Why Data Onboarding is so Difficult and How to Solve It

Data onboarding is the process of ingesting online and offline data into a product’s operational system(s) in order to successfully use that product. This process is an essential, yet difficult, part of collaborating with your customers and partners.

During onboarding, customers and companies need to upload their data in order to use the product and see value. But the customer's data comes from various sources and in formats that don't often align with your target system. Cleaning and ingesting customer data take weeks leading to terrible customer experience.

Below are the top 3 reasons why this problem is so difficult to solve at scale.

What Makes Data Onboarding Difficult?

1. Messy data

Just think of all the different methods, files, syntaxes, and systems used to share data—CSV, JSON, TSV, XML, APIs, data warehouses, emails, CRMs, and ERPs. Ingesting external data into a format and style that your systems can understand and use is no easy task, which is why companies rely on dev and data teams to support their data onboarding efforts.

Even if CSV is successfully imported, the lack of standard formatting renders a lot of it useless. Your operational system can quickly become riddled with errors, inaccuracies, duplicates, etc. when data isn't formatted or validated before being ingested.

Without the team or tools to manage the data onboarding process, it's difficult for customers to realize the value of your product. CRMs, inventory management software, ERPs, and product lifecycle management software all need clean data coming in to execute properly.

2. Duct-taped solutions

Imagine your company is onboarding customer data via CSVs. Let’s say your customer’s CSV has a field called "PO #," but your schema requires a field called "Purchase Order No." The data won't import properly due to the different schemas, and without validations someone from your team must manually transform the data before it’s ingested.

Dev teams attempt to solve these recurring data onboarding problems by dedicating extra hours toward data wrangling, creating in-depth documentation for non-technical teams, and/or building and maintaining in-house solutions.

Unfortunately, these solutions cause further downstream problems. 

1. The more custom scripts and glue code you create, the more people, time, and money you'll need to maintain each solution. Devs grapple with formatting errors, custom scripts, and maintaining APIs and building connectors. 
2. Companies struggle to scale in-house data onboarding solutions as the company grows. Custom data uploaders require constant tweaks from technical teams to handle the multiple data importing scenarios, files, and formats.
3. Digging through documentation is cumbersome for customers and internal teams who want to quickly upload their data. That’s assuming you even have documentation that’s up-to-date and clearly written.
   

Throwing more internal resources to manually onboard customer and partner data is a losing game—the high cost isn't worth the small bump in efficiency. Having developers and engineers to fix bugs and wrangle messy data takes away resources for investing in new products or technology.

3. Expensive

People make mistakes, even highly-trained engineers, developers, and data scientists. But errors made during data onboarding cause headaches for your customers. Manual data wrangling demands meticulous attention to detail and the ability to sort through thousands of fields to select and correct errors. 

Data onboarding difficulties lead to several major issues: error-prone data, high data-wrangling costs, frustrated internal teams, and a sub-par customer experience. 

Take a second to consider how costly building an internal tool and manual data cleaning is for companies. The nationwide average salary for a software engineer is just under $120,000. That number jumps to about $160,000 in San Francisco and $130,000 in New York City and Seattle. If the average company employs a team of 10 engineers who spend 45% of their time on data wrangling, it costs upwards of $540,000 per year on data cleaning.

Building and maintaining integrations take a lot of developer time. And that time has a financial impact to the company. Your dev team should focus on product work that's bound to drive a business forward. New product features, incredible functionality, innovative components, and a delightful customer experience can take place when teams aren’t bogged down with external data onboarding.

Most importantly, internal teams have the time to provide an excellent customer experience. A study by Gallup revealed that companies that successfully engage B2B customers realize 63% lower customer attrition, 55% higher share of wallet, and 50% higher productivity. 

Engaging customers has clear benefits, but it also shows you respect your partners' and customers' time. Since data onboarding is one of the first touchpoints a company has with customers, it's the perfect opportunity to delight and impress with a quick, simple experience.

Solve Your Data Onboarding Problems with Osmos

Although companies can't avoid receiving messy data, they can use solutions to simplify and improve their customer data onboarding process. Let's look at three common scenarios where Osmos' data onboarding solutions can have a positive impact.

1. Companies want to provide a self-serve data onboarding experience for their customers and partners.
2. Companies want to provide a self-serve data onboarding experience for their internal teams to upload data on behalf of their high-touch customers or partners.
3. Companies want to automate customer and partner data imports on a recurring schedule.
   

Customer data needs to be cleaned before onboarding is complete. That's where Osmos can help.

Using Osmos' solutions, both technical and non-technical teams can tackle the most challenging parts of the data onboarding process—data transformation. Osmos Uploader and Osmos Pipelines are simple to customize and scale, so your team can give your customers and partners the freedom to share their data and you can control how that data is received.

Solution #1 - Osmos Uploader

Enable your dev teams to quickly a build a smart data uploader and embed it into your product or web property. When a customer or partner needs to share data, they simply click the upload button. This brings them to the Osmos Uploader interface, where they can clean and map data without having to write code. In just a few clicks, our no-code data transformations help them upload their data so it matches your system's schema. It's the easiest way to empower end-users to send you clean data, every time.

‍

Solution #2 - Osmos Pipelines

Turn one-time data onboarding into automated data ingestion. They enable your internal teams to pull data from a Source Connector (file stores, databases, apps, APIs, or emails) and automatically clean and validate the data before sending it to the Destination Connector. You can set them up to run on a recurring schedule or manually trigger them at any time. There's no need to worry about having the most up-to-date data when it's automatically ingested into your operational systems.

Powering our data onboarding solutions is a real-time, AI-powered data transformation engine that lets end users easily teach the system how to clean and map data with QuickFixes, AutoClean, formulas, and column mapping.

• QuickFixes are one-click, data cleanup that easily clean up your data for the most common scenarios (ie. date, text, numeric etc.). You can combine multiple QuickFixes to both cleanup your data and resolve errors easily.
  

• SmartFill is simple to use AI-powered data cleanup that learns and detects a pattern from examples to the clean data at scale. Involves transforming your input data by providing examples of the clean data in the output column.

• Column mapping is used to directly map a single source column (from the left table) to a single output column (in the right table). Column mapping is useful when the source data in a specific column doesn't need to be transformed or cleaned up, just mapped to an output column.
  

• Formulas are used to complete complex transforms on data from one or more input columns. We have several predefined formulas (such as CONCAT, DATE, ADD, IF, IFERROR etc.) that can be used together.

• Data validation - Ensure uploaded data is correctly formatted, mapped, and ready to use every time.

Simplified data onboarding experience will become the standard, as companies adopt no-code technologies and prioritize the customer experience.

As more companies adopt no-code technologies, customer expectations and business growth opportunities will inevitably reshape. Now is the time to eliminate your data onboarding challenges with Osmos’ dev-friendly solutions designed for non-technical end users.