How to Turn Your Company's Data into Business Intelligence

In an increasingly digital and data-driven world, companies that can transform raw information into business intelligence gain a competitive edge. But what does business intelligence actually mean?

This concept refers to the ability to collect, organize, and analyze data to generate strategic insights and support more accurate decision-making. Instead of merely accumulating information, business intelligence enables data to become a valuable asset for business growth and innovation.

By applying business intelligence, your company can improve operational efficiency, anticipate market trends, and identify growth opportunities. This results in greater competitiveness, more precise decision-making, and a significant strategic advantage.

In this article, we will explore how to turn your company's data into business intelligence, highlighting the key tools, processes, and benefits of this approach.

Why should companies invest in Business Intelligence?

Investing in business intelligence is no longer just a competitive advantage; it is a necessity for companies looking to grow sustainably and strategically. The ability to transform data into valuable insights helps reduce risks, optimize processes, and identify opportunities that might otherwise go unnoticed. Here are some key reasons to adopt this approach:

Reducing Risks in Strategic Decision-Making

Making decisions based solely on intuition can be risky. Business intelligence allows managers to analyze historical data, trends, and projections to make informed decisions and reduce uncertainty. With dashboards and analytical reports, it is possible to visualize future scenarios, assess risks, and proactively address potential issues.

Identifying New Market Opportunities

Companies that leverage business intelligence can identify consumer behavior patterns, emerging trends, and untapped market niches. With analytical tools, businesses can better segment their customers, develop products and services that align with their needs, and even anticipate industry changes ahead of the competition. This provides a crucial competitive advantage for sustainable business growth.

Better Resource Management and Increased Efficiency

Business intelligence also contributes to more efficient management of financial, human, and operational resources. With accurate, up-to-date data, managers can monitor key performance indicators (KPIs), identify production bottlenecks, and optimize internal processes. This results in less waste, better allocation of investments, and increased team productivity, making the company leaner and more efficient.

Real-World Examples of Business Intelligence Success

Many companies worldwide have already reaped the benefits of business intelligence and gained a competitive edge in their markets. A classic example is Netflix, which uses data analysis to recommend personalized content to its users, predicting preferences and increasing engagement. Similarly, Amazon enhances the shopping experience by analyzing browsing data and consumer behavior, suggesting relevant products, and optimizing its supply chain.

Beyond large corporations, small and medium-sized enterprises can also leverage business intelligence to improve operations. Through Business Intelligence (BI) platforms, smaller companies can track performance metrics, optimize marketing campaigns, and gain deeper insights into customer behavior, boosting profitability and market competitiveness.

A great example is the Santa Apolônia retail chain—an equal client. By implementing a BI project within the network, the company was able to reduce shipping costs and streamline the operational work of locating products in the Distribution Center (DC) through order analysis across its units.

How to turn data into Business Intelligence?

For a company's data to become a strategic asset, it is necessary to follow a structured process, from data collection and organization to analysis and visualization. Below, we detail each essential step to transform raw data into business intelligence:

1. Data Collection: The Foundation of Business Intelligence

The first step in business intelligence is data collection, which must be carried out in a structured and efficient manner. The more precise and organized the collected data is, the more reliable the subsequent analyses will be.

Data can be collected from various sources, divided into internal and external sources:

Internal sources: These include systems such as ERP (Enterprise Resource Planning), CRM (Customer Relationship Management), sales records, financial reports, customer feedback, and marketing campaign performance. Any system or spreadsheet where structured information is recorded can be used.

External sources: These include market research, competitor data, industry trends, social media, and even public databases with economic information.

To ensure data is effectively used, standardization is essential. This means eliminating inconsistencies, duplicates, and discrepancies between different sources. It also involves ensuring that data is generated correctly from the beginning, with well-organized processes to avoid unnecessary corrections and manipulations. A well-structured database facilitates analysis and reduces errors in decision-making.

2. Data Processing and Organization

After collection, data must be processed and organized to ensure quality and usability. Raw data often contains errors, duplicate values, or incomplete information.

Data cleaning involves processes such as:

• Removing irrelevant or inconsistent information.

• Standardizing formats (dates, currencies, units of measurement).

• Completing missing data when possible.

• Tools for organizing large volumes of data.

Given the growing amount of available information, it is essential to use tools that help structure and efficiently store data, such as:

• SQL and NoSQL databases (MySQL, PostgreSQL, MongoDB) for storing and organizing large volumes of data.

• Big Data platforms (Google BigQuery, Amazon Redshift, Hadoop) for large-scale data processing.

• ETL (Extract, Transform, Load) software such as Azure Data Factory, Apache Spark, and Pentaho Data Integration, which assist in data extraction and transformation before analysis.

3. Data Analysis for Strategic Insights

Once organized, data needs to be analyzed to generate strategic insights. There are three main data analysis techniques applied to business intelligence:

Descriptive Analysis: Provides an overview of past events, helping companies identify patterns and trends. Example: Analyzing customer purchasing behavior over the past months.

Diagnostic Analysis: This phase interprets the data and formulates hypotheses on why certain patterns occurred.

Predictive Analysis: Uses statistical modeling and machine learning to forecast future scenarios. Example: Predicting demand for a specific product based on historical data.

Prescriptive Analysis: Suggests actions to take based on previous analyses. Example: A recommendation system suggesting specific actions to increase sales.

4. Data Visualization: Making Intelligence Accessible

Even with advanced analyses, data only has a real impact if presented clearly and accessibly to decision-makers. Creating interactive dashboards allows managers and teams to visualize information intuitively and quickly understand key insights.

These dashboards can include dynamic charts, heat maps, and key performance indicators (KPIs). Various tools are available to transform complex data into comprehensible visualizations, such as:

• Power BI: Microsoft’s platform with integration to various data sources and advanced analysis features.

• Tableau: A widely used solution for creating interactive dashboards and dynamic visualizations.

• Google Data Studio: A free tool that allows the creation of customized reports with integration to multiple data sources.

• Google Looker: A cloud-based business intelligence platform that enables interactive reports and advanced analyses with customized data modeling, integrating with real-time data sources.

• QlikView/Qlik Sense: BI solutions that offer associative data analysis, enabling dynamic explorations and real-time insights.

How to Present Insights Clearly for Decision-Making:

• Use easy-to-interpret charts and tables, avoiding excessive visual information. A good reference for this is the book Data Storytelling, which provides best practices in the field.

• Highlight the most important KPIs for each business sector.

• Enable interactive filtering and real-time updates for easy navigation through data. For end users, the ability to explore data with their preferred segmentation makes a significant difference.

Turning data into business intelligence is a process that requires structure, technology, and strategic analysis. From data collection and organization to insight visualization, each step plays a fundamental role in helping companies make more informed decisions, reduce risks, and identify opportunities.

Best practices for turning data into effective Business Intelligence

For the process of transforming data into business intelligence to generate real value for a company, it is essential to follow best practices that ensure efficiency and strategic alignment. Below are key pillars for a successful business intelligence implementation:

Define Clear and Measurable Objectives for Data Analysis

Before collecting and analyzing data, a company must define the problems it wants to solve and the metrics that will be used to measure success. Without well-defined objectives, there is a high risk of collecting irrelevant data or generating unhelpful analyses.

Align data analysis with business goals: For example, if the goal is to increase sales, business intelligence should focus on analyzing customer behavior and optimizing the sales funnel.

Select key performance indicators (KPIs) to track results and set realistic deadlines and targets. Continuous monitoring allows for adjustments and improvements over time.

📌 Practical Example: The Stúdios Mormaii network aimed to improve unit management. To achieve this, they defined key business indicators such as lead conversion rates into students, studio occupancy rates, and student retention rates. Management dashboards helped identify top-performing studios, map best practices, and replicate them across other locations.

Involve All Departments in the Process

Business intelligence should not be the sole responsibility of the IT or data analytics team. To obtain truly valuable insights, all departments must be involved, ensuring a broad and integrated business perspective.

Create a data-sharing environment: Establish processes so that different areas (finance, marketing, sales, HR, operations) have access to relevant information.

Conduct meetings and workshops on data: Engaging different teams helps identify new opportunities and challenges that can be addressed with data.

Use accessible dashboards across the company: Tools like Power BI allow each department to visualize its metrics and track the impact of its actions.

📌 Practical Example: At Stúdios Mormaii, the Business Intelligence project involved managers from various franchise units. This approach expanded the business perspective of the project, and after three years, the franchise leadership observed significant performance differences between franchisees who adopted business intelligence in daily management and those who did not.

Invest in Team Training on BI Tools

For business intelligence to be effective in practice, the team must be prepared to interpret data, use BI tools, and make data-driven decisions.

Train employees in tools like Power BI and hold periodic meetings to share knowledge and discuss case studies applied to the company’s reality.

📌 Practical Example: At Santa Apolônia, a retail chain in Santa Catarina, data-driven decision-making was initially done using Excel spreadsheets, managed exclusively by the leadership team. After implementing a BI project with equal's support, employees across various departments were trained to use dashboards and analyze key indicators. Today, these dashboards are utilized by everyone, from in-store sales associates to the company’s CEO.

Adopt a Continuous Improvement Mindset

Business intelligence is not a project with a beginning, middle, and end—it is a continuous process of learning and refinement. As data and market needs evolve, companies must adapt to these changes.

Best practices for maintaining BI evolution:

• Regularly review KPIs and reports to ensure that the analyzed metrics remain relevant to the company’s goals.

• Stay updated on new technologies: BI tools, Big Data, and Artificial Intelligence are constantly evolving—follow industry trends.

• Collect internal feedback: Encourage employees to suggest improvements in data analysis processes.

By implementing these best practices, companies can maximize the value of their data, improve decision-making, and gain a lasting competitive advantage in their market.

The importance of specialized professionals in Business Intelligence

Business intelligence goes far beyond simply collecting data. To properly analyze information and convert it into strategic insights, it is essential to have qualified professionals who can interpret data, develop analytical models, and ensure information reliability.

Additionally, collaboration between different departments and support from specialized consultancies can enhance results, making business intelligence a true competitive advantage.

Roles and Responsibilities: BI Analysts, Data Scientists, and Data Engineers

Each professional involved in business intelligence plays a crucial role in transforming data into actionable insights.

🔹 BI Analyst (Business Intelligence Analyst)

Responsible for querying, organizing, and analyzing data to create reports and interactive dashboards.

Works with tools such as Power BI, Tableau, and Google Data Studio.

Supports managers and teams in interpreting information for decision-making.

Needs to understand business areas to maximize value.

🔹 Data Scientist

Develops predictive models and advanced algorithms for data analysis.

Uses Machine Learning and Artificial Intelligence techniques to predict trends and patterns.

Works with programming languages such as Python and R.

🔹 Data Engineer

Responsible for the company’s data infrastructure, ensuring that storage and processing systems are efficient and secure.

Works with SQL and NoSQL databases, as well as Big Data tools like Hadoop and Apache Spark.

Develops data pipelines to integrate different sources and ensure data quality.

By relying on a well-structured team of specialized professionals, companies can maximize the value of their data, improve decision-making, and gain a sustainable competitive edge.

Advantages of Hiring Specialized BI and Data Analytics Consultancies

Not all companies have an in-house team qualified to implement a robust business intelligence strategy. In such cases, hiring a specialized consultancy can be the ideal solution to accelerate the process and ensure more effective results.

📊 Benefits of Hiring a Specialized BI Consultancy

• Access to highly qualified professionals: Consultancies employ experts in BI, data science, and data engineering who can develop tailored solutions for your business.

• Faster and more efficient implementation: With extensive experience, consultancies enable businesses to adopt business intelligence without undergoing a lengthy internal learning curve.

• Reduction in operational costs: Instead of hiring and training an entire team, companies can outsource this service.

• Constant updates with new technologies: Consultancies stay up-to-date with the best practices and tools in the market, ensuring that businesses remain ahead of the competition.

Specialized professionals play a crucial role in business intelligence, ensuring that data is processed, analyzed, and used strategically. Collaboration between different departments and the support of specialized consultancies can accelerate the process and maximize the benefits of data analysis.

Investing in business intelligence is essential for any company that aims to grow strategically, enhance operational efficiency, and make more accurate decisions. Transforming raw data into valuable insights requires a structured process, qualified professionals, and the right tools.

Accelerate Your BI Strategy with equal BI

If your company wants to speed up this process and achieve concrete results, partnering with equal BI is the best solution. Our team of specialists is ready to help you structure your data, implement interactive dashboards, and develop business intelligence-driven strategies.

📩 Contact us today and discover how to turn your data into a competitive advantage for your business!