Four Data Onboarding Trends To Enhance The Customer Experience In 2022

By Daniel Hussem - Director of Marketing & Product for Troparé Inc.

Click here to read the original article on Forbes.com

Data is being transferred, shared and moved between businesses more frequently than ever before. Because of this, data errors or modifications related to formatting, structure and content can be significant bottlenecks with costly consequences for mid-sized and enterprise organizations.

If you are looking for some extra guidance on how to optimize your data onboarding experience in order to avoid broken, problematic or incorrectly formatted data files from entering time-consuming (recurring) data onboarding processes and leaving a bad impression on your clients, here are a few trends you can leverage to improve your workflow moving forward.

1. Data Validation

Companies typically conduct data validation by completing metadata trending analyses. This ensures imported data is correctly formatted, properly standardized and ready for action. Common onboarding errors data validation techniques can help you avoid relate to:

• Sizing: You should work to reduce the number of import failures due to file size requirements.
• Data type: For example, you should make sure you don’t have a field categorized as a date field when it should be a dollar amount.
• Formatting: For example, if you have a phone number column in your file using format 9492010577, which should be (949) 201-0577, or if special characters like semicolons and commas are wrongfully splitting cells, make sure to correct these issues.
• Invalid characters: Ensure UTF-8 encoding and/or remove special characters.

Manual data validation is a valuable way to ensure your incoming data is up-to-par but is also hard and extremely tedious work. Depending on the number of files, data fields and products you have, it’s easy to get lost spending countless hours checking and re-checking your work. However, data validation is an important step, and if you have the manpower available, this may be a viable option for you

2. Data Differencing

Imagine that you’re an organization that receives recurring data dumps in the form of new client data, form submissions, invoices and price sheets and that aside from receiving that data in a set format, you wish to be informed of any content variation between files. Data differencing does just that by outlining any data discrepancies and variations between two or more files.

Common onboarding advantages data differencing offers relate to:

• Data matching and comparing: For example, you should make sure headers are uniform in recurring files and keep track of when new columns are added or deleted.
• Missing data: Make sure you know when files are onboarded with empty columns or missing data above a set threshold.
• Value comparison: For example, if you receive recurring vendor price sheets, you want to know if there are price variations, product variations or unit variations between files.

3. Machine Learning

Data onboarding provides an ideal use case for reaping the benefits of machine learning. Due to the number of data transfers that occur, in addition to the ongoing evolution of what that data is comprised of in terms of volume, type and complexity, machine learning can automatically learn, validate, map, fix and transform as much of the data into your ideal onboarding state as possible.

The advantages of machine learning within data onboarding are substantial; it can help you save time, effort and resources.

In order to swiftly start utilizing the benefits of machine learning, develop a defined data template incoming files have to adhere to, collect an assortment of validated incoming files, and utilize these as the basis around which to create your model.

4. Converting ETL Into EVTL

Given a traditional extract, transform, load (ETL) workflow, the extract-transform-load process lacks a critical validation component. By converting ETL into an extract-validate-transform-load (EVTL) workflow, you can detect or deal with broken or incorrectly formatted data prior to entering the time-consuming transform and load phases.

By inserting a systematic validation component within a traditional ETL workflow, you can:

• Prevent wasted time: Applying systematic data validation upon file arrival will enable you to reject broken data prior to letting incorrect files infiltrate your data onboarding process.
• Streamline the onboarding experience: Know that the data you are onboarding adheres to your exact requirements and is ready for action.
• Maximize resources: Spend less time cleaning and wrangling your data and more time using it.

Some companies choose to only validate data after the “load” phase or forgo validation altogether, which vastly increases the risk that they will introduce problematic data into their production environment. This has the potential to greatly corrupt ongoing data flows when they could avoid all this by validating data at earlier stages.

The Takeaway

Data onboarding is often the first impression people have with your organization, whether you are onboarding a new client or a new vendor or receiving recurring data files. It’s a critical part of the overall customer experience.

Don’t let bottlenecks, complications and barriers be the first impressions customers have with your business. You can realize a smooth, efficient and streamlined data onboarding process in conjunction with adamant validation components so that you can spend more time utilizing your data rather than spending valuable time and effort trying to figure out how to fix it.