When we talk about internal management and performance measurement, there is no way to reach clear and true-to-life results without involving data science and the different paths to obtain these values. Business intelligence and data science are critical to the health of a strategy. But why?
To find out and understand the role of each of these two fundamental topics, check out this post! In it, you'll get an in-depth look at what it takes to improve marketing intelligence. Keep reading!
In this content you will see:
- Data and the modification of the marketing concept: understand the relationship
- What is Business Intelligence?
- Main applications of Business Intelligence
- Data mining and report creation
- Data warehouses and performance metrics
- Descriptive and statistical analysis
- Visual analysis
- What is Data Science?
- Main applications of Data Science
- Goal calculation
- Conversion
- Generating numerical reports
- Logic construction
- Building custom dashboards
- What are the applications in strategic marketing?
- How do these items impact lead capture?
- Start implementing your strategy!
Data and the modification of the marketing concept: understand the relationship
Different marketing actions are carried out to reach the same objective: success. And to achieve this goal, different tools can complement what was done, in addition to the possibility of measuring performance during and after the completion of the action.
For marketing or any other industry, business intelligence revolves around one point that is the measurement of all work: data. Measured according to the possible metrics and what one wants to understand, it is through them that what was possible to notice about the efficiency of the action plan outlined is translated.
In Brazil, any use of data for marketing must comply with the LGPD. The National Data Protection Authority (ANPD) details roles such as controllers and processors and provides guidance on good governance, minimization, and transparency practices. In practical terms, this means mapping legal bases, recording purposes, reviewing consents, and maintaining audit trails throughout the process. In addition to reducing regulatory risk, well-governed data increases the reliability of analyses and, consequently, the quality of BI and Data Science decisions.
To understand the importance of each of them and how their application in the market is fundamental, check out their definition below!
What is Business Intelligence?
Business Intelligence, literally translated “Business Intelligence”, also known simply as BI, refers to the collection, analysis of data and translation of information that can be useful for the management of the company and/or business. Its main role is to offer subsidies to guide decision-making, like a light in a dark cave that makes it possible to choose the best path, away from obstacles that can delay or hinder the path to the point drawn as an objective.
BI can be understood as fundamental, since it corresponds to such a relevant stage. Using it properly, it is possible to achieve results such as:
- Monitoring the company's performance;
- Identify and validate market trends;
- Identify bottlenecks;
- Project results based on data obtained;
- Analyze customer behavior and brand impact.
Main applications of Business Intelligence
Check below some examples of what Business Intelligence can promote from its correct use by marketing professionals and data scientists for the health of the company.
Data mining and report creation
Based on the technology used through artificial intelligence, it is possible to obtain information taken from the database, enabling the creation of reports.
Data warehouses and performance metrics
Being fundamental to BI, Data Warehouses is, in Portuguese, the data warehouse, and it is with this system that business intelligence manages to develop and organize itself in a different way. With it, it is possible to collect data from specific platforms in order to understand them properly.
Therefore, there are specific performance metrics for elements such as website, blog, landing page, among others.
Descriptive and statistical analysis
For different types of marketing, data collection is indisputably important. With this, it is possible to have information relevant to the business at hand, from understanding the number of users who showed interest to analyzing the path taken from the capture to the consolidation of the purchase or registration.
Visual analysis
To understand if the nutrition flow is effective, analyze the growth of social networks or even manage to translate results, the visual analysis made possible is a way to make information more democratic to carry out a clearer work and without communicational noise.
If you use the old Google Data Studio, it's worth remembering that it has been renamed Looker Studio. The change (initiated in October 2022) came with deeper integrations with BigQuery and Looker, while maintaining compatibility with connectors from the marketing ecosystem. In practice, this expands the possibilities for unified dashboards (organic, paid media, CRM, and revenue), preserving data governance and metric consistency across teams.
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What is Data Science?
Using mathematical models to perform statistics and data prediction, Data Science or data science, in Portuguese, is a methodology that has three objectives: to collect, analyze and translate data to provide the company with information that was previously difficult to access and time-consuming to interpret.
A useful distinction for those working with data-driven marketing is the difference between BI and Data Science. Broadly speaking, BI focuses on descriptive/diagnostic analysis — understanding what happened and why — through reports and dashboards. Data Science, on the other hand, moves toward predictive/prescriptive analysis, modeling what might happen and what to do next (e.g., conversion propensity, next best offer). This complementarity avoids blind-eye decisions and supports more accurate predictions and testing.
This feature, in addition to optimizing time, allows it to be an automatic process, with low human labor demand. With it, it is possible to analyze strategies and carry out increasingly precise planning, based on data that match reality.
Among its many benefits is testing.
Having reliable sources and with agility is to attribute more quality and efficiency to the project. This is a guideline that enables the creation of panels that are independent of other people to carry out procedures and analyses, a factor that could end up slowing down and hindering the progress of strategic planning and the implementation of actions.
Main applications of Data Science
With this strategy, it is possible to expand and optimize operations, offering customers even more precision and information about the actions planned and put into practice. Among its functions, it is possible to perform some that are extremely relevant. Check out, below, five examples of activities that this system makes possible!
Goal calculation
This is a data analysis to estimate the contact goals needed to reach the customer goal, as well as the maturation time between being a contact and becoming a customer and the conversion calculation between a phase and another in its life cycle.
With this action, it is possible, even in the strategic planning, to build more realistic goals, which take into account data and not customer desires. Thus, there are no unnecessary strains or demands for not reaching a utopian or distant goal.
Conversion
This calculation seeks to detail the conversion of contacts into customers and align which patterns the contacts follow, optimizing which campaigns to invest in. With this action, it is possible to improve the strategy based on real information and accurate calculations, performed quickly and automatically.
In this way, it is possible to understand different scenarios and carefully analyze which action will be most effective to achieve the goals outlined since the Kick-Off.
Generating numerical reports
Translating the results for clients, as well as using graphs and images, makes understanding the numbers easier, showing the results quickly to all stakeholders.
In a visually organized way, it is possible to make the information accessible and even more useful to simplify understanding and show the results graphically.
Logic construction
This is an analysis of the behavior of numbers and association with other environmental variables to optimize the use of resources. With this, different types of reasoning can be architected.
Building custom dashboards
Dashboards are customized with important customer information. Containing various data on different aspects, the main point is to be able to make arrangements and filter those numbers that correspond to the needs.
With this, it is possible to avoid unnecessary information and focus more on those that make the most sense for the objective or moment in question. With the possibility of merging different types of data, the possibilities become gigantic, and all this in a practical and efficient way.
Image: Relationship between Business Intelligence, Data Science and Marketing
What are the applications in strategic marketing?
For strategic marketing or relationship marketing, Data Science is highly effective to better define the actions to be taken, in addition to bringing the user and qualified leads closer together by understanding their needs and pains, improving the performance of the product or service by enabling a more effective advance in the sales funnel.
Predictive models and personalization algorithms tend to boost performance when connected to journeys and offers. Recent evidence shows that consumers expect personalized interactions — and become frustrated when they don't — while companies that combine AI and data achieve measurable gains in efficiency and revenue per customer by orchestrating messages, channels, and timing in real time. To capture this value, start with use cases that have a clear impact on acquisition and LTV and continually test increments.
How do these items impact lead capture?
If, by making a cake, you are not able to please your intended audience, the most effective way to understand the reason for the rejection is to analyze the behavior of the people who accepted or refused that cake. In this way, it is possible to make the necessary changes so that the cake can please the majority, being a public success.
So is the use of data for marketing. When we talk about lead conversion, the meaning is linked to this whole concept of achievement, analysis and optimization. If leads aren't heard and won over, they're gone. If the material and communication are correctly aligned, it is possible to be increasingly assertive about what will be offered.
For this reason, it is essential to invest in data to improve not only nutrition flows but also capture and conversion itself.
Measurement is currently undergoing two shifts: a focus on first-party data in Google Analytics 4 (GA4) and adaptation to the third-party cookie landscape in Chrome. In 2025, the UK CMA noted that Google had backed away from completely disabling third-party cookies and adopted a user - choice model —but the market direction remains "privacy-first." In practical terms, prioritize consent capture (user-ID, first-party lists), modeling in GA4, and CRM/CDP integrations to reduce cookie dependency and maintain attribution quality.
Start implementing your strategy!
Did you see how important it is to have a more assertive overview with the use of these two strategies working together? Although it is not an easy mission and requires dedication and investment, being able to count on these strategies can guarantee the success of your actions.
This entire process is beneficial, from the sales team with more information and directions to those responsible for formulating large campaigns. Using BI and Data Science is to gradually and considerably reduce the assumptions that guided decision making.
This is a market trend increasingly used to add value and quality. Therefore, not using this union represents a risky blind path.
Key takeaways from Business Intelligence and Data Science: The combined use of Business Intelligence (BI) and Data Science is essential for data-driven marketing strategies, ensuring more assertive decisions and eliminating guesswork. While BI organizes and analyzes information descriptively and visually, allowing performance tracking and identifying bottlenecks, Data Science advances toward predictive and prescriptive analytics, calculating goals and conversion rates, and building personalized dashboards. Together, these approaches strengthen lead capture and nurturing, improve personalization in the sales funnel, and optimize campaigns. By complying with the LGPD, using first-party data, and integrating with tools like GA4 and CRM, companies can combine intelligence, compliance, and performance in their digital strategies.
To understand more about the benefits, check out the blog post “What is Big Data and How to Use It in Customer Acquisition!”. Login now!
Business Intelligence and Data Science: How to Use Data to Optimize Marketing
What is Business Intelligence (BI)?
Business Intelligence is the process of collecting, organizing, and analyzing data to support business management and guide strategic decisions. It acts like a “beacon,” helping managers understand performance, market trends, bottlenecks, and customer behavior.
What are the main applications of Business Intelligence?
BI can be applied in different areas, such as:
- Data mining and reporting: using AI to transform databases into insights.
- Data warehouses: organizing and integrating data for consistent analysis.
- Performance metrics: monitoring websites, blogs, landing pages, and campaigns.
- Descriptive and statistical analysis: explaining what happened and why.
- Data visualization: dashboards and charts that improve team communication.
What is Data Science?
Data Science goes beyond BI, using mathematics, statistics, and machine learning to predict scenarios and recommend actions. While BI describes past and current events, Data Science anticipates what may happen next and suggests the best course of action.
What are the main applications of Data Science?
Some of the most relevant functions include:
- Goal calculation: setting realistic targets based on real data.
- Conversion calculation: identifying customer patterns and improving strategies.
- Numerical and graphical reports: making results clear and easy to understand.
- Logic building: analyzing behavior patterns to optimize resources.
- Customized dashboards: filtering and highlighting metrics relevant to specific strategies.
What is the difference between BI and Data Science?
BI focuses on descriptive and diagnostic insights—understanding what happened and why. Data Science advances to predictive and prescriptive insights, pointing out what could happen and what actions to take. When combined, they eliminate guesswork and make marketing more accurate.
How are BI and Data Science related to marketing?
These tools allow businesses to track the sales funnel, optimize campaigns, and better understand the customer journey. BI provides clarity on current performance, while Data Science helps predict lead behavior and personalize relationship strategies.
How does data usage impact lead generation?
By analyzing engagement, behavior, and conversion data, companies can adjust communication and offers, making lead generation more effective. BI and Data Science help identify funnel bottlenecks, build precise segmentations, and increase conversion rates.
What legal considerations need to be taken into account?
In Brazil, data usage must comply with the LGPD. This means mapping purposes, reviewing consents, maintaining transparency, and documenting processes. Strong data governance not only ensures legal compliance but also builds trust and improves analysis quality.
Why is investing in BI and Data Science essential today?
Both practices are crucial for businesses seeking sustainable growth. They optimize campaigns, improve lead generation, and support data-driven decision-making. This increases predictability, strengthens personalization, and provides a competitive edge in the digital market.
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