Imagine going to a large nearby store where you see all products kept in a haphazard manner.
But, there is a store opposite it which has its products well stacked in sequence and order.
Where would you go? The second one!
Data wrangling is like this. It takes all the messy, complicated data and cleans it to make sense of the data so that it is easy to use.
If data wrangling is cleaning up the mess then don’t you think the data has to be accurate to get valuable insights? Businesses rely on data scientists to understand data and bring in leads and customers. It thereby is a crucial step to sort the relevant data from the least necessary data.
Trustworthy data becomes a necessity in this case. Before data scientists, use their skills to make sense of that data, data wrangling can play a significant role since. Here are a few reasons for the same:
1. Provides credibility to the data
The question on credibility of data arises when
The data retrieved is not consistent with the solution expected.
Data is outdated
Technological and social changes are not factored in the solution. Hence they do not give important outcomes.
Remove these through the use of data wrangling.
Data wrangling brings out the best features of data. It is done through the mix of communication skills and common sense.
It helps plan solutions to problems. It not only gets the more accurate and relevant data but provides valuable insights. Experience and mistakes are another way to add to it.
Data wrangling lends data credibility by
Identifying the exact data sets that are required to arrive at solutions
It only picks data that is recent and consistent with the problem at hand
It factors in the changing technical and social factors. These factors impact business decision making and reduce redundancy.
2. Simplifies the process and provides actionable information
Information collection and application cannot be done if the data cannot be used.
For this, data wrangling needs to have a clear structure to follow.
There is no formula that you can use for this process. The following is the basic structure that you can follow for your data cleaning
Identify the data
Provide a structure to the data
Clean the data that makes it complicated and inaccurate
Add your own relevant inputs
Validate for consistency
Execute the data by publishing it
These steps help de-clutter the data into meaningful insights. They are practical and can be used to create actionable steps.
3. Makes it easy to explain data to employees and stakeholders
Information validation to employee and stakeholders is necessary to gain their trust.
Employees have to be convinced of the need to engage with large data sets. Stakeholders need a valid reason to spend time on data wrangling.
Even though data should be in its simplest form, it needs to add value. The drawback is that data wrangling does not always provide simple data in a homogeneous environment. Rather there is a muddled mix of an unknown schema of data that can get confusing.
The easiest way to do so is:
Use a mix of visual tools and machine learning to simplify data
Use tools that your staff is familiar with
Carry out regular training on new tools and technologies entering the market
When employees start believing in data wrangling and its benefits, this, in turn, can help convince stakeholders of its relevance. As pointed earlier, visualizing data critical to gain valuable insights out of it. Tools like Tableau are helpful in creating stories out of data. A good Tableau training can help you master the platform and aid you in creating actionable value out of data.
Wrapping it Up
Data wrangling works beyond cleaning the data. It lends the much-needed backing to provide valuable and actionable insights.
These ideas can give a new direction to your business. In turn, it helps guide it towards a positive result.
These are some of the roles of data wrangling. Can you list some more? Let us know in the comments.