Data munging is considered as an important process in which data gets cleaned, extracted and identified. It is also a way of integrating a perfect dataset together. This dataset plays an important role in common methods like exploration and data analysis.
So, what is the actual data munging meaning? What is data munging, for both small and big organizations? Above all, is data munging python different from other routines? If these are questions in your mind, keep reading.
Both data cleaning and munging are known as data wrangling. It helps in understanding data sources, which need to be merged and processed. Also, it plays an important role in understanding the overall quality of data. According to experts, more than 70% of the time experts focus on identification, integration and cleansing of data. A common data munging example and usage would be during formatting and re-engineering. The changes made during data munging makes it much simpler for the consumption of raw data. In the world of data munging, raw data is known as “Data Lake”.
When it comes to data munging, you need to be aware of its differences from data mining. Technically, data mining is a process where hidden patterns in a big dataset are identified. On the other hand, data munching is a superset of the mining technique.
Whether you take up data munging in Perl, or data munging in r – there are multiple steps to be followed. In fact, special data munging pandas skills need to be acquired too. With this being said, here are a few important data munging techniques to be remembered.
One, you need to engage in data discovery. This is an important step that involves understanding and learning the actual location of your data. “Discovery” needs to be done with utmost care. Why? It is the best way to find productive results from the analytical methods. Two, you need to find the right data munging tools for structuring. Data often comes in a raw format. The moment you start analyzing, you will be aware of the need for restructuring. This is because structured data often results in a better solution.
After you define data munging, take care of discovering and structuring – you can opt for cleaning. Cleaning is a process where noisy and inconsistent data can be removed. The noise in any dataset has to be removed before you venture into analytical approaches. The next process would be enriching. In this step, you will make use of cleaned data. All the cleaned data is carefully enriched. Enrichment strongly depends on the kind of data you can extract from the existing dataset. Often, this is new information for any organization or in-house dataset. Nevertheless, some organizations tend to source data from third-party marketplaces too. Data enriching prepares the data for two important processes: validation and publishing.
Data validation focuses on checking the overall quality and consistency of the data. Most companies consider validation as an activity. It helps in verifying if potential issues with the data are addressed or not. Also, necessary transformations are performed on the data to make it more accurate, and appropriate too. According to experts, data validation needs to be performed in several dimensions.
The last step in data munging would be publishing. This is where you plan, and deliver data. Data is used as a part of your projects. It could be your current or future projects.
Now that you know what is data munging in Python, and pandas data munging is: you need to know the importance of this procedure too! Big or small, data munging is important for any company. This is a time-consuming process that comes with many useful pieces of information. It helps the company develop routines that are repeatable and standardized.
In fact, companies use this process to transform data into a format that will be of some use. Once data is transformed into an appropriate format, it can be reused multiple times. Upcoming technologies like cloud and data warehouse depend on data munging. This means, data munging is one of the ways by which you can generate governed, flexible and fast information to support modern platforms.
Finally, concepts like NoSQL and Data Lake would not explode if data munging is not achieved. Individuals need to access raw data, and this becomes possible only with effective analysis and transformation of data. Data specialists invest lots of time and effort in transforming, cleaning and verifying data themselves. In such places, data munging removes a major workload.
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