The world of data is deep, complex and always expanding. It’s easy to understand why having the right data in the first place can make all the difference in business. Business leaders rely on data and information to make business decisions. When this information is incorrect, it could lead to significant downfalls, missed opportunities and unnecessary risks. The process of data wrangling exists to ensure that data is ready for automation and machine learning to combat this.
But the time-consuming nature of data wrangling could mean that your business decisions may be delayed and cause undesirable consequences. Automation tools have helped to resolve the slow and all too often manual process of data wrangling. Let’s take a look at how it works and what automation tools can do for you.
Data wrangling refers to the process of cleaning, organising and enriching raw data so that it can be used for decision making promptly. Raw data is any piece of repository information that has yet to be processed or integrated into a system. It can come in the form of text, images and database records, for example.
Data wrangling, also called data munging, tends to be the most time-intensive aspect of data processing. Data scientists note that it can take up about 75% of their time to complete. It’s time-intensive because it’s essential to be accurate since this data is pulled from various sources and then often used by automation tools for machine learning.
Data wrangling includes:
In the simplest of terms, data wrangling is so crucial because it’s the only way to make raw data usable. Many times in a practical business setting, customer information or financial information, comes in different pieces from different departments. Sometimes, this information gets stored on various computers across different spreadsheets, and on different systems including legacy systems leading to data duplication, incorrect data or data that can’t be found to be used. To create a whole picture of what is happening within a business, it’s best to have all data in a centralised location so it can be used. This is just one way in which data automation tools help the data wrangling process along.
Good data-wrangling involves piecing together raw data and also understanding the business context of data. In this way, good data wrangler will be able to interpret, clean and transform data into valuable insights. You can leverage data automation software like SolveXia to help eliminate disconnected data and map the data seamlessly together within your business as it collects data from various sources and systems so it can be accurately processed for reporting and provides real-time analytics and insights while also improving compliance.
Automation tools also reduce errors, maps out processes to reduce critical man dependency, removes low-value manual tasks, so staff can focus on the high-value tasks that matter, and saves employees time so they can provide more and better insights to the business.
You can approach data-wrangling as you did in the past by hiring a data analyst to perform the work manually. But, data is growing all the time, and a manual approach is not scalable or efficient. While coding and engineering work, to a certain extent, it doesn’t scale as well as an automated software tool does. Just like you use technology solutions in departments like marketing to help with automated email marketing, you can use data automation technology solutions to help manage and utilise your raw data for insights.
No matter how you approach data wrangling, through manually coding or software systems, there is a 6-step approach used to complete the data wrangling process. These core activities include:
Here is where you try to understand data and what it is about. Before you clean the data or fill in missing information, it’s crucial to know what the data is going to be used for. With this knowledge, you can better organise the information. Once you understand why you need the data, you will be able to determine the best approach to analyse it.
In most instances, companies have data stored with no organisation. When data is input and coming from different sources, there’s no structure. As such, data needs to be restructured to be used. Based on step one, you can understand how to categorise and separate data based on what it will be used for.
Before you can start to input data into any analysis software systems, you need to make sure that it’s clean. Cleaning data removes duplicates, null values and relies on formatting to make data high quality. You’ll also want to standardise data. This is where you’ll write all information in a column in the same way, i.e. “CA,” “Calif,” and “California.” Cleaning data is crucial to data mapping and data accuracy. Automation software connects directly with systems, and you can set up rules to automatically clean. Map data are removing any guesswork and saving vast amounts of time by automating this very manual low-value task.
Is your data ready to be used after cleaning? That’s for you to analyse and decide. If you think that you need to augment or add additional data to make it better, then you can enrich the data by finding ways to add more information. You can use existing data to derive additional information. For example, if you work in insurance and need to underwrite home insurance, then you’ll likely want to know crime rate data in the city to assess risk better.
Your data may be clean and enriched, but if it isn’t accurate, you will run into problems. To make sure that your data is valid and credible, you can run a check across all the data to ensure that attributes are typically distributed.
For an organisation to use the data after the wrangling process has been completed, you have to publish and share the information. This could come in the form of uploading the data to an automation software or storing the file in a location where the organisation knows it is ready to be used. It’s also a good idea to document the steps taken and logic used in the data wrangling process for future reference.
We’ve touched on a lot of the technicalities of data wrangling, but what does it all mean in practice? To understand why data wrangling is so essential, let’s take a look at how automation tools help to achieve data-wrangling goals.
Data wrangling is a necessary component of any business. It is used to transform raw data into actionable information. This essential workflow has been done manually, but it doesn’t have to be this way.
With manual data wrangling, your data analyst is bogged down, transforming data and filling in gaps rather than spending valuable time performing analysis. Consider a data automation tool like SolveXia to help you with data wrangling, data management and automated analytics to boost your decision-making process, with more precise and more accurate insights and real-time analytics and reports.