5 Features You Should Look Out For In Your Data Projects Tool

Data project tools are becoming increasingly important for businesses of all sizes. We have more data than ever, and if we want to make meaningful decisions based on it, we need to have a way to manage and process it all. To do this, you not only need a good data projects tool, but you need to have one that fits the specific needs of your business.

Getting the Right Data Tool

Before you buy any data tool, it’s a good idea to spend some time conducting research into your options. For example, Redshift is one of the most popular data tools on the market. However, it’s not the right solution for everyone. Maybe the cost is too much or it doesn’t work well with the rest of your software stack. In this case, you would want to explore Redshift alternatives and see which one is best for you.

The right data tool will not only perform well for your business, but it will do so at a cost that is suitable for you. As you’re exploring your different data project options, there are certain features you’ll want to keep in mind. Below are 5 of these features you should look for in your data projects tool to ensure you are getting the best possible software for your business.

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1. Integrations

The first thing to look for is the available integrations. You likely already have software tools you use for data collection, marketing, and sales. You’ll want a data project tool that can easily integrate with your existing software, making it easy to share information across the different tools. 

Most data project tools list the software they can automatically integrate with and provide directions on how to do so. If you don’t see your specific software listed, consider contacting the developer of the data tool to see if it is possible to connect.

You should also look for data project tools that feature ETL. ETL stands for Extract, Transform and Load. ETL is essential for data integration, as it allows you to access different types of data from various sources in one location. To learn more about ETL, and how it impacts data integration, you can look for many guides around the web.

2. Data Size

Next, you should look at how well any data tool you’re considering performs at handling different dataset sizes. You want to get a tool that matches the amount of data you’ll typically be processing. Some data tools excel at handling very large data sets, but they can also cost more or lack other features. If you only have small datasets you’re working with, you wouldn’t need one of these tools.

On the other hand, if you have a lot of data to process, getting one of the data tools that are better with smaller datasets could significantly limit your efficiency. Explore each data tool and see the amount of data they are able to process effectively. You may need to read some outside reviews to better determine the optimal data size for that tool.

3. Speed

Speed is another big feature to look for. You need a data tool that is available to provide you with insight and analysis in a timely manner. When you’re working with larger datasets, it can take some data project tools longer to process everything than others. Speed goes hand-in-hand with the data size a tool is able to process effectively. 

If possible, see if the data tool you’re considering comes with a free trial. With it, you can then test out the speed of the tool on your datasets yourself. Try performing different tasks with your datasets and seeing how long it takes to accomplish everything. Then compare this against other data tools and see which is best. While a few seconds may not seem like a big deal, when you add up all those seconds over the course of your business’s lifetime, it adds up to a lot of lost time.

This guide provides more information on how to performance test different big data applications.

4. Ease of Use

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If this is your first time using project data tools, you should look for one that is built for newer users. You’ll want a tool that is easy to use from the start and doesn’t take much instruction to learn all of its features. Some data tools are more suitable for experts who have a long history of using this type of software. Unless you have someone on your staff with this experience, you are better off finding a tool that comes with an easy-to-use interface.

5. Pricing Model

Finally, you should look for software with a pricing model that suits your financial situation. Some tools require a monthly subscription while others want you to sign up for an entire year. Others base their pricing on how much data you process or how much time you spend processing it. For example, BigQuery charges $5 per TB of data while Snowflake has a per-second data consumption model.

It’s a good idea to estimate what you’ll likely pay through all the different pricing models available to you. If you can convert these numbers into common terms, for example, how much you’ll pay each month, you can then compare each tool against one another. 

In Conclusion

There are a lot of tools out there that can help you collect, process, and analyze your data, with more being created all the time. Rather than settling on the first one you find or whichever one is currently the most popular, you should find the tool that’s right for you.

However, your work isn’t done once you select a tool. As time goes on, your needs may change. It should be a regular process within your business to analyze your current data processing needs and determine whether your current tool is still performing well for you. Always be on the lookout for new data project tools and be willing to consider whether another tool would better serve you. Your goal is to always have the right data project tool for your business so that you can make good use of all the information you’re collecting.

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