What are the most effective data reporting tools for different use cases?

The choice of tool recommended depends on the business goals of the organisation as well as the potential end users because different tools fit perfectly for different use cases. So, to decide on the choice of tool, I often have different conversations with stakeholders to understand their business goals and data needs. Also, questions relating to the volume of the data, as well as the technical know-how of the consumer, would help determine the appropriate tool. While there are great tools that are among the top players, like Power BI and Tableau, there are instances where other tools, like Looker, Sigma, Preset, etc., would be a better choice. In summary, proper conversation with necessary stakeholders and use case will be needed

Choosing the most effective data reporting tool depends on factors such as data complexity, team collaboration requirements, interactivity needs, and budget constraints. If the task is straightforward using spreadsheets like Excel or Google Sheets can be quite effective. For complex analysis and visualization, specialized tools like Tableau or programming languages such as Python and R are highly effective. It's important to select a tool that perfectly fits the unique demands of your data reporting project.

Well , Lot of data reporting tools are present with different features. In the organisation level we need to understand the business case , Data volume , Data cleansing part , End user perspective. Then we can decide which Data reporting tool Is helpful for all the scenarios and cost effective. Let’s talk about one example here I have requirements only Adhoc report and not using large amount of data Then we can prefer Excel or similar kind of tool. If you want to cleanse your data , then build your data , using advanced function then we can prefer Tableau , PowerBI , Looker , Microstrategy and Spotfire. Based on the business requirements we can analyse and use those data reporting tools as per needs

Data driven story telling is an invaluable skill that most of the data analysts struggle with and to overcome these challenges today's BI solutions like Power BI, Tableau, Microstrategy have baked in some amazing capabilities for an effective data story telling. Talking specifically about Tableau one can use Arria Natural Language plugin and can generate intuitive reports that not only shows interactive graphs but also translates what the graphs are showing. Similarly in Power BI Key Indicators Visualization is one amazing tool which generates auto insights telling the user the causes for performance deterioration in an easy to understand. In Microstrategy you can use narrative science's plugins to generate data narratives.

There is no doubt on what appeals better to demonstrate a data in a better effective manner. - It appeals across all level - It provides for easy comprehending - It ensures the same data can be displayed in periodic manner. Hence visually the changes are easier to spot

Static reports are appealing when you have to send periodic information for stakeholders Especially when your stake holders use various media to consume the data, like some might want a print from thier secretary, some might want to see a doc in thier mobile. This helps them.

When you need to send statics reports, Reporting Services (SQL Server or Power BI) is a good tool to prepare it, because the ability to connect to data models and properly represent results on a sheet. if we combine this tool, with data-storytelling + UX perspective from a Graphic Designer professional, we are going to take a high impact report for our users in the organization.

The static report has fixed content and layout which means Static reports display a specific dataset and set of visualizations that do not change unless the report is manually updated or regenerated. The layout of static reports is typically designed by the report creator and remains the same each time the report is viewed or generated.

I once worked on a project that used R to generate PowerPoint slides. Honestly, it was quite a pain because there was not much documentation around for that use case, but the main benefit was that the PowerPoint slides could easily be edited by someone else without any experience in R.

• Microsoft Excel: Traditional static reports. • JasperReports: Open-source and pixel-perfect. • Crystal Reports: Robust for static reporting.

Storytelling is an art, where we can utilize data reporting tools efficiently to present insights and share effective business outcomes making thoughtful decisions. Beginning to ensure that data is well cleaned and ready to use by various reporting tools like Tableau, PowerBI, Looker, Data Studio and a lot more. Where these tools aren't suffice to share compelling stories, what impacts is how do we interpret data to share insights for business. Utilizing various templates, charts, filters, images, and animations to create impactful dashboards and storyboards can help to extract value out of data.It's also important to make the story relatable to data and the business use case making it more ideating and realistic in the challenging world.

Probably the single most important thing about transforming data into wisdom, is through a compelling story. This doesn’t have to be told through words or a person presenting but can be done through good visual design.

Most of the data visualizations tools like Tableau and Power BI provides all the templates, charts and capabilities to integrate with open source programming which helps to implement out of the box functionalities to help us to create a data story which will help the business users to understand easily

This tool is used when the involvement and time dedicated by customer is absolute. This is more interactive media and needs dedicated , personal and physical presence by the customer. Best suited for two pizza team size teams.

Data storytelling in data engineering is the practice of using data and analytics to tell a compelling and meaningful narrative. It involves taking raw data and turning it into a story that conveys insights, trends, and valuable information to a diverse audience. Data storytelling is a crucial part of data engineering, as it helps make data more understandable and actionable for decision-makers.

Data exploration often requires a combination of tools. Python, SQL, and Databricks can be used to access, process, and analyze the data. Jupyter Notebook and Azure Databricks can be particularly beneficial for interactive and collaborative data exploration sessions. These tools provide an environment for data scientists and analysts to experiment, visualize, and gain insights from the data.

Taking some time to learn the shortcuts and all the features of RStudio really pays off in the long run if you're working with R. There are so many things you can do in RStudio - easily inspecting a data frame, filtering and visualizing data, switching between panes, and much more. My advice: if you're an R programmer, use RStudio and definitely take the time to learn the shortcuts!

Data exploration is the uncovering of hidden patterns, trends, and anomalies within datasets. It helps identify valuable insights that can inform business strategies and decision-making. By diving deep into data, organizations gain a competitive edge, as they can adapt and innovate based on a thorough understanding of their information. Furthermore, data exploration fosters data-driven innovation, enhancing problem-solving and efficiency across various industries.

For Data Engineers, understanding data exploration tools is advantageous, even if it's not their main focus. Alternatives to Jupyter Notebook, RStudio, or Google Colab include tools like Apache Zeppelin, Databricks, and Observable. These platforms are crafted for crafting data exploration notebooks that blend programming, data analysis, and results in a unified document. They feature interactive elements like widgets, graphical displays, and geographical visualizations, enabling diverse data manipulation and visualization. These tools are particularly suitable for data science, research, and prototyping, offering insights into how Data Engineers' work facilitates diverse analytical endeavors.

• Tableau: Drag-and-drop data exploration. • Google Data Studio: Free and user-friendly. • Metabase: Open-source data exploration.

Though relatively new, completely open source, and projected to be the modern orchestration tool replacement for Airflow - Mage is a very powerful and easily scalable data ingestion and transformation engine with the flexibility to choose any of Python, SQL & R in the pipeline development. DAGs can also easily be tested in development real-time. Databricks Delta Live Tables are also very powerful for a completely end-to-end ETL process at scale. StreamSets is also a powerful modern data ingestion, transformation, and job scheduling tool.. You can also develop and integrate your own custom processors to use in the DAG. It has comprehensive product documentation and so easy to pick up.

These are areas that have grown beyond IT scope. It is an area that has got customized according to company. One can't recommend this is standard way of performing this task.

Airflow, Luigi or Prefect are great to orchestrate ETL processes. The flexibility and underlying framework allows for complex DAGs, that are also great to split up huge SQL processes into smaller steps to parallelize and increase maintainability. However, one key to deliver business value and make most of your data is self-service. If a product manager would like to schedule a SQL query as a data source for a report, you don't want to tell him to write an Airflow DAG with Python. You also want to avoid these tasks stacking up on your desk. So instead, integrate simple automation tools to allow for self-service scheduling. Examples: scheduled queries in BigQuery or Cloud Scheduler. There are similar solutions in other environments as well.

Data automation is pivotal in reducing manual errors and accelerating data processing, increasing efficiency and reliability. It streamlines repetitive tasks, allowing teams to focus on more strategic activities and innovation. Automation ensures data consistency, improves data quality, and supports real-time decision-making by providing up-to-date information.

In the realm of data automation, particularly for scheduled or trigger-based tasks, Data Engineers should be aware of tools like Apache NiFi, Talend, and Dagster. These platforms excel in building and managing data pipelines, crucial for streamlining reporting workflows. They come equipped with functionalities like dependency management, automatic retries, notifications, and comprehensive logging. This makes them indispensable for tasks in data engineering, ETL processes, and DevOps. Their integration helps ensure efficient and reliable data flow, reflecting the critical role of Data Engineers in automating and optimizing data processes.

To add some free tools here, using Google Collab for your Notebook allows a team to work on it when and where they want. Jupyter Notebook has also now developed real time collaboration which is great for the right situation. If real time collab is not the goal but more access to the same data and code, Github is an obvious and great choice to use which is freely accessible to all.

It is important to lean on the community. Over the years we managed to foster an everlasting online collaborative presence and it translated into tools like GitHub, where you can share your knowledge with the world. Rely on it.

• Google Data Studio: Real-time collaboration. • Microsoft Teams: Collaboration within Teams. • Looker: Data platform with collaboration.

- Power BI Workspaces allow teams to build, share, and collaborate on reports and dashboards. - Databricks Unity Catalog centralizes metadata, promoting collaboration on data assets. - Azure Synapse offers unified data processing and storage for seamless collaboration. - SQL Role-Based Access Control ensures secure data access based on user roles, enhancing collaboration.

A lot would come down to how much degree of involvement and span of time customers spend with the project teams The way we serve the data to them changes according to disposable time they have in hand.

A key idea is prioritizing user autonomy in data manipulation. Our data pipeline design caters to diverse user needs and timelines. Placing the user at the core consistently yields positive results. Operational teams, for example, require near-real-time data with minimal processing for quick issue diagnosis. We use Metabase for visual and SQL-based analysis. Simpler solutions like Tableau or Looker Studio suit users not needing real-time data, not needing to build dashboards or not have previous knowledge about data analysis. Empowering the user enhances the data-driven culture.

There is no right or wrong answer here; it depends on various factors, such as compatibility with the current tech stack, scalability, budget, familiarity, and user experience