Faced with intense competition and uncertainty in the business world, it can be very difficult for companies to make the right decisions. Knowing that making relevant choices is essential for the proper development of these companies, decision makers are constantly looking for effective decision-making tools. In these circumstances, business intelligence is a reliable solution. In this article, discover an overview of business intelligence, with special attention to how it can impact your business.
Understanding the business intelligence process requires knowledge of certain fundamentals, including its definition and history.
Also referred to as Business Intelligence (BI), this discipline consolidates all processes involved in collecting, analyzing, and processing raw data to facilitate well-informed business decisions. It operates through a comprehensive suite of tools, methodologies, and techniques that, combined, work together effectively. The concept of Business Intelligence was first conceptualized in 1958 by Peter Luhn, who laid down its foundational principles. The vision for BI materialized in the 1990s, a period during which it became a standardized practice, significantly propelled by contributions from figures such as Howard Dresner.
Since the 2000s, marked by the widespread adoption of computers and the Internet, Business Intelligence (BI) has seen extensive growth. This expansion is attributed to the vast volumes of user data available and the critical need for managers to access accurate insights into their business operations. What’s more, the Business Intelligence process has undergone remarkable development since 2010, driven by major corporations’ interest in big data and the opportunities presented by leveraging available data.
Business Intelligence (BI) relies on four fundamental components. The initial step is data collection, where information is gathered from diverse sources within the organization, facilitated by the Extract-Transform-Load (ETL) process. Following this, data storage (or modeling) takes place, involving the organization of structured data into specialized databases, such as data warehouses or data marts, to ensure accessibility for users who will conduct queries and make decisions.
The next phase is data presentation, during which stored information is displayed to users through dashboards, reports, or statistical tools. This step aims to make the information easily interpretable, supporting swift and informed decision-making. Additionally, the collected data can be utilized with analytical tools like OLAP (Online Analytical Processing) cubes, which allow for multidimensional data analysis and data mining to uncover correlations.
Several key distinctions set Business Intelligence (BI) apart from traditional data analysis. The primary difference lies in their respective utilities: BI leverages collected and current data to aid in decision-making, whereas traditional data analysis is often employed to forecast future trends.
Another notable contrast is the nature of the data utilized; BI typically deals with structured data, while data analysis can involve both structured and unstructured data. Lastly, BI generally focuses on gathering information from internal sources, whereas data analysis encompasses data collection from both internal and external sources.
There are several tools and technologies used in the business intelligence process.
The cornerstone tools in Business Intelligence include, first and foremost, ETL (Extract, Transform, Load) solutions such as Informatica, Microsoft’s SSIS, or Oracle’s ODI for data collection. Additionally, RDBMS (Relational Database Management System) software is vital for creating data warehouses or data marts to house structured data, including options like SQL Server, DB2, MySQL, and Oracle.
The third critical technology involves data presentation. For this, dashboard construction tools are essential, with solutions like Power BI, IBM Cognos, or OBIEE standing out.
For those aiming to consolidate data within their business intelligence framework, incorporating OLAP (Online Analytical Processing) cubes is advisable, utilizing technologies such as SSAS, Hyperion ESSBASE, or Power Pivot. While data analysis and data mining technologies are optional, they add considerable value, as do Data Visualization tools, which enhance the presentation of data, making it more engaging and insightful.
Today, new technologies are reshaping the landscape of Business Intelligence (BI). Among these innovations are mobile business intelligence tools, which empower decision-making directly from smartphones. This accessibility fosters broader adoption of BI practices and facilitates quicker decision-making processes.
Furthermore, BI has expanded into the cloud, with cloud applications allowing companies to store, process, visualize, and utilize their data more efficiently without significant investments in hardware and software. Cloud-based BI solutions offer the advantage of being accessible anytime and anywhere, which streamlines the decision-making process for business personnel.
The integration of artificial intelligence (AI) into BI presents further advancements. AI enhances the understanding of structured data and unveils new business opportunities that companies can pursue. It also recommends actions aligned with business objectives, leveraging its predictive capabilities to inform strategic planning.
A good understanding of business intelligence and its impact on a company’s business requires knowledge of how this discipline helps in the creation of strategic decisions .
BI enhances strategic decision-making through two key elements: dashboards and visualization. Dashboards present key performance indicators (KPIs) to decision-makers, enabling them to assess their business’s progress against the company’s strategic goals.
Data visualization provides a graphical representation of the indicators and other information displayed on the dashboard. This graphical approach allows decision-makers, such as Directors of Information Systems or analysts, to gain a clearer understanding of their company’s current status and foresee potential shifts. Visualizations can be adjusted based on various observation parameters, including time, geographic location, age, and more, offering a dynamic tool for in-depth analysis.
Business intelligence (BI) can be leveraged to enhance sales within an e-commerce business, as an example. By gathering, processing, and visualizing user data from the online commerce platform, various insights can be derived. For instance, if a company aims to market a specific product to customers under 30, analyzing the sales data of this product over time within this demographic can be enlightening.
Should the data show that a significant portion of the targeted customers consistently purchases the product throughout the observation period, the company can infer that the product genuinely appeals to this customer segment. This insight enables the company to more effectively market the product to consumers under 30 and to allocate more resources to marketing efforts to boost sales. Conversely, if the product fails to resonate with the targeted demographic, decision-makers can investigate which products are favored by these consumers and focus on enhancing those instead.
Additionally, the company can examine whether the product aimed at the under-30 demographic is being adopted by older or younger consumers. Through this analysis, BI tools can reveal whether the initially proposed product is valued by a significant portion of the market, warranting further investment. So, by utilizing business intelligence, the company can make strategic decisions to manage its operations more effectively and intelligently.
Business intelligence primarily enables companies to make informed decisions that steer their operations towards success. It aids any organization in boosting its productivity, understanding the challenges it encounters, and accurately identifying customer expectations. This results in a significant increase in the company’s revenue and profitability, gains in market share, and better customer satisfaction.
There are some precautionary measures to take if you want to practice in the field of business intelligence.
It is important to know the various professions relating to business intelligence before getting started. These include, for example, the professions of BI developers, Data Analyst, Data Scientist, Data Engineer, Data Architect, BI Consultant, etc. Each of these professions in the field of business intelligence has prerequisites that you must know before considering their exercise.
A master’s degree in fields relating to computer science, statistics, or information and communication technologies is generally necessary. You must also consider the prospects for development, the tasks carried out on a daily basis, and the skills necessary to exercise each of these business intelligence professions.
To pursue a career in business intelligence, it’s advisable to seek education from a reputable institution. This could involve enrolling in a specialized master’s degree in business intelligence or obtaining a professional certification. Such training should equip you with the essential skills required in this field, including:
Additionally, a strong command of technical English is important to grasp the various concepts related to business intelligence fully.
Business intelligence is an indispensable tool that every company should utilize to guarantee business growth. It is continually evolving, with advancements in mobile technology, cloud computing, and artificial intelligence significantly expanding BI’s capabilities. If you’re considering implementing it in your business or specializing in this field, conduct thorough research and take the necessary steps to leverage its full potential.