Data exploration is the first step of data analysis used to explore and visualize data to uncover insights from the start or identify areas or patterns to dig into more.
Collecting the right data plays a huge role in Data exploration as all the analysis is done on collected data. In this post, we will see how the right kind of data is selected.
Selecting the right Data –
Following are some data-collection considerations to keep in following for your analysis:
How the data will be collected :
Determine if you will collect the data using your own resources or receive (purchase it) from another party. Data that is collected by yourself is called first-party data.
Data sources :
If you don’t collect the data using your own resources, you might get data from second-party or third-party data providers. Second-party data is collected directly by another group and then sold. Third-party data is sold by a provider that didn’t collect the data themselves. Third-party data might come from a number of different sources. Choosing third-party data leads you to perform some extra work on data, if there is not any other option then settle down with Third-party data.
Solving your business problem :
Datasets can show a lot of interesting information. But make sure to choose data that can will help solve your problem question. For example, if you are analysing trends over time, make sure you use time series data — in other words, data that includes dates.
How much data to collect :
If you are collecting your own data, make reasonable decisions about sample size (Sample size refers to the number of participants or observations included in a study.). A random sample from existing data might be fine for some projects, but other projects might need more strategic data collection to focus on certain criteria. Each project has its own needs.
Time frame :
If you are collecting your own data, decide how long you will need to collect it, especially if you are tracking trends over a long period of time. If you need an immediate answer, you might not have time to collect new data. In this case, you would need to use historical data that already exists. Choosing the right time frame will reduce the amount of unnecessary data.
Thank you for reading.
Have a nice day!
References — Google Data Analytics Professional Certificate
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