You’re a self-taught data analyst. How can you develop your Business Intelligence (BI) skills?

Costumo sempre dizer, aprenda o conceitos das coisas, o resto vem! N?o aprenda uma ferramenta, aprenda o conceito por trás daquela ferramenta!

Before delving into advanced business intelligence (BI) tools, it's crucial to establish a solid foundation. The excitement of AI and real-time dashboards can distract us from building a strong base, which is essential for maximizing tool value. Establish a clear understanding of your data environment, including key data sources, systems, and architecture. Map out data location and flow, ensuring data quality and consistency by documenting definitions, metrics, and rules. Develop proficiency in fundamental tools like SQL, Excel, and data visualization, as they serve as the backbone of data analysis. Collaborate with data and IT teams, demonstrating the transformative potential of BI. Begin with small-scale initiatives.

As a self-taught data analyst, my main goal will be to improve my BI abilities by practice and constant study. My first step is to become proficient in BI principles including data warehousing, ETL procedures, and data visualization. It is helpful to use online lessons and courses, and I will make use of the free versions of business intelligence software like Power BI and Tableau. Gaining practical experience requires applying academic knowledge to real-world initiatives, even hypothetical ones. By sharing expertise and keeping up with the newest trends and technology in the BI area, active involvement in online BI groups will improve my learning.

Data Warehousing: Learn about data warehousing concepts, including the processes of data extraction, transformation, and loading (ETL), as well as the architecture and design of data warehouses. Data Modeling: Understand the basics of data modeling, including the construction of dimensional data models and understanding star schemas and snowflake schemas.

As a self-taught data analyst, you're already halfway to becoming the Da Vinci of data. Think of BI as your trusty paintbrush – it's all about how you wield it. First, immerse yourself in the world of BI by reading up on the latest trends, attend workshops, and network with fellow data enthusiasts. It's like seasoning your dish with the perfect blend of spices – a little SQL here, a dash of Tableau there, and voila! Next, never underestimate the power of hands-on experience. Dive headfirst into real-world projects. Roll up your sleeves and get your hands dirty with some messy data. Whether you're sculpting insights from spreadsheets or painting a picture with pixels, embrace your inner BI and let your imagination run wild!

Data Visualization Tools: Tools like Tableau, Power BI, and Qlik are industry standards for creating interactive reports and dashboards. Choose one to start with and master it by learning its features, functionalities, and best practices for visualization. SQL: SQL (Structured Query Language) is essential for querying databases. Becoming proficient in SQL allows you to extract and manipulate data efficiently, a fundamental skill for any BI professional. ETL Tools: Familiarize yourself with ETL tools such as Talend, Informatica, or even Python scripts for data preparation and integration tasks.

?? El BI abarca una amplia gama de herramientas y tecnologías, desde herramientas de visualización como Tableau y Power BI hasta plataformas de análisis de datos como SQL, Python, y R. Dedica tiempo a investigar y seleccionar las herramientas que mejor se adapten a tus objetivos de aprendizaje y las necesidades del mercado. No necesitas aprenderlas todas; empezar con una o dos herramientas clave y llegar a ser competente en ellas puede abrirte muchas puertas.

When you start learning tools always work on real life projects to solve real world problems. Avoid watching tutorials continuously. These projects will help you to practice and apply your skills in a real world scenario precisely with tools.

Lots of great info by others, but there's a new game in town: AI. I've used ChatGPT since it first came out and it's very valuable for quickly getting answers and examples, especially code examples (they can be wrong, so some iterations might be necessary). For instance, I was using some supply chain trucking delivery data. I asked it to give me a list of possible valuable insights (particularly on profitability) based on the data I gave it. It came up with excellent suggestions. If you have dirty data, you can ask it to write code to clean it up. I'd encourage you to lean on this AI capability--it will accelerate your development. Also, becoming a prompting expert is now a thing in tech, so you will be learning that as well.

Choosing the right BI tools is essential for effective data analysis. While Power BI excels in integration with Microsoft products, Tableau leads in visual analytics. Meanwhile, SQL is indispensable for complex querying. Considering one's specific needs, budget, and the tool's ease of use before making a choice is crucial. Utilizing free trials and demos can significantly help in making an informed decision without upfront commitments.

It is not all about the technology. You must immerse yourself into learning your business and processes. Then you can use your technical and analytical skills to deliver meaningful business insights!

Aqui refor?o a quest?o do portfólio de servi?os! Tenha um portfólio diversificado em rela??o a negócios e em rela??o a ferramentas! Aproveite neles para também demonstrar sua habilidade de escrita! Fica a dica!

The optimal approach to mastering Business Intelligence involves immersing oneself in real-world data across diverse domains while strategically utilizing various BI tools such as Tableau, Power BI, and Qlik. The adoption of multiple tools not only enhances technical proficiency but also fosters adaptability to different organizational preferences. Engaging with practical business challenges refines the ability to identify precise decisions required for effective problem-solving. The synthesis of hands-on experience with diverse datasets and a comprehensive toolset cultivates a versatile skill set, positioning individuals as adept professionals in the ever-evolving landscape of Business Intelligence.

Every skills can be strengthen by practice. With real data, usually the database is large enough to let you see interesting results. With the results, you can observe different patterns, which inspire you to think of another way to present the data. If you have rights to build your own BI solutions from work, you have to listen the problems that users are facing. While you're resolving the problems, you learn much from there.

Personal Projects: Start with personal projects or find datasets online (e.g., Kaggle, Google Dataset Search) to practice your skills. Try to mimic real-world business scenarios by setting objectives, extracting and transforming data, and building dashboards to communicate your findings. Freelancing or Volunteering: Offering your BI services as a freelancer or volunteering for nonprofits can provide practical experience with real-world data and scenarios.

Online Communities and Forums: Join online communities and forums dedicated to BI, data analysis, and related topics. Platforms like Reddit, Stack Overflow, and LinkedIn groups offer spaces where you can share your work, ask questions, and receive feedback from experienced professionals and peers. Professional Networks: Connect with professionals in the BI field through platforms like LinkedIn or industry events and conferences. Reach out to mentors, colleagues, or industry experts . Peer Reviews: Form a peer review group with colleagues or classmates who are also learning BI or working in the field. Share your work with each other and provide constructive feedback on areas for improvement.

Vejo que é de extrema importancia ter um mentor! Tenha algum profissional mais experiente que você que lhe auxilie em sua evolu??o!

Feedback from real users always help you understand their actual needs and the problems. By investigating and seeking for solutions, you can learn the technical skills, business skills and communication skills. If you have technical issues, Google is your best teacher, except you're using a tool that is not common. And usually there's a support from the software vendor, which you can also seek help from. If you're working in groups/teams, asking those who have more experience is also a short path. Some may say communities/forums could help. However, with over 10 years experience on BI, I haven't raised a question in any communities/forums.

For aspiring BI professionals, it's crucial to embrace qualitative feedback over quantitative metrics when starting out. Instead of focusing solely on creating flawless dashboards, prioritize developing a strong analytical mindset. Actively seek critical but constructive feedback to speed up your learning curve. Engage with end users to understand which data is most impactful and how they use it. Collaborate with data scientists on statistical approaches and designers on effective visualizations. Join online communities and subscribe to industry publications to stay informed. As you progress, identify mentors who can challenge your ideas and provide valuable insights. Seek their feedback on your data models, analysis and dashboards.

?? La retroalimentación es una herramienta de aprendizaje poderosa. Comparte tu trabajo con comunidades en línea, mentores o colegas para obtener sus opiniones. Las plataformas de redes sociales profesionales como LinkedIn o foros especializados en datos pueden ser excelentes lugares para conectar con expertos en BI que pueden ofrecerte consejos valiosos y constructivos. No tengas miedo de preguntar y estar abierto a críticas; es una parte esencial del proceso de aprendizaje.

As a self taught data analyst, I have lived through the challenges of implementing and learning BI skills. The best way is to learn-apply-take feedback and repeat. I have picked my tool and I have been learning everything I can about that. In my opinion, keeping yourself up to date with the new updates and advancements can do wonders for your career.

As a self taught data analyst, I have lived through the challenges of implementing and learning BI skills. The best way is to learn-apply-take feedback and repeat. I have picked my tool and I have been learning everything I can about that. In my opinion, keeping yourself up to date with the new updates and advancements can do wonders for your career.

BI won't stop evolving - never! Evolution frequency in BI has reached daily and even hourly! Every new version of each tool brings new features which make your work simpler and even make some analysis possible which may have been impossible in previous versions. New ideas and concepts come into play in high rates. Read news and update changelogs to find what's new in each tool and platform.

Technologies keep evolving. 15 years ago, not much talk about big data or data lake on cloud. And some are fade out. For example, I don't hear people mention OLAP cube. No matter is for your own interest or for your career, you should be open-minded to understand new technologies. Try them out if you have a chance to compare the knowledge that already have.

Você precisa ter consciência de que a área de dados é um ambiente de estudo perpétuo. Você precisa se inspirar e estar apaixonado pelo que faz para ter algum tipo de sucesso. N?o é o tipo de profiss?o ou posi??o da qual você poderá descansar por muito tempo. é necessário ter organiza??o de suas agendas e se permitir aprender algo novo sempre.

* Master the fundamentals of data analysis. This includes understanding how to collect, clean, and analyze data. * Learn how to use business intelligence tools. There are many different BI tools available, so it's important to choose one that meets your needs. * Develop your communication and presentation skills. You need to be able to effectively communicate your findings to stakeholders. * Network with other BI professionals. This is a great way to learn from others and stay up-to-date on the latest trends. By following these tips, you can develop your business intelligence skills and become a successful data analyst.

Domain Knowledge: Understanding the specific industry or domain you're working in is crucial. BI is most effective when it aligns with the business context and objectives. Developing domain knowledge enhances your ability to ask relevant questions and provide valuable insights. Data Ethics and Governance: As BI professionals, it's important to be aware of data ethics and governance. Ensure that your BI practices adhere to data privacy regulations and ethical standards. Understanding the legal and ethical aspects of data usage is essential for responsible BI.

In my opinion, the primary distinction between a Business Intelligence (BI) developer and a data analyst lies in their approach to fetching and cleaning data through the Extract, Transform, Load (ETL) process. Both roles necessitate a comprehensive understanding of star schema, ETL tools, SQL procedures, and high-level Create, Read, Update, and Delete (CRUD) commands, however, a BI developer is responsible for implementing and setting up cloud services, data ethics, and data management rules. On the other hand, data analysts typically receive these elements already prepared. To be successful in their work, both roles must possess a strong understanding of these concepts.

What makes BI not just about technology is that it is a creative discipline as well. It takes experience to produce quality BI assets that are content rich yet easy to use. I don’t see this suitable for just any technical person, but I would start with reading books by Tufte and others on visualization techniques. Also, try to get a mentor who has experience as you will need guidance.

Real life scenarios are not something that can be easily self-taught based on my years at Bank of Montreal, Oracle and a few other as data analyst(they didn't even have the term yet). At a high level, I had to do ETL, analytics, reporting, cleansing, automation, which all sound like something one can pick a book or online course to learn. However, trouble shooting, data lineage backtracking, security incidents handling, infrastructure induced issues handling, and more cant really be practically taught, because the actual efforts to resolve each can be weeks. No one would spend the time to create a course that last that long for just one such task.