete.
Before, requests were handled by extracting data from data warehouses and stored in separate OLAP storage locations. Now, OLAP analyses are run alongside databases for quick processing.
Mobile Business Intelligence
Mobile BI enables users to view their BI applications on portable devices, such as tablets and smartphones. Mobile BI technologies are programmed to prioritize the user-friendly interface and data viewing.
For example, many mobile BI dashboards only show a few vital KPIs and visualizations for easy reference, and to avoid overwhelming the viewer.
Real-Time Business Intelligence
Real-time BI analyzes data as it is created and inputted into the application, giving users the most up to date information.
Management can view real-time metrics on operations, customer behavior, financial health, market trends, and other various areas. Common real-time analytics include checking credit scores, inventory levels, stock trading, and sales.
Operational intelligence (OI), also known as operational BI, is a type of real-time analytics that streamlines data sharing to the managers of various systems. OI tools focus on enhancing decision-making and responsiveness to arising issues.
Software-as-a-service (SaaS) BI tools are cloud-based programs that deliver data analysis applications to clients for a subscription. This service offers multi-cloud support, enabling businesses to launch their BI applications on different platforms.
Open Source Business Intelligence
Open-source BI (OSBI) is software that is offered in two versions - the community edition, which is free, and the subscription-based product, which includes customer support from the provider. Some OSBI providers offer free, limited services for startups and individual users.
Embedded Business Intelligence
Embedded BI collaborates and implements BI and data visualization functions into established business applications, enabling employees to analyze data within their primary software. While embedded BI is typically integrated into software by vendors, it can also be programmed into customized applications.
Collaborative Business Intelligence
Collaborative BI is considered to be a process rather than a tool as it combines several BI systems to enable multiple users to work simultaneously on the same data. This streamlines data sharing, analysis, and workflow on team projects.
For example, several employees can view, highlight fundamental datapoints, leave comments, and post questions on data reports via messaging boards and discussion tools within BI software.
Location intelligence (LI) is a specialized type of BI that enables users to evaluate geotargeted data with map-based visualization features. This function offers insights into operations that depend on location, such as selecting a property for retail stores, launching target marketing campaigns, and logistics management.
What to Look for in a Business Intelligence Solution
Companies looking for a BI solution first need to determine their needs and business objectives. Then management should look for a user-friendly software with-
A Universal Platform
By choosing a universal platform solution, all employees can access data and integrated applications in a centralized location. This streamlines data gathering, sharing, and analyses, promoting workflow and productivity.
Businesses with legacy BI solutions are unable to integrate their existing systems, such as inventory management, point-of-sale (POS), and forecasting software, due to compatibility issues. This makes it challenging to consolidate data to generate insights and reports on various operations.
However, a single platform enables seamless integration to create an end-to-end solution that allows employees to retrieve, analyze, and interpret data simultaneously. This avoids any compatibility problems and saves time navigating through various systems searching for information and tools.
A cloud-based BI solution allows employees to access the dashboard, regardless of their location, optimizing accessibility. For example, retailers with cloud-based BI systems can connect employees from all store, warehouse, and corporate locations to streamline data exchange and communication.
Cloud-computing solutions also fit the needs of any sized business, making it an excellent tool for both larger enterprises and small businesses looking to expand.
Most modern BI solutions have the capability to connect multiple databases. However, management should double-check with the provider to ensure the system has preprogrammed connections to avoid potential technical issues. This functionality makes sure that data integration is quick and seamless.
Prebuilt connections also eliminate the hassle of manually connecting data warehouses, avoiding system malfunctions.
To optimize the ease of BI, businesses should consider augmented analytics, which utilizes machine learning techniques. With machine learning, BI solutions can automate standard, repetitive tasks such as data gathering, evaluating, interpreting, and sharing.
Advanced systems can even automatically prepare structured data by collecting information from various sources, consolidating relevant data, and generating reports. This functionality eliminates the need for human intervention, reducing the risk of human error.
Users should be able to search for information in one centralized location, rather than navigating through several disjointed systems. Some models offer a semantic layer that enables users to explore data using standard business terms.
With augmented analytics, employees can set parameters and enter requests that enable the system to filter through large volumes of data and retrieve relevant results.
Embedded machine learning enables augmented analytics to incorporate forecasting models without the need for software coding. This gives businesses the ability to detect emerging trends and threats.
Most modern BI solutions have data visualization features that automatically transform a set of data into visual aids, such as pie charts and graphs. This enables viewers to easily digest data and trends, as opposed to trying to recognize patterns from several metrics and raw data.
Data visualization also enables management to develop actionable insights from the relationships between variables and trends. On the other hand, solutions without data visualization capabilities would require additional help from analysts to interpret information.
Employees can even handpick which visual aid they would like to utilize. For example, line graphs are great for showing progression, while pie charts represent different elements that make up a structure.
While it is essential for BI systems to handle multiple simultaneous users, they also must cater to individual users who may not need automation.
Self-service allows easy navigation with simple point-and-click and drag features, while dashboards are user-friendly and comprehensive. This software should also provide interactive elements, such as step-by-step tutorials, so users are able to learn how to operate different functions.
Self-service should also give users full control over their data, from where it is stored to how it is analyzed. This enables management to collaborate internal and external information to create unique insights and perspectives. However, this also means that employees are responsible for creating their own data-based reports.
Now more than ever, business people are working around the clock and from different locations, whether it be from home or various company facilities. This means that many companies need a solution that can connect all employees, regardless of their operating hours and location.
With mobile accessibility, employees can access their company's BI solution via cloud-based software or mobile applications, from anywhere at any time. Many mobile BI solutions offer voice-enabled verification, real-time alerts, and interactive visualizations. This enables users to view data and internal systems from their mobile devices.
BI is a powerful tool that handles several crucial data processes, from consolidation to analysis. With the right solution, organizations can optimize data management to enhance their decision-making and expansion efforts.