Executive Summary: ISG Provider Lens™ Advanced Analytics and AI Services - Brazil 2024
19 Feb 2025
by Marcio Tabach
The individual quadrant reports are available at:
ISG Provider Lens™ Advanced Analytics and AI Services - Data Modernization Services - Large - Brazil 2024
ISG Provider Lens™ Advanced Analytics and AI Services - Data Modernization Services - Midsize - Brazil 2024
Brazilian companies are keeping pace with the AI revolution; however, they face maturity challenges
This report presents the results of the ISG Provider Lens Advanced Analytics and AI Services™ study. It is the fifth annual edition of the ISG Provider Lens™ analytics services study with an updated title that gives prominence to AI in provider services.
Advanced analytics and AI services has been frequently debated in the specialized and non-specialized media and press because of its potential to make companies more efficient and productive and its possible risks to organizations and society. Much of this controversy has been caused by the spread of generative AI (GenAI) and its adoption at an
exponential speed.
Much of the debate ignores the semantic differences between GenAI and traditional AI, usually relegating the latter to the background.
However, the results of this survey show that traditional AI still plays a significant role in Brazilian organizations’ data journey.
Various data science solutions, such as demand forecasting, preventive maintenance, abandonment forecasting (also known as churn forecasting) and anomaly detection, are part of provider offerings and have been increasingly used by large and midsize enterprises. In 2024, GenAI has been used in combination with traditional AI in data science solutions.
GenAI can be used as a data transformer (for example, converting voice content into text), in image recognition or as a user interface for natural language searches. It is extensively used in sentiment analysis solutions where various
statements on consumers’ and stakeholders’ social networks are classified as positive, neutral or critical and then consolidated to form a thermometer of the customer’s perception of the brand. Another example is the exploitation
of numerical data learned by the model, which is queried using SQL codes to provide answers about company indicators.
The adoption of GenAI has also helped disseminate techniques, such as the adoption of Knowledge Graphs, which service providers rarely offer. This technique improves the accuracy and explainability of GenAI agents by organizing learning information into a hierarchy in models. Many service providers use the Neo4J platform to build graphs, which is also used to build structured data models.
This year, the study has assessed a wider range of client companies and industries served. An instance is the presence of law firms that hardly appeared in previous versions. Law firms have increasingly sought service providers
to implement GenAI solutions with strict governance standards.
This year’s study also features several service providers that were not mentioned in previous reports. Some of them are established business consulting companies that have incorporated data science and business intelligence (BI) offerings. They stand out for their in-depth knowledge of business processes, which enables them to identify opportunities to automate processes, promote productivity gains and mitigate business risks. Consultancies also excel at using companies’ proprietary data as a strategic source of competitive advantage, playing a key role in their clients’ data journey.
Many new companies mentioned in this study focus exclusively on data and analytics, especially in the midsize enterprise segment. These providers have sophisticated offerings, managed services and technology-agnostic projects. Many have partnerships with major cloud and data platforms, such as AWS, Microsoft Azure, Google Cloud Platform, Databricks, Snowflake and Denodo. Some of them also have multinational clients and awards from platform vendors. Based on this increase in service providers who bring important skills to projects and the increase in sectors served, we can infer that the Brazilian market is growing, consolidating and, above all, becoming more mature.
As highlighted in the ISG Provider Lens™ Analytics Services study in 2023, governance has played a key role in an organization’s data journey. Many service providers reported challenging stories at various levels of their customers’ analytics maturity. There are many cases where data is trapped in silos — on cloud or on-premises — and some scattered across different systems and company files. Although data governance plays a significant role in technology-based companies, a lot of learning is still needed in non-tech companies.
This study highlights the ability of service providers to deal with the diversity of analytical maturity levels. In this context, maturity assessment methodologies have gained importance. Service providers need to promote workshops and training for clients to achieve the expected results for projects. Providing data literacy is crucial to the success of projects.
The adoption of GenAI has further increased the need for data governance programs as the technology works with unstructured data such as contracts, emails and call center recordings. When data science was restricted to structured data such as databases, it followed a certain level of governance, including information security, restricted access and storage in data lakes. Unstructured data, however, does not have these processes and is not stored in
governed data lakes.
Data mesh architecture has become the guiding principle for implementing data modernization projects. In this architectural model, data is integrated and democratized through access control processes, data quality, such as master data management (MDM), data lineage, observability and legal compliance. It is then provided to different business areas according to organizations’ needs. The use of data catalogs or data fabric enables the organized management of an organization’s data environment.
In this process of becoming data-driven, advanced BI and reporting services solutions have also undergone modernization. Service providers have developed solutions that integrate databases to build dashboards. This enables self-service analytics for various company areas, preventing conflicting versions in different dashboards. Another step toward modernization includes the construction of GenAI agents that allow data to be navigated using natural language interfaces.
With these agents, users can ask datarelated questions and get automated graphs and answers without the need for complex calculations or data manipulation. These modernization approaches can empower citizen data scientists — business professionals who use data to support decision-making. The report also shows that customers enable major migration of analytical reports through automation to modernize data architecture and improve governance or save costs on selfservice BI platform licenses.
The controversy surrounding AI contributes significantly to the evolution of the Brazilian business mentality, demonstrating a more mature understanding of the importance of data-based decisions and data-driven journey to maintain competitiveness. However, service providers face complex challenges, mainly related to the different levels of analytical maturity among companies, which require customization of solutions, the ability to evolve together and flexibility in implementation.
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