You’re a business architect with a data analysis problem. What tools do you turn to?

For the purpose of Extraction data and connectivity to the desired Source. There are plenty of connectors and direct connections available. It varies from BI Tool /Software you would like to use and connect your data

Is your data nestled in relational databases? SQL scripts will be your trusty steed. Do web APIs hold your treasures? JSON, XML, and HTTP requests are your passports. But fear not, data explorers! A vibrant arsenal of tools awaits. Power BI, the BI champion, bridges the gap between diverse data sources. It wrangles raw information, sculpts it into shape, and builds insightful models. Alteryx, the data whisperer, cleanses, blends, and enriches data from all corners of your ecosystem. Python, the programming powerhouse, wields libraries like pandas, requests, and Beautiful Soup to conquer databases, files, web pages, and APIs. The power to transform diverse data into actionable knowledge lies within your grasp.

Firstly, I believe Microsoft Excel remains a versatile and widely-used tool for basic data analysis. Its spreadsheet functionality allows for data sorting, filtering, and basic statistical analysis. Excel is suitable for quick exploratory data analysis and can be a foundation for more complex analyses. I also think that SQL is an essential tool for managing and querying databases. Whether working with structured or relational databases, having a strong command of SQL allows business architects to extract, manipulate, and analyze data efficiently. Tools like DBeaver or Microsoft SQL Server Management Studio can enhance the SQL querying experience.

In my experience SQL IDE like Toad, SQL/ Teradata navigator are good tools when it comes to relational databases, these tools need to be layered or integrated with accelerators for better performance, like Dremio etc. challenges with data analysis is scalability of the products/ tools we use and expectations on latency. Preference is always to keep your data federated and not centralize as that will cause challenges with storage and refreshes

Microsoft Power BI comes handy in your data analysis journey. There are several connections to different data sources. With a gateway in place, multiple data sources can be pulled together to get meaningful insights from your data. Having a data lake can also provide a one stop shop for your data to support a single source of truth for better intelligence.

Moreover, I believe in leveraging business intelligence (BI) tools such as Tableau or Power BI. These tools provide robust data visualization capabilities, allowing business architects to create interactive dashboards and reports. BI tools enhance the understanding of complex datasets and facilitate effective communication of insights to stakeholders. In my opinion, Python with libraries like Pandas and NumPy is powerful for more advanced data analysis and manipulation. Python's flexibility and extensive libraries make it a go-to choice for business architects dealing with large datasets, statistical analysis, and machine learning applications.

As an Enterprise Architect establishing analytics platforms, I deploy Excel for basic analysis but leverage advanced languages like R and Python for complex statistical modeling, while empowering citizen data scientists with self-service BI tools like Power BI to visualize data stories without coding, together providing a spectrum of analysis functionality.

For data analysis, I leverage a combination of statistical software like R or Python's pandas library for exploratory data analysis and statistical modeling. Visualization tools such as Tableau or Power BI aid in presenting insights effectively. Machine learning libraries like scikit-learn or TensorFlow are essential for predictive modeling tasks. Advanced analytics platforms like SAS or IBM SPSS offer comprehensive capabilities for statistical analysis and modeling. Additionally, I might use data mining techniques and algorithms for pattern discovery and clustering. Collaborative platforms like Jupyter Notebooks facilitate iterative analysis and documentation of findings, fostering team collaboration and knowledge sharing.

In 20 years I never needed either R or Python, although I learned them both. The Microsoft suite is enough for anything but building LLMs, and with integration, I’d bet Power Bi could get you most of the way there. I regret learning to code, there’s nothing I couldn’t do without it (or cutting and pasting it, D3 for example). Also, what happened to MS Access? Does everyone just pretend that that’s SQL? Because it isn’t. Learned SQL too, didn’t really need it. Learn how to run code and ask a halfway decent AI to do the rest, you’ll be fine unless you haven’t figured out that data analysis is a people business - give them more than they expect, but not more than they asked for.

Data analysis: Employ robust data analysis tools to derive insights. Options include: - Power BI or Tableau: For creating interactive visualizations and analyzing data trends. - Python with Pandas: For in-depth data manipulation and analysis. - R Programming: Especially useful for statistical analysis and data modeling.

" A picture says a thousand words" - words to live and die by, especially in the data world. I believe that if you have to over-explain everything using words, then your visualisation was not clear enough at the start. Work on visuals that are clear, not just clever. A non-data person should be able to look at a Tableau dashboard and understand what is going on in the business. Use colours to show negatives and positives, use different visuals to tell the story. Our job is to provide actionable insights to business in a clear and concise way.

When you work with data, you may have different options to explore, validate or optimize it. But the most important part is how you communicate your findings and recommendations to your audience. Data storytelling is the essential skill that helps you deliver clear and compelling messages from your data analysis. It is not just about creating charts and graphs, but about crafting a narrative that connects the data points with the business goals and actions.

Additionally, I think Jupyter Notebooks can be a valuable tool for combining code, visualizations, and narrative in a single document. They facilitate an interactive and collaborative approach to data analysis, making it easier to share insights and findings with both technical and non-technical stakeholders. In my experience, using data visualization tools like Google Data Studio or QlikView can enhance the presentation of analytical results. These tools allow business architects to create dynamic and interactive visualizations that can be easily shared and understood by a broader audience.

Tailor data communication to the audience and purpose. For formal presentations, use Word or PowerPoint for polished documents or slides. For informal, collaborative discussions, leverage tools like Miro to enable real-time feedback. Adapt your approach based on the formality and interactivity required for effective stakeholder engagement.

For me, the choice of tools for data communication depends on the nature and purpose of the intended message. Knowing your tools help, knowing your audience is even better.

I would start the other way. Understand the customer and business problem you are trying to solve for. This will help focus in your efforts and continue with data exploration and analysis with the goal in mind. It will also speed up the process. There are numerous tools that can help however you need to be clear on the outcome you are trying to achieve.

Furthermore, I believe in utilizing ETL (Extract, Transform, Load) tools like Apache NiFi or Talend for efficient data integration and transformation. ETL tools streamline the process of preparing data for analysis, ensuring that it is clean, accurate, and ready for use in business architecture initiatives., from my perspective as a seasoned business consultant, addressing a data analysis problem as a business architect involves using a combination of tools. Microsoft Excel and SQL are foundational, while business intelligence tools like Tableau, Python for advanced analysis, Notebooks for interactive documentation, data visualization tools, and ETL tools play crucial roles in ensuring a comprehensive and effective approach to data analysis

Beyond the specific tools, a successful data analysis strategy entails addressing critical considerations. Prioritize security and compliance, ensuring that the chosen tools adhere to industry standards. Effective cost management is key; regularly monitor and optimize cloud service expenses to maintain efficiency. Scalability is paramount; evaluate the capability of chosen tools to accommodate growing data volumes and evolving business needs. A holistic approach, considering data sources, analysis techniques, communication tools, and additional considerations, ensures a robust and adaptive foundation for successful data analysis endeavors.

Certamente o Power BI e seus painéis chamativos são apenas a ponta do iceberg. O que poucos vêem é que por trás existe todo um trabalho de engenharia de dados que permite a ingestão e tratamento de todo o conteúdo que alimentará os BIs e reports, e fornecerão aos analistas e especialistas assim, estruturas confiáveis para cruzamentos de dados para a obtenção de insights.

Empezar una transformación digital real en la compañía comienza con quick wins que muestran el impacto que tiene una cultura data driven. Procesos automatizados que: - disminuyen el tiempo de procesamiento en un 95% - reducen del error humano - poder hacer un deep dive de la información - dan rapidez y acceso visual de la información Con estas mejoras de rápida implementación la empresa empieza a confiar en invertir en un transformación digital y data driven. Luego se podrá implementar modelos predictivos con machine y deep learning y también el uso de IA generativa.