Learn about the role data munging plays in data analysis, as well as its significance and processes. Furthermore, learn about the range of tools from which you can choose from for data munging.
Data munging, or data wrangling, is an important step in data analysis. Data munging is about processing the raw data, turning it into a usable form. This article explains some of the basic ideas about data munging.
To understand the role of data munging, let us look first at the data infrastructure needed for smooth data analytics.
The most important part of the data infrastructure is the data warehouse. A data warehouse is a type of data management system that is solely intended to perform queries and analysis and often contain large amounts of historical data. Data warehouses contain the following elements:
Data munging enters the picture as an important part of the ELT solution.
The ELT (or sometimes ETL, where transformation comes first before loading) operations form the so-called data pipeline. The data pipeline delivers data from the sources to the databases comprising the warehouse. Data munging is an important part of the transformation processes of the ELT operations.
Inconsistencies between the data coming from different sources pose a challenge to data analysis. Data munging is important in the data pipeline because it smoothes out inconsistencies and the variation in formats between the data from different sources. This makes data usable. Therefore, you can also imagine data munging as the preparation stage for data analysis.
Trifacta lists six steps comprising data munging. We will discuss them briefly and then demonstrate them by using an example data set.
We have the following dataset as an example. As you can see, while the columns were neatly defined, they are separated by spaces, which may pose problems when we blindly load them into an app for data analysis.
There are also inconsistencies in the Branch column and in the Date of birth column in terms of the formats followed. Fortunately, the other columns are pretty consistent.
We have sorted these entries into a table that separates them into individual cells. This will make it possible for the analysis software to correctly identify information by identifying the header, thus identifying the variables to be stored. While not shown here, at this point, the variable type is also identified, which is important in data analysis.
Using the information from the header of the table, each entry is scanned for inconsistencies with the standard format for data analysis. For example, the entries in the Branch column are cleaned by completing the entries or rewriting them in the standard format (from 5th to 5th Avenue). Some entries in the Date of birth are also cleaned to conform to the standard format (from January 2, 2000 to 1/2/2000).
Using the given information we can derive the following information:
We can already calculate these given a certain date. Let’s say we calculate them with January 1, 2021 as the given date. The table now looks like the following, with additional columns:
This process can involve pre-processing the data to look for inconsistencies among them. For our example, the last two entries were flagged during the validation step because their ages when they started working is way below the lawful minimum age. This means that they have to check again from the source. Fortunately, they are just typo errors. The validated data set now looks like as follows:
Alooma lists three main challenges that affect the ETL processes, which include data munging:
These challenges can be solved by studying the raw data, planning how to process it, implementing the plan, and then testing whether the setup works. These take time, but this will free up a lot of time that can be used to analyze the data later on.
As you have seen, data munging is not a pretty process; in fact, it is a fairly routine but time-intensive task. Data munging accounts for 50% to 80% of work by data analysts! There is therefore a strong push to automate data munging.
Fortunately, tools are being developed to help automate at least a portion of the process. Alooma lists the following types of tools:
To help you choose the right tool, ChartIO’s Off the Charts blog came with the following deciding criteria, copied verbatim:
These are essentially what makes a good tool for data munging.
I know an app that does that.
It’s called Lido.
In fact, Lido goes beyond data munging, it already includes its own data analysis tools, thus not only freeing you from the potentially laborious process of data munging but also lets you see what happens to your business real-time. You can easily use this without requiring technical expertise. It allows you to gather data from the most common ecommerce tools and services in the market. Most of all, you can afford this. Let our platform do it for you!
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Sources:
https://www.oracle.com/ph/database/what-is-a-data-warehouse/
https://www.trifacta.com/data-wrangling/
https://www.alooma.com/blog/what-is-etl
https://towardsdatascience.com/data-munging-a-perspective-view-783b4a3bee58
https://analyticsindiamag.com/the-importance-of-data-munging-for-data-preparation-in-analytics/
https://blog.datumize.com/why-let-go-of-data-munging-challenges-and-issues
https://www.infoq.com/articles/ml-data-processing/
https://www.sciencedirect.com/science/article/pii/S2405896315001986
https://medium.com/@ODSC/automating-data-wrangling-the-next-machine-learning-frontier-64db0d3b19a4
https://theappsolutions.com/blog/development/data-wrangling-guide-to-data-preparation/