Introduction What is Data Wrangling? Principles of Data Wrangling How Would Have Been To The World If The Data Wrangling Process Didn't Exist At All? 6 Steps of Holistic Data Wrangling 5 Smart Tips for Effective Data Wrangling A Few Questions To Warp Off Your Wrangling Process
Introduction to Data wrangling
What would our world be like if Data Wrangling process did not exist?
Do you feel data-wrangling is annoying? Yet many feel it. But the truth is in this process lies all the possibilities and hidden opportunities. Data is the gold mine, and you need to discover what is suitable for your business and what matters for your goals or visions you have seen for your brand to grow or stay ahead in the competition.
The process could be tiresome, but one can easily tackle the biggest headache of organizing datasets and accuracy if your data set is clean in less time.
Please read this blog to find out in-depth knowledge about the data wrangling process, its principles, six steps in holistic data wrangling, and five smart tips to boost your data wrangling process.
And what would the world be like if data-wrangling didn’t exist? To find your answers, let’s dive in.
What Is Data Wrangling?
Data wrangling is the process of cleaning, structuring the raw data into the desired format for better and intelligent decision making. This process is getting ubiquitous, adding fun to the tiresome data cleaning process. And with growing technologies, data has become more diverse and unstructured, demanding more often collecting, cleaning, and organizing deep and accurate analysis.
At the same time, the demand for decision-making is faster and accurate to be ahead in the competition and acquiring more customers. And if you use the data wrangling process, you will always be ahead in the competition on the technical resources.
Therefore, handling vast datasets can be exhausting, as always. Data wrangling is a self-service data preparation model to tackle unstructured and complex data to move away from IT-led preparation. And for this, more and more businesses started using data wrangling tools to prepare data before the analysis process.
Principles of Data Wrangling Process
- Understand what kind of data is available and select those that make a difference.
- Choosing the correct data to use and getting to all levels of their details.
- Meaningfully combine multiple data sources to make it enrich
- Deciding the outcome for better and in-depth analysis
How Would Have Been To The World If The Data Wrangling Process Didn’t Exist At All?
To me, everything had gone messed up; there wouldn’t be anything particular, all mixed up, and you know data is ubiquitous – it’s terrible to mess with it.
Data is vast, the fuel of tomorrow that drives any business from zero to well-established shortly. Therefore information is crucial to make any decision or to be successful in the highly competitive world. But there are high chances that data is ubiquitous in unstructured format – incomplete, unreliable, and faulty and their analysis is crucial to discover new patterns.
Data wrangling dodge the risk of duplication, redundancies, data that adds no meaning to the goal. It cleans such data and saves storage space for further data – making it full of enriched data, which once for always remains as the critical part of the analytical process.
Although it’s a time-consuming and high-priced process when done manually, that’s why there were many eyes on automatic processes to solve the large dataset in minimum time, accurately, while it reduces the human efforts in the data clean-up process.
6 Steps of Holistic Data Wrangling
Each data project demands a different and unique approach to ensure it’s the final dataset they wanted to go with, which is reliable and accessible. Though methods are other, the six fundamentals that every person goes through are very identical. Let’s understand them in detail.
Discovery leads to new opportunities, which means familiarizing yourself with data to know how you should proceed. It’s an opportunity to discover new patterns in the data and find out the missing entities that need to get addressed. It’s a crucial step in data wrangling, as it forms all the activities in the queue.
Raw data adds no meaning unless they get segregated; because they are incomplete with proper actions. Data structuring is the process of converting the raw data into a meaningful format.
Data cleaning is the process of chopping off the redundant data that might distort your analysis or make your dataset less crucial. This cleaning can be anything from deleting the empty cells, outliers, data duplication, and standardizing input that doesn’t give any identity. The goal is to have datasets with no reproduction but full of values.
Once you understand the existing database and convert all data into meaningful ones, your next step is to look out for all the necessary data in the database. If it is well and good, but in case it isn’t, you can enrich the database by incorporating the value from other databases to have a clear-cut idea of what data is available to use.
Data validating works on corroborating data in both ways: being consistent and good quality throughout. Getting multiple issues during validation is a common thing. You need to resolve them and analyze them, as this is an automated process that requires programming.
Publishing is the last step in the entire process. Once you ensure data gets validated, you can publish them but make sure there is duplicate or fake data that is irrelevant to your business. Your data has to be credible and follow your business’s goals and objectives.
5 Smart Tips for Effective Data Wrangling
Data has become gold for marketers and companies. 85% of the consumers will safe-manage their brand relationships without any assistance by 2020, according to Forbes’s research. With customers getting more brilliant, the data must be clean and reliable to solve their problems and give them what they demand. And there is nothing better than a data wrangling process.
Data wrangling – also called data cleaning, data munging refers to various processes to transfer raw data into meaningful ones. The objectives remain the same, though the methods differ from project to project – cleaned data with more readable formats. It can be a manual or automated process based on the amount of data to be wrangled.
Here Are Five Crucial Tips To Power-up Your Data Wrangling Process:
1. Keep Yourself Updated With Latest Happening In The Data Wrangling Process
Maintain a journal – collect all hacks; that can make your research pinpoint and give you results in the minimum time. Identify the best approaches to every problem and keep a note of the best solution.
2. Be A Part Of Communities And Read Marketing And Business Case Study
Communities bring you great minds, help you find out people with the same interest, rich in their skills, and have some excellent knowledge shared in the comments. Get engaged with them and build your relationships, and remember it will help you in many ways.
3. Collaborate With Other Team In Your Corporate
The marketing team can help you in many ways, from getting you the correct information to the number of sales or the number of carts abandoned – always build a good relationship with them, and you always have a solution ready in your pocket.
4. Keep Your Focus On Processes And Final Results
Of course, results are essential at the end of the day, but your approach towards solving the process is right to get the best results. Look for the ways where the result will be accurate and less time-consuming – your go-to process as always.
A Few Questions To Warp Off Your Wrangling Process:
- Will your process ensure scalability and granularity?
- How can you use the same data in other methods and queries?
- Can tools give you accurate results or better do it manually?
Data wrangling is a fundamental process you can do manually or automated through coding. That’s true as ist principle is simple, and there are six easy steps to the data wrangling process.
But what would have been a world without data wrangling? For me, everyone would have gone mad with tons and tons of data everywhere around. There would be a complete mess choosing the correct data and making intelligent decisions, and most of the data are in an unstructured format.