Automating data validation with bots

Data cleaning is essential when it comes to data analytics or uploading batches of data into legacy systems. Most of the time, data cleaning are actions to take based on a set of rules and are often repetitive in nature.

Data cleaning can easily be the part where people spend most of their time on. Regardless of the project type, data cleaning usually consists of the following actions:

  • Merging multiple datasets
  • Removing empty or irrelevant observations
  • Check for typos or making capitalization consistent across data sets
  • Check for observations that are ‘not applicable’
  • Filtering out the variables that you don’t need and handling missing information
  • Categorizing data into specific types or categories
  • Changing data types

After finishing the validations, you may have already spent hours on cleansing the dataset and haven’t even started your actual work! This is where bots can bring in values and ease your burden from eyeballing thousands or millions row of data.

This article focuses on how robotic process automation (RPA) bots can automate the data cleaning step.

RPA bots are software robots that mimic human actions. It is best when used for manual and rule-based tasks. Not only that, the bots can improve efficiency and work satisfaction among employees, it also significantly reduces error rate prone to human actions.

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The RPA framework

An RPA bot can be broken down into four main steps: Getting the data — validating the data — doing the core process (in this case, data analytics) — and providing the automation results and output. In the case of data cleaning, RPA bots are most valuable in the first two phases, automating the input gathering and validation steps.

Inputs are files/sources fed into the bots for processing. Depending on how complicated your process is, there may be various channels to access to get all the data you need. The good thing about RPA bots is that they work fine on top of almost all systems, including legacy ones. Therefore, with proper configuration, bots can automate data extraction through multiple sources, easing the data gathering step.

After the extraction, the bots will store the raw data in a pre-defined template. Preferably, using a standardized Excel template could reduce the number of errors generated by the bot. A side note about automating data extraction is to ensure that the extraction process is structured and standardized as much as possible. Else, the bots will face errors and throw out too many exceptions. Files generated through a system could be used as templates as well, as long as they are in a consistent format. So, the rule of thumb of selecting or preparing a template is to use the one with a fixed structure.

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Process flow of a bot doing data cleaning work.

At this step, bots will do the data validation based on the pre-defined rules, including the actions listed above. After doing the validation, the bots can also be used to make further comparison (like a VLOOKUP in Excel), within the dataset itself or with other sources. The comparison can help to improve data quality by assisting the users to automatically identify any similarities or deviations. After the comparison, data can also be transformed into categorical data for qualitative analysis by the bots.

Any result outputs generated by the bots can be either send through an email or print in a new text/Excel file. If users are doing data cleaning to upload data batches into legacy systems, then the bots can be configured to save the cleaned datasets into a designated template in shared folders to be uploaded into the system(s).

Improved efficiency, lower error rate, and better data quality are some of the benefits you would gain by using a bot to do data cleaning. As the bots’ free people from the manual work of data cleaning and validation, people would have time for analytical work.

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Photo by JESHOOTS.COM on Unsplash

So, if you found yourself taking too much time doing data cleaning work, you should think about outsourcing the manual work to a bot!

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Changing the world with data points, one word at a time. #naturalLanguageProcessing #textMining #sentimentAnalysis

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