Posts in category: Business

Having a grasp of key data analysis concepts is becoming a challenge for more and more professionals. This knowledge is no longer exclusive to analysts or data scientists. The number of roles requiring data-driven decision-making is increasing, meaning that more leaders and employees need to understand these key concepts. In this article, we’ll walk you through 5 key concepts that will help you start better understanding the world of data analysis.

1. What’s the difference between structured and unstructured data?

This classification of data is pretty simple and relies on data structure. As for structured data, it must be organized into columns, and each column must have a header with clear information about what it contains.

A popular example for most professionals is the use of Excel spreadsheets, but SQL databases are also considered structured. Their format enables easy data handling.

Unstructured data, on the other hand, doesn’t follow this structure. It includes free text, images, videos, or audio in formats like Word, PNG, PDF, etc. It also includes social media comments and emails.

In terms of volume, unstructured data makes up 80% of business data, and analyzing it requires advanced tools. Some of these technologies include machine learning, natural language processing (NLP), and more.

As for their use, structured data is crucial for calculating concrete business figures like sales or transaction counts. Unstructured data is useful for qualitative analysis, such as opinions, social media sentiment, voice recordings, etc.

2. What is an ETL?

After learning the first of the key data analysis concepts, let’s move on to a well-known acronym: ETL. More than just a process that Extracts, Transforms, and Loads data into a defined destination, it’s important to understand its significance in the face of growing data volumes within companies.

When working with data, we need it to be available, high-quality, and non-redundant, all while saving time and avoiding human errors. You might relate if you’ve ever spent hours in Excel cleaning data it’s time-consuming and error-prone.

An ETL process is automated and scalable. It extracts data from different sources, transforms it, and then loads it into a data warehouse for analysis. Here’s a more detailed breakdown:

• Extract: Connects to data sources such as CRMs, ERPs, production systems, or APIs.
• Transform: After extraction, the cleaning and normalization process begins, ensuring data meets quality standards.
• Load: With clean and normalized data, the ETL loads it into data warehouses.

Once complete, the data is ready for use in business intelligence tools.

3. What is a dashboard used for?

You’ve likely seen a dashboard in result presentations or company reports. But beyond just presenting data, a dashboard helps track key metrics for your business or department.

A dashboard summarizes the most important information on one screen visually and clearly, using charts, tables, and other visual elements. It should have just the right amount of information — not too much — to make visual analysis effective.

A key feature of dashboards is that they can show historical data or real-time information. The latter is only possible with business intelligence platforms, like BI4Web, that connect to data sources in real-time.

Main advantages:

• Simplifies data: Turns large volumes of data into easy-to-understand visuals.
• Centralizes information: Monitor your KPIs all in one place.
• Supports quick decision-making: If something goes wrong, you’ll see it and can act immediately.
• Customizable: Different departments (like marketing or sales) can have their customized dashboards.
• Real-time updates: Ideal for making immediate adjustments.

Example: If you run an online store, your dashboard might show today’s sales, best-selling products, and the number of current website visitors.

4. KPI (Key Performance Indicator)

With that many metrics available, it can be overwhelming to focus on what matters. That’s why it’s crucial to define Key Performance Indicators (KPIs), metrics that help measure performance and the overall health of your company or department. They are essential tools for measuring the accomplishment of strategic objectives.

How to define this key data analysis concept effectively:

• They must be specific, measurable, and aligned with goals, for example, conversion rate, cost per acquisition, and customer satisfaction.
• Choose KPIs that align with your company’s strategy: It’s better to track 5 meaningful metrics than 100 irrelevant ones.
• You can complement structured data, such as sales volume, with unstructured data, like customer sentiment analysis.
• Use them daily for effective and proactive management.

5. What does it mean to be data-driven?

You’ve probably seen companies describe themselves as “data-driven,” but do you know what it truly means?

Essentially, a data-driven company makes decisions based on data rather than gut feelings or subjective opinions. It doesn’t mean human experience is irrelevant, but prioritizing data when making decisions provides better results.  

In a data-driven organization, data analysis is the first place to turn when making any decision, regardless of scale.

To make this possible, the company must cultivate a data culture and ensure employees have access to dashboards, predictive analytics, and other business intelligence tools. It also requires collaboration between business users and technical teams, who form a powerful duo: business experts contribute domain knowledge, and technical teams bring data expertise.

In Summary: 5 Key Data Analysis Concepts Every Professional Should Know

1. Structured vs. Unstructured Data: Structured data (like Excel) is organized and easy to analyze; unstructured data (like images or emails) is less organized but highly valuable.
2. ETL (Extract, Transform, Load): The process that prepares data by cleaning and organizing it for analysis.
3. Dashboard: A clear and visual screen that shows key data points using graphs and charts.
4. KPI (Key Performance Indicator): Metrics that show whether goals are being achieved or not.
5. Data-driven Decision Making: Using real data — not guesses — to make better business decisions.

We’re confident these concepts will help you better understand and make the most of the data analysis world. If you want to explore platforms like BI4Web, which make using data for decision-making incredibly easy, we suggest two things:

First, visit our free demo by clicking here.

Second, request a free trial so you can explore all the benefits of BI using your data.

Using storytelling is highly effective in improving message retention among audiences. For this reason, it is widely used in advertising strategies. However, it’s not the only field where it can be applied. In the business world, it helps enable different stakeholders to interact more easily with the key data from each department within the company.

Where does storytelling’s effectiveness come from?

The main reason for its effectiveness relies on something deeply human: the act of telling stories. That’s why, even in primitive societies, stories served as powerful vehicles for transmitting knowledge, beliefs, and ideas. Despite all the technological advancements we’ve experienced, stories continue to be powerful tools for sharing information.

That’s why, when it comes to improving information retention among stakeholders, it’s best to move away from random data—mainly because random data is more difficult to remember.

Data storytelling, also known as narrative data, is a technique that uses data to tell a story. In other words, it’s a way to communicate the information extracted from data analysis through a story in a clear, coherent, and concise manner. Data storytelling helps the intended audience retain the information and enables the company to achieve more persuasive and impactful results. Moreover, using data increases credibility and builds trust.

It’s important to distinguish between data storytelling and data visualization. Data visualization represents data graphically, using different visual tools to make the analysis easy to understand. However, it lacks the narrative thread that data storytelling provides. This doesn’t mean one is better than the other—they serve different purposes.

Essential Elements of Data Storytelling

With the above in mind, it’s important to understand the key elements to consider when using storytelling in your data communication strategy: data, narrative, and visualization.

• Data: This is the heart of what we want to convey. It must be properly validated, as it provides the foundation for reliable information.
• Narrative: This provides the context in which the data becomes relevant. It’s crucial to choose narrative elements that bring the audience closer to the data.
• Visualization: This involves selecting specific elements that bring the data and narrative to life. It includes everything from charts to the font used to present the data.

How to Apply Data Storytelling

Here are some steps you can follow to implement it:

1. Define the objective: This step is key, as it builds up criteria for decision-making throughout the process. It’s important to know what you want to convey and what the key points will be.
2. Know your audience: Understanding the audience helps you identify what they already know and provides the necessary context to craft a relevant and powerful story.
3. Gather data: Choosing data is easier when the previous steps have been clearly defined. Balance is crucial here: the amount and relevance of the data will influence the effectiveness of the storytelling.
4. Organize the data: Once selected, the data must be structured. This can be done chronologically, hierarchically, etc. This step helps make the data more understandable and memorable.
5. Create the narrative: It’s helpful to follow the basic Aristotelian structure: introduction, development, and conclusion.
   • The introduction provides the topic and context.
   • The development shows the relationship between the data.
   • The conclusion presents the main message or takeaway.
   • It’s also recommended to include comments and explanatory notes.
6. Data visualization: Create charts and visual aids to help the audience better understand the data. Visualizations should always be easy to interpret, and the graphics should support the narrative.
7. Evaluate the results: Revisit the objectives defined at the beginning, as they provide the framework to assess success and identify areas for improvement.

In addition to following the steps above, it’s crucial to use the insights gathered during the evaluation phase to make the data storytelling message increasingly relevant within the company. This could range from creating a more enriched context to adjusting the criteria used to select which data to present.

To conclude, having the right tools is essential. That’s why we invite you to try BI4Web—the business intelligence tool with the most native graphical representations. This allows you to choose the best visual option to make your data easier to understand and strengthen data-driven decision-making in your company. Request your free trial and enjoy 15 days of access to BI4Web.

The benefits of AI for retail are numerous. In this article, we talk about those with the highest impact on the company and how to apply them.  

It is worth highlighting that a key element of retail success is selling high-turnover products—those that sell quickly—while also achieving the highest possible profit margins. To address both aspects, AI enhances in-depth retail data analysis, allowing decision-makers to work based on data-driven insights.

How Does an AI-Powered Stock Strategy Work?

An essential part of the stock strategy involves forecasting. Which products should be ordered from suppliers? In what quantities? How often should restocking occur? AI helps decision-makers answer these and other key questions.

Tools like the DataGate Orchestration Platform can analyze collected data and provide insights on these questions. How do they do this? AI-powered solutions analyze data from sales, supplier orders, returns, and more. By doing so, they identify patterns, trends, and anomalies, which they then use to make accurate forecasts.

It is important to note that both the quality and quantity of data directly impact the accuracy of these forecasts. Poor-quality data can distort results, while insufficient data can lead to imprecise predictions due to a lack of information.

How Can AI Enhance Pricing Strategy?

Another crucial aspect of retail operations is pricing. What price should be set for each product? How often should prices be updated? What should the profit margin be? AI-powered solutions can help answer these questions by analyzing data and forecasting market trends, improving the ability to create and execute a successful pricing strategy.

Can AI Improve Internal Processes?

Yes, AI can help optimize various processes. For example, it can make warehouse management more efficient by identifying products based on their turnover rate and determining the ideal placement to minimize unnecessary movement.

Additionally, AI can automate the review of supplier invoices to ensure they are error-free. It reduces manual labor for employees, allowing them to focus on more strategic tasks.

Another process that benefits from AI is customer service. AI-powered chatbots can enhance customer interactions by learning from past interactions and providing personalized assistance. Unlike traditional chatbots, AI-driven versions remember past conversations, saving time and delivering more relevant responses to customer inquiries.

Personalized Customer Experience with AI

AI also enables retailers to offer a personalized shopping experience by analyzing customer behavior patterns. This allows businesses to create tailored promotions at the moment when a customer is most likely to make a purchase, leading to higher retention rates and increased average ticket size.

Conclusion

AI has the potential to transform the retail industry by analyzing large volumes of data, automating processes, and improving operational efficiency. It enhances the ability to provide customers with highly relevant and personalized experiences while optimizing internal processes to boost competitiveness.

If you want to implement these and other AI benefits in your retail business, you need a partner like RCM Software. Contact us—we would be delighted to help you maximize the advantages of AI for your retail operations!

Business intelligence has become key to improving the company’s productivity and competitiveness. One of the main advantages is that it facilitates business decision-making by making information available clearly and visually. It makes it easier to detect opportunities, needs, trends, and problems.

Although many of the benefits mentioned above are widely known, many companies do not take advantage of the business intelligence potential. This results in the chosen solution operating inefficiently and ending up becoming underutilized. In this scenario, BI value will be called into question for not delivering the expected benefits for the investment.

Can you identify the inefficiencies that affect your Business Intelligence?

The ability to answer the above question results in the capacity to take advantage of the benefits of business intelligence. That is why we present 3 points that will help you identify the inefficiencies that affect your business intelligence and correct them.

Unnecessary accumulation of data

The volume of data generated by people and companies is increasing, so it is likely that data accumulation without a clear strategy will happen. It impacts costs since having data stored and available generates costs that are hardly attributable to a process inside the company. In other words, accumulated data does not provide any benefit to the company but does generate expenses.

In a similar scenario, the stored data has a purpose but is not well organized, which makes finding information difficult. It directly impacts the company´s competitiveness because its response time becomes inefficient.

It is crucial to have an updated data strategy that meets the needs of companies and their stakeholders. As a result, only data that responds to some need or interest of the company will be stored. Likewise, they will be correctly hierarchized so that the storage and availability strategies are the most cost-efficient.

As a direct benefit, it will be much easier to find the data you need promptly, and you will eliminate all inefficient costs from your enterprise data management.

Lack of integration between systems

There is an interesting parallel between the accumulation of data mentioned earlier and the accumulation of tools and platforms in companies. This similarity is because both accumulations occur due to a lack of strategic vision guiding the decisions made.

In the case of software, the enterprise digital ecosystem is growing without a clear north, so commonly, there are not all the necessary integrations for information to flow efficiently. One of the main symptoms is the duplication of effort since it must do redundant tasks of data compiling, cleaning, and validation. As a result, data processing times and the likelihood of data having mistakes increases. This directly impacts BI’s ability to display complete and quality information that facilitates decision-making based on data.

As in the previous point, it is important to have a strategy with a complete view of the data that the organization works with, how data flows, etc. As actions to be taken, you may establish processes with ETL and choose BI tools that have the necessary integrations.

At a more global level, we can say that you should strive for centralized data management and technology infrastructure that allows interoperability between systems, that is to say, that can exchange data securely and automatically, regardless of geographical or organizational boundaries.

Lack of training

As with any organizational change, data-driven decision-making requires people’s involvement. It is inefficient to acquire the most advanced technology in data management and business intelligence if the work team is still using spreadsheets because it is the option they feel more comfortable with.

To combat this inefficiency, it is important to continuously train all levels of responsibility so that information flows properly. This ranges from the person who enters the customer’s data into the system to the senior executive who makes decisions based on the company’s data. Incorporating the data culture into the organization’s DNA will result in taking advantage of the tools and solutions that the company acquires.

In conclusion, we can highlight the impact that resolving the three points mentioned above has on companies on an ongoing basis. While it is crucial to have an initial strategy with integrated data management, it is also vital to ensure it remains current and timely.

If you want to learn how to manage data and get the most out of business intelligence, request a free trial of BI4Web and discover all its advantages.

This year, we are participating again in a key event for the global tech scene: MWC 2025 in Barcelona.

During MWC 2025, visitors can explore all the latest updates to our catalog of business tools and solutions that we have been working on at RCM Software. At our booth, you’ll find a dedicated team ready to explain the full functionalities of our products and how they help businesses achieve their goals.

BI4Web: Business Intelligence for Every Company

The latest version includes artificial intelligence to enhance its predictive analytics capabilities. Discover all its advantages in the webinar—click here to watch it.

DataGate Orchestration Platform

A comprehensive platform that helps you manage all your company’s data centrally and efficiently. No matter where your data is stored, DataGate ensures complete availability and straightforward management. Additionally, its AI features enhance businesses to take advantage of data.

DataGate GDP for Progress® OpenEdge

Our white-label framework enables Progress® OpenEdge developers to continue working on the web without needing to learn HTML, CSS, or JavaScript. Its drag-and-drop interface makes the development process quick and intuitive.

We look forward to seeing you at MWC 2025 from March 3rd to 6th at Congress Square, Fira Barcelona. If you’d like to schedule a visit, click here.

The selection of artificial intelligence for SMEs has become a critical decision for its impact on competitiveness in the current market. According to the BARC & Eckerson Group survey The Future of BI & Analytics: Adopting Generative AI for Analytics: Early Trends, Lessons and Best Practices, only 11% of the companies have fully implemented artificial intelligence. The other respondents stated that they were in the process of implementing it, evaluating options, or just talking about it. It is noteworthy that only 13% of companies do not have any type of AI currently working in their processes.  

As can be deduced from the study, there is still a long way to go when it comes to artificial intelligence in companies. It is why we share with you this guide in which you can find tips to overcome the challenge of choosing among all the available options on the market. 

As a preliminary step, we recommend you go through a clear definition of the business needs.

Types of AI and its benefits

Our starting point is to present the different types of AI available in the market. With this in mind, you can start to have a clearer picture to make informed strategic decisions.  

There are different classifications for artificial intelligence, but for this guide, we will use the next one: 

• Machine learning. It emulates the human learning process, allowing computers to learn from training with data. Its uses can range from fraud detection in financial services to customer service through chatbots with personalized answers based on previous interactions.  
• Deep learning allows you to analyze images with artificial intelligence. This way, it can identify faces or biometric patterns in images and videos. A very important application is the identification of people for civil authorities as well as the identification of manufacturing parts in the factory quality assurance processes. 
• Generative artificial intelligence can create texts, audio, images, or even videos with a quality that makes it harder to differentiate its results from other sources. Some of the more popular models work with a chat interface that receives the prompt (query) and delivers the result in the requested format.

The benefits of artificial intelligence for SMEs are diverse and vary depending on the chosen technology, the digital maturity of the company, and the scope of implementation. Here you can find some of them:

• Automation of routine tasks, for example, fraud detection in financial transactions. 
• Creation of analytical summaries of large volumes of data to have a more efficient response and to improve competitiveness. Learn about the advantages of AI in BI in our latest webinar. 
• 24/7 customer service through digital agents that respond to chats, e-mails, and other enabled digital channels. It impacts earning customer loyalty since it provides constant support and personalization in the response.
• Enables customization of products and services portfolio. It helps companies to stay relevant to increasingly informed and demanding customers.   

How to choose the AI that your SME needs

After seeing the types of AI in the market and some of its applications, we recommend you follow the next steps in your decision process.

Identification of processes

The first step is to examine in detail the current operations to determine which processes are ideal candidates for one or more of the AI applications. An example of this could be the invoicing process in which it is needed to make image recognition with AI to enter the data into the ERP. This increases speed and decreases human error in the scanning process. Explore other processes such as documentation and data management, payment and invoicing processes, administrative tasks or report analysis. When you finish, also identify the relationship between the processes to have a clear picture and to create an achievable action plan.

Make a second check before proceeding since, according to McKinsey’s study “The state of AI in 2023: Generative AI’s breakout year”, AI has the potential to automate between 60% and 70% of the time employees spend on routine tasks. 

Available resources analysis

Once the previous step has been completed, it is time to evaluate the existing infrastructure and capabilities, this includes technological resources (current infrastructure, data quality, etc.), human resources (AI experience, willingness to change, abilities, etc.), and lastly financial resources. This last one is crucial since it determines the speed of implementation.

Evaluate the data quality and reliability.

As mentioned above, we emphasize this point since the quality and reliability of AI tools largely depend on the quality of the data used to train them.  

If you don’t follow this step correctly, the result of the implementation can be drastically affected and even turn into a waste of time and money. 

Definition of objectives

As in any project, it is vital to establish clear and measurable objectives for AI implementation. Objectives should be well-aligned with business strategies and have well-defined KPIs. This way you will be able to have a proper measurement of the project progress from the beginning.

Technical criteria

In this respect, it is important to count on the support of your company’s technical team, since they are the ones who can know in detail what is most convenient for the company’s digital ecosystem.  

• Scalability and compatibility: key factors for SME growth. These two aspects ensure that the AI investment is sustainable over time, grows with the company, and adapts to challenges of growth. Your infrastructure team should be heavily involved in this point so you don´t overestimate or underestimate the expected needs.   
• Ease of integration and security: Covering this point you can ensure that your digital ecosystem has a secure data flow both inside and outside the organization, depending on the nature of each company. Besides avoiding security breaches, you can rest assured that all the digital ecosystem components can communicate with each other.
• Hardware and software specifications: If you need to acquire new hardware or software for the implementation of AI, do not forget to verify that their technical specifications are compatible with the needs of your digital ecosystem and the AI solution you are implementing.

Return of Investment

Finally, it is worth noting that the investment in artificial intelligence for a business represents a significant financial commitment that requires careful planning. Make sure your budget allocation covers upfront costs such as licenses, implementation, training and infrastructure, and other ongoing expenses such as support and maintenance.

Involve your team

Choosing the right artificial intelligence tools for SMEs is not an easy task, but following the right steps can represent a milestone in the history of each company. Involve your team to make all the steps easier to follow and less resistant to change. Implementing AI in SMEs is not just a technology change but an evolution of mindset. It implies humans working in tandem with artificial intelligence.

The benefits of AI for manufacturing have been increasing as its implementation in the industry advances and as manufacturing companies’ digital maturity grows. Some examples include process automation and supply chain optimization. This is completely transforming the way goods are produced, and here we’ll explain the benefits this transformation brings to the manufacturing industry.

Improved operational efficiency

AI’s analytical capabilities surpass those of any human, especially when it comes to large volumes of data generated by companies. This ability allows for real-time detection of bottlenecks, workflow optimization, and the prediction of issues before they occur. As a result, downtime is reduced, and productivity is maximized.

Predictive maintenance

AI enables timely maintenance, meaning it can be performed at the precise moment, in a planned manner, and in the shortest possible time. This way, unplanned production interruptions become increasingly rare. It also results in cost reduction, as the useful life of machines is extended.

Quality and process control

Anomalies and defects can go unnoticed by the human eye, making AI crucial in the quality control processes that companies undertake for their products. AI achieves this through automation and its hallmark precision.

Responding to market personalization

Mass production of customized products requires a production model that is both cost-effective and flexible. AI enables the shift from a rigid system to a more adaptable one, allowing for dynamic adjustments in production settings. This ensures profitability and customer satisfaction.

Supply chain optimization

The complete vision provided by AI in analyzing company data helps in more effective planning and reduces material fluctuations that could slow down or halt production and delivery times.

Cost reduction

As noted earlier, more efficient resource usage directly impacts costs, as the same resources yield greater results. This reduction is the sum of all efficiencies achieved across the areas where AI is implemented within the company.

Safer workspaces

The analysis of data from sensors or cameras can highlight behaviors that increase workplace risks. In some cases, AI can even automatically stop a machine if the operator isn’t wearing all the required personal protective equipment.

Conclusion

The benefits of AI are transforming the manufacturing landscape by making processes more efficient, personalized, and safer. From intelligent automation to predictive maintenance, companies that adopt AI are better equipped to face modern market challenges and seize new growth opportunities. In an increasingly competitive environment, integrating AI into manufacturing processes is not just an advantage—it’s a necessity.

Adopting AI not only boosts productivity and reduces costs but also drives innovation, allowing companies to stay at the forefront of the industry. If you want to learn how to implement AI in your company, contact us, and we’ll be happy to assist you.