Business Intelligence in Action: How Companies Harness BI?

How many times have we heard that a company has millions of data and doesn't know what to do with it? Everywhere the message is that data offers great value, although depending on how we formulate it, this is not entirely true. Without a good analysis that extracts information from it, data is practically useless. To extract this valuable information companies can use business intelligence. Business intelligence is the use of data to get information that serves as a solid basis for safer decision making. To do this, companies consume data in different ways. Here are some of them:

How do companies use bi

What is business intelligence?

Business Intelligence (BI) is a powerful framework that transforms raw data into actionable insights, enabling organizations to make informed decisions, optimize operations, and gain a competitive edge. Through data analysis, visualization, and reporting, BI empowers businesses to understand past performance, identify trends, and forecast future opportunities, paving the way for strategic growth and success.

Why do companies need business intelligence?

Companies need business intelligence (BI) because it serves as a strategic compass that guides their decision-making and helps them navigate the ever-changing business landscape effectively. 

In today's data-driven world, businesses are inundated with vast amounts of information from various sources. Business intelligence acts as a powerful beacon amidst this data deluge, transforming raw data into valuable insights. By utilizing BI tools and practices, companies can:

  1. Data-Driven Decision Making: BI empowers organizations to base their decisions on concrete data and objective analysis rather than gut feelings or intuition. It enhances the accuracy and reliability of decisions, leading to better outcomes.

  2. Competitive Advantage: With BI, companies gain a deeper understanding of their market, competitors, and customer behaviors. Armed with this knowledge, they can identify untapped opportunities, address weaknesses, and capitalize on their strengths, ultimately gaining a competitive advantage.

  3. Performance Monitoring: Business intelligence provides real-time and historical insights into key performance indicators (KPIs) and metrics, enabling companies to monitor their progress toward goals and track performance over time. This facilitates quick course corrections and ensures strategic alignment.

  4. Identify Trends and Patterns: BI tools help companies identify emerging trends and patterns in their data. By spotting market shifts, customer preferences, or operational inefficiencies early on, organizations can proactively adapt and stay ahead of the curve.

  5. Operational Efficiency: BI optimizes processes by identifying bottlenecks, streamlining workflows, and reducing inefficiencies. Data-driven process improvements lead to cost savings, increased productivity, and enhanced operational efficiency.

  6. Customer Insights: Understanding customer behavior, preferences, and needs is critical for any business. BI enables companies to analyze customer data, personalize interactions, and deliver superior customer experiences, fostering loyalty and retention.

  7. Risk Management: BI tools provide comprehensive risk analysis, enabling companies to assess potential threats, vulnerabilities, and market fluctuations. Armed with this information, companies can implement risk mitigation strategies and make informed contingency plans.

  8. Regulatory Compliance: In industries with stringent regulatory requirements, BI helps companies maintain compliance by providing accurate and auditable data records.

  9. Strategic Planning: BI plays a pivotal role in long-term strategic planning. It allows companies to evaluate the effectiveness of existing strategies, explore new opportunities, and align resources with their objectives.

  10. Data Visualization and Communication: BI's data visualization capabilities facilitate clear and concise communication of complex information. It enables stakeholders to grasp insights quickly, enabling more effective communication and collaboration across the organization.

In summary, business intelligence is a vital asset for companies seeking to unlock the potential of their data, make informed decisions, drive innovation, and thrive in today's dynamic business environment.

What is self-service BI?

Self-Service Business Intelligence (BI) is an empowering approach that allows business users, regardless of their technical expertise, to independently access, analyze, and visualize data to derive valuable insights. It puts the power of data exploration and reporting directly into the hands of non-technical users, freeing them from reliance on IT or data specialists.

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Some of the advantages of self-service BI for companies are:

  1. Faster Decision-Making: Self-service BI enables business users to access real-time data and generate reports instantly. This agility accelerates decision-making processes, helping companies seize opportunities and respond swiftly to market changes.

  2. Empowerment of Business Users: By eliminating the need for technical assistance, self-service BI empowers business users to be self-reliant in data analysis. They can create customized dashboards, reports, and visualizations tailored to their specific needs and preferences.

  3. Reduced IT Burden: With self-service BI, IT teams are freed from creating and maintaining numerous reports for different departments. Business users can directly access the data they require, reducing the burden on IT and enabling them to focus on more strategic tasks.

  4. Better Data Governance: While promoting data accessibility, self-service BI solutions can also enforce proper data governance policies. Administrators can define access controls, data permissions, and quality standards, ensuring data security and accuracy.

  5. Improved Collaboration: Self-service BI fosters collaboration among teams. Users can easily share reports, insights, and interactive dashboards, encouraging cross-functional discussions and fostering a data-driven culture.

  6. User-Friendly Interface: Most self-service BI tools offer intuitive drag-and-drop interfaces, making data analysis and visualization user-friendly for non-technical users. This reduces the learning curve and encourages wider adoption across the organization.

  7. Deeper Data Exploration: Business users can explore data freely, drilling down into details, applying filters, and conducting ad-hoc analysis without waiting for IT-generated reports. This freedom encourages curiosity and a deeper understanding of the data.

  8. Real-Time Insights: Self-service BI provides access to up-to-date data, ensuring that decisions are based on current information. This real-time aspect enables companies to respond quickly to emerging trends and capitalize on opportunities.

  9. Cost Savings: By empowering business users to handle their data needs independently, companies can reduce the cost associated with specialized BI training and dedicated IT resources.

  10. Enhanced Data-Driven Culture: Self-service BI encourages a data-driven culture, as it fosters a sense of ownership and accountability among business users for their data analysis and decision-making processes.

In conclusion, self-service BI equips companies with a powerful tool that democratizes data access, promotes data-driven decision-making, enhances collaboration, and optimizes resource allocation. By unlocking the potential of their data through self-service BI, companies gain a competitive edge in today's fast-paced business landscape.

What is embedded BI?

Embedded business intelligence is defined as the integration of reports, dashboards and analytics views into an application. Information is displayed and managed on a BI platform —for example, Power BI Embedded and embedded directly into the application's user interface to improve context and data usability. That is, with BI embedded you can have the graphics, performance indicators and KPIs of your BI inside your CRM, PMS, CMS, Customer Data Platforms (CDP) or others, without having to go check your business intelligence software. The advantage of using embedded BI is that it reduces the cost and time involved in creating reports and analysis.

With BI embedded BI joins the application user experience and provides customers with an enriched work context and information within the applications they already use. In this way, users can get better and faster decision making on their own with interactive dashboards and integrated analysis. In addition, these dashboards and reports can be customized by combining multiple data streams according to your specific needs, unlike traditional reporting software.

With the use of business intelligence embedded, users can base their decision making on BI while still performing their normal daily tasks. Embedded BI can also be part of workflow automation, so it will determine certain actions based on parameters set by the user.

Data Discovery

Data discovery is a user-directed process by which unknown or unusual patterns and values can be discovered in data. Data discovery consists of collecting data from its various databases and silos and consolidating them into a single source that can be evaluated easily and in real time. It allows you to discover, in a few clicks, the factors that contribute to a trend as soon as it has been discovered.

With data discovery, the user searches for specific elements or patterns in a data set. Visual tools make the process dynamic, easy to use, fast and intuitive. Data visualization now goes beyond traditional static reporting. BI visualizations have evolved and increased to include geographic maps, thermal maps, pivot tables, and more, allowing you to create presentations that faithfully reflect discoveries.

Self-service Analytics

Self-Service analytics allows end users to easily analyze their data by creating their own reports and modifying existing ones without the need for training. For example, if an organization only needs one report per year, it can dedicate IT resources to this task. On the other hand, if this organization has 1000 employees and each of them requires several reports on a daily basis, the IT team will not be able to manage the demand.

Self-service analytics or ad hoc reports offer users the ability to create reports quickly, allowing them to get data analytics in minimum time. End users can analyze their data by dynamically modifying or adding calculation functions to a report. This flexibility lessens the burden on the technical department, freeing up development resources. This gives business users the ability to take control of their own analytical needs and helps them extract maximum value from both their data and their application. In this way, the IT team manages interactive reports that each end user can filter to find the information they need.

Augmented Analytics

Augmented analytics offers automation of data analysis through machine learning, natural language processing (NLP) and Large Language Models (LLM). This advanced use, manipulation and presentation of data simplifies data to present clear results and provides access to sophisticated tools for business users to make day-to-day decisions with confidence. Users can go beyond opinion and prejudice to get a true picture and act on data quickly and accurately.

Augmented analytics solves the problem that many organizations still have with the generation of knowledge from data.

What augmented analytics does is to alleviate a company's dependence on its data scientists by automating the generation of knowledge in a company through the use of advanced machine learning algorithms and artificial intelligence.

An augmented analytics engine can automatically process a company's data, clean it, analyze it and turn it into actions for executives or marketing professionals with little or no supervision by a technician.

The consumption of data for use in business takes many forms and each of them can be used alone or with others. Each company, department or specific situation will require one way or another to analyze the data, although the goal of these processes and technologies is similar: to get a good basis for making good business decisions and optimize the processes within the company.

Advanced Analytics

Advanced analytics is a cutting-edge discipline within the field of data science that goes beyond traditional data analysis techniques. It encompasses a sophisticated set of statistical, mathematical, and machine learning methods to extract profound insights, predict future trends, and uncover hidden patterns from complex and voluminous datasets. By employing algorithms like neural networks, decision trees, clustering, and natural language processing, advanced analytics unlocks the full potential of data, enabling organizations to make informed and strategic decisions that lead to transformative outcomes. This futuristic approach leverages the power of data to solve intricate problems, drive innovation, and gain a significant competitive advantage in today's data-centric world.



In conclusion, business intelligence (BI) is a powerful tool that enables companies to transform raw data into actionable insights, driving informed decision-making and strategic growth. BI empowers organizations to make data-driven decisions, gain a competitive advantage, monitor performance, identify trends, and improve operational efficiency. Self-service BI further enhances these benefits by empowering business users to independently access, analyze, and visualize data, fostering faster decision-making, reduced IT burden, and enhanced collaboration. Embedded BI integrates reports and analytics into applications, providing users with enriched work context and information within familiar interfaces. Data discovery, self-service analytics, augmented analytics, and advanced analytics are additional techniques that unlock the full potential of data, enabling organizations to uncover hidden patterns, predict future trends, and make transformative decisions. To fully harness the power of data, companies should embrace these various approaches to business intelligence. To learn more about implementing a successful self-service BI model, check out the e-book "10 best practices for a self-service BI model". Engage further with the blog by exploring the wide range of topics related to business intelligence and data analytics to stay ahead in today's fast-paced business landscape.