You’re starting a new business intelligence project. What are the best data analysis tools to use?

In addition to traditional commercial tools, there are open-source projects like Apache Superset or Metabase that provide powerful features and flexibility for customization, backed by a highly specialized community. By exploring these less conventional options, you may uncover excellent tools that align perfectly with the unique needs of your project.

In the past I have used Tableau, currently we use Power BI and it does do an excellent job. Independently of what you use, the important part is to have a trustworthy dataset and star schema to start. At this point you need to work with the business units on what they would like to see. Showing samples to the stake holders is very important. Some prefer raw data, others prefer less detail and more trends. The tools, as far as I know can get the job done and deliver a strong user experience, as long as your data is solid and that the BI team understands the requirements. An iterative approach has proved to be the best strategy in my current company.

Concordo com a opini?o dos colegas que afirmam que ao iniciarmos um novo projeto de business intelligence, temos que nos certificarmos em primeiro lugar da qualidade das fontes de dados, da veracidade das informa??es e que tenhamos um tratamento adequado destes,para que se transformem em informa??o relevante para os usuários. Inicie sempre com muito diálogo com as áreas que demandam as visualiza??es das informa??es, pois temos o compromisso de garantir que o resultado visual final deve ser relevante, intuitivo e muito fácil de utilizar. Muita aten??o ao Storytelling. A história das informa??es desejadas deve estar bem contada, cadenciada. As ferramentas que hoje utilizamos s?o Power BI, SQL, Cognos IBM e Power Apps para automatiza??o.

Data analysts should always consider the following aspects before selecting any tools which are as follows: ? How the tool is helping to prepare the data ? Whether the selected tool can be a helping hand to process the data or not ? Whether it is justifying the analysis aspect or not ? What makes it different and more functional than other tools in terms of visualization Overall, the simple way to select a particular tool for data analysis is to verify the above criteria. Once it is verified, it really does not matter how advanced the tool is. Sticking to the basics and keeping things simple is the way to go. ??

In my opinion depending on the data source, volume of the data and nature of the data decides the best data analysis tool to work for. If you are working on cloud services data then Microsoft Power BI is the best tool to work with and apart from its data visualization concepts the tool is quite equipped to transform the data and then prepare appealing visualizations. DAX and M-Query is quite powerful to derive hidden insights from the data. Also business users who worked on excel in traditional ways of working find it easy to upgrade to Power BI ways of working

Primeiramente, a IA pode identificar padr?es e tendências ocultas em grandes volumes de dados, proporcionando insights valiosos para tomadas de decis?o estratégicas. a IA pode prever comportamentos de consumidores, otimizando estratégias de marketing e vendas. A IA também contribui para a inova??o de produtos e servi?os, analisando feedbacks de clientes para melhorar a oferta. Além disso, a IA auxilia na detec??o e preven??o de fraudes, analisando transa??es em tempo real e identificando atividades suspeitas. Portanto, a integra??o da IA na análise de dados n?o só melhora a compreens?o do mercado e dos clientes, mas também impulsiona a inova??o e a eficiência, trazendo um valor significativo ao negócio.

Bom, algumas ferramentas como o Microsoft SSAS, SSIS e SSMS podem ser utilizadas para a cria??o dos processos de BI, para dados estruturados e que obedecem o pipeline de dados ETL. Quando estamos tratando de dados n?o estruturados o ideal é termos um pipeline com ELT, extraindo e carregando os dados com Python ou R, Databricks, para manter o dado no data lake e posteriormente fazer as transforma??es com essas linguagens de análise de dados.

Principais pontos para determinar a estrutura ideal de ferramentas para análise de dados: 1. Qu?o automatizada é a leitura e coleta de dados? 2. A confiabilidade dos dados coletados está assegurada? 3. Qual é o volume de dados a ser analisado? 4. Com que frequência os dados s?o atualizados? 5. Qual é a complexidade dos KPIs a serem desenvolvidos? 6. Qual é o or?amento disponível para a constru??o dessa análise? Responder a essas perguntas é crucial antes de selecionar a ferramenta mais adequada. Com o vasto leque de solu??es disponíveis no mercado atual, direcionar a escolha certa para o contexto do projeto de análise de dados pode resultar em economia significativa de tempo e recursos financeiros.

The selection of a data analysis tool should be driven by the specific requirements of the project. Key factors to consider include the volume and variety of data you’re working with, the technical proficiency of your team, and the complexity of the analyses required. For instance, Excel might be suitable for ad-hoc analysis and quick insights, but for handling large datasets or real-time data processing, a combination of SQL databases and programming languages like Python would be necessary. Furthermore, the tool's scalability, cost, ease of integration with existing systems, and the availability of support and community resources are crucial considerations.

When using AI for data analysis, it's crucial to focus not only on the accuracy of the model but also on interpretability. By employing interpretable AI models like decision trees or rule-based systems, experts can gain valuable insights into how the model arrived at specific predictions, helping build trust, identify biases, and improve explainability in complex data analysis tasks.

To leverage AI, one can use machine learning frameworks such as TensorFlow or PyTorch to develop predictive models. These models can analyze large datasets to identify trends, forecast outcomes, and provide decision-makers with actionable insights. Additionally, AI-driven tools like natural language processing (NLP) and image recognition can process unstructured data, which constitutes a significant portion of big data, to extract valuable information. However, successfully applying AI requires a clear understanding of the business problem, selecting the right models, and having a well-prepared dataset.

A integra??o da Inteligência Artificial (IA) na análise de dados oferece oportunidades significativas para insights avan?ados e automa??o de tarefas complexas. Algoritmos de aprendizado de máquina podem prever tendências, segmentar clientes, identificar anomalias e analisar texto e imagens. Essa abordagem, quando implementada com coleta de dados abrangente, treinamento eficiente de modelos e monitoramento contínuo, proporciona às empresas uma vantagem competitiva ao transformar dados em decis?es estratégicas e operacionais mais informadas.

La IA es una excelente herramienta para acelerar el proceso de obtención de datos claves y relevantes de un estudio. Es importante que los datos a evaluar estén muy adecuados al tipo de empresa en el cual nos desempe?amos.

AI can process large volumes of data at incredible speeds, offering insights and foresight into future trends, which is invaluable for strategic planning. It can also automate mundane tasks, allowing analysts to focus on more strategic work. The main challenges include the need for large and high-quality datasets, the risk of bias in AI models, and the complexity of explaining AI decisions. Moreover, there’s the cost of setting up AI infrastructure and the need for skilled personnel to develop and manage AI systems.

O uso da Inteligência Artificial (IA) na análise de dados oferece benefícios significativos, incluindo eficiência aprimorada, tomada de decis?o informada, identifica??o de padr?es complexos e personaliza??o de recomenda??es. No entanto, enfrenta desafios relacionados à interpreta??o e transparência dos modelos, qualidade e viés dos dados, seguran?a e privacidade, custo de implementa??o, e quest?es éticas. Ao superar esses desafios, as organiza??es podem explorar o potencial transformador da IA para impulsionar a inova??o e a eficácia operacional na análise de dados.

Para utilizar eficazmente a IA na análise de dados, é essencial estabelecer objetivos claros alinhados à estratégia da organiza??o. A coleta de dados deve ser rigorosa, evitando viéses, e a transparência nos modelos é crucial para confian?a. Medidas robustas de seguran?a e treinamento contínuo dos modelos asseguram precis?o, enquanto a avalia??o ética e a documenta??o completa garantem práticas responsáveis e conformidade regulatória.

Best practices involve starting with a clear problem definition, ensuring your data is clean and well-structured, and selecting the right model based on the problem type. It's vital to divide your data into training and testing sets to validate your models properly. Once deployed, these models require regular monitoring and updating to maintain accuracy over time. It’s also crucial to establish a framework for ethical AI, considering the impact of your models on privacy, fairness, and regulatory compliance.

When using AI for data analysis, it’s essential to adhere to best practices for effective and ethical outcomes: 1. Establish specific goals for your AI initiatives. 2. Clean and preprocess data to improve model accuracy. 3. Know the strengths and limitations of your AI models. 4. Protect data integrity and comply with privacy regulations. 5. Select tools and infrastructure that match your project’s needs. 6. Combine the expertise of data scientists, domain experts, and business analysts. 7. Regularly assess model performance and update as needed. 8. Track model performance and data drift over time. 9. Opt for models that provide clear, interpretable results. 10. Ensure AI applications are fair, transparent, and accountable.

Data analysis isn't just about technology; it's also about people and processes. The human element — from the data analysts to the end-users — plays a crucial role in the success of any data initiative. Training and change management are as important as the tools themselves. Additionally, with the increasing importance of data in strategic decision-making, governance and security cannot be overlooked. Ensuring data integrity, quality, and compliance with privacy laws like GDPR or CCPA is essential. Finally, scalability and future-proofing your data infrastructure will allow your organization to grow without being hindered by early technical decisions.