As businesses have to rely on data more and more it is of growing importance how this data is ordered and handled. Thus, a lot of data analyst’s time is spent structuring and cleaning data before the actual analysis. This process of cleansing large data sets and transforming them into structured formats is known as data munging. Alternatively, this procedure is also referred to as data wrangling and is known to be the foundation of efficient data analysis. Thus, it is worth looking at the beneficial effect this process has on business.
Clarity and organization
The main objective of the data munging process is to produce clarity and order in the data sets. This is done by going step by step through the data in various data sets, usually employing spreadsheets and scripts for filtering. The unwanted data is filtered out, leaving only what is most necessary for the goals at hand.
The final format of the data will depend on the type of data we are dealing with. Usually, it would be one of the four main formats – de-normalized transactions for transactional data, analytical base table for varied data, time series for when time sequences are relevant, or document library for when we are dealing with textual information.
Data munging will in one form, or another involve the same general steps. It will be initiated by data discovery and organization then moving to clean. After the data is cleaned of unnecessary or erroneous units, if needed it will be enriched to fill the gaps. Data munging will conclude with validation and publishing, which is preparing data for future use.
This is how data that is at first hard to read and unstructured is turned into very useful sets of information that are then utilized for decision making and other important business tasks. Thus, no wonder that data analysts spend most of their time munging data. It is at the very essence of proper data handling.
The greatest benefits of data munging
Data munging is used everywhere where there is any work to be done with large volumes of data. And these days many businesses certainly fall into that category. Thus, businesses utilize data munging for added value in many ways.
Here are five of the greatest benefits of data wrangling in business.
1) Enhanced decision-making. With the clarity that is provided by data munging it is much easier to uncover important insights lying within the data. This leads to better usage of the available data and enhanced decision making. As it is much easier to see the underlying patterns after the data is structured, the managerial and financial decisions would be better-informed using such well-ordered intelligence.
2) Improved overall quality of the data. As data-wrangling involves removing errors and fixing such issues as empty fields and redundancies in the data set it leads to higher data quality. And the quality of data that is used is extremely important in business as low-quality data instead of being an asset may very well drag the firm down by leading to continuous mistakes.
3) Time efficiency. Although data munging is at first a time-consuming procedure, in the end, it actually goes on to save time. Working with unstructured data leads to constant delays as it is necessary to go through different formats and different data sets to get the needed information. Additionally, there are likely to be errors that will further delay the analysis. After data is unified into a singular format, the analysis runs smoothly and efficiently.
4) Cost efficiency. Naturally, time efficiency also leads to cost efficiency. When we are working with well-ordered data and less time is spent in using it for knowledge extraction or model building, that time then can be invested in other tasks. Furthermore, error avoidance also saves money as it is often costly to fix the issues which are noticed late.
5) Unified system of data sharing. Finally, data munging allows sharing the information in a unified format with all the departments concerned with this data. As different data may be collected in various formats and stored differently, to share it efficiently needs to be structured in a set structure. When we have the data prepared this way, the data is equally understandable and accessible to all the users.
Doing your homework
Data munging is where the hardest and most intensive preparation is done before doing the actual analysis. Thus, it is often tempting to skip this step and try to analyse poorly structured data or to rush the process.
However, these temptations should be resisted as it is very important to do the work attentively and with care. One can think of it as doing your homework before taking on an important task. If the homework has been neglected, it is unlikely that the task completed right. But when coming prepared, there is nothing to fear of in doing the job.