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These days, data is what governs our everyday lives as well as business fortunes. They can come from diverse sources, at different times, and are available in different formats. Within this data lies invaluable insights waiting to be gleaned by data scientists, but before that they would need the data in proper order and in a consistent format to be able to carry out analysis.

In order to make sense of something that is found by you in an entirely garbled format/layout, you would first go ahead to arrange it in a way that would remotely make sense and make it feasible for further analysis.

This is exactly where data wrangling comes into the picture.

With the help of cleaning, structuring and unifying cluttered and complex data into sets, data wrangling ensures that data becomes easy to access and analyse. It makes certain that there is no unarranged stack of data during analysis. This is needed because if there is even one element out of place during this step, then the analysis will run a wrong course, thereby leading to incorrect outcomes, thus making the entire process counterproductive and futile.

There are certain distinct steps in data pre-processing:

  1.     Data cleaning
  2.     Data integration
  3.     Data transformation
  4.     Data reduction

Data pre-processing is a necessary pre-requisite to data wrangling. Data wrangling is used to convert raw data into a format that is convenient for consumption.

Also known as data munging, this method follows certain steps such as:

1 – Extracting data from several sources,

2 – Sorting out data using algorithms,

3 – Reducing data to discernible chunks and

4 – Storing them into a database ready for further analysis.

Difference between ETL/Data Wrangling:

ETL, which is short for Extract, Transform and Load, is a tool that is used to pull out data from databases and place it into another, more relevant database. Due to their similarity, in the sense that they both aid sorting of data, ETL and Data Wrangling are often confused. 

Here are a few differences that demarcate the similarity between the two and thus help you understand Data wrangling better.

1. The user-base is different:

Data wrangling caters to the belief that people who know and understand data should be the ones exploring and preparing data. This means that it is tailored for business analysts, line-of-business users, managers and many others like these. On the contrary, ETL is focused on IT based end users who receive requirements from their business counterparts. They are required to implement pipelines using ETL tools to deliver the desired data to the systems in a specified format.

2. Data that is arranged is different

The occurrence of data wrangling solutions came out of necessity as data is generated at a breakneck pace these days. Much of the data that business analysts have to deal with comes in various formats and are either too big or complex to work with using traditional tools like Excel. Data wrangling provides the right solution to this issue as it is specifically designed to handle a diverse range of data of any complexity lengths.

ETL on the other hand is made to handle data that is usually well-structured. It is not made to process data that is large or complex or that which requires extraction and derivation.

3. Use cases are different

Uses cases when it comes to data wrangling are more exploratory in nature and are conducted by smaller firms or departments before launching out into something major like an organization. Data wrangling users are typically trying to work with new data sources or new combination of data sources. ETL extracts, transforms and loads data into a centralized data warehouse that can be used for reporting and analysis, as and when the need arises.

Role of data wrangling in analytics process

The degree to which data is useful largely depends upon one’s ability to wrangle it. And though there is a considerable advancement in technology, analysts are struggling to work with large and complex sets of raw data. It has been noted that arranging data into discernable chunks eats up at least 50-80% of an analysts time. That is why Data wrangling is such a boon.

Data wrangling is, as you must have known by now, is the ability to wrangle raw, messy data into something that is feasible to be analysed. It is because of this pivotal nature of data wrangling that it has now become the entire front end of analytical processes all over the globe.

Modern day data comprises of datasets that contain variables of different lengths and classes. Many mathematical and statistical calculations operate on different types of data. Data wrangling aligns all this into one understandable string of data that can be easily processed and analysed by tools.

How to improve the effectiveness of Data Wrangling?

Considering how important Data Wrangling is to the analytical aspect of things, improving its efficiency is of prime importance. The more accurate are the results generated, courtesy data wrangling, the more efficient would the strategies be that are made in the light of data emanated from it.

1. Data mapping

Mapping data is too often seen as the most arduous of tasks and is one of the biggest causes of delays and mistakes. One of the ways this can be tackled is to play around with the data. This may not sound as economically beneficial but this is one of the best ways to cut down from spending hours mapping data. Data labs can come in handy where data analysts have the opportunity to use potential data feeds and variables within to learn which are actually predictive or useful for either analysis or modelling.

2. Recruiting non-IT data specialists

Incorporation of non-IT data experts is a move that modern day businesses have stopped doing and that has lead to all the conundrum in the first place. Though it is true that data needs analysts and specialists, it also needs the services of experts from data modelling, data quality and also those from metadata. 

3. Deliver value to justify investment

It is necessary to investigate data requirements so as to be able to sketch out decisions that can help score higher business potential and value. This however has to be very precise in nature and nothing can be left on sheer randomness. Providing value is a term that leaders use these days instead of the term “use cases”.

What other steps do you follow to enable effective data wrangling? Do write to us and let us know

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