In episode 96 of the AI/Hyperautomation Minute, Aaron Back explores the DAM landscape and the impacts of synthetic data.
This episode is sponsored by Acceleration Economy’s Digital CIO Summit, taking place April 4-6. Register for the free event here. Tune in to the event to hear from CIO practitioners discuss their modernization and growth strategies.
Highlights
00:43 — Artificial intelligence (AI) is nothing without data. Machine learning (ML) is a subset of AI, which also requires data. The acronym for the combination of these three elements — data, AI, and ML — is DAM.
01:32 — Why should you care about synthetic data? Aaron highlights an Acceleration Economy Data Modernization Top 10 company, Qlik, that is “doing some phenomenal things.”
Which companies are the most important vendors in data? Check out the Acceleration Economy Data Modernization Top 10 Shortlist.
01:50 — In January, Dan Sommer, Senior Director of Global Marketing at Qlik, outlined three key data trends for 2023 — one of which involved synthetic data. Aaron references Sommer’s point stating, “One data-related glaring gap was the lack of enough readily available real data on pandemics to prepare for such a crisis.” This is precisely where synthetic data becomes incredibly valuable, Aaron notes.
02:37 — Synthetic data allows organizations, data scientists, and others to fill in gaps where there’s a lack of real data. It can help simulate outcomes for AI/ML models.
03:16 — Databricks, another Acceleration Economy Data Modernization Top 10 company, has a project called “Data Generator” and it has resources on GitHub. This tool is used to generate large simulated synthetic data for tests.
03:33 — Oftentimes, when people hear the term “synthetic,” they think of “fake” or “a copy of the real thing.” In this case, Aaron explains, the data generated is creating data from a given context of the real data and the parameters that are fed into the model to provide something useful.
Which companies are the most important vendors in AI and hyperautomation? Check out the Acceleration Economy AI/Hyperautomation Top 10 Shortlist.
04:00 — Aaron recommends not using synthetic data in a real-world scenario unless it’s absolutely necessary. It could lead to the validity of the data being questioned.
04:40 — Aaron challenges those using AI and ML to use synthetic data where necessary — but use it with caution. For those who are decision-makers, ask your data providers the right questions and ensure that the insights they’re providing aren’t full of synthetic or simulated data that could hurt your decision-making process.
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