Here's how you can cultivate a logical mindset in approaching complex problems in Business Intelligence (BI).

Sempre comento que a curiosidade é a base da análise de BI. Não tenha medo de fazer perguntas, mesmo que pareçam simples ou óbvias. Essa abordagem ajuda a explorar diferentes ângulos de um problema e descobrir insights que não são imediatamente evidentes.

Here are 3 pointers to help you cultivate that mindset: Practice Deliberate Calm: When faced with uncertainty or challenging situations, it’s crucial to remain calm. Next time you encounter a complex BI problem, pause, and deliberately calm yourself. Consider alternative approaches before diving into solving it. Expand Your Perspective: Avoid falling into the trap of binary thinking (right/wrong, good/bad). Instead, challenge your understanding of the problem. Ask questions like, “What am I not seeing here?” and "What else might be true Decompose Complex Problems: Break down intricate BI problems into smaller, manageable parts. This process, known as decomposition, allows you to analyze each component step by step.

Everyone starts out of curiosity, then that curiosity penetrates you deeper and deeper allowing you new oportunity and challenges. It all starts with a challenge to learn something new and to find in what you are exploring.

"Unearthing Hidden Gems 💎 in Your Data" In BI, curiosity isn't a luxury, it's the ignition switch. By asking "why" and relentlessly exploring data patterns, you unlock deeper insights. Approach challenges with a sense of discovery – the more inquisitive you are, the more hidden gems you'll unearth. This curiosity fuels better questions, leading you to the data that unlock true value. Based on my experience, fostering a culture of curiosity within BI teams fosters innovation and drives data-driven decision-making across the organization.

🧠 Understand context: Dive deep into the problem's background and its impact on various aspects of the business. 🔍 Gather data: Collect relevant data points and analyze them to uncover patterns or correlations. 🛠️ Utilize tools: Employ business intelligence tools like Tableau or Power BI to visualize data and identify trends. 🔄 Iterate solutions: Develop multiple hypotheses and iterate through them to find the most logical and effective solution. 🌟 Example: When faced with a decline in sales, I delved into sales data, used Tableau to visualize trends, and iteratively tested strategies, ultimately implementing targeted marketing campaigns that boosted sales. 🧠🔍🛠️🔄

My approach is to question my output data to see if it’s logically correct based on business context being analyzed. Then perform data validation with a familiar sample of the dataset being analyzed.

Ser curioso abre portas!!!! Mas mais importante que ser curioso, é ser resiliente. É se deparar com algo complexo e desafiador, aprender, executar, testar, falhar e começar novamente. Aprender com error e continuar… isso aqui é uma das grandes lições que vivi sendo curiosa.

Logical mindset will come through conscious active practice. 1. Break apart the complex problem - all problems are combinations of smaller puzzles each contributing to the challenge/solution 2. Identify the top 3 sub-problems - 80% of the solution to a complex problem will be contributed to 20% of the smaller puzzles you are resolving 3. Get Technical But keep end goal always in sight - Remember to take a step back after solving each sub problem and look at overall complex problem All that will remain is the business context. Business context and story will come from knowledge about the problem and will have to be added onto the business intelligence which you have performed.

In the realm of Business Intelligence (BI), curiosity acts as a powerful catalyst for innovation and effective problem-solving. By cultivating a mindset that thrives on asking questions and unraveling the 'why' behind data patterns, you open doors to deeper insights and meaningful discoveries. Embrace each BI challenge with a sense of wonder, unafraid to venture into uncharted territories of data exploration. The more curious you are, the more likely you are to spot intricate patterns and hidden opportunities within your data. This inquisitive approach not only helps you ask the right questions but also guides you to unearth the most valuable data, leading you towards actionable insights that can drive significant business impact.

Curiosity is an essential component in Business Intelligence (BI) analysis, allowing for the exploration of different perspectives and uncovering hidden insights within data. By adopting a curious mindset, BI professionals can ask probing questions, challenging their own perceptions and leading to deeper discoveries. This approach not only drives innovation but also fosters a data-driven decision-making culture throughout the organization.

In addition if-then statements and logical operators, you can build new concept for data statements in BI. With Boolean logic you can combine multiple conditions when filtering or query data. Implication in two logical relationship between new oportunities can help you to drive into the your data. Condition probability like a measure you can occurring into the next level into variables in your data. My mastering in these logical concepts. you can enhance your abilities to analyzie data, identify patterns or new opportunities in Bussines Inteligent.

BI is all about logic building at its core and not about using different tools/query languages. In order to solve any BI problem -Think what you want to convey as the final result -Build a logic in your head; how can I use the data available to me to reach there -If its too complicated try to start with a pseudo code -Once you understand the logic convert the logical steps into script/query. Don't worry about efficiency right now -Take a sample data and replicate the logic in your script. Get to your final output/result. Hurray! your logic is sound! -Now work on your script/query to optimize it and make it more efficient

Nestes 12 anos trabalhando com BI, é muito direta a ligação entre as regras do negócio da organização, e todo o dinamismo envolvido, e a respectiva lógica que precisa atender a estes anseios, assim como ser flexível para acompanhar as mudanças frequentes, aliás, a única certeza que temos! Não importa a linguagem, ou ferramenta, mas sim o nível de adaptabilidade da solução que será desenvolvida!

Un aspect qui manque de mon point de vue, c'est la modélisation des données. C'est primordial de savoir faire la différence entre une donnée de fait et une donnée de référence. On aggrege des faits, on historise un référentiel. L objectif étant de pouvoir rejouer un traitement ou un dashboard dans le passé et que le résultat soit le même.

To create a logical mindset in BI problem-solving, you need to learn logic. Professionals who understand logical principles can break down difficult problems into doable parts, look at how data points relate to each other, and come to logical conclusions. BI professionals can approach problems in a planned way, find patterns, and come up with good answers by improving their deductive and inductive reasoning skills. Mastering logic also improves the ability to think critically and make decisions. This helps professionals manage the complex world of business intelligence (BI) with accuracy and confidence, which leads to success in making decisions based on data.

Logical thinking allows us to create associations and consequently build a framework of reasoning. We cannot forget that it is the basis of programming. Therefore, when we talk about data analysis and visualization of insights for business, we arrive at the importance of valuing logical reasoning.

In the realm of Business Intelligence (BI), a firm grasp of logical principles is paramount. Get comfortable with key constructs like if-then statements, logical operators, and causality. These tools empower you to construct robust arguments and hypotheses based on your data. For instance, logical operators can help you sift through data to pinpoint specific trends. When analyzing sales data, you might employ an if-then statement to investigate how promotional campaigns correlate with spikes in sales numbers. Understanding these logical frameworks enhances your ability to extract valuable insights from data, enabling you to make informed decisions and drive business growth.

In the realm of Business Intelligence (BI), a firm grasp of logical principles is paramount. Get comfortable with key constructs like if-then statements, logical operators, and causality. These tools empower you to construct robust arguments and hypotheses based on your data. For instance, logical operators can help you sift through data to pinpoint specific trends. When analyzing sales data, you might employ an if-then statement to investigate how promotional campaigns correlate with spikes in sales numbers. Understanding these logical frameworks enhances your ability to extract valuable insights from data, enabling you to make informed decisions and drive business growth.

Understanding logical principles is crucial for Business Intelligence (BI) analysis. By familiarizing themselves with conditional statements, logical operators, and concepts of causality, BI professionals can build solid arguments and hypotheses around their data. Additionally, mastering boolean logic enables the combination of multiple conditions for data filtering or querying, while understanding conditional probability opens up new analysis possibilities. These concepts not only enhance analytical skills but also aid in uncovering valuable insights for strategic decision-making in BI.

Ferramentas de BI fazem análises preditivas, que nada mais é do que utilizar técnicas de aprendizado de máquina e estatísticas avançadas para prever tendências futuras com base em dados históricos. Essas ferramentas de BI incorporam algoritmos preditivos que podem ser aplicados diretamente nos dados, permitindo que os usuários visualizem previsões e insights acionáveis.

Analyzing data methodically is crucial in BI for complex data sets. First you can identify object before you diving into data, use specific question and try to answer the problem solving and gathering the issue. With data preparation, you can dive deeply in your objectivites and hypotheses and determine your data patterns, organizing and removing necessary issue. Use statistical technique and metods to analyze your data visually represent with tools that allow you to explore your data.

You can not build any BI solution with only logic and optimized script. You need to have clean and reliable data. In most cases, you won't have that so it is your first task to prepare your data for success. This is what I typically do when I am using any new data source -Look for nulls in columns. If there are too many nulls try to look for distribution and see if its viable to replace those nulls with appropriate values -Look for data types of all the columns. Try to fix them and make is universal across the data source (Ex. datetime columns, currency columns) -For columns with categorical values, do a simple group by and see how the data is distributed and if its reliable or not -Spend enough time on data cleaning before scripting

A qualidade dos dados é crucial para resultados confiáveis. Isso envolve processos de limpeza, deduplicação e validação de dados. A governança de dados, por sua vez, refere-se a políticas e procedimentos que garantem a segurança, privacidade e uso adequado dos dados. Ferramentas de BI modernas incluem funcionalidades para monitoramento da qualidade dos dados e implementação de políticas de governança.

When fixing BI problems, looking at data helps you think more logically. It teaches professionals how to organize knowledge, find patterns, and draw conclusions that make sense. This process teaches discipline in dealing with difficult issues and encourages structured decision-making based on facts and reasonable conclusions. By getting better at data analysis, BI professionals develop a methodical way of handling problems that helps them deal with tough problems in the BI field.

To cultivate a logical mindset in approaching complex problems in Business Intelligence (BI), start by breaking down problems into smaller, manageable components. Develop a structured approach to analyze data, identify patterns, and draw conclusions. Utilize logical reasoning and critical thinking to evaluate different solutions objectively. Practice problem-solving techniques such as root cause analysis and hypothesis testing to uncover insights. Continuously refine your analytical skills through practice, learning from both successes and failures. Finally, stay open to feedback and collaboration, as working with others can often lead to more robust and innovative solutions.

Business Intelligence (BI) hinges on meticulous data analysis. To develop a logical approach, practice dissecting data systematically. Begin by deconstructing intricate datasets into simpler, more digestible components. Search for trends, irregularities, and connections. Utilize tools such as pivot tables or functions in spreadsheet software to organize and sift through data. This approach enables you to pinpoint fundamental structures and associations within the data, which is essential for logical, effective problem-solving.

A careful and thorough analysis of data is crucial for success in BI. While meticulous analysis allows for the identification of patterns and valuable insights, proper data preparation ensures its reliability and accuracy. Additionally, BI professionals are encouraged to adopt a logical approach when dealing with problems, organizing knowledge, identifying patterns, and making structured decisions based on facts and reasonable conclusions. These texts highlight the importance of a methodical and disciplined approach to data analysis in BI, aiming to tackle complex challenges and generate meaningful insights for organizations.

Data analysis is more than a process; it is an art that requires curiosity and precision. By splitting complex data sets, we are not only simplifying information, but also discovering patterns that shape the future of business. Tools such as pivot tables are the brushstrokes that reveal the complete image, allowing us to paint strategic scenarios based on concrete evidence. The real magic happens when we turn data into actionable insights that drive business success.

It’s crucial to critically assess and challenge assumptions to foster a logical mindset in BI. This practice prevents erroneous conclusions based on unverified beliefs. For example, if you presume that customer satisfaction is driven solely by product quality, you might overlook other influential factors, such as customer service or delivery efficiency. To avoid such oversight, systematically test each assumption against actual data. This rigorous approach ensures that your analysis remains grounded in verifiable information, leading to more accurate and logical decision-making. By consistently applying this method, you can enhance your problem-solving skills and make more data-driven decisions in BI.

We all have our assumptions and biases. From my point of view it is crucial to set aside these biases while working on a BI project. I think central BI teams who are not dedicated to the needs of a specific department of the organization would do better in this part because they would work with multiple teams with different interests and assumptions. So they can see and work with a diverse set of perspectives not going wrong in the direction of biases.

When solving BI problems, it's important to develop a logical attitude by questioning assumptions. It makes professionals question their biases, preconceived ideas, and what they think is generally true. This encourages critical thought and objectivity. By questioning assumptions, BI professionals avoid making mistakes in their thinking and look at things from different points of view, which leads to more thorough analyses and stronger findings. This way of thinking promotes being flexible, open-minded, and ready to think about all the options. This makes it easier to solve problems when dealing with difficult BI issues.

In the realm of Business Intelligence (BI), assumptions can present significant hurdles to logical reasoning. It's crucial to challenge these assumptions to avoid veering off course. Continuously test hypotheses against the available data. For example, assuming customer satisfaction hinges solely on product quality might overlook other factors like customer service or delivery times, which could also impact satisfaction levels. By rigorously scrutinizing each assumption, you guarantee that your conclusions are grounded in data and align with logical reasoning.

Go for logical reasoning, by developing an assumption, then go for testing the hypothesis against gathered, analysed data. Test your assumptions, validated by data.

Presenciei e até já participei de soluções lindas, tecnicamente impecáveis, com DataViz impecável, que não conquistaram os clientes. Isso acontece em 100% dos casos onde o viés do desenvolvimento está carregado pelo lado técnico, mas distante da dor, da real necessidade do cliente! Para que isso não ocorra, o processo de empatia precisa ser pleno e, nos times que lidero, faço questão que essa etapa seja de viver literalmente o dia a dia do cliente, passando ao menos 2 semanas sentando ao lado, vivendo a dor... assim o desenvolvimento tende a ser mais efetivo!

As with every other computing problem, BI problems also benefit a lot with iteration. There was a problem, you tried a solution and it worked, but that is not the end. In order to make that solution the best solution, you need to iterate and improve. as BI at its core is logic building, there is always the opportunity to iterate your solutions and come up to the new best solution. As there is no wrong approach in BI, its in your hands to keep on iterating and find the best approach for the problem at that time.

My go to technique to solve complex problems be it in BI or other software development is to breakdown the work/problem into smaller pieces - something that can be solved in a 2 week sprint and move the needle. The next step is to gather feedback/reflect on what worked and where things need to improve. The sooner you can create a report or data set - even in a scrapy MVP format ready and put it in end user's hands to see what worked or not the better the end state is going to be.

Ser analista de dados é automaticamente estar sempre em revisão com o que construiu para seu cliente. Haverá sempre mudanças pois o fluxo de informações e do setor que esta analisando pode mudar instantaneamente. Ter estruturado a etapa de ajustes e Sprints para entregas de cada aba é essencial para o bom funcionando da área de Analytics.

Iterating on solutions is an important part of developing a logical mind when handling BI problems. It includes improving and refining solutions in a planned way by going through cycles of analysis, experimentation, and change. This method helps professionals develop a disciplined and methodical way of thinking by teaching them how to objectively review results, find ways to make things better, and change their methods as needed. By using iteration, BI professionals build resilience, adaptability, and a dedication to constant growth. This leads to success in solving difficult BI problems in a clear and logical way.

I think creating robust pipelines can help us iterate through solutions as fast and accurate as possible. If we have a good pipeline, the BI team won't need much effort in responding to changes in business need and trying new things.

I think having a plan to get feedback from the users of the BI solution can help identifying what parts of our approach could be improved. Also reviewing the solutions with other data teams like data science can help us find our biasses and fallacies.

With continuous learning, you can play a crucial role in conveying insights derived from data analysis to stakeholders in a meaningful way. Every story should have a clear starting point, relevant data, highlight key, significant trends,business goals or objectives, because every story offering actionable recommendations on next step based on the insights.

Infusing a qualitative mindset into BI means delving beyond numbers to understand human stories. Context, engage stakeholders, and qualitative research. Cultivate empathy and creativity to uncover richer insights and make more impactful decisions. 3 key points: -THE HUMAN ELEMENT -CONTEXT BESIDES DATA -EMPATHY -CURIOSITY

É fundamental estar atento às inovações na área de Business Intelligence (BI) e não hesitar em apresentar novas ideias aos clientes. Essas inovações podem ser cruciais para alavancar o "cross sell" e expandir os serviços de BI para outras áreas. É importante acompanhar de perto as atualizações e novidades, como as do Power BI, e buscar validar e implementar aquelas que agregam valor ao cliente. Demonstrando essa proatividade, evidencia-se um compromisso contínuo em oferecer soluções além do esperado, o que fortalece o relacionamento com o cliente.