Qin Li
Qin Li
Head of Finance and Admin
December 9, 2022

5 Principles to Consider When Evaluating No-Code AI Data Management Solutions

5 Principles to Consider When Evaluating No-Code AI Data Management Solutions

According to a recent Tamr survey, Nearly 95% of data leaders anticipate investing more in AI/ML in 2023. And it’s no surprise, really.

For years, companies have relied on traditional master data management (MDM) processes that are primarily rules-based and human-driven. But as the volume, variety, and velocity of data grow, so does the demand for clean, accurate data.

As we head into 2023, it’s clear we’ve reached a tipping point. As the demand for more efficiency and greater scalability grows, so does the demand for clean, curated data. But many organizations realize that traditional MDM solutions simply can’t keep up. Instead, they need to embrace AI/ML to automate the mastering of data across the organization. That way, they can break down data silos and deliver clean, accurate data for analytics and decision-making.

Embracing AI and machine learning will enable organizations to clean their dirty, messy data at scale and with speed. And by using the machine, they can include more data sets as inputs as they organize and clean their data because the machine is significantly more efficient and scalable.

Many vendors claim to offer no-code AI solutions. Still, when evaluating these solutions, you must look for ones from reputable vendors who understand your business problem.

So, where do you start? Below are five principles that we believe the best no-code AI MDM solutions embrace.

1. Integrates easily

No-code AI should use platforms and modules that are simple to integrate so that they are easy to tailor to a company’s particular needs. That way, business users may utilize domain-specific knowledge and quickly develop AI solutions thanks to no-code solutions.

2. Accelerates processes

Cleaning data, classifying, organizing data, training, and debugging the model are all necessary steps in creating unique AI solutions. AI should be able to automate repetitive operations, allowing businesses to perform tasks faster.

3. Costs less than custom AI

No-code AI should be low-cost and easy to implement for organizations wanting to implement AI with less stress and without the need to hire staff with AI expertise.

4. Empowers business intelligence at scale

End users should be able to utilize no-code AI to build new solutions without knowing how to code, which improves business efficiency, productivity, ROI, and customer retention.

5. Enables you to realize quick time to value

No-code platforms should allow you to experiment with your concept on a budget and in a limited amount of time.

Finally, it’s important to remember that AI alone is not enough. It’s critical that you also keep humans in the loop, as they are the ones who can provide critical feedback and context to ensure the accuracy of the model results.

To learn more about Tamr’s human-guided, machine-learning-driven approach to data mastering, visit tamr.com.