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4 Factors to Consider While Choosing a Data Analytics Tool

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Choosing the right data analytics tool for your business takes a lot of work. There are so many options for data analytics tools available in the market for the extraction of data insights. And you need to evaluate all of them before choosing one for your business. So, here are some essential factors you should consider before making a decision about which tool will help your company grow:

1.   Data Type

Before choosing a data analytics tool, you should evaluate the data type you want to work with. Data types can be classified as structured or unstructured. Structured data is organized into tables and columns, while unstructured data has no predefined structure. The volume and complexity of your data also matter when selecting a particular tool for analytics purposes. Besides, other factors like where your data is coming from (internal or external sources) and how secure it needs to be considered when selecting an analytics tool for extracting meaningful data insights because they may affect how well the devices perform in their intended job roles.

2.   Learning Curve

The learning curve is the time it takes to become proficient in using a tool. Learning curves depend on the type of tool and how it is used, so you should consider this factor when choosing a data analytics tool. For example, if you plan to use an open-source tool such as Python for your next project, you can start by evaluating your team members’ backgrounds in programming languages such as Java or C++. If there are no programmers on your team with skills in these languages, but they have experience with Python, this will help them learn how to use the open-source tools faster than if they had no prior programming experience.

3.   Type of Analytics

Descriptive Analytics

It is a process of analyzing past and present data. It helps in answering questions such as, what happened? What does it show? Where did it happen? Why did it happen? When did it happen? Who was involved? Etc.

Examples: How much time your customers spent on purchasing an item or how many people visited a website at a given time are examples of descriptive analytics.

Predictive Analytics

This type of analysis helps predict future trends based on historical data and current patterns by using various advanced statistical techniques like machine learning algorithms and artificial intelligence algorithms to analyze large amounts of data coming from multiple sources such as social media posts, customer feedback, product reviews, etc. This can lead to better decisions by businesses that can take advantage before their competitors do so (i.e., being proactive rather than reactive).

Examples: Marketers use predictive analytic tools to predict sales figures based on historical buying patterns, while companies looking into hiring new employees often use these tools for job applicants’ background checks before making final decisions about hiring them.

4.   Big Data Requirements

There are two aspects to consider in this regard. The first is the volume of data you have to work with, and the second is its variety, and both can affect your choice of tool. For example, if your company has a lot of data but very little variety (i.e., all your data consists of customer transactions), then it might be okay to use a spreadsheet program like Excel instead of a more expensive big data software solution that allows ad hoc querying and analysis capabilities (although even this would be preferable over using only spreadsheets). On the other hand, if you have very little variety but a great deal of volume, you may need a tailor-made data analysis application. 

In conclusion, there is no best data analytics tool, as each one has its own merits and demerits. So, the right tool choice depends on several factors, such as the type of data you need to analyze, the number of users who will use it, and so on.

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