Digital transformation

Enterprise digital transformation: key strategies to be strong by 2026

by Business

The business calendar doesn’t stop, and the start of 2026 is closer than it seems. In this context, technology has evolved from a support function and has become a strategic pillar of the business. What five years ago represented a competitive advantage is now simply the starting point.

Even so, digital transformation is still perceived in many organizations as a vague or overly ambitious investment. The reality is that, when approached with a clear and pragmatic vision, its impact is reflected directly in operational efficiency and the bottom line. Preparing a company for the future does not require adopting futuristic technologies overnight, but identifying and eliminating the inefficiencies that slow growth on a daily basis.

Below, we propose a diagnostic exercise based on common situations that act as indicators of digital maturity. A starting point to identify where the real opportunities for improvement lie within your organization today.

Digital health indicators: where does your company stand?

1. Systems integration and a single source of truth

In many companies, departments such as sales, logistics, or administration work with applications that are not connected to each other. The result is silos that require data to be manually transferred from one system to another.

The risk: duplicated tasks and human error. When teams spend time copying and pasting information between spreadsheets, ERP systems, or CRMs, productivity suffers. An infrastructure ready for 2026 is based on interoperability: systems that communicate automatically and ensure a single, consistent, and reliable dataset for the entire organization.

2. Mobility and remote operations

A key question: could your critical operations continue at 100% if physical access to the office were interrupted tomorrow? Dependence on on-premise servers, network drives, or paper documentation severely limits an organization’s ability to respond to unforeseen events.

The goal: decouple productivity from location. True digitalization makes it possible to approve budgets, manage inventory, or access records securely from anywhere. If a company needs to be physically present to operate, its model is fragile in the face of unexpected scenarios.

3. User experience as a competitive advantage

Customer expectations—both in B2B and B2C environments—are shaped by leading technology companies. This translates into simple, agile processes with minimal friction. Endless forms, bureaucratic procedures, or unintuitive interfaces are no longer just an annoyance: they are a direct cause of abandonment.

The reality: digital friction is one of the main enemies of conversion. Optimizing user experience is not an aesthetic choice, but a strategic decision that directly impacts profitability, satisfaction, and customer loyalty.

4. Data-driven decision-making

Many leadership teams still manage their business by looking backwards, analyzing monthly reports to understand what happened. While this approach remains useful for historical analysis, it is insufficient in environments that demand fast reactions.

The evolution: today’s technology allows traditional analysis to be complemented with real-time information. Dashboards updated instantly with data on sales, cash flow, or production enable faster, better-informed decisions. Moving from intuition to data-backed certainty is one of the defining traits of companies that lead their industries.

A journey that doesn’t have to be taken alone

Undertaking a digital transformation is not just about implementing technology, but about doing so with sound judgment, business vision, and expert guidance. Having the right partner makes the difference between a one-off investment and sustainable, long-term change.

On this path to 2026, RCM Software and our partner network support companies throughout the diagnosis, definition, and execution of their digital transformation. Through solutions designed to integrate data, optimize processes, and support decision-making, we help organizations build a solid, scalable technological foundation aligned with their strategic objectives.

The future is not improvised: it is designed today. And doing so with the right support is the first step toward leading tomorrow.

    Transform your software with embedded analytics

    Transform your software with embedded analytics

    by Business

    Data analytics continues to gain ground within companies and has evolved from an attractive add-on to becoming an indispensable element for decision-making and business operations.

    In this context, CRM and ERP product developers face the need to integrate data analytics into their solutions. Independent Software Vendors (ISVs) must therefore choose between building an in-house solution or integrating third-party tools, which we call embedded analytics.

    In many cases, in-house development is ruled out because it is not cost-efficient and takes longer. It also requires dedicating internal resources to ongoing development and support.

    Integrating third-party tools or components improves delivery times and allows the ISV to focus on developing and supporting its own products.

    What is embedded analytics?

    Embedded analytics refers to analytics built into tools or solutions whose primary purpose is not data analysis, for example, a CRM or an ERP. This allows users to access analytics without leaving the product. From the user’s perspective, charts, reports, and dashboards feel like a native part of the software.

    What are its main advantages?

    Using embedded analytics in your products directly impacts competitiveness and increases perceived value. Here are its key advantages:

    1. Improved perceived product value

    Integrated analytics makes the software no longer seen just as a management tool but also as a decision-making support tool. This empowers users to extract value from data within the tools and applications they already use. The increase in perceived value can also open the door to creating premium pricing options with advanced features.

    2. Increased engagement, loyalty, and reduced churn rate

    Expanding the product’s scope from management to decision-making means users rely on it for more critical tasks, such as marketing budget allocation, stock strategy, etc. As a result, usage frequency increases and the likelihood of switching to another product decreases.

    3. Faster decision-making

    Without embedded analytics, the process of leveraging data for decisions can be as long as exporting, cleaning, analyzing, and finally deciding. With embedded analytics, users can view key information within the application and quickly move into analysis and decision-making.

    4. Reduced friction and improved user experience

    With an integrated analytics solution, everything retains the same look, design, and flow as your main application. There are no new interfaces or learning curves—allowing for a smooth, simple, and intuitive experience.

    5. Improved core product functionalities

    By delegating data analysis to expert companies with mature products, ISVs can devote 100% of their resources to developing new features that keep their product competitive in an increasingly fast-evolving market.

    What challenges may arise when using embedded analytics?

    We’ve already covered the benefits for both ISVs and end users. Now let’s look at some challenges that may arise—and how to overcome them.

    What should you consider when choosing an embedded analytics solution?

    There are many providers offering embedded analytics solutions. Here are some criteria to help you make the best choice:

    Easy integration

    Choose options that allow integration without unnecessary additional steps and that make it easy to embed data analytics into your product.

    Customization level

    Another key factor is how flexible the tool is when integrating into your user interface. This helps reduce friction and improve user experience.

    Licensing flexibility

    Every business model has specific needs, so we recommend choosing a licensing model that fits your model—or can adapt to it. This gives you the freedom to make product decisions that keep it competitive.

    What is the outlook for embedded analytics?

    Embedded analytics will continue to gain traction, as reflected in the Data, BI and Analytic Trend Monitor 2025 survey conducted by BARC. In this survey, 30% of companies reported already using embedded analytics, and 16% said they are planning to implement it within the next 12 months.

    With BI4Web, you can enjoy easy integration, world-class features, and the licensing flexibility you need. Contact us, and our team will help you begin your journey into embedded analytics.

      The new mission of data manager

      The New Mission AI Brings to the Data Manager

      by Business

      The arrival of artificial intelligence in the business world is having a gradual yet steady impact, particularly in areas like data analytics. That’s why we want to present some of the challenges it brings, how to face them, and how the role of data manager can truly make a difference.

      What are the main changes AI brings?

      The data manager’s main mission used to be summarized as keeping data organized and available. However, today’s landscape introduces new demands; here are some of the most relevant ones:

      • Data quality and preparation: Keeping data clean, well-prepared, and properly governed becomes more important, as the success of AI relies on the quality of the data it uses.
      • Technological and cultural adaptation: This includes integrating new formats such as images, videos, and text. It means also adopting new architectures, processes, and mindsets.
      • Governance, traceability, and ethics: Being prepared to comply with legal frameworks and uphold principles of ethics, privacy, and transparency.
      • Taking on a more strategic and bridging role: Strengthening collaboration between business, technology, and data teams. This helps build the synergy needed for a company to remain competitive.
      • Enabling self-service without losing control: Carefully assessing which self-service options align with the company’s needs without compromising data governance.

      Best practices to take on these challenges

      Govern your data: Now is the time to review how your company defines and enforces its data governance policy. It’s essential that it includes clear roles, policies, and monitoring mechanisms. Adjust the plan to your company’s current reality so it is ready for the future.

      • Automate tasks: Identify and automate routine data quality, cleaning, and lineage tasks to save time and improve efficiency.
      • Store the right data: Evaluate whether the data being stored and processed serves a business purpose. Sometimes data is managed without a clear goal, leading to unnecessary resource consumption.
      • Promote a data culture: Everyone who interacts with data in your company should understand their role. Internal training programs can help strengthen data governance and improve results.
      • Don’t forget ethics and privacy: These must be integral components of your data strategy to prevent bias, ensure transparency, and comply with regulations.

      Common myths about AI

      Along with following best practices, it’s important to avoid decision-making based on unfounded beliefs. Here are some of the most common myths:

      • AI doesn’t make mistakes: This is one of the most widespread misconceptions. Human oversight must remain a key part of the process.
      • The data manager is only a technician: Thinking of this role as purely technical disconnects it from the business and limits its impact.
      • Rigidity equals good data governance: In reality, balance is the key to maintaining both governance and agility.

      What are the benefits of the data manager’s evolving role?

      The main opportunity this evolution brings is to rethink the role as a more integrative position. This paradigm shift enhances the potential of data analytics and AI, while driving better-informed decision-making and stronger organizational structures.In conclusion, there are many opportunities in this space; this is why at RCM Software, we’ve developed solutions and tools designed with this integrated vision in mind. If you’d like to learn more about our ecosystem of business-oriented solutions, get in touch with us, and we’ll reach out to you

        The Gantt diagram

        Plan Smarter: The Gantt Chart Comes to BI4Web

        by Business

        As we mentioned in the news about BI4Web version 25, this new release introduces the Gantt chart as a new control. Here, we’ll explain in more detail what this control is and what its main benefits are.

        What is a Gantt Chart?

        Let’s start with a basic definition. The Gantt chart is a widely used visual tool for project tracking. It helps map out the tasks required to carry out a project, the dependency relationships between them, and the time needed to complete each one.

        How Does It Work?

        On the left-hand column, you list all the tasks, while the top section shows the time axis. The duration of each task is represented by the length of the horizontal bar displayed in front of it.

        How to Use the Gantt Chart

        Here are a few simple steps to help you use it effectively:

        1. Define the tasks. Break the project down into specific tasks.
        2. Set the task duration. The time can be expressed in days or weeks — the key is to make a realistic estimate.
        3. Set dependencies. Define which task or tasks must be completed before the next one can begin.

        When Should You Use a Gantt Chart?

        It’s highly recommended for projects that include multiple tasks with deadlines. Here are some of the most common use cases:

        • Product launches. The Gantt chart is very useful for mapping all tasks — for example, market research, prototype development, testing, marketing, and distribution.
        • Event planning. These can include corporate events such as a product launch party. Some of the tasks you could define are venue rental, hiring presenters, and so on.

        Best Practices for Using the Gantt Chart

        Now that you know how it works, here are some tips to help you get the most out of it:

        • Break down tasks: Make sure tasks are small and manageable. Tasks that are too general tend to be difficult to manage.
        • Set clear dependencies: Take the time to verify that dependencies are correctly connected. This will help you avoid unexpected delays and give you a more realistic view of the project’s progress.
        • Use management software: For example, the Gantt chart included in BI4Web version 25 lets you track all tasks in real time, explore possible scenarios, and identify the critical path.
        • Be realistic with timelines: Allow some buffer time for tasks — overly tight deadlines can create unnecessary bottlenecks.

        What Are the Advantages of BI4Web’s Gantt Chart?

        One of its main advantages is that it allows users to explore hypothetical scenarios without affecting the data sources. For example, you can see how delaying a task by two days would impact all dependent tasks. It also makes it easy to identify the critical path, the longest sequence of interdependent tasks whose delay directly affects the project’s overall timeline.

        If you want to discover all the advantages of BI4Web, including the Gantt chart, request a free trial today.

          Who are you giving the keys of your Business Intelligence

          Who are you giving the keys to your Business Intelligence?

          by Business

          Having business intelligence in your company is a key element for improving decision-making capabilities. But who should you give the keys to your BI within your organization? Do all employees need access?

          Well, we can start by saying that free access without any governance will overshadow the benefits of BI in the company. That’s why having an adequate data governance strategy is essential to make the most of it.

          Here are some recommendations you should consider when determining who should have access to your BI.

          First, properly structure your company’s information

          With a clear definition of the data your company will work with, it becomes easier to establish the level of access each employee should have. Let’s look at an example: if your company has a sales team organized by countries, regions, and cities, you can group data by that structure and provide access for each team member accordingly. This way, a salesperson with a smaller scope will only be able to access their own sales data, while someone managing multiple regions will have broader access privileges.

          Think about making your employees’ work more efficient.

          Evaluate each employee’s role and how they currently perform their tasks. Ask yourself if you can make it easier for them to access the information they need to do their jobs. This will help you identify opportunities to grant the right level of access to the right people in less time. A BI tool that provides valuable information to its users becomes an indispensable part of their work, leading to greater efficiency and faster response times.

          Here are some examples of the types of information different roles may need access to:

          Executive management: They need consolidated reports to make strategic decisions. With BI4Web’s pyramid navigation, they can move from a global overview down to the level of detail necessary to understand what’s happening in the company.

          Department managers or process leaders: They need access to specific data to manage teams and projects. You can make their work easier by providing access not only to their areas of responsibility but also to related data from other departments that impact their work—always with a clear governance strategy so they don’t end up overwhelmed with unnecessary information.

          Data analysts: They perform more specialized analyses, so their access should be limited to their analytics area.

          Follow the principle of least privilege.

          This principle states that each person, application, or system should have only the permissions and access strictly necessary to perform their functions. A simple example: if someone’s role is to analyze sales, there’s no need for them to access HR data.

          Ensure confidential and sensitive data is protected.

          In addition to making sure every employee sees information relevant to their role, you must also consider that some data requires additional protection due to its confidentiality or sensitivity.

          Examples of confidential information include financial data, key client data, or HR reports.

          Control the level of access.

          Viewing data is not the same as modifying or deleting it. Therefore, each role must have the appropriate access level according to its responsibilities within the company. Here are some examples of access levels a user might need:

          A. Read-only
          Allows employees to view data without making any changes. Ideal for those who need to make data-driven decisions but are not supposed to modify the information.

          B. Creation of analyses or reports
          This level of access allows users to create dashboards and data visualizations.

          C. Full or administrative access
          These users can configure the system, manage permissions, and perform critical BI maintenance tasks.

          Choose a BI solution that makes all of this easy.

          With BI4Web, you can securely provide your employees with the data access they need. Request a free trial and see for yourself how you can start making data-driven decisions safely.

            BI4Web Version 25

            BI4Web Version 25: faster, more powerful, and easier to use

            by Business

            BI4Web version 25 is now available, packed with improvements designed to make your work faster, more efficient, and more enjoyable.

            Studio: take full control

            • Test parameters: new tab in the selectors to instantly find the information you need.
            • Separate Edit and AI: decide when a control can be editable or work with artificial intelligence.
            • Save your favorite templates: new templates for controls and HTML that you can reuse in any project.
            • Improved KPI: corrected text formatting and better performance.
            • Export errors: quickly and easily generate error lists when updating projects.
            • Updated connectors and SQL: includes new subselect and window functions.
            • Optimized performance: slow connections are no longer an issue thanks to the migration to standard requests.

            Gantt Chart: total flexibility

            • New control to manage your diagrams with ease.
            • Modify without touching the database: adjust critical paths directly from the viewer.
            • Critical path visualization: manage projects more efficiently and safely.

            Other key improvements

            • Grid and Pivot Grid now accept format strings correctly.
            • Updated projects screen: open or update versions without complications.

            With BI4Web Version 25, working on your projects has never been easier: more speed, more control, and new tools to optimize your workflow.

              Does your company needs a data hub

              Guide: Does Your Company Need a Data Hub?

              by Business

              To answer this question, let’s start by defining what a data hub is. Its main function is to centrally manage the use of data across different sources and destinations within the company. This management includes data integration, governance, and distribution.

              In other words, a data hub is key to avoiding the creation of data silos because it enables the necessary connections for a company’s digital ecosystem to be 100% interconnected. Additionally, it allows the application of policies that ensure data quality and proper distribution across different processes and applications.

              Given the benefits that come with implementing a data hub, you may be wondering when the right time is to adopt one. To help you answer that question, here’s a practical checklist:

              Business Needs

              A data hub makes sense when business and technology teams demand faster and more reliable access to data. To identify these needs, ask yourself the following questions:

              • Do we need to integrate information from multiple sources (ERP, CRM, SQL/NoSQL databases, files, APIs)?
              • Do we struggle to access reliable data for decision-making?
              • Are business areas requesting self-service access to data without depending on IT?
              • Do we want to enable advanced analytics, AI, or centralized reporting?

              Current Data Situation

              These questions will help you quickly diagnose the current state of your company’s data:

              • Is data scattered across silos?
              • Are there duplications, inconsistencies, or lack of traceability?
              • Is data governance limited or nonexistent?
              • Has IT become a bottleneck for delivering datasets?

              Technical Aspects

              Infrastructure is key to ensuring that a data hub is scalable and efficient. Use these questions to assess your company’s technical maturity:

              • Does our current infrastructure support centralized integration?
              • Do we have APIs, connectors, or basic pipelines in place?
              • Do we need compatibility with cloud, on-premises, or hybrid environments?
              • Can we handle future growth in data volume?

              Internal Resources and Capabilities

              A data hub is not only about technology — it also requires people and processes:

              • Does the company staff have sufficient knowledge of ETL/ELT and modern architectures?
              • Is there a defined data governance area or role?
              • Is there financial backing and a defined budget from management?
              • Are key users prepared to adopt the use of a data hub?

              Expected Value

              To estimate expected value, you can use the following questions as a guide, considering your current data analysis needs:

              • Will it reduce data preparation time for analytics?
              • Will it improve the quality and reliability of information?
              • Will it provide competitive advantages? For example, better customer experience, more efficient processes, or new opportunity management.
              • Is it possible to define clear ROI goals?

              Risks and Change Management

              Implementing a data hub goes hand in hand with change management:

              • Have you assessed, or do you have the means to assess, the complexity of implementation?
              • Is there a training plan for users?
              • Is data security and privacy compliance in place, for example with GDPR?
              • Have you created a roadmap grounded in your company’s reality, or do you have the means to do so?

              Conclusion and Results

              While it’s true that a data hub can be a powerful driver of digital transformation, not every company is ready to take that step. If your company meets most of the points on the checklist, the time has probably come. Otherwise, create a work plan that aims to strengthen the weak points until you reach the maturity needed to make a data hub implementation a reality.

              The key is to view the data hub as a strategic investment that will generate value from data in critical areas, ultimately boosting the company’s competitiveness. If you’re interested in learning about a data hub that can help you unlock all these benefits efficiently and AI-powered, we invite you to explore DataGate Orchestration Platform.

                Challenges of Progress OpenEdge Applications

                Challenges of Modernizing Progress OpenEdge Applications and How to Overcome Them

                by Business

                Application modernization is not without challenges, and Progress OpenEdge applications are no exception — especially because it involves moving from a desktop environment to the web. In this blog, we’ll explain what those challenges are and how to overcome them.

                How to meet the front-end needs of Progress OpenEdge applications?

                Front-end decision-making has a major impact on both the modernization process and the application’s subsequent performance. There is a path we’ll call the “traditional” route, which involves building the front-end using web technologies.

                In other words, you’ll need to hire additional specialized staff to handle creation and maintenance. The downside is the increase in costs and potential compatibility issues between the chosen web technologies and OpenEdge.

                To avoid the complexities and challenges of expanding the team, it’s advisable to choose a platform with a drag-and-drop interface. This way, the Progress developers you already have on your team can manage the front-end without relying on third parties.

                This option also makes it easier to transfer your team’s existing business logic knowledge to the front-end. It provides greater agility in both the modernization process and the applications’ ongoing performance.

                What strategy should you use to avoid application downtime?

                Downtime can be a real headache for users, so it’s best to avoid it whenever possible. For OpenEdge applications, the ideal approach is to use a hybrid work model that allows them to continue running both on desktop and web.

                With this work model, modernization can move forward gradually without interrupting service for desktop users. Another advantage is that modernization results can be delivered without having to wait until the very end of the process. This positively contributes to change acceptance, which is so critical in this type of project.

                How to make the most of the ABL code you’ve already developed?

                A platform that integrates natively with Progress OpenEdge will prevent disruptions and allow you to fully leverage your back-end development without compatibility issues or similar problems.

                How to manage APIs?

                API management can become a significant challenge if handled individually, which is why it’s best to opt for an approach that avoids such individual management. For example, a platform that connects directly to the Progress database through global methods. This way, you can optimize time usage and focus on less operational matters.

                Choose DataGate GDP for Progress OpenEdge

                With DataGate GDP, you can modernize Progress OpenEdge applications while overcoming all the challenges mentioned, with an agile solution that ensures a fast, secure, and modern move to the web.

                In addition to the advantages already mentioned, it also ensures no data loss thanks to its state machine that writes directly to the Progress database.

                If you’d like to learn more about how DataGate GDP works, contact us and our team will answer all your questions.

                  Data Driven companies

                  Tips to Transform Your Company into a Data-Driven Organization

                  by Business

                  Becoming a data-driven organization is the goal of many companies. The main motivation behind this transformation is its direct impact on competitiveness. This change involves an evolution in both technical and cultural aspects within the company. Here are some practical tips to help you foster a culture where every decision is based on data rather than intuition.

                  Transformation Starts with Leadership

                  The DNA of the organization reflects the actions taken by its leadership. That’s why it’s crucial for figures such as the CEO or members of senior management to be leaders of this transformation as well.

                  With this in mind, the first actions should focus on ensuring that leaders use data to support their decisions and share this in meetings with employees and collaborators. This will create a cascade effect, leading employees to also start using data in their decision-making.

                  For a higher chance of early success, we recommend limiting the use of data to a single project at the start. Use high impact and low complexity as selection criteria. It’s also necessary to involve a senior leader to generate the desired impact and build initial trust.

                  Transformation Is Built with Small Wins

                  In line with the previous point, it’s crucial to continue promoting small wins at different levels within the company. This is achieved through a test-and-learn experimentation culture. This is how companies like Amazon and Booking test hypotheses, analyze results, and adjust strategies based on real data.

                  The goal of this experimentation culture is for employees to gradually integrate data usage as the rule, not the exception.

                  Promote Data Literacy

                  Speaking the language of data is an essential skill to ensure everyone in the company has the necessary tools to integrate data analysis into their work. This is why promoting data literacy is fundamental—so that even employees without a technical role can learn basic concepts such as KPIs, metrics, and results.

                  A notable example of promoting data literacy is Airbnb’s Data University program, which, since 2016, has aimed to provide data analysis training to all employees, regardless of their role. This way, everyone in the company is on the same page and works more efficiently.

                  Make Information Accessible so Your Company Loves Data

                  Alongside training and promoting data analysis, building a digital ecosystem that makes data accessible is key. That’s why it’s necessary to choose the right software for each case. The guiding principle is to select software that adapts to the company’s processes and users. This helps reduce resistance to change, shortens the learning curve, and increases the adoption rate.

                  In this regard, choosing the right business intelligence platform helps build a data culture—mainly because employees can access the data they need and turn it into an indispensable part of their work.

                  We can conclude that the faster users integrate data into their work and directly see its benefits, the higher the long-term adoption rate will be.

                  Create an Environment with Governed, Reliable, and High-Quality Data

                  Trust in data comes directly from its quality, and the only way to ensure it is by having a well-executed data governance strategy.

                  Data governance encompasses key aspects such as defining data quality criteria, implementing processes to guarantee quality, and assigning roles within the company responsible for each process. As a result, you will have data your team can trust to make decisions.

                  When Is the Best Time to Become a Data-Driven Company?

                  The best time is now. Regardless of your company’s size or type, making data-driven decisions is becoming an essential trait for staying competitive.

                  The sooner you start applying the tips mentioned in this article, the sooner you’ll begin the journey your company needs to take to become truly data-driven.

                  If you want to see your data in action on a business intelligence platform designed for today’s business needs, request your free trial of BI4Web. This way, you can start your digital transformation journey with expert guidance.

                    Impact of AI on Data Analysis

                    What is the impact of AI on data analysis?

                    by Business

                    Artificial Intelligence has been making its way into various aspects of personal and business life for several years now. More and more tasks and processes are being partially or fully handled by AI. This, of course, includes the use of AI in data analysis—an area that directly affects how companies make decisions.

                    A key aspect of using AI in data analysis is that it gives data analysts many more tools to work faster and more accurately. This is thanks to improved interaction with data, for example by generating graphs from natural language prompts or creating forecasts through user-friendly interfaces.

                    To explore its impact further, here are some specific use cases:

                    Forecasting Within Reach of Business Intelligence Users

                    BI users can now make AI-based forecasts without needing help from the development team or experts in advanced statistics. This is mainly because they can choose the necessary parameters through a user-friendly interface that delivers results quickly. BI platforms like BI4Web offer AI-powered forecasting for end users in just a few clicks.

                    One interesting result of this is that it speeds up data-driven decision-making by reducing the friction in adopting a data-driven culture within the company.

                    Fast Anomaly Detection in Large Data Volumes

                    Large volumes of data make anomaly detection more complex. However, with AI, it becomes possible to quickly identify which data points are anomalies, despite the volume. This functionality—also available in BI4Web—lets users rapidly detect any irregularities in the data. With this optimization, more time can be spent on strategic tasks.

                    A Specific Feature: AI-Generated Dashboards

                    AI’s generative capabilities go beyond the images, texts, and videos flooding social media. In data analysis, AI also shows its strength by generating dashboards from prompts.

                    This way, users can create dashboards that meet all the company’s standards without needing support from the development team. This feature will be available in the upcoming version of BI4Web.

                    AI-Enhanced Analysis

                    Data analysts are no longer alone. AI can summarize large amounts of information in just a few words, highlighting the most relevant aspects and making them easier to understand. This is another feature coming to BI4Web in its next version. As a result, analysts are much less likely to overlook key insights in their data.

                    AI is becoming a vital tool in data analysis, directly impacting a company’s competitiveness by improving analytical capabilities, response speed, and precision.

                    If you’d like to experience AI integrated directly into your BI, contact us and request a free trial to see firsthand the benefits BI4Web can bring to your business.