You're drowning in data for your visualizations. How can you efficiently gather it to save time?

- Defining your goals before moving forward with your visualizations. Knowing your objectives clearly will help you gather the information that really counts. - Not all data isn't equally relevant, Concentrate on gathering data that directly advances your objectives is very helpful. - By automating repetitive tasks, you can focus on analyzing data and producing insightful visualizations.

Firstly we need to get the key understanding of what exactly the business is running on so accordingly we can pick the data set. Have a clear understanding on overall data movement with respect to filters and actions we are willing to showcase. Example : Retail sector doesn’t need much focus on the processing units as major part discuss with logistics and sales. So we need to get an idea of overall business to achieve the best insights and data optimization.

All visualizations enable inferences, but taking your audience beyond aggregations and dashboards will wow them. Think "seeking causes and alternatives" and "bottlenecks and workarounds." Here, the focus is on relationships between entities, and storing data in knowledge graphs makes this possible.

The key lies in understanding your audience's priorities. By prioritizing insights that resonate with their interests, you streamline the data collection process. This approach not only ensures relevance but also enhances the effectiveness of your visualizations. Drowning in data doesn't necessitate displaying all of it. Instead, focusing on the most meaningful datasets that align with audience interests enhances clarity and impact. This approach not only saves time but also ensures that your visualizations are purposeful and resonate effectively with your stakeholders.

Define the priorities first. Evaluate the quality of the sources, if it is in accordance, test the data to guarantee. With the priorities and the acknowledge that the data is good, select the most imporant data to show. As long as you advance on the analysis, you pull more data. Keep it simple.

Optimisation can also be achieved by few internal steps we take at time of development. Example : Tableau gives us feasibility to hide unnecessary fields at the source level and we can go with Level of Detailing expressions (LOD’s) which doesn’t impact more on load time comparatively than using table calculations and window functions.

Power Query in Excel emerges as a valuable but often overlooked resource for simplifying repetitive tasks. Its capability to extract, transform, and clean data from multiple sources can drastically reduce manual effort. By maximizing Power Query's functionalities, data professionals can allocate more time to refining visualizations and deriving meaningful insights, thereby optimizing their analytical workflows.

Make sure that your data is good and trusted to use. Depending on the tools, you can automate the data collection, it will save much time that you can spend on generating value instead of collecting. Tools as Python, Power Query (using datamart or dataflows) and others may help on that, depending on the scope of the project.

There is an API for almost anything but be careful, some of the APIs cost money. Still, it's better to pay for a good API than to collect the data incorrectly by yourself. Over time, I've found that the investment in reliable APIs pays off in the quality and accuracy of my visualizations. Ive once created an analysis on the german stock market and bought some weather data. That might cost 10-50 EUR/Dollar, but its still worth in my opinion. With a few lines of code, you can pull the latest datasets directly into your analysis environment, ensuring that your visualizations become GREAT.

Leveraging APIs can streamline data collection by allowing you to programmatically access vast amounts of data. First, understand how APIs work and review their documentation to identify relevant APIs that provide the data you need. Set up access by registering for API keys and use programming libraries like requests in Python to interact with the APIs. Write scripts to make API calls, automate these requests to regularly pull data, and parse the JSON or XML responses. Store the fetched data in databases or files, update datasets regularly, and implement error handling for failed requests. Optimize your API calls to stay within rate limits and keep your API keys secure to manage sensitive data appropriately.

Data cleansing is essential, involving removing duplicates, correcting errors, and handling missing values. Though time-consuming, clean data underpins effective visualizations. Tools like Excel, R, and Python automate many data cleaning tasks. For instance, Python's pandas library offers functions to drop missing values or replace them with statistical imputations. Using these tools, you can streamline data preparation, ensuring accuracy and readiness for analysis. This foundational step enhances the quality and reliability of your visualizations, making the initial effort worthwhile for better outcomes.

Efficient data collection for visualization starts with thorough data cleaning, including removing duplicates, correcting errors, and handling missing values. Utilizing tools like pandas can automate these tasks, saving valuable time and ensuring a solid foundation for impactful visualizations.

You can use Power BI to connect LIVE to datasets in the service. Different sources or drillthrough from one dataset to another is possible. Integrating multiple datasets can improve your visualizations but also adds complexity. To save time, plan your integration strategy carefully before you start merging data uncontrollably. Use tools that allow for seamless merging of datasets. Consistency in data formats and understanding the relationships between datasets are key to efficient integration. PBI can help you tackle all of these problems, but be aware that this is an advanced topic which might have a high complexity level.

Implement Master Data Management practices to maintain a single version of truth across all datasets. This ensures that all integrated data is accurate and consistent. Use data integration platforms like Apache NiFi, Talend, or Informatica to automate the data integration process. Automation reduces manual effort and minimizes errors. Regularly perform data quality checks to ensure that the integrated data is free from errors and inconsistencies.

It's probably not your job to really gather the data, but you need to be able to query it from warehouses. In my experience, being good at querying data is very important to ensure you can work on your stakeholders requirements.

?? Learn SQL; itss the most important language, although other languages emerge. In my experience, SQL has been indispensable for querying and managing data effectively, providing a strong foundation for any data-related task regarding a database such as Snowflake, Google Big Query or a classical SQL Server.

Don't be afraid to start small. One insight from one facet of a large database is enough to get you started. Perhaps one thing will lead to another question: "I wonder how X works related to Y?" And on it will go. You don't have to understand it all to begin.