What does a business intelligence analyst do?

A Business Intelligence Analyst who focuses on data visualization has an essential role in making data understandable and useful for a business. They collect data from different sources, clean it up, and then create easy-to-read charts and reports. Using special tools like Tableau or Power BI, they design interactive visual displays that help people in the company understand what's happening and make better decisions. The analyst works closely with other parts of the business to know what kind of information they need and ensures the data is accurate and secure. Overall, their goal is to turn complex data into a story that everyone in the company can understand and use to improve how they work.

A Business Intelligence (BI) specialist must think strategically about data collection. A well-thought-out data strategy guides the collection of the right data, tailored to the specific needs and strategical goals of the organization. Without a clear strategy, businesses risk gathering irrelevant or low-quality data, leading to flawed insights and poor decision-making. A strategic approach to data collection also involves considering data sources, storage, and governance, ensuring data security and compliance with regulations. By focusing on collecting the most relevant data in a structured manner, a BI specialist can significantly enhance the quality of business insights, leading to more informed, data-driven decisions.

They may also design and conduct surveys, interviews, or focus groups to collect qualitative data from customers, employees, or other groups. They need to ensure that the data they collect is accurate, relevant, and reliable for their analysis.

While many BI analysts focus on visualisation, some of us focus on the data and have other teams specifically for visualisation. In a world full of pretty dashboards, the importance of the underlying data can often be overlooked. In a large corporation like a bank, managing and understanding the underlying data can be a full time job, and in reality the data can be equally if not more important. No matter how pretty the dashboard - if the data is inaccurate or misunderstood visualisation is of no point whatsoever. If the data is not timely, the dashboards also lose potency as being a tool used for high-level decision making. For example, financial data needs to be reconciled to ledger before being useful.

Analyze data: Evaluate and interpret complex business data to provide valuable insights. Create reports: Develop clear and concise reports for management to support decision-making. Identify trends: Recognize patterns and trends in data to help the organization make informed choices. Dashboard design: Build visually effective dashboards for easy consumption of key performance indicators. Strategic recommendations: Provide actionable recommendations based on data analysis to improve business performance.

De modo geral o analista deve resolver problemas. "Quais os principais motivos que levam um cliente a desistir de uma compra?" As perguntas surgem e o principal é avaliar todas as variáveis envolvidas, além de possíveis ferramentas que ser?o utilizadas. Nem tudo precisa de um algoritmo de machine learning complexo, que leva dias treinando e aprimorando as saídas. As vezes um KPI no power BI ja é suficiente pra balizar a tomada de decis?o. Avaliar a complexidade da solu??o e viabilidade da mesma é essencial. 1) Identificar problemas 2) Possíveis solu??es 3) Avaliar complexidade e viabilidade 4) Implementar solu??o 5) Apresentar os dados

Data analysis is the systematic process of inspecting, cleaning, transforming, and modeling data to uncover meaningful insights, patterns, and trends. It involves applying statistical and computational techniques to interpret complex datasets and extract valuable information that can drive decision-making and inform strategies. Data analysis plays a crucial role in various domains, including business, science, healthcare, and technology. In business, it helps organizations understand customer behavior, optimize operations, and identify market opportunities. In science, it enables researchers to discover correlations, validate hypotheses, and generate new knowledge. In healthcare, it facilitates disease diagnosis, .

- Employing statistical methods and analytics to interpret data patterns. - Identifying trends, correlations, and insights to support business decision-making.

A análise de dados é essencial tecnicamente, pois revela padr?es, tendências e rela??es ocultas nos dados, transformando informa??es brutas em insights significativos. Isso possibilita a extra??o de informa??es valiosas para a tomada de decis?es informadas, identifica??o de padr?es e compreens?o profunda, fundamentando estratégias e a??es eficientes, além de otimizar processos.

I agree, but I would recommend leveraging the help of each department so that you know what data points to focus on and what ideas or limitations they see that you can investigate. Having each department share business questions is very helpful in planning how to analyze the data.

Tool Proficiency: Tableau, Power BI, Excel User-friendly layouts Interactive elements for exploration Incorporate narratives, scenarios, and case studies Make complex data relatable Tailor visualizations for different audiences Provide relevant insights for specific purposes

A visualiza??o de dados é crucial porque transforma informa??es complexas em representa??es visuais claras, facilitando a compreens?o, identifica??o de padr?es e tomada de decis?es mais informadas e eficazes.

- Creating visual representations (charts, graphs) to communicate complex data findings. - Using visualization tools to present information in a user-friendly format.

-Dashboards claros e diretos -Foco na apresenta??o para o usuário final do dado (Gerente, Vendedor e etc) -Responder as perguntas corretas

An example of storytelling I used was a Marketing funnel. The visualization clearly told a story of how many people saw our adds and funneled down to the final conversion. The visual clearly shows where the customers dropped off on their journey, which helps marketing identify their areas of focus.

1. Facilitate business leaders with crisp and actionable intelligence by analyzing data from business lens 2. Ensure clear definition of key KPIs 3. Ensure standardization in reporting of KPI across organization

A comunica??o dos dados é vital porque facilita a transmiss?o eficaz de insights, promove entendimento coletivo e apoia decis?es informadas, promovendo uma colabora??o mais eficiente e uma a??o orientada pelos dados.

- Translating analytical results into understandable insights for non-technical stakeholders. - Collaborating with teams to convey the significance of data-driven conclusions.

It's crucial to have a deep understanding of the business, identify your target audience, deliver valuable insights derived from data, and actively seek their feedback. Effective communication with stakeholders plays a pivotal role in this process.

In my experience this can be the most important part of the job. The Business Intelligence Analyst is the gatekeeper of the data and they need to guide members of the organization through their dashboards and data sets. Change is important for growth, and the negative correlation of it can be avoided with great communication. Training sessions have been very helpful for my team. As well as succinct visuals and dashboards to promote a consistent understanding of the data.

For Business Intelligence, it's like having many tools in a toolbox. But the real skill is in understanding what the business needs and telling a meaningful story using the available data. It's about making sense of information to help businesses make smart decisions.

There are so many different skills to learn. Most of them have been mentioned. To stay ahead of everyone else I recommend following other people in the industry and leveraging YouTube and other learning platforms to continuously improve your technical skills.

Soft skills are also important for a business intelligence analyst; they should have curiosity and creativity to explore new insights from data, as well as critical thinking and problem-solving to identify data challenges and issues. Communication and collaboration with different stakeholders is also necessary. Finally, the analyst should have business acumen and domain knowledge to understand the business goals.

The blend of technical, analytical, and soft skills I've developed is fundamental to my role as a business intelligence analyst. Proficiency in SQL, Python, and visualization tools, combined with critical thinking and business acumen, has been crucial in addressing complex business challenges. This varied expertise is essential for navigating the complexities of data analysis and visualization in today's data-driven world.

- Developing proficiency in data analysis tools (like: SQL, Python, R). - Possessing strong analytical, problem-solving, and communication skills.

Planos de carreira em business intelligence s?o essenciais, pois oferecem diversidade de op??es para analistas. Podem evoluir para papéis como cientista de dados, engenheiro de dados, arquiteto de dados ou analista de negócios, proporcionando oportunidades de especializa??o e crescimento profissional conforme interesses e habilidades.

There are so many different entry points to business intelligence and many more career paths. The skills that serve you well in these rolls are also highly transferrable between companies and industries. Make sure you have a solid mix of useful tech skills AND people skills, e.g. stakeholder management, presentation, negotiation, communication, mentoring, critical thinking etc.

- Progressing from entry-level roles to senior positions based on experience and expertise. - Specializing in specific industries or aspects of business intelligence.

Data engineers are more technical roles that involve building and maintaining the data infrastructure and pipelines. Alternatively, a data architect is a more strategic role that involves designing and overseeing the data architecture and governance.

Know your audience! A dashboard, presentation, story or simple insight can be inspiring or ignored depending on how you pitch it. Who you're communicating to, what they need to understand, their skill level, their available time - it all needs careful consideration for maximum impact.

Something that I found useful so far is building connections among fellow BI analyst to understand different perspective and learn approaches. It can save you from making costly mistakes in your projects.