The last stage reached in organizations’ efforts to use information for gaining a sustainable competitive advantage is Business Intelligence (BI) applications. In today’s economic environment, business intelligence has become an essential part of effective decision-making and information management processes.
To maximize a company’s revenue and performance, it is crucial to gather the necessary data, analyze it quickly, and generate insights to make informed decisions. The easiest and most reliable way to achieve this is through business intelligence (BI) applications.
What is Business Intelligence (BI)?
Broadly defined, BI encompasses applications and technologies that help organizations make better business decisions by collecting, storing, analyzing, and reporting necessary data. BI involves the analysis and reporting of organizational business data to understand existing business performance and make data-driven business decisions. These processes are primarily carried out using computer software and technology.
Let’s break down the BI process into steps while also explaining the commonly used technical terms throughout the process:
1. Discovery and Intelligence Gathering
The first step in the BI process is identifying the need for information and gathering data through intelligence activities. Organizations require specific information to guide strategic and tactical business decisions, such as “How can we stay ahead of the competition?”, “What are the global trends?”, or “Should we invest in a new brand?”. This information can be gathered through various methods (operational data entry, market research, surveys, etc.) from diverse sources, such as media, economic reports, e-cards, POS systems, mobile applications, CRM, and ERP systems. As technology advances, both the volume of data collected and the techniques for collecting it continue to grow. BI software integrates with these data sources to enable real-time data collection. For instance, it can transfer data in real time from an ERP system that holds financial and operational data or from a CRM system that contains existing customer information. Today, BI applications can even collect data from mobile platforms.
2. Consolidating and Managing Data
The second step involves relating the gathered information to one another and consolidating it into a structure that is easy to analyze. This process involves organizing scattered data into a central system to enable faster and more cost-effective analysis while preventing duplicates. Sometimes, the operational data and their existing technological platforms allow for this, while other times, they do not. In such cases, another system is needed to store and integrate only the necessary data. The platform that consolidates all data in a single center, allowing for easy, fast, and accurate analysis, is known as a Data Warehouse. A data warehouse is a platform that enables easier access to information and, in turn, faster and more effective querying. The process of extracting only the necessary data, cleaning redundant information, and transferring it to the data warehouse is called ETL (Extract, Transform, Load). Data stored in a data warehouse is placed into data sets that allow for filtering, grouping, and categorical differentiation. These intelligent data structures within data warehouses are known as Data Warehouse Cubes. Due to their structure, which allows analysis and manipulation from multiple perspectives, they are referred to as “cubes”. Sometimes, there is a need for analysis on a relatively smaller data set targeting a smaller user group. These smaller, manageable data structures are stored in a platform known as a Data Mart. While a data warehouse stores all organizational data, data marts are a subset that contains information specific to certain processes, such as sales and marketing.
3. Analyzing Data
The next step is to analyze the available data to understand the company’s business performance or to determine actions that will help achieve the desired goals. The integrated and organized data structure created in the previous stage enables comprehensive and targeted analysis. BI analysis technologies allow for the analysis of information stored in smart environments like data warehouses, as well as operational data. One frequently used method in the BI analysis process is Online Analytical Processing (OLAP). OLAP allows for simultaneous, multidimensional cross-queries without being limited by dimensions or groupings, providing high performance for queries at all levels thanks to its database cube structures. Sometimes, the collected data may contain answers to questions we have never thought to ask. These data may include information such as trends, correlations, and dependencies that are impossible to derive with traditional reporting structures. This type of analysis is developed through techniques known as Data Mining. Using various algorithms and methodologies, data mining identifies relationships, predictions, trends, and patterns through computer-assisted analysis of large data sets. Another analysis method is Benchmarking, which involves comparing a business process and performance measurements against the industry’s best practices to learn tools and methods for achieving higher quality, faster, and cheaper results. One of the key performance metrics is Key Performance Indicators (KPIs), which are success indicators that help measure progress and improvement toward organizational goals. BI applications play a crucial role in reporting these indicators effectively.
4. Decision-Making
The final step is interpreting the analysis results and making decisions. This process involves choosing one or several options from multiple alternatives based on logic, interpretation, foresight, and predictions. The outcome is either an action or a new idea. Methods and presentation techniques that enable even those who are not experts to easily interpret analysis results are part of this step. Visual tools are used to reduce complexity and uncertainty. One of the BI capabilities used for this purpose is Executive Dashboards, which are visual reports stripped of technical jargon and complexity. Another frequently used tool by executives is performance dashboards containing key performance indicators. Throughout this process, data is transformed into information, information into insights, insights into decisions, and decisions into actions. BI is merely a tool that provides environments like data warehouses for data consolidation, advanced analytics techniques like data mining, and easy-to-understand reporting formats. However, decisions are made by managers.
Why Are Companies Investing in BI Solutions ?
The simplest answer is, “To make more accurate and faster decisions!” In today’s environment, you need to be fast, high-quality, and cost-effective simultaneously. You must respond to your competitors’ moves promptly. Whether you are launching a new campaign, entering new markets, or making a tactical decision, you need to be quick and effective before conditions and the environment change. BI applications meet precisely this need. As discussed above, BI solutions consolidate data into a common “smart” platform, providing more efficient analysis capabilities than traditional reporting systems. This allows us to see what is happening in our business processes and with our customers faster, more comprehensively, and—most importantly—at a much lower cost. BI enables a multidimensional view of customers, suppliers, market conditions, business processes, financial performance, and potential risks.
Future Trends
- In recent years, surveys indicate that senior management considers BI their preferred decision-making tool. Transforming raw data into insights has been documented as a “key success factor” in competitive environments. Today, BI applications are extending beyond data and technology, influencing business processes, skills, and even organizational cultures.
- BI solutions, supported by artificial intelligence, are expected to become increasingly sophisticated, with applications that can accurately interpret human speech and natural language.
According to the principles of analytical philosophy, scientifically derived knowledge is the most reliable basis for rational decision-making. However, to understand whether a piece of information is correct, its analysis is also essential.
With advancements in AI-powered technologies, we are getting closer to applications that can accurately perceive human speech.
According to CIO magazine: “Although business intelligence does not tell business users what to do or what will happen if they take a certain course, neither is BI only about generating reports. Rather, BI offers a way for people to examine data to understand trends and derive insights.”
