Raw data is information that has been collected into a database but has not been formatted or analyzed. By itself, raw data holds little meaning, but once it has been analyzed, it becomes processed data, which is typically easier to understand, better displayed, and has the potential to lead to actionable insights.
Portions of this definition originally appeared on Datamation.com and are excerpted here with permission.
Raw data is data in its initial state that is collected from sources like databases, files, spreadsheets, and source devices such as cameras. It can be manually written down or typed, recorded, or automatically input via machine.
Examples include:
- A month’s worth of every purchase at a store with no further structure or analysis
- Every second of footage recorded by a security camera overnight
- The grades of all of the students in a school district for a quarter
- A list of every movie being streamed by a video streaming company
- Open-ended responses to a survey question
Regardless of source type or collection method, raw data is just one type of data with potential energy. The data needs organization and analysis to be actionable. Fortunately, all the information is there to create benchmarks, ask questions, and process the data as well as to create visuals to show what is happening with the dataset.
How is raw data processed?
Raw data is handled by data analysts, who use software and artificial intelligence (AI) to aid in each step of the process. They start by organizing and cleaning the dataset, ensuring duplicates and outliers are removed.
The next step is an initial analysis, which may involve data manipulation, especially if the raw data is based on human responses to a question. Analysts must determine if the respondents inaccurately replied to the question in a way that will change the results and review the quality of the question to decide if the responses are relevant for further analysis.
What is raw data’s importantance?
Raw data serves several purposes, especially when full data visibility is key to statistical and predictive analysis.
- It’s the starting phase of all data analysis and is necessary to make data-based decisions.
- It has a high level of integrity since it has yet to be manipulated or formatted.
- AI and machine learning methods can only analyze data in a raw format.
- Raw data can act as a backup resource, which can be referred to once a dataset has been processed or manipulated.
With raw data in hand, it’s time for data analysis. Take a look at some of the best methods to make sense of the data at Datamation.com.
