Data Onboarding in 2024: 4-Step Guide, Challenges & Solutions

McKinsey has identified that companies that achieved success in their digital transformation efforts focused on proprietary assets such as data¹. This is because as companies implement digital solutions, they need to onboard or migrate their data to the new systems as an initial step of their digital transformation journey. That is where data onboarding comes into play.

According to a recent report on ‘the state of data onboarding,’ which surveyed around 100 companies and ~5000 respondents (via Twitter):

To remedy that, we have curated this article to explore data onboarding to help business leaders streamline this crucial process for a successful digital transformation. 

Our research covers the following:

• What is data onboarding?
• How it differs from data migration?
• How to complete data onboarding in 4 steps?
• What are the challenges in data onboarding, and how to overcome them?

What is data onboarding?

Suppose you purchased a new laptop or smartphone. As a first step, you would onboard all your previous data (files, pictures, etc.). This process also applies to businesses and is called data onboarding. 

Data onboarding is the process of collecting offline data from various components of your business, preparing it for use in a specific data platform (CRM, PDM, ERP, etc.), and transporting it to that platform. Whenever a company starts working with a software vendor, the first step the vendor needs to do while signing them up is data onboarding.

Data onboarding vs. migration

Data onboarding is often confused with data migration, a process in which data from one data platform is moved to another data platform. However, data onboarding involves collecting data from disparate data sources within an organization and transferring it into a new system. On the other hand, data migration involves moving data between two existing platforms.

Data migration is usually used during mergers and acquisitions of companies, while data onboarding is used while implementing a new digital solution in the business, especially in the case of SaaS. However, sometimes data migration can occur during the data onboarding process since it also involves transferring data.

How to complete data onboarding in 4 steps?

There can be 4 steps to complete the data onboarding process.

1. Data gathering

In this step, data must be extracted from different parts of the organization, such as different departments, data stored in data warehouses, data from other data systems, etc. In this process, it is important to ensure that the extraction is done in a secure manner without data corruption or loss.

2. Data preparation

In this step, the data must be prepared for loading into the new system by validating data types and formats, eliminating duplicates, and ensuring data are in compliance with relevant data regulations. This step involves processes like data anonymization, data transcription, and data preprocessing.

• Data anonymization: Data anonymization is the process of transforming data by replacing data points with synthetic data or aggregating them to eliminate personal information. This is useful for marketing purposes when data involves offline customer data, including personally identifiable information (PII).
• Data transcription: Data transcription is the process of converting data from a source format to a destination data format. For instance, paper-based invoices can be converted into digital invoices.

• Data preprocessing: This step involves data cleaning, data transformation, and data improvement. To learn more about data preprocessing, check out this quick read.
• Data digitization: This can sometimes also be a part of the data preparation process since some companies have analog data. Data digitization is the process of converting data from its original analog form into digital data. This data can include photos, documents, audio, video, and other data formats. Data digitization makes data more accessible and easier to use in a digital environment. To learn more about the best practices of data digitization, check out this quick read.

3. Data uploading

Data uploading is one of the last steps in the data onboarding process. It means taking the data that has been collected and prepared and putting it into the new system. It involves migrating the data from the previous server to the new system’s server. 

4. Data validation

Data validation is the process of data verification in which data accuracy and completeness are checked. This helps to ensure that data quality is maintained during data onboarding. As a final step, data should also be tested to ensure data accuracy.

What are the main challenges of data onboarding?

Data onboarding can be a complex process involving data from different sources and formats and especially with companies with complex operations, diverse product lines, and a large customer base. The main challenges in data onboarding include: 

1. Data quality

One of the most common challenges of data onboarding is ensuring quality and that the data must be collected accurately and in the right format.

Recommendations

We recommend that data providers use automated data cleansing tools that can detect errors such as duplicate entries or missing data points. Additionally, data should be validated regularly to ensure accuracy over time. To learn more, check out our article on data quality assurance best practices.

2. Scalability

This challenge occurs due to the size of the data since more data requires more processing capacity and specialized hardware to store large volumes of data. 

Recommendations

To address this issue, cloud-based solutions can be used since they offer easy scalability with minimal upfront costs.

3. Data security

This is another data onboarding challenge that businesses face due to the surge in data breaches and the enforcement of stricter data protection rules. Data must be stored securely and in compliance with regulations such as HIPAA², GDPR³, and CCPA⁴. 

Recommendations

To ensure data security, data providers should use secure data storage solutions such as encryption algorithms and data anonymization techniques. Check out this article to learn more about the top data security best practices for organizations. 

Further reading

• AI/ML Data Collection: Guide, Challenges & Top 4 Methods
• Quick Guide to Crowdsourcing
• Top 7 Product Data Management (PDM) Best Practices

If you need help finding a vendor or have any questions, feel free to contact us:

References

1. “Three new mandates for capturing a digital transformation’s full value” Mckinsey Survey Jun 15, 2022. Retrieved: Jan 04, 2023.
2. “The HIPAA Privacy Rule” HHS, Office for Civil Rights (OCR) Mar 31, 2022. Retrieved: Jan 05, 2023.
3. “General Data Protection Regulation” GDPR. Retrieved: Jan 05, 2023.
4. “California Consumer Privacy Act.” Wikipedia Dec 01, 2022. Retrieved: Jan 05, 2023.