What BI tools are essential for an entry-level position?

Power Query sem duvida é a ferramenta primordial para quem busca uma vaga de nível júnior. Ele pode te ajudar tanto em Excel quanto em Power BI, consegue se conectar a inúmeras fontes de dados, incluindo arquivos de texto, APIs, CSVs e JSONs. Você também consegue fazer uma limpeza nos dados, alterar tipagens, mesclar tabelas e realizar algumas funções de ETL nele. É realmente uma ferramenta muito boa e que deve ser explorada por todo juninho.

La mayoría de los programas de formación en temas de datos te enseñan Python. Pero desde mi punto de vista lo más importante que tiene que dominar alguien que está recien partiendo es SQL. SQL te da independencia para conseguir tus datos y te ayuda a entender como funciona el sistema (por las relaciones entre tablas). Ambas son habilidades muy poderosas para trabajar eficientemente en equipos de BI.

Mastering data sources is crucial for BI success. In an entry-level BI position, proficiency in accessing diverse data is vital. Identify and connect to relevant data sources—internal or external, structured or unstructured. Examples include databases, spreadsheets, web pages, APIs, social media, and text files. Develop skills in using methods like SQL queries, ODBC drivers, RESTful services, or web scraping to seamlessly connect with various data sources, laying a strong foundation for effective BI analysis.

Para ser capaces de obtener información de la mayor cantidad de fuentes de datos que podamos necesitar, hay que combinar el aprendizaje de herramientas específicas que nos permitan acceder a ellos (como Power BI, Qlik, Tableu, etc.) y lenguajes como SQL, Phyton y R.

Identifying essential data sources - internal databases, websites, APIs, social media, spreadsheets, and text files - is the first step in effective Business Intelligence (BI). It is critical to efficiently connect these using SQL, ODBC, RESTful services, or web scraping. This preliminary stage establishes the framework for a thorough study, allowing organizations to make educated decisions based on a thorough understanding of operations, market trends, and consumer behavior. In today's data-driven world, integrating these disparate data sources is critical.

Mastery of Power Query is a great skill for entry level BI roles for two reasons - its ubiquity, as part of both Excel and Power BI, and its immense power and flexibility for data preparation and data modeling. Gil Raviv, who led the integration of Power Query into Excel 2016 estimates that 50% of data cleaning/prep problems can be addressed solely through use of the Power Query UI. By learning how to apply lightweight editing to the underlying code in the formula bar, Raviv states this proportion rises to approximately 2/3, and by learning how to code in the M language within PQ and apply recursion and advanced iterator functions, he claims 99.9% of data problems can be addressed using PQ - a tool that sits on nearly everyone's desktop.

Una vez identificado donde están los datos relevantes, e idealmente que estos datos ya estén centralizados en un datewarehouse, es hora de prepararlos para el analisis. Este paso generalmente tiene que ver con cruzar tablas, limpiar datos, aplicar lógicas de negocios y calcular KPIs. Todo realizable en SQL, directamente desde una herramienta de visualización o a través de vistas / vistas materializadas. En esta etapa recomendamos usar DBT como una herramienta para preparar los datos para analistas y herramientas de visualización. Con DBT puedes versionar este proceso, documentarlo para trabajar en equipo y traer buenas practicas de desarrollo de software como aplicar tests.

In my experience, Power Query is a great place to start. A good grasp of this tool will decrease the amount of code you need to run in DAX. The best part is that most problems you may want to solve can be addressed with just a few clicks. For example, filtering out specific words from a long string (text).

In an entry-level BI role, mastering data preparation is essential. This involves cleaning, transforming, and enriching data for analysis. Tasks include removing duplicates, handling missing values, standardizing formats, merging tables, creating calculated fields, and applying filters. Proficiency in tools like Excel, Power Query, Alteryx, or Python is crucial for efficient and accurate data preparation. Developing these skills ensures you can harness the full potential of data for meaningful BI insights.

Finding joy in dealing with messy data from various sources before designing fancy dashboards is a crucial detail for a fulfilling BI career. Understanding, cleaning, and making necessary transformations to data from different datasets is often the most time-consuming step in projects. Being conscious of the time spent here is important.

I think the emphasis on software tools for data analysis largely misses the point. I see many Data Analysts who are highly skilled in creating dashboards and data visualizations, but have no foundational knowledge of descriptive and inferential statistics. Without that core knowledge and understanding, you have no ability to properly formulate and test hypotheses about your data, no language to accurately communicate uncertainty, no rigorous way to evaluate differences, or to distinguish signal from noise. A skilled analyst with that knowledge and Excel will run circles around someone lacking that knowledge but possessing the SQL, Python/R, Tableau/Power BI "stack", in terms of ability to generate valuable, actionable business insights.

Make it a point to understand the fundamentals of DAX. Knowing DAX (Data Analysis Expressions) is crucial in data analysis because it’s a powerful formula language used in Microsoft Power BI, Excel, and SQL Server Analysis Services. DAX allows you to create custom calculations, manipulate data, and build sophisticated models, enhancing your ability to derive meaningful insights from your data. It enables you to perform complex calculations and aggregations, making it an essential skill for anyone involved in data analysis or business intelligence. I’m all you must build your thinking skills first.

Data analysis should majorly not be tools focused. At the end of the day you'll be limited to tools that your company uses for this tasks. I believe that having a better understanding of the core concepts of data analysis is more important than tools.

I think one of the key tenets of Data Analysis is being able to ask the right questions. SQL, R, Python, etc. are great for being able to interrogate data from a technical perspective but the real value comes from knowing what questions to ask, determining what doesn't make sense, and really trying to understand what the data is telling us. I could aggregate data a number of ways, cluster it, etc. but that doesn't necessarily mean it will make sense or provide any kind of valuable insight. If you can define the problems accurately you can (in general) quickly create accurate solutions. Giving someone Tableau and calling them a data scientist is like giving someone a microwave and calling them a chef.

"Data analysis in Business Intelligence is not just about numbers; it's the art of uncovering stories hidden in data, turning raw information into actionable insights, and empowering businesses to make informed decisions that drive success." 📊✨ #DataAnalysis #BusinessIntelligence #InsightsDrivenDecisionMaking Master basic data analysis techniques. Excel is a fundamental tool for entry-level positions, and learning functions like pivot tables, VLOOKUP, and basic statistical analysis is beneficial. SQL is also a valuable skill for querying databases.And mandatorily it is recommended to learn a vizualization tool such as Power Bi or Tableau based on users choice.

I strongly recommend that Data Analysts, regardless of which visualization tool they use, study and master the principles of the International Business Communication Standards (IBCS), which provides a common "semantic notation" for all business reporting. In my opinion, application of IBCS standards to data visualization eliminates almost all of errors seen repeatedly in BI reporting with regard to use of color, choice of visuals, labeling and more. Research shows that IBCS-compliant reporting decreases user interpretation errors by 61%, and reduces user processing time by 46%. A free detailed guide to the current #IBCS standards can be found on the IBCS website.

Las dos herramientas más comunes que hemos identificado en empresas para el analisis de datos es Power BI y Tableau. Creo fundamental que un profesional que este partiendo en el mundo de los datos sepa algunas de las dos para la visualización de datos. El combo Power BI / Tableau + SQL ya te da habilidades para trabajar como #DataAnalyst.

Having a working knowledge of the most popular visualization tools such as Tableau, Power BI, or Qlikview is certainly helpful. But it is also important to understand the concepts of effective visualization design, regardless of tool. In most cases, learning the tool is fairly straightforward once you are proficient at selecting the type of visualization will work best for the story you are trying to tell.

In BI, finding the perfect balance between requirements and meaningful insights in data visualization is crucial. While requesters may not always articulate their visualization needs precisely, having the right data and a proficient BI tool empowers users to create impactful charts. As a BI professional, I've learned that filling dashboards with pertinent insights, rather than irrelevant information, is key. This skill not only enhances communication clarity but also amplifies the strategic value of the delivered insights. It highlights the importance of a collaborative approach, ensuring visualizations align with stakeholders' actual needs and objectives.

Become proficient in data visualization tools such as Tableau, Power BI, or Google Data Studio. Creating compelling visuals is crucial for conveying insights to non-technical stakeholders. "Data visualization is not just about pretty charts; it's the art of transforming raw data into a compelling narrative, unlocking insights that drive informed decisions in the world of Business Intelligence. It's not just about what you see; it's about what you understand and how it empowers you to act." #DataVisualization #BusinessIntelligence #InsightsInColor 📊✨

It is key to familiarize yourself with platforms that can carry out end-to-end BI projects. One of the most popular being the MSBI stack which allows you to integrate and migrate on-premise data, carry out ETL with SSIS and also create cube calculations in SSAS. The outcomes are loaded into the database/data warehouse, most preferably SQL Server. SSRS or Power BI can be used to report on the data.

"Embarking on a journey in Business Intelligence? 🚀 Dive into the dynamic realm of BI platforms where innovation meets actionable insights. Beyond the powerhouses like Microsoft Power BI, Tableau, and QlikView, explore emerging platforms like Looker, Domo, and Sisense, each offering unique features to elevate your analytics game. As data becomes the backbone of strategic decision-making, mastering these platforms equips you to transform raw information into a narrative that guides businesses toward success. Stay curious, adapt to evolving technologies, and let the data tell its story. Ready to unleash the potential of BI platforms? Let’s embark on this transformative journey together! 💡📊 #BusinessIntelligence #BIPlatforms

If you are new in your career and not sure which software is best to get started, I recommend Microsoft BI and Tableau. Most people are learning these tools in college and some in high school. The companies looking to invest in data driven decisions will want to invest financially in one of the more common platforms used by small and medium sized businesses. These BI options are also easier to find talented resources who know the software. When purchasing an end-to-end full suite BI Platform, companies must recognized that they may need to hire and train talented BI experienced employees who might not have the full suite of experience. It is likely that the candidates with the full suite of experience will be a highly paid employee.

Creo que la habilidad no técnica más importante para cualquier profesional del mundo de los datos es la comunicación efectiva. Poder comunicar y socializar los aprendizajes del analisis de datos son fundamentales para que el conocimiento no se pierda. Estos insights también pueden evolucionar al ser claramente explicados con personas de negocios dentro de la organización. Comunicar aprendizajes con la organización es fundamental para hacer un buen trabajo como Data Analyst

Starting a career in data analytics, especially in Business Intelligence (BI), it's best to begin with SQL. SQL is fundamental, the core language for interacting with databases. It's essential to grasp how data is structured and managed. Once you've got SQL down, move on to a BI tool like Power BI for data visualization. Power BI is great for turning complex data into clear, actionable insights. As for Excel, it's useful, but you can learn it on the fly. It's more intuitive and can be picked up as you progress. But remember, SQL is the cornerstone. Master it first, then dive into BI tools, and use Excel as a supplementary skill. This approach sets a strong foundation in the BI field.

The most important skill for an entry level BI position is SQL. Period. It’s foundational to gathering and cleansing your data and has carryover to syntax used on front end tooling like PowerBI or Tableau.

Vou citar habilidades não técnicas, além das pontuadas, do meu dia a dia e, que entendo serem importantes para todo profissional de inteligência de negócios: 1 - Comunicação 2 - Engenharia de requisitos 3 - Matemática 4 - Estatística descritiva 5 - Visão detalhada dos processos de negócio 6 - Qualidade de software 7 - Testes 8 - Storytelling e visualização da informação Essas habilidades são essenciais a todo o momento, no cotidiano do trabalho em Business Intelligence!

Develop essential BI skills, including understanding key performance indicators (KPIs), dashboard creation, and report generation. Knowledge of business processes and the ability to translate business requirements into actionable insights is vital. BI is booming: The BI field is rapidly expanding, offering a wealth of promising career opportunities for skilled individuals. Essential skills: Hone your data analysis, problem-solving, communication, and data visualization abilities to excel in BI. Get your foot in the door: Build relationships with BI professionals and actively seek out mentorship to gain valuable industry insights. Remember to tailor these points to your specific BI expertise and career goals.

Any data visualization tool can be helpful as long as the visualizations are produced in a structured way. Here are few things to elevate your reports: - Put the most important KPIs on top of the report - With reporting, less is more. Try to keep less, but engaging and relevant visuals that narrate a story - Use colours sparingly. A rainbow themed report will take away from the essence of your message, use colours to draw attention to the figure.

“Se vc não sabe para onde vai, qualquer caminho serve”. Já dizia o gato de Alice no país das maravilhas! Em business intelligence isso JAMAIS pode acontecer! É premissa básica ter o conhecimento acerca do caminho a se seguir. George Polya, um matemático, escreveu em 1945 um livro intitulado “How to solve it?” que, em português foi traduzido para “A arte de resolver problemas”. Mesmo não sendo uma arte, como propõe a tradução brasileira, devemos entendo que existem processo e técnicas, que segundo Polya são: 1 - Compreensão do problema (O que?) 2 - Planejamento (Como?) 3 - Execução 4 - Avaliação de resultados Esses pilares sustentam o desenvolvimento do seu BI e as técnicas podem ser encontradas na Eng. de Requisitos! Tmj!

Falou tudo... Não importa a ferramenta... Elas vêm e vão... Cada empresa escolhe a sua e depois trocam... Resolva problemas... Entregue informações úteis...

A Business Intelligence vai muito além de ferramenta, é um conceito que busca trazer inteligência para o negócio, assim o primeiro Bom conhecimento é como fazer perguntas. Ferramentas como o 5W2H é uma boa ajuda, existem bons livros na literatura de como podemos levantar requisitos de uma boa forma. A matriz de dados também é muito usada para situações de projetos de BI. Depois de saber fazer as melhores perguntas, será possível identificar a melhor ferramenta para resolver o problema levantado através de BI.

Cloud Platforms:Cloud platforms like AWS, Azure, or Google Cloud Platform, as many BI tools integrate with these services. Coding Skills: Basic programming skills can be advantageous like Python or R for more advanced analytics. Industry-specific Knowledge: Tailor your skills to the industry you are entering. Understanding specific industry trends and challenges to provide meaningful insights. Communication Skills: Are crucial in BI roles. Practice translating complex technical concepts into clear, non-technical language for diverse audiences. Continuous learning to stay updated on the latest BI tools and trends. Networking with professionals in the field, both online and offline, can provide valuable insights and opportunities for growth.