Analysis of data and transforming it into some meaningful insights has become an integral part of an organisation. Data Munging is the process by which the data is identified, extracted, cleaned as well as integrated in order to gain a good dataset that is suitable for both exploration and analysis. Data Munging can also be referred to as data wrangling and it includes various aspects such as data quality, merging of different sources, reproducible processes, managing data, etc.

It has been estimated that a staggering 70% of the time spent on analytic projects is concerned with identifying, cleansing and integrating data due to the difficulties of locating data which is scattered among many business applications, the need to re-engineer and reformat it in order to make it easier to consume, and the need to regularly refresh it is to keep it up-to-date. This cost, along with recent trends in the growth and availability of data, has led to the concept of a capacious repository for raw data called a data lake, which is a set of centralized repositories containing vast amounts of raw data.

Why Is It Important

Data munging plays a crucial role in an organisation. The process can be time-consuming but the valuable insights it is producing plays an important role in the organisation. The wrangled data can be organised into a standard repeatable process which can be moved and transformed in a common format and can be reused later for multiple times.  

Steps For Data Munging

According to Trifacta, one of the established leaders of the global market for data preparation technology, data wrangling involves mainly six core activities. They are mentioned below.

1. Discovering: In this process, you understand and learn what is there in your data and to find the best way for some productive analytic explorations.
2. Structuring: Data is usually in the raw form. While analysing the data, it needs to make sure that the data is restructured in the way which suits better during the analytical procedures.
3. Cleaning: Inconsistent and noisy data cannot be used to gain meaningful insights in an organisation. The noisy data needs to be cleaned before it is used for analytical approaches.
4. Enriching: In this process, the cleaned data is enriched by analysing what new data can be derived from the existed data. This new information is sometimes available in in-house databases, but, and increasingly so, may be sourced from marketplaces for third-party data.   
5. Validating: Validating is the activity that surfaces data quality and consistency issues, or verifies that they have been properly addressed by applied transformations. Validations should be conducted along multiple dimensions.
6. Publishing: Publishing refers to planning for and delivering the output of your data wrangling efforts for downstream project needs (like loading the data in a particular analysis package) or for future project needs (like documenting and archiving transformation logic).

How Is It Different From Data Mining

Data mining is a process of discovering some specific hidden patterns in a large dataset whereas data munging is a superset of data mining which involves various process such as cleaning, transforming, integrating, etc. in a large dataset for decision-making. The outcome of a data mining process is meaningful pattern whereas the output of a data munging is a meaningful insight.

Skills Required For Data Munging

A data wrangler solves all the data related issues right from the integrating, cleaning, and transforming. Data is everywhere but it is mostly in the raw form. A good data wrangler requires adequate skills such that he/she can integrate information from various data sources. Most often organisations choose data wranglers with a specific set of skills such as a wrangler with efficient knowledge in a statistical language such as R, Python, etc., adequate understanding in the business context, knowledge in other programming languages such as SQL, PHP, Julia, Scala, etc.