Everyone who was involved in the vendor evaluation process would undoubtedly concur that picking the right technology partner or vendor is a make-or-break decision for any company. Choosing a tool for smooth data onboarding from clients and partners is a multifaceted challenge. It must handle growing data complexities while aligning with the organization’s specific needs; otherwise, it risks becoming expensive shelfware, perpetuating manual coding of integration scripts.
To evaluate data onboarding vendors, start by identifying essential, desired, and optional features. Understand data management intricacies, integration needs, and business requirements. With this foundation, create a list of specific features for comparison.
Ultimately, choose the data integration tool that best fits your use cases, budget, resources, and skills—not necessarily the most highly rated or feature-packed product.
As a rule of thumb, it’s essential to remember a guiding principle: when integrating new technology, around 20% pertains to the product itself, while a substantial 80% revolves around effective change management. So, as crucial as the product and its features may be, the way you select it and introduce it to your organization can often be even more significant.
Defining selection criteria for data onboarding tools and product evaluation
When it comes to simplifying the selection process, one practical approach is to prioritize the features within data onboarding tools. We can classify these features into four categories: ‘must-haves,’ ‘should-haves,’ ‘nice-to-haves,’ and ‘items we won’t use under any circumstances.’
The ‘must-have’ features are the foundation of clarity. It’s crucial to eliminate any tool that lacks these essential attributes quickly. They are the non-negotiable prerequisites for a successful data integration solution.
The ‘should-have’ features fall between mandatory and optional. These capabilities significantly impact integration efficiency, scalability, and maintainability, although they aren’t absolute necessities.
The ‘nice-to-have’ features often act as differentiators when deciding on a product. These extra perks can sway the final decision, favoring a particular solution.
However, during the evaluation process, you might encounter nuances. For example, a product may seem to meet your criteria but come with certain caveats. These might include the need for custom coding to bridge integration gaps and the requirement to acquire add-on products, possibly from third-party sources, to compensate for missing functionality or promises of future releases.
These exceptions introduce complexities that require attention. Evaluators must consider not only the essentials but also these nuanced factors. This comprehensive approach is the key to a thorough product assessment, minimizing surprises after the selection.
When it comes to the shortlist of selected vendors, you might notice that many of them offer a very similar set of features and functionality. Each vendor will assure you that they can meet all your needs, so it’s likely you’ll have more than one strong contender. In this scenario, a comparison matrix like this can be a valuable visual tool to simplify the evaluation process.
In a scenario with two top contenders, non-functional requirements become even more crucial. Aspects like user-friendliness, quality of support, update frequency, the product roadmap, and the presence of experienced individuals familiar with the tool can often hold more significance than specific features.
Listing the data onboarding features
Capturing Data From Diverse Sources
One of the common challenges we face is dealing with data that comes in various formats from different sources. So, the first step is to carefully list all the potential data sources—those currently used by your company and clients, as well as those we might adopt in the future. Make sure any vendor you’re considering can handle all of these sources.
The range of potential source types is extensive, covering everything from relational databases, flat files, and specialized application messaging technologies to industry-specific formats like, for example, FHIR, HL7, and EDI X12 in healthcare.
This is particularly important when predicting the structure and format of future client data is challenging. Ask vendors about their adaptability—how quickly they can accommodate new connectors and their expertise in handling new data formats.
Keep in mind that some tools might have limitations on the number of sources you can integrate. So, it’s crucial to thoroughly evaluate this aspect before committing to a vendor.
Data Transformation
When it comes to data onboarding, we face a fundamental reality: the source and destination are almost never the same. That’s why foundational data transformation features become the star of the show. These features cover a range of capabilities, such as converting data types, manipulating dates, handling strings, dealing with NULL values, and performing mathematical functions.
Similarly, data mapping capabilities play a crucial role in this landscape. Functions like joining, merging, looking up data, aggregating information, and substituting values are the key elements of effective data integration. They make it possible to translate data from where it originates to where it’s needed with precision and reliability.
Automation
A reliable data integration tool aims to reduce manual interventions and simplify processes. Here are some critical automation features your tool should include:
- Data Type Management: The tool should handle changes in data types, such as converting float to integer, with minimal manual adjustments.
- Automatic Schema Evolution: Applications can evolve, leading to changes in schemas (e.g., adding or removing columns, modifying names). Your tool’s connectors should adapt to these changes automatically, reducing the need for post-integration fixes.
- Connector Monitoring: The tool should regularly monitor connectors and address any issues proactively. This helps maintain data flow integrity without relying heavily on manual intervention.
Security and Compliance with Data Regulations
Your data tool needs relevant certifications like SOC 2. Stringent location-based or vertical-specific requirements, exemplified by GDPR for EU clients or HIPAA in healthcare, entail substantial penalties for non-compliance and damaging reputations, especially in heavily regulated sectors.
A robust data onboarding tool must surpass mere compliance, offering features like column blocking and hashing to fortify data security. The ever-evolving regulatory landscape demands continuous review, ensuring adaptability in tool selection, alignment with data governance, and addressing integration-specific challenges.
In the face of mounting cyber incident costs, averaging $9.05 million for U.S. companies, safeguarding data has become paramount. Granting selective access within your chosen tool, from read-only for interns to admin privileges for data architects, is essential.
Employing strong encryption methods like AES and RSA ensures data security during transit. But when implementing a data onboarding solution, consider how it connects to your data source and where it stores data. For native cloud sources, this is straightforward, but for relational databases, connection options vary from firewall rules (less secure) to SSH tunneling (more secure with added costs) to site-to-site VPN (most secure but complex). Storage location, whether in the U.S. or EU, is also crucial.
While privacy breaches and data breaches are real risks, a proactive, multi-layered security strategy tailored to your organization’s unique vulnerabilities can mitigate these threats. Therefore, your data onboarding tool must align seamlessly with your company’s security framework, ensuring the security of data integration in the digital age, where technical sophistication and vigilance are vital for data integrity and corporate resilience.
Ease of Use
Your data onboarding tool should be all about manageability, user-friendliness, and an intuitive UX that makes data integration, transformation, and automation a breeze.
You should assess the sweet spot for your team, both in the present and looking ahead, within the spectrum that ranges from flexibility—where any developer can implement custom code—to ease of use—where users operate in a highly guided and controlled manner, and the tool can handle many functions that traditionally only developers could perform.
However, how easy the tool is to use largely depends on the skills of your data integration developers. It’s essential to evaluate your team’s skill set. A no-code interface can streamline the data onboarding process, saving costs by eliminating the need for coding skills and simplifying connector setup and data transformation within data pipelines.
One of the most critical areas where many data integration tools fall short is maintaining existing pipelines. Pay close attention to how the tool helps identify and locate problems so users don’t have to reverse engineer the whole process to make a change. Self-documentation and diagnostic capabilities are of utmost importance.
Extensibility
For some organizations, dealing with unconventional data sources that lack native support can be crucial. If this applies to your situation, look for vendors who offer custom source configuration options.
Certain tools offer flexible solutions for this purpose, allowing you to create custom data extraction methods or use programmatic APIs. This adaptability ensures you have the flexibility to work with sources beyond typical vendor support.
In your integration strategy, extensibility is vital as it needs to handle both current and future data volumes. To effectively manage scalability, connect it to real-time triggers and automate resource allocation in sync with your implementation processes.
Scalability
One of the critical requirements for any data integration technology is its ability to handle large volumes of data.
Even if your data isn’t massive right now, it’s wise to ensure that the underlying computing infrastructure can handle significant data loads in the future. This way, you won’t have to overhaul your data integration approach down the line.
For many SaaS companies, another critical consideration is how well the technology can manage concurrent data integration tasks. Sometimes, the promise of handling vast amounts of data doesn’t necessarily mean the ability to manage multiple tasks simultaneously without queuing them up and processing them one at a time.
Additional data integration tool selection criteria
When choosing data integration tools, some extra factors need consideration. These factors are usually part of the evaluation, but it’s crucial to understand that they can vary based on your organization’s needs and importance.
- Deployment Time: Swift implementation is crucial for businesses with time-sensitive needs. Delays in deploying data integration can hinder decision-making and cause missed opportunities. Choosing a tool with a rapid deployment capability is essential for seizing timely opportunities and ensuring a quick time-to-value.
- Loading Performance: Assess how well a tool handles data loading, considering factors like integration complexity and data volume. Compare performance across specific use cases to make an informed choice.
- Training: Determine the training options offered by the vendor, which may include in-person classes, online courses (live or prerecorded), or web recordings. Choose based on your team’s learning preferences and needs.
- Documentation and Support: Evaluate the availability of comprehensive documentation and support mechanisms. Differentiate between developer online help and technical documentation. Consider the vendor’s support methods, such as online Q&A, chat, in-person discussions, and on-site support, along with pricing.