Arquivo de Advanced Analytics - F5tci

Semantic Layer vs Ontology. Porque a IA tornou ambas essenciais?

admin — Tue, 23 Jun 2026 10:56:41 +0000

In the AI era, the difference between a semantic layer and an ontology has become one of the most critical topics in modern data architecture.

For years, organizations focused on ensuring information access, data quality, and consistent metrics. That’s why semantic layers emerged: a way to create a shared language across systems, teams, and analytical tools.

But the rise of AI agents, and enterprise copilots introduced a new challenge: it’s no longer enough to ensure everyone calculates ‘"revenue" the same way. Now we must ensure intelligent systems actually understand what "revenue" means within the context of the business.

That’s why semantic layers and ontologies are taking on complementary roles in AI‑driven data architectures.

Semantic Layer vs. Ontology in summary

A semantic layer defines how data should be consumed through consistent metrics, relationships, and analytical models.

An ontology defines the meaning of business concepts, the relationships between those concepts, and the rules that enable intelligent systems to interpret context and make decisions.

In the age of AI, you can’t have one without the other.

What is a semantic layer and why does it remain essential?

A semantic layer is the layer that brings data closer to the business. Its purpose is to create a consistent representation of information, regardless of the complexity of the underlying systems.

In Microsoft Fabric, semantic models act as a logical description of the analytical domain, including tables, relationships, and metrics that can be consumed by dashboards, applications, copilots, and AI services.

In practice, a semantic layer answers questions such as:

How should revenue be calculated?
What is the official definition of this metric?
Which data should be used?
How can we ensure consistency across reports and teams?

This layer remains fundamental for Business Intelligence, Analytics, and Data Governance. But there is a limitation that becomes increasingly evident as AI evolves from a support tool into a decision‑making mechanism. A semantic layer alone cannot represent all the business context that sits behind the metrics.

Ontology: What is it, and how does it differ from a semantic layer?

Instead of defining only metrics and analytical relationships, it formally models the business domain, concepts, entities, relationships, rules, constraints, and dependencies.

While a semantic layer answers the question "how should I consume this data?", an ontology answers the question "what does this concept mean within the organization?"

This makes it possible to create a shared representation of the business that can be used by people, applications, workflows, and intelligent agents. More importantly, it allows organizations to make explicit the knowledge that usually exists only in the minds of teams.

Who is considered an active customer?
When does a sale count toward a given KPI?
What exceptions exist within a sales process?
What relationships exist between customers, contracts, products, and services?

Historically, these answers were scattered across documentation, internal procedures, and tacit knowledge. The ontology aims to turn them into a formal, reusable structure.

Semantic Layer vs Ontology: The difference in practice

The best way to understand the difference is not through the technology itself, but through a real business problem. In an organization with multiple systems, it is relatively common to find three different definitions for "active customer":

The CRM considers any customer with recent sales activity to be active.

The ERP considers any customer with invoicing in the past twelve months to be active.

The Marketing team considers any contact who has interacted with recent campaigns to be active.

The semantic layer can ensure that each dashboard uses the correct definition for each context. But the challenge appears when an AI Agent receives an instruction that seems simple: ‘"Identify the active customers with the highest churn risk". Before executing the task, the agent needs to know which of the three definitions it should use.

This is no longer a question of metrics. It is a question of meaning.

This is precisely where the ontology adds value: it provides the context intelligent systems need to interpret business concepts the same way an experienced team would.

Why do AI and AI Agents need ontologies?

For the first time, we are beginning to see systems that not only retrieve information, but can recommend actions, trigger workflows, or execute decisions with different levels of autonomy.

Without explicit context, an agent may produce answers that sound plausible but are misaligned with the real business rules. And as these agents begin to operate at scale, small ambiguities can quickly turn into incorrect decisions repeated hundreds or thousands of times.

This is why the conversation around enterprise AI is gradually shifting from models to the governance of meaning.

The real challenge is ensuring that AI understands the business in the way the organization intends.

How does Microsoft Fabric use semantic models and ontologies?

For several years, the semantic model was the primary business abstraction within the Microsoft ecosystem. It was the layer responsible for transforming technical structures into information that analysts and decision‑makers could actually use.

With the introduction of Fabric IQ, a clearer distinction is beginning to emerge between two different responsibilities.

The analytical representation of the data.
The representation of the meaning of the business.

This evolution matters because it directly addresses one of the biggest challenges in enterprise AI: creating a governed source of context that can be shared across users, applications, and intelligent agents.

In practice, this means the architecture stops being only data‑oriented and becomes knowledge‑oriented. The semantic layer remains responsible for delivering metrics, KPIs, and analytical models. The ontology takes on the role of representing concepts, relationships, policies, and business rules.

The result is an architecture that is far better suited for AI Agents, because it clearly separates two different questions:

How to access the information?
How to interpret that information?

We believe this separation will gradually become a common feature of enterprise architectures designed for AI, regardless of the underlying technology.

Source: https://learn.microsoft.com/en-us/fabric/iq/overview

What should organizations do to prepare their data for AI?

In our experience, in most organizations the data exists, the dashboards exist, the metrics exist, what often does not exist is a formal and shared definition of the organization’s most important business concepts.

This is precisely why many AI initiatives start by exposing inconsistencies that were already there long before AI itself arrived.

The relevant questions then become:

Who defines critical business concepts?
Where do the business rules live?
How are they governed?
Who validates exceptions?
How do we ensure that an AI Agent interprets the business in the same way a senior team does?

Organizations that answer these questions first will be better positioned to scale AI safely and sustainably.

FAQ: Semantic Layer and Ontology

Are a Semantic Layer and an Ontology the same thing❓

No. The semantic layer provides consistent metrics, calculations, and analytical definitions. The ontology models concepts, relationships, and business rules.

Does an Ontology replace a Semantic Layer❓

No. They are complementary components. The ontology provides context and meaning, while the semantic layer provides governed access to data.

Why does AI need an Ontology❓

Because AI Agents and copilots require explicit context to interpret business concepts, apply rules, and make consistent decisions.

Does Microsoft Fabric support Ontologies❓

The evolution of Fabric IQ points to an architecture where semantic models and ontologies coexist to provide both governed access to data and governed context for AI.

Conclusion

For years, organizations prioritized democratizing access to data. In the coming years, the challenge will be democratizing meaning.

Companies that manage to turn tacit knowledge into governed context will be better prepared to use AI Agents in a scalable, safe, and business‑aligned way.

If your organization has already invested in Data Governance, this is the right moment to assess whether your architecture is prepared not only to answer questions, but to support systems capable of acting on the answers.

Because in the era of AI, competitive advantage gradually shifts from the data an organization owns to the way it defines, governs, and shares its meaning.

Is your architecture ready for AI Agents? Talk to our team of experts for a quick assessment of your semantic layer and data governance.

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O centro do BI e dos dados está a mudar?

admin — Mon, 18 May 2026 11:32:43 +0000

For years, the dashboard was the central metaphor of BI and data.

Building a good dashboard used to be a sign of analytical maturity. The platform was the product, and knowing how to navigate it was a valued skill.

“The center of gravity in BI may now be starting to shift and probably not in the way most organizations expected. With the rise of the MCP (Model Context Protocol), the AI agents , and conversational interfaces, the most important question is no longer how we present data. The real question becomes: do we truly understand what our data means?

The latest evolutions in platforms like Qlik Cloud and Microsoft Fabric reinforce the same trajectory. Qlik’s MCP Server, Qlik Answers, its agentic experiences, and Microsoft’s accelerating investment in Copilot, semantic models, and OneLake‑native agents signal a decisive shift in how users engage with enterprise data: away from manual navigation and toward contextual, natural‑language interactions built on governed semantic models.

Something in this relationship is indeed changing.Not abruptly, but in a way that is structural enough to deserve the attention of any organization that invests seriously in analytics.

What exactly are we talking about when we talk about MCP?

MCP is not just another conversational interface on top of dashboards. It is an open protocol, adopted across virtually the entire relevant data ecosystem in 2025. It is not the bet of a single vendor. It is shared infrastructure, and that is precisely why the shift it introduces is structural, not incremental.

The most significant shift may be something else entirely: the ability to consume analytical capabilities outside the BI platform itself. For years, access to enterprise data depended on dashboards, filters, and manual navigation. With MCP, AI agents can now query semantic models, business context, and governed data directly, without the user ever opening a traditional analytics tool.

A manager can simply ask an assistant: "What were last quarter’s margins by region?"

And receive an answer built from semantic models, certified metrics, access permissions, business context and governance. Without opening dashboards or navigating interfaces.

The user no longer needs to adapt their thinking to the structure of the tool, the system now interprets the context of the decision.

What changes and what remains:

Dashboards are not going away. But the most vulnerable segment appears to be what we might call "administrative BI": reports built to answer repetitive questions and pages filled with dozens of KPIs that are rarely consulted.

When an agent can answer directly from governed data, part of that layer becomes redundant. Yet there are contexts where visualization remains extremely relevant. Operations, logistics, and retail teams still depend on immediate visual reading:

alertas;
heatmaps;
time series;
anomaly detection;
continuous operational monitoring.

Similarly, management teams continue to align through scorecards and visual storytelling. And the detection of complex patterns, as distributions, dispersion, correlations, and outliers, remains a domain where visual analytics still holds clear advantages over natural language.

The most reasonable conclusion is not the disappearance of the dashboard. It is its eventual shift in role. Dashboards stop being the center of the analytical experience and become one of several delivery mechanisms for business intelligence.

The real problem is not technological.

When an agent answers: "margin dropped by seven percent", the critical question is no longer "which chart?" but rather: ‘"who defined margin?" and ‘"is that definition consistent across the organization?’"

Concepts such as revenue, churn, active customer, margin, stop being merely technical metrics. They become strategic assets. Because, as we’ve already discussed, in previous topics,an AI trained on weak or inconsistent definitions doesn’t minimize error, it magnifies it, packaged with the illusion of precision.”

For years, many organizations concentrated their effort on visualization tools. The layer of meaning, semantic governance, metric ownership, business vocabulary was often solved implicitly, dashboard by dashboard, team by team. In the new paradigm, that approach no longer scales.

Security: the most underestimated topic.

As AI agents begin interacting directly with enterprise platforms, the risks become significantly more complex than in traditional BI. Plausible but incorrect answers, loss of auditability… The most dangerous risk in analytics is not the technical error itself, but the wrong answer that sounds convincing.

Therefore, the responsible adoption of this paradigm requires robust foundations:

granular access control;
full auditability of interactions;
context validation;
consistent governance;

MCP increases the dependency on a strong data governance strategy.

What fundamentally changes for data teams?

“If this transition materializes, the value of BI teams may progressively shift. From the ability to build dashboards to the ability to govern meaning, certify metrics, and manage business vocabulary. To structure consistent semantic layers and create data products designed not only for human consumption but also for AI agents. Perhaps the most important change is not technological, it is organizational.

In summary:

MCP won’t make dashboards disappear, but it may remove the need to browse them to get to the right answer, and that fundamentally shifts BI’s center of gravity:

from interface to semantics;
from visualization to governance;
from navigation to context.

The future of BI may belong to the organizations that most effectively govern the enterprise meaning embedded in their data.

Let's Talk?

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Adoção de IA: o que cria valor e o que pode amplificar o caos

admin — Fri, 24 Apr 2026 09:32:55 +0000

Copilot, Copilot Studio, Azure AI Foundry. The building blocks of Microsoft’s AI ecosystem are officially on the table.

What truly matters for organizations isn’t the product announcements, it’s knowing what’s worth investing in, at what moment, and in what priority order.

In the previous articles of this series, we examined the maturity of the Azure Data Stack and Microsoft Fabric, and explored the causal link between poor‑quality data and the failure of AI initiatives.

And this article focuses on the tools available today, offering a critical view of where they create value and what should be considered before adoption.

Three layers, three distinct purposes

The Microsoft AI ecosystem is effectively structured into three layers, each designed with a different purpose:

Microsoft 365 Copilot

Focused on individual and team productivity. It requires no additional development, only licensing and activation.

The value is immediate, but it depends directly on data quality and organizational discipline.

Key question: Are the data structured and governed well enough to generate useful context?

Copilot Studio

Focused on process automation and conversational experiences. It requires configuration, integration with data sources, and the definition of workflows. It is suitable for repetitive, well‑structured processes, not for complex or non‑deterministic decision scenarios.
It’s suitable for repetitive, well‑structured processes, not for complex or non‑deterministic decision scenarios.

Azure AI Foundry

Data‑stack maturity isn’t a technical detail, it’s the primary determinant of the outcome.

The adoption sequence is not optional. Moving directly to Foundry without addressing data quality and data governance is equivalent to building on unstable foundations.

The maturity of your data stack isn’t a technical nuance, it is the single biggest driver of results.

Microsoft 365 Copilot: real value, real limitations

Copilot in Microsoft 365 is highly effective for concrete, well‑defined tasks:

automatic meeting summarization in Microsoft Teams.
automatic creation of first‑draft documents in Word.
natural‑language data exploration in Excel.

The productivity gain is tangible. However, the quality of the output is proportional to the quality of the information available. Organizations with disorganized data, inconsistent documents, and unstructured collaboration practices will only amplify those problems.

AI doesn’t fix poor‑quality data, it scales it.

Copilot Studio and AI Agents: different strategic purposes.

Copilot Studio and AI Agents are often grouped together, but they operate in fundamentally different paradigms.

Copilot Studio: A low‑code platform for creating conversational assistants with predefined logic.

structured dialog flows.
integration with enterprise systems such as SharePoint, Dataverse, and custom APIs.
responses based on configured data sources.

The behavior is predictable and controlled.

AI Agents (Azure AI Foundry): Goal‑oriented systems that operate autonomously:

they receive a task.
access the required tool.
autonomously decide how to execute it.
they can chain multiple actions without human intervention.

In practical terms:

Automated FAQ → Copilot Studio
proposal analysis with data validation and response generation → AI Agent

Confusing these two models frequently results in poor architectural choices and misaligned expectations.

Azure AI Foundry: capability and complexity.

The Azure AI Foundry is currently Microsoft’s most comprehensive platform for enterprise‑grade AI development.

Key components:

Model Catalog: access to multiple models (OpenAI, Mistral, Llama, Cohere), enabling you to choose the right model for each scenario.
Prompt Flow: orchestration of AI pipelines, including RAG, output evaluation, and quality control.
AI Agent Service: development of autonomous agents with memory, tools, and evaluation mechanisms.

Key challenges:

learning curve.
the complexity of implementing RAG pipelines over enterprise data sources, especially when governance and quality vary.
the challenge of integrating AI solutions with legacy systems that were not designed for modern workloads.

Data and AI: the point where strategy, governance, and intelligence converge

With Microsoft Fabric, OneLake acts as a unified data layer. This allows applications in Azure AI Foundry to access information directly without data movement, reducing latency and complexity.

The Fabric Data Agent introduces a new interaction layer: natural‑language queries with semantic context over enterprise data. Microsoft Purview complements this by enforcing data governance:

prompt auditing to track usage, enforce governance, and ensure responsible AI practices.
data classification to ensure sensitive information is identified, protected, and governed consistently.
access control to ensure that only authorized users and systems can interact with sensitive data and AI workloads.

In regulated environments, this layer is foundational.

Challenges that organizations often underestimate:

Data Quality
sets the upper limit on the value any AI initiative can realistically deliver.
Total cost of adoption.
includes far more than technology: spanning integration work, team training, change management, and the continuous maintenance of data‑governance processes.
User adoption.
it is not automatic. Making the technology available does not guarantee its use.
Vendor dependency
a strategic decision with long‑term impact.
The Model Catalog provides partial mitigation at the model layer, but it does not address dependency at the architectural or operational‑process level.

What we recommend:

Start with the data: Without a stable and well‑governed data stack, every AI initiative turns into a series of workarounds and compensations.
Define the problem before choosing the tool: Copilot, Copilot Studio, and Foundry address fundamentally different needs. Selection should start from the use case, not from whichever technology happens to be on the shelf.
Integrate data governance from the start: Purview, data policies, and access controls must be defined as core architectural choices, not as activities postponed to the end of the project.

The Microsoft AI ecosystem is both technologically robust and tightly integrated, enabling organizations to build, govern, and scale AI with consistency and confidence.

But the real differentiator is not the technology, it is how it is adopted. Organizations that respect the maturity sequence, align use cases with the right tools, and structure their data from the start are the ones that turn AI into real advantage. The rest simply experiment with technology without achieving sustainable impact.

Planning AI initiatives in the Microsoft ecosystem?
We can help you define the right sequence: data, use cases, and technology.

👉 Book a meeting

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Copilot no Power BI: Nova Geração de Analytics com IA

admin — Tue, 25 Nov 2025 12:39:41 +0000

Copilot & Power BI: For the Next Generation of Analysts!

Imagine a meeting where decisions are guided by real-time, easily accessible data, presented in a clear and intuitive way. Where there’s no longer a need to wait for a technical report or decipher complex formulas. That’s what Copilot in Power BI makes possible — a qualitative leap that is redefining the role of the data analyst.

But there’s an important detail: this advancement doesn’t happen in isolation. Copilot is an integral part of Microsoft Fabric, Microsoft’s unified data platform, and it’s precisely this integration that unlocks its full potential.

Generative AI in Power BI: What’s Changing?

Power BI has always enabled the creation of interactive reports and advanced analytics. As discussed in our previous article, “Power BI in Microsoft Fabric: Data-Driven Data Visualization”, the introduction of Copilot makes this experience even more natural and accessible:

🧠 Natural Language: users can type questions such as “Which products have the highest margins over the last 8 weeks?”, and Power BI responds with visualisations and insights.
⚙️ Automatic Measure Creation: you define the objective, and Copilot generates the corresponding DAX measure — with step-by-step explanations.
📊 Dashboard Suggestions: Copilot proposes report pages based on the model’s data, speeding up exploration.
🔎 Contextual Analysis: identifies trends, anomalies, and correlations without the need for complex formulas or filters.

Why is Microsoft Fabric Essential?

Copilot doesn’t work in isolation. The underlying infrastructure is critical — and this is where Microsoft Fabric comes in as a strategic pillar. Fabric provides:

A unified platform for structured and unstructured data (including Lakehouse, Data Factory, and Real-Time Analytics);
Centralized management of capabilities and computing resources, ensuring performance and scalability for AI workloads;
Integrated Data Governance, enforcing security and access policies across the entire data lifecycle.

Caution: Copilot Limitations

Copilot offers many benefits, but there are some aspects to consider:

Poorly structured data can lead to unreliable responses❗
Need for human validation: suggested measures are not always perfect❗
Licensing: not available in all Power BI subscription plans❗
Organizational maturity: without a basic data-driven culture, the impact will be limited❗

Preparation Checklist

Before planning and activating your AI investment, check whether your company is ready in this context:

Is the data organized?
Is there a Data Governance policy in place?
Do the teams already use Power BI regularly?
Is there openness to upskill non-technical users?
Is the investment in licensing feasible?

Why Move Forward?

The combination of Copilot and Power BI is a tangible competitive advantage. But more than that, it’s an opportunity to transform how organizations work with data. By adopting Microsoft Fabric, companies gain the technological foundation that supports this transformation with robustness, scalability, and security.

📈 The future of data is conversational, automated, and intelligent. It’s just one workspace away.

📩 📩 Ready to see if Copilot and Fabric are the right fit for your organization?

We help design that path — with data, strategy, and simplicity.

Talk to our team of experts. We're ready to help.

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Microsoft e Qlik renovam liderança em BI e Analytics!

admin — Wed, 25 Jun 2025 11:51:46 +0000

Gartner Magic Quadrant 2025: Microsoft and Qlik Reaffirm Leadership in BI and Analytics Platforms

What Is the Gartner Magic Quadrant for Analytics and BI Platforms?

The Gartner Magic Quadrant for Analytics and Business Intelligence Platforms is an annual analysis that evaluates and positions leading market tools based on their ability to execute and the completeness of their vision.

These platforms enable organizations to:

Model, visualize, and analyze data to support informed decision-making;
Create interactive dashboards and automated reports;
Optimize operations based on real-time data.

With the growing use of AI, automation, and data integration, the 2025 Magic Quadrant reflects a new phase of modern Business Intelligence.

Gartner Magic Quadrant for Analytics and Business Intelligence Platforms 2025

Key Trends for Business Intelligence in 2025

Gartner highlights three major trends that are transforming the landscape of BI and Analytics platforms:

💡 1. Generative AI (GenAI) at the Core of Analytics

Leading platforms, such as Power BI and Qlik Sense, are incorporating generative AI to automate:

The creation of reports, visualizations, and metrics;
Generation of insights from natural language;
Pattern discovery based on machine learning.

These capabilities boost productivity for both analysts and decision-makers.

🌍 2. Democratization of Analytics

Modern BI platforms are making data access easier and more secure:

Collaborative and self-service dashboards;
Integrated Data Governance with content certification;
Multi-platform access, from web to mobile.

🔗 3. End-to-End Integration

Solutions like Microsoft Fabric stand out by unifying components such as:

Data lakes, data warehouses, and data engineering;
Data science and real-time operations;
Analytical layers within a single data ecosystem.

Microsoft Power BI 2025: Consolidated Leadership with Fabric and Copilot

Microsoft maintains its leadership position in the 2025 quadrant, highlighting Power BI as a key component of Microsoft Fabric. The integration of tools such as OneLake, Spark, Real-Time Analytics, and Copilot with generative AI makes Power BI a comprehensive and AI-assisted BI platform.

🔑 Key Strengths:

Dominant market presence, facilitating adoption and support;
Copilot in Power BI, boosting productivity with AI;
Flexible licensing with per-user or capacity-based options.

⚠️ Points of Caution:

Changes in Fabric pricing and licensing may cause confusion;
Dependence on Azure Cloud for certain advanced features;
Challenges in managing workload isolation in shared environments;

Qlik Sense 2025: Innovation in Associative Analytics and Cloud-Agnostic Solutions

Qlik continues to be recognized as a leader thanks to its data-centric approach and innovation in AI and automation. The Qlik Cloud platform provides a robust solution for organizations that value flexibility and exploratory insights.

🔑 Key Strengths:

High customer satisfaction and retention;
The associative model itself, which allows exploring data without predefined paths;
Compatibility with multicloud environments and enterprise applications.

⚠️ Points of Caution:

Lack of a proprietary cloud ecosystem, which may limit corporate strategies;
Lack of a serverless architecture, which can be a constraint in data lakehouse environments;
NLQ (Natural Language Query) capabilities are still somewhat limited compared to other providers offering more robust natural language interactions.

Relevance of This Report for Your Organization

The 2025 Gartner Magic Quadrant reinforces a clear message: the best BI platforms are those that:

Incorporate generative AI and advanced automation;
Operate as comprehensive and scalable data platforms;
Help organizations build a data-driven decision-making culture.

Both Microsoft and Qlik reaffirm their leadership in BI and Analytics platforms. Each represents a strong strategic choice for organizations looking to modernize their analytics practices and gain a competitive edge

How Can We Support Your Analytics Journey?

At F5tci, we are specialists in implementations using Microsoft Power BI, Microsoft Fabric, Qlik Sense Client-Managed, and Qlik Cloud Analytics.

We help companies define data strategies aligned with business objectives and implement modern analytics solutions, with a focus on AI and cloud.

📩 Want to discuss your BI and Analytics roadmap for 2025?

Talk to our team of experts. We're ready to help.

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Power BI no Microsoft Fabric: Visualização de dados data-driven

admin — Thu, 22 May 2025 09:39:27 +0000

Power BI in Microsoft Fabric: Native Visualization for a Data-Driven Culture

Microsoft Fabric is revolutionizing the way organizations access, transform, and design the visualization of their data. At the heart of this evolution is Power BI, now a native tool within this unified platform. With this integration, data visualization becomes smoother, faster, and more secure — from the lakehouse to the dashboard.

In this article, we show how Power BI fits within Microsoft Fabric, explore the impact of Direct Lake mode, and explain how this approach accelerates a data-driven culture in modern organizations.

What Is Microsoft Fabric?

Microsoft Fabric is Microsoft’s new analytics platform that unifies tools such as Power BI, Azure Synapse Analytics, Azure Data Factory, and OneLake into a single SaaS environment. Its goal is to simplify the analytics lifecycle: from data ingestion to visualization.

If you’re not yet familiar with the core concepts of this architecture and want to understand its impact, we recommend reading our previously published article: Modernizing Analytics with Microsoft Fabric.

With OneLake as the central repository, all services access data from a single point, improving consistency and performance. And this is where Power BI comes in…

Power BI as a Native Component of Microsoft Fabric

With the arrival of Fabric, Power BI is no longer just a standalone visualization tool and becomes an integral part of the ecosystem. This means:

Direct connection to Fabric’s Lakehouses and Warehouses;
Real-time visualizations using data from OneLake;
Integrated collaboration with other Microsoft tools (Excel, Teams, etc.);

If Power BI is already in use within the organization, no migration is required — dashboards, semantic models, and reports remain valid, now with default access to Fabric functionalities.

Direct Lake: Real-Time Data, No Compromises

One of the biggest advantages of this integration is the new connection mode: Direct Lake. This feature allows Power BI dashboards to access data in OneLake without importing or duplicating it.

Benefits of Direct Lake:

Real-time access to data;
Elimination of manual or scheduled refreshes;
Scalability for large data volumes;
Optimized performance with direct Lakehouse reading;

In practice, this allows the creation of always-up-to-date dashboards without overloading the infrastructure or compromising analysis speed.

Real-World Application Example

Let’s consider the following common scenario within an organization: a finance team needs to monitor the company’s operational costs in real time. With the Fabric platform, the process works as follows:

The technical team imports and processes the data — according to business logic — into a Lakehouse in Fabric, which serves as the central repository. Tools such as data pipelines, Spark notebooks, or manual uploads are used to bring in financial data (e.g., operational expenses, revenues);
Power BI connects to the Lakehouse through the OneLake catalog. Relevant tables are selected, and a semantic model is created in Direct Lake mode, allowing direct access to the data without the need for import. Relationships between tables are defined, measures are created using DAX, and hierarchies are configured as needed;
With the semantic model ready, interactive reports are developed in Power BI, incorporating relevant charts, tables, and KPIs;
The published report can be embedded in Microsoft Teams channels, allowing leadership and other stakeholders to access information in real time, fostering a data-driven culture.

Without external integrations and without redundant processes.

More Than Dashboards: A New Way to Do Analytics

With Power BI natively integrated into Fabric, the focus is no longer just on creating dashboards. Now, it is possible to:

Engage business users in the analytics process from the source;
Reduce dependence on the technical team for data updates;
Implement security and compliance policies across the board;

And with Copilot for Power BI, generative AI further assists in report creation. Using natural language commands, it is possible to automate:

The creation of visualizations, using simple commands such as: “show operational expenses by quarter and region”;
The creation of DAX measures using prompts like: “create a metric to compare actual costs with the budget”;
Adding natural language descriptions and narratives to dashboards, making it easier for non-technical decision-makers to interpret the data.

Why Is This Integration a Game-Changer?

Microsoft Fabric and Power BI together represent a new generation. This native integration reduces the time from data to decision, democratizes access to information, and puts the power of analytics in the hands of the entire organization. If the goal is to create a truly data-driven culture, this is the right architecture.

Do you want to turn your data into decisions quickly, visually, and in an integrated way?

Talk to our team of experts, experienced in Power BI and Microsoft Fabric, to help you modernize your end-to-end analytics architecture.

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IA, automação e qualidade de dados com Qlik

editor — Thu, 24 Apr 2025 16:02:45 +0000

How to apply AI, automation, and data quality with Qlik

Qlik, traditionally recognized for its strong visualization and analytics capabilities, has been expanding its scope with solutions increasingly focused on data intelligence—integrating data quality, automation, and augmented intelligence (AI) into its core.

In a landscape where organizations increasingly rely on real-time data to make critical decisions, the need for robust, scalable, and intelligent platforms has never been clearer.

Data quality in the Qlik platform

Before talking about AI, it is essential to address data quality. After all, intelligent models are only as good as the data that feeds them.

Data governance and integration tools (Qlik Data Integration & Talend)

Qlik strengthened its portfolio with the acquisition of Talend, integrating data quality and data governance capabilities directly into the Qlik Cloud platform. Key highlights include:

End-to-end data lineage: full traceability of data origins and transformations.
Regras de qualidade automatizadas: deteção e tratamento de inconsistências.
Integration with data governance and compliance policies.

These mechanisms ensure that the data used in dashboards is not only visual but also accurate, traceable, and auditable.

AI and Automation in Qlik: Intelligence within reach

Qlik is democratizing AI by offering no-code, intuitive tools for business teams. The goal extends beyond dashboards: it’s about empowering decision-makers with AI capabilities that were once reserved for data scientists.

Recent highlights include:

Qlik AutoML

Qlik AutoML allows users to build predictive models directly in the Qlik Cloud interface—no coding required. This feature enables:

Create predictions using historical data.
Assess key metrics, including accuracy, precision, and recall.
Apply the results directly to dashboards and workflows.

All delivered visually, intuitively, and with high impact for both data and business teams.

Qlik Answers: AI-Powered Conversational Business Intelligence

Among Qlik’s most recent AI innovations is Qlik Answers—a natural language feature leveraging generative (RAG) technology.

Qlik Answers enables users to:

Interact with data directly through natural language queries.
Get visual responses with insights automatically generated and interpreted.
Explore data in an assisted and contextualized manner.

By removing technical barriers, this enables non-technical users to make informed, data-driven decisions—especially in business areas less familiar with SQL or conventional BI practices.

Qlik Application Automation: Data and Action Orchestration

Qlik Application Automation is a low-code platform for orchestrating tasks and data workflows. It allows users to:

Seamlessly integrate with platforms like Slack, Teams, Salesforce, and others.
Streamline dashboard updates and automate alerts and notifications.
Design smart workflows driven by events and business rules.

Practical Use Cases: Leveraging AI in Decision-Making Workflows

Consider a scenario in which Qlik uses sales and behavioral data to predict the probability of a customer contract cancellation. This AutoML-generated prediction then initiates an automated workflow through Application Automation that:

Automatically updates the CRM with the predicted churn information.
Notifies the account manager;
Initiates a sales follow-up workflow.

Such orchestration empowers real-time decision-making using trusted data, actionable insights, and explainable AI—all seamlessly within the Qlik ecosystem.

Qlik demonstrates a strong commitment to the future of data analytics. With capabilities such as AutoML, Application Automation, and Qlik Answers, the platform is evolving beyond a traditional BI tool into a full-fledged ecosystem for augmented, data-driven intelligence.

Why Rely on a Qlik-Expert Consultancy?

Although the platform offers powerful capabilities, achieving success requires both technical know-how and strategic insight. We support our clients in:

Design and implement a consistent data flow;
Implement AI practically, delivering tangible results;
Ensure robust data governance, high performance, and security across Qlik environments.

Our certified specialists accompany every phase, from architecture through to final delivery, ensuring that technology translates into tangible business value.

📣 Are you ready to bring AI and automation into your Qlik environment—practically, securely, and with measurable business impact?

Book a technical consultation with our specialists and unlock the full potential of your data platform.

Frequently Asked Questions (FAQ)

❓ What is Qlik AutoML?

Qlik AutoML is a no-code automated machine learning capability embedded in Qlik Cloud, allowing organizations to generate predictions from historical data and seamlessly apply them within dashboards.

❓ How does Qlik Answers work?

It is a generative AI–powered feature that allows users to engage with data through natural language, receive explanatory visualizations, and explore insights in an assisted manner.

❓ What can I automate with Qlik Application Automation?

This includes everything from dashboard updates and alerts to integrations with external applications and business workflows triggered by events or predictive insights.

O conteúdo IA, automação e qualidade de dados com Qlik aparece primeiro em F5tci.

Modernização de Analytics

admin — Wed, 26 Mar 2025 16:03:57 +0000

When and Why to Move to Microsoft Fabric for a Unified Data Platform

Microsoft Fabric is Microsoft’s strategic platform for analytics modernization, designed to unify and simplify data management. It is an end-to-end solution that brings together data storage, ETL (Extract, Transform, Load), analytics, and machine learning capabilities—eliminating the need to manage multiple disconnected services.

But when is the right time for an organization to migrate to Microsoft Fabric? What are the economic benefits and the differentiating capabilities that set this platform apart from competing solutions? This article explores these questions and helps organizations make informed decisions about modernizing their data architecture.

What’s New and Innovative About Microsoft Fabric?

Fabric stands apart from other platforms by being a fully SaaS (Software as a Service) solution, natively integrated into the Microsoft ecosystem, eliminating the need for manual configuration of multiple services. Among its key innovations are:

OneLake: A unified, enterprise-wide data lake that enables centralized storage without the need to replicate data across services such as Power BI, Azure Synapse Analytics, and Azure Data Factory.
Azure Data Factory & Azure Synapse Analytics: Built-in ETL and data processing tools that reduce the complexity of data integration.
Compatibility with Multiple Analytics Engines: Supports T-SQL, Spark, KQL, and Data Science workloads.
Enhanced Data Governance and Security: Centralized control over data and compliance with regulations such as GDPR.

Unlocking Economic Value with Microsoft Fabric

Migrating to Microsoft Fabric can deliver significant long-term cost savings. Key economic benefits include:

Reduced Operational Costs: By leveraging Fabric as a SaaS solution, organizations can eliminate the overhead of managing complex Azure or on-premises infrastructure, driving efficiency and cost savings.
Simplified Licensing: The consumption-based billing model helps avoid over-provisioning costs.
Reduced Integration Overhead: Organizations currently using Azure Synapse Analytics, Azure Data Factory, and Power BI separately can consolidate everything in Fabric, lowering integration costs.
Automatic Scalability: The platform automatically adjusts resources based on demand, ensuring cost-efficient operations.

Recognizing the Turning Point

Although Microsoft Fabric introduces innovative capabilities, not every organization needs to migrate immediately. The ideal timing often arises when the following indicators are present:

Fragmented Data Infrastructure: Organizations using separate tools for ETL, storage, and data visualization can benefit from consolidating everything within Fabric.
Data Governance and Security Challenges: Organizations struggling to maintain compliance with data protection regulations can benefit from centralized management through Microsoft Purview within Fabric.
Data Governance and Security Challenges: Organizations struggling to maintain compliance with data protection regulations can benefit from centralized management through Microsoft Purview within Fabric.
Expansion into Advanced Analytics and Machine Learning: Microsoft Fabric integrates AI and machine learning tools, enabling a seamless transition to predictive analytics models.
Need for Integration with the Microsoft Ecosystem: If an organization already uses Power BI, Azure Entra ID, and Microsoft 365, migrating to Fabric enhances interoperability across tools.

Conclusion

Microsoft Fabric is an innovative platform that can simplify and optimize data management, delivering significant benefits for organizations looking to modernize their analytics infrastructure. However, the decision to migrate should be guided by genuine needs for operational efficiency, cost reduction, and enhanced data governance.

If your organization is facing challenges such as fragmented systems, high costs, or difficulties scaling analytics, it may be the right time to explore Microsoft Fabric.

Get in touch for a tailored evaluation of your data architecture and unlock the full potential of your analytics environment!

O conteúdo Modernização de Analytics aparece primeiro em F5tci.

Qlik: Líder na Inovação em IA!

admin — Thu, 13 Mar 2025 11:07:46 +0000

Qlik lidera na inovação em IA e qualidade dos dados!

Now available: Gartner® Magic Quadrant for Augmented Data Quality Solutions.

This research analyzes emerging technologies and the vendor landscape to help leaders make better purchasing decisions. For Qlik, in the age of AI, data quality is not just important—it is essential. By embedding data quality processes across the entire platform, Qlik ensures that every stage—from data integration to insight—is built on a foundation of trust and quality.

This year, for the 6th consecutive year, we highlight the competitive advantages it delivers: 6º ano consecutivo, damos destaque às vantagens competitivas que entrega:

AI and Data Quality Innovationdados: Qlik remains at the forefront, enabling AI use cases with Qlik Trust Score™ for AI, new integrations with OpenAI and Pinecone to support Retrieval-Augmented Generation (RAG) scenarios, curated data products, and API-driven data preparation.
Unstructured Data: The acquisition of Kyndi, combined with Qlik Cloud’s robust structured data capabilities, delivers optimized solutions for processing and managing high-quality structured and unstructured data.
Enhanced Data Quality with AI and ML: Automated data standardization, anomaly detection, and intelligent correction—all powered by models that learn from metadata and human feedback;
Hybrid and Multi-Cloud Support: With support for on-premises, hybrid cloud, and SaaS options, Qlik provides the flexibility to optimize costs and scale efficiently.

In addition to this recognition, Qlik also maintains a leadership position in the Gartner Magic Quadrant for Analytics and Business Intelligence Platforms, as well as the Gartner® Magic Quadrant™ for Data Integration Tools. This consistent presence underscores Qlik’s ongoing commitment and investment in innovation—a true partner for organizations in driving informed decision-making.

O conteúdo Qlik: Líder na Inovação em IA! aparece primeiro em F5tci.

Qlik: Líder na inovação e competência!

admin — Tue, 25 Jun 2024 15:49:04 +0000

Qlik nomeada líder pelo 14º ano consecutivo!

Já está disponível o relatório de 2024 da Gartner® Magic Quadrant for Analytics and Business Intelligence Platforms. O relatório considera anualmente a opinião de especialistas e as avaliações de utilizadores concentradas numa única experiência. Nesta representação gráfica, a Qlik®, posiciona-se como líder pela capacidade de execução, aliada à visão da empresa, adequadamente posicionada para o futuro:

This year, for the 6th consecutive year, we highlight the competitive advantages it delivers: 14º ano consecutivo, damos destaque às vantagens competitivas que entrega:

Analytics End-to-end: a aquisição da Talend em 2023 permitiu à Qlik® otimizar a sua base de dados e a integração de dados. Acrescenta-se a vantagem de ser pioneira na integração de Large Language Models (LLM), e fica claro que a Qlik® criou um poderoso fluxo de trabalho de dados para apoio à tomada de decisão.
Reconhecimento renovado do mercado: A Qlik® conseguiu destacar-se por meio de aquisições estratégicas notáveis, em particular a aquisição da Talend e a aquisição da Kyndi, empresa reconhecida pelo seu papel inovador em processamento de linguagem natural, pesquisa e inteligência artificial (IA) generativa.
Serviço agnóstico para os principais fornecedores cloud. Quer esteja a usar AWS, Google Cloud, Microsoft Azure ou até mesmo Alibaba Cloud, o produto funciona perfeitamente em todas as principais plataformas cloud. A Qlik® pode também ser uma escolha confortável para organizações com implementações multicloud com uma ampla gama de aplicações empresariais.

Leia as reflexões de Christopher Powell (Chief Marketing Officer da Qlik) acerca do posicionamento de líder pelo 14º ano consecutivo. Leitura obrigatória para relembrar os 12 meses de inovação que antecedem este reconhecimento que, segundo palavras do autor, comprova a força e amplitude das capacidades da empresa.

O conteúdo Qlik: Líder na inovação e competência! aparece primeiro em F5tci.

Arquivo de Advanced Analytics - F5tci

Semantic Layer vs Ontology. Porque a IA tornou ambas essenciais?

Semantic Layer vs. Ontology in summary

What is a semantic layer and why does it remain essential?

Ontology: What is it, and how does it differ from a semantic layer?

Semantic Layer vs Ontology: The difference in practice

Why do AI and AI Agents need ontologies?

How does Microsoft Fabric use semantic models and ontologies?

What should organizations do to prepare their data for AI?

FAQ: Semantic Layer and Ontology

Conclusion

O centro do BI e dos dados está a mudar?

For years, the dashboard was the central metaphor of BI and data.

What exactly are we talking about when we talk about MCP?

What changes and what remains:

The real problem is not technological.

Security: the most underestimated topic.

What fundamentally changes for data teams?

In summary:

The future of BI may belong to the organizations that most effectively govern the enterprise meaning embedded in their data.

Adoção de IA: o que cria valor e o que pode amplificar o caos

Three layers, three distinct purposes

Microsoft 365 Copilot: real value, real limitations

Copilot Studio and AI Agents: different strategic purposes.

Copilot Studio: A low‑code platform for creating conversational assistants with predefined logic.

AI Agents (Azure AI Foundry): Goal‑oriented systems that operate autonomously:

Azure AI Foundry: capability and complexity.

Data and AI: the point where strategy, governance, and intelligence converge

Challenges that organizations often underestimate:

What we recommend:

The Microsoft AI ecosystem is both technologically robust and tightly integrated, enabling organizations to build, govern, and scale AI with consistency and confidence.

Planning AI initiatives in the Microsoft ecosystem? We can help you define the right sequence: data, use cases, and technology.

👉 Book a meeting

Copilot no Power BI: Nova Geração de Analytics com IA

Copilot & Power BI: For the Next Generation of Analysts!

Why is Microsoft Fabric Essential?

Caution: Copilot Limitations

Preparation Checklist

Why Move Forward?

📩 📩 Ready to see if Copilot and Fabric are the right fit for your organization?

We help design that path — with data, strategy, and simplicity.

Microsoft e Qlik renovam liderança em BI e Analytics!

Gartner Magic Quadrant 2025: Microsoft and Qlik Reaffirm Leadership in BI and Analytics Platforms

What Is the Gartner Magic Quadrant for Analytics and BI Platforms?

Key Trends for Business Intelligence in 2025

Microsoft Power BI 2025: Consolidated Leadership with Fabric and Copilot

Qlik Sense 2025: Innovation in Associative Analytics and Cloud-Agnostic Solutions

Relevance of This Report for Your Organization

How Can We Support Your Analytics Journey?

📩 Want to discuss your BI and Analytics roadmap for 2025?

Power BI no Microsoft Fabric: Visualização de dados data-driven

Power BI in Microsoft Fabric: Native Visualization for a Data-Driven Culture

What Is Microsoft Fabric?

Power BI as a Native Component of Microsoft Fabric

Direct Lake: Real-Time Data, No Compromises

Benefits of Direct Lake:

Real-World Application Example

Without external integrations and without redundant processes.

More Than Dashboards: A New Way to Do Analytics

Why Is This Integration a Game-Changer?

Do you want to turn your data into decisions quickly, visually, and in an integrated way?

IA, automação e qualidade de dados com Qlik

How to apply AI, automation, and data quality with Qlik

Data quality in the Qlik platform

AI and Automation in Qlik: Intelligence within reach

Qlik AutoML

Qlik Answers: AI-Powered Conversational Business Intelligence

Qlik Application Automation: Data and Action Orchestration

Why Rely on a Qlik-Expert Consultancy?

📣 Are you ready to bring AI and automation into your Qlik environment—practically, securely, and with measurable business impact?

Frequently Asked Questions (FAQ)

Modernização de Analytics

When and Why to Move to Microsoft Fabric for a Unified Data Platform

What’s New and Innovative About Microsoft Fabric?

Unlocking Economic Value with Microsoft Fabric

Recognizing the Turning Point

Get in touch for a tailored evaluation of your data architecture and unlock the full potential of your analytics environment!

Qlik: Líder na Inovação em IA!

Qlik lidera na inovação em IA e qualidade dos dados!

Qlik: Líder na inovação e competência!

Planning AI initiatives in the Microsoft ecosystem?
We can help you define the right sequence: data, use cases, and technology.