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Last updated on 2024年5月26日

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How can you find a data analytics platform that offers advanced data visualization capabilities?

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In the era of big data, having a robust data analytics platform is crucial for making informed decisions. As you delve into the world of data, you'll find that advanced data visualization capabilities can significantly enhance your ability to interpret complex datasets. These visual tools transform numbers into graphical representations, making trends and patterns immediately apparent. To find a platform that offers these capabilities, you'll need to consider several factors, including the variety of visualization options, customization features, and integration with other data sources.

本文章的要点总结

Assess needs first:

Start by identifying your unique data requirements and who will use the visualizations. This ensures the platform you choose can handle your specific data types and analysis needs effectively.
Evaluate integration:

Ensure the data analytics platform integrates smoothly with your existing systems for a seamless workflow. This reduces manual input errors and saves valuable time.

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1 Assess Needs

Your first step in finding a suitable data analytics platform is to assess your specific needs. Consider the types of data you work with and the insights you aim to glean from them. If your analysis requires tracking real-time data, you'll need a platform that offers dynamic visualizations. For historical data analysis, static visualizations might suffice. Think about who will be using the visualizations and in what context, as this will influence the level of complexity and interactivity required.

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Ricardo Cácio

Data & AI | Top Data Engineering Voice | Top Data Analytics Voice | Top Business Intelligence Voice | Microsoft and Databricks Certified Professional
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In my opinion, some points should be taken into consideration, such as: Start by defining your specific requirements for data visualization; Evaluate visualization capabilities; Choose a platform that provides an intuitive, easy-to-use interface for creating and customizing visualizations; Consider data connectivity; Evaluate interactivity resources; Evaluate performance and scalability; Evaluate integration with analytical tools; Ensure the platform provides robust security features to protect sensitive data and comply with data privacy regulations and Evaluate the cost of the platform and consider return on investment (ROI) based on the value it provides in terms of insights improved productivity gains and decision-making capabilities.

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Harish Vishwa Senthil

Business Intelligence Analyst, dentsu || ex - Kotak Life Insurance
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As per my knowledge, choosing a suitable data visualization platform involves the key considerations listed below, -Firstly, identify your specific needs, including the types of data and visualizations required. -Evaluate the platform's ease of use, customization options, and interactivity. -Ensure it integrates well with your necessary data sources and existing tools. -Consider the BI platform's ability to handle large datasets and its scalability. -Assess the security features, options to share & download the report and other compliance related regulations. -Finally, compare costs, support options, and take advantage of free trials to test the platform.

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Ed C.

Triumph with marketing analytics and powerful storytelling
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See how your stakeholders want to work. If they don't use existing reporting tools, getting more like them won't improve information flow. Also consider what your talent pool is capable of.

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2 Compare Features

Once you've outlined your needs, compare the visualization features across different platforms. Look for a range of chart types, such as bar graphs, line charts, heat maps, and scatter plots. The best platforms offer a library of templates that you can customize to fit your data. Additionally, consider the platform's ability to handle large datasets and perform complex calculations, as these capabilities will affect the accuracy and speed of your visualizations.

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Laura Porsch

Data Analyst | Business Analyst | Python | SQL | Excel | 3+ Years Experience in Data Analysis and Data Visualization | I transform data into strong stories and actionable insights | Volunteer at the Banff Food Rescue
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Make sure the platform offers everything you need. If you are working a lot with geolocations, for example, you will need a wider range of map options. A lot of platforms now also offer the help of AI to create visualizations. Maybe this is something you want?

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Ed C.

Triumph with marketing analytics and powerful storytelling
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There is no such thing as advanced visualization. Any chart has to have clear and intuitive and commonly interpreted meaning to your audience and what matters is contextual understanding of the report builder. The tool is nearly irrelevant.

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3 Evaluate Integration

Integration capabilities are essential for a seamless analytics workflow. Examine whether the platforms can easily connect to your existing databases, CRM (Customer Relationship Management) systems, and other data sources. This integration allows you to pull in data from various sources without manual input, saving time and reducing the likelihood of errors. A platform with robust API (Application Programming Interface) support will offer greater flexibility in how you can manipulate and visualize your data.

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Ed C.

Triumph with marketing analytics and powerful storytelling
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If you want a reporting tool that pulls in data from platforms, you'll probably be disappointed by how much configuration you'll have to manually do. Better separate the api integration layer from the visualization tool. You'll be less beholden to expensive vendors that charge you a lot to do little.

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4 Test Usability

Usability is a key factor in choosing a data analytics platform. A platform with an intuitive user interface will lower the learning curve and enable quicker adoption across your team. Look for platforms that provide clear documentation and responsive customer support. It's also beneficial to choose a platform that allows for collaborative work on visualizations, as data analysis is often a team effort.

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Ed C.

Triumph with marketing analytics and powerful storytelling
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This really depends on your stakeholders and talent. I don't think anyone would go through a logical process and end up with tableau if it were up to people that appreciate things like speed, logical data structures, good pricing, etc. It's the least usable reporting tool to data people. But you'll probably end up with tableau and regret it.

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5 Check Scalability

Consider the scalability of the platform as your data analytics needs may grow over time. The platform should be able to handle an increasing volume of data and more sophisticated analytics without significant performance degradation. It should also offer advanced features like predictive analytics and machine learning capabilities that you might need in the future.

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6 Review Costs

Finally, review the costs associated with each platform. While it's tempting to opt for a free or low-cost solution, ensure it doesn't compromise on the essential features you need. Some platforms may offer a subscription model with different tiers of service, so you can choose one that fits your budget while still providing the advanced visualization capabilities you require.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Ed C.

Triumph with marketing analytics and powerful storytelling
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When you end up with tableau, Costco has pretty good prices on bulk kleenex for the team. Remember they only take Visa now and the pizza slice plus hot dog and unlimited soda is the best thing you can buy in America under $4. Toss the hot dog bun and use the pizza slice as a bun to save on carbs. Thank me later.

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Ysobel Rose

Retail & Digital Media ?? Amazon Ads, Walmart Connect, Instacart Ads, Pinterest Ads
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Ha! “Blended” carbs.

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How can you find a data analytics platform that offers advanced data visualization capabilities?

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1 Assess Needs

2 Compare Features

3 Evaluate Integration

4 Test Usability

5 Check Scalability

6 Review Costs

7 Here’s what else to consider

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