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Informatica adds AI assistant, GenAI development interface

The longtime data management vendor's new capabilities simplify use of its platform, enabling nontechnical users to work with its tools and making experts more efficient.

Informatica on Tuesday launched generative AI-powered chat capabilities and a low-code/no-code interface for developing generative AI applications. Both simplify use of the vendor's tools so that more employees within organizations can use its platform and those already using its platform can be more efficient.

Informatica unveiled Claire GPT and the low-code/no-code development interface during Informatica World, the vendor's annual user conference, held this week in Las Vegas.

Based in Redwood City, Calif., Informatica is a data management vendor whose Intelligent Data Management Cloud (IDMC) enables customers to integrate and prepare data for analysis. Claire, meanwhile, is the vendor's AI and machine learning engine. First introduced in 2017, Claire is integrated throughout IDMC to enable AI and machine learning during all phases of data management.

Informatica unveiled plans in May 2023 to infuse Claire with generative AI through partnerships with Microsoft and OpenAI.

Now, Claire GPT is generally available, while the low-code/no-code development interface is in preview. Both make it easier to use a platform that plays an important role in helping organizations manage and operationalize data and are therefore significant, according to Stephen Catanzano, an analyst at TechTarget's Enterprise Strategy Group.

"[Informatica] is innovative, simplifying data management and empowering AI," he said. "Excellence in data management leads to excellence in AI. Informatica can help organizations get there and reap the benefits of AI."

[Informatica] is innovative, simplifying data management and empowering AI. Excellence in data management leads to excellence in AI. Informatica can help organizations get there and reap the benefits of AI.
Stephen CatanzanoAnalyst, Enterprise Strategy Group

Informatica was recently a rumored acquisition target of Salesforce before talks between the vendors fell through. Investors did not react positively to the potential purchase, with the stock prices of Salesforce and Informatica both falling when news of the potential acquisition was made public in April.

Simplifying data management

Data management and analytics platforms have historically been complex, requiring users to have coding knowledge to interact with data and data literacy training to understand and interpret outputs. Vendors have tried different tactics through the years to make their tools more accessible, but had only limited success.

Generative AI might finally be the technology that simplifies the use of complex data management and analytics platforms.

When integrated with data management and analytics tools, generative AI enables true natural language interactions and can automatically create content such as summaries of analyses. As a result, generative AI has the potential to enable nontechnical users to work with data as well as make data experts more efficient.

Given that potential, many data management and analytics vendors have made generative AI a focus of product development over the past couple of years. In addition to Informatica, competitors such as Alteryx and Boomi have developed generative AI capabilities, as have tech giants including AWS, Google, Microsoft and Oracle that provide data management tools as part of their larger offerings.

A circular graph showing the top seven benefits of generative AI for businesses.
Enterprises might receive these seven benefits when using generative AI.

Claire GPT is an AI assistant that can be embedded throughout IDMC and enables users to talk to their data in natural language. The tool integrates with an enterprise's proprietary metadata management system to understand the enterprise's operations and respond to queries related to those operations.

By doing so, Claire GPT enables more widespread use of Informatica's IDMC by providing a natural language interface. In addition, Claire GPT comes with automation capabilities that improve user efficiency, provide security and governance through large language models (LLMs) hosted by Informatica, and help users break down complex data management operations with multistep reasoning capabilities.

Kevin Petrie, an analyst at BARC U.S., noted that Claire has enabled data teams to prepare and govern data with automation, AI and metadata since it was first released seven years ago. Claire GPT builds on Claire's capabilities.

"This announcement extends the role of GenAI within Claire -- a vision they articulated last year -- by using a natural language interface to handle commands that previously required mouse clicks or manual scripting," Petrie said.

Beyond addressing ease of use and productivity like many generative AI assistants, Claire GPT has the potential to be more accurate than some other natural language tools, given Informatica's access to metadata, he continued.

"Informatica's extensive installed base gives it a rich metadata repository on which it can train and fine-tune GenAI language models," Petrie said. "This differentiates Informatica because they can produce models that are less likely to hallucinate, expose sensitive data or cause other governance issues."

Like Claire GPT, the low-code/no-code development interface aims to both broaden use of Informatica's platform as well as make those already using its tools more efficient.

However, unlike Claire GPT, the low-code/no-code development interface isn't an assistant that helps users carry out complex tasks. Instead, it is an environment that aims to enable users of all skill levels to develop generative AI applications and add generative AI capabilities to existing applications to inform business decisions.

The interface includes drag-and-drop capabilities, customizable templates and prebuilt techniques for generative AI development. In addition, it comes with support for a variety of LLMs and vector databases so that customers can use their preferred platforms in conjunction with Informatica as they develop the data pipelines that feed and train generative AI applications with proprietary data.

LLMs supported by Informatica include, among many others, those from Anthropic, Google, Meta and OpenAI. Similarly, vector databases supported by the vendor include, among others, Pinecone and Elastic.

Informatica has historically been agnostic in terms of enabling customers to use the vendor's platform in conjunction with any other data system. Support for a variety of LLMs and vector databases to let customers choose their preferred tools for generative AI development fits with that ethos, according to Informatica CEO Amit Walia.

"Different LLMs have different strengths," Walia said. "Customers want to use different LLMs, so our goal is to make sure we support every LLM. Customers want choice, and we want to give them that choice so they can leverage the best technology for their use case."

Catanzano, meanwhile, said the addition of the low-code/no-code generative AI application development interface is an important addition for Informatica users.

There is a shortage of software developers. Informatica's low-code/no-code interface for application development helps mitigate the dearth of developers, according to Catanzano.

"This addresses the growing skills gap by making it easier for lower-tech people to manage tasks which once needed coding experience," he said.

Petrie, however, cautioned that although the low-code/no-code interface for generative AI development is compelling and addresses a real need, trained experts will still need to be involved in the application development process.

"The tool will still require expert human oversight," he said. "Data engineers, data stewards or business-oriented data consumers will need to carefully inspect the quality of outputs they get from these language models before they put anything into production. The caveat of data democratization [is that] you still need ... experts to ensure the artifacts and data outputs are trustworthy."


In addition to Claire GPT and the low-code/no-code development interface, Informatica unveiled enhanced partnerships with Microsoft and Snowflake. The partnership with Microsoft now includes three new integrations, while the partnership with Snowflake includes two new integrations.

As Informatica plots future product development, generative AI will continue to be a primary focus, according to Walia. That includes both further integrating generative AI with IDMC as well as providing customers with tools to develop their own generative AI applications.

"Our goal is to make sure all IDMC capabilities can be viewed through a GPT interface so users can ... leverage complex data management very easily," Walia said. "Also, we want to ... scale IDMC capabilities to support our customers to operationalize generative AI."

In particular, data governance and data quality are key to helping customers develop trustworthy AI models and applications, he continued.

Beyond generative AI, Informatica aims to help those users that still have not migrated their data operations to the cloud to do so.

"The reality is that they will not get the benefit of generative AI if they do not modernize fast," Walia said.

Informatica's plan to continue focusing on enabling customers to develop generative AI applications is wise, according Catanzano. So is its intent to infuse generative AI throughout its own platform.

Petrie, meanwhile, noted that Informatica does not yet support unstructured data such as text, audio files and images. Unstructured data is estimated to now make up more than 80% of all data. Adding support for such data is critical as customers attempt to develop accurate generative AI models trained on their own data so that the models can be used to inform business decisions.

"Informatica says it will soon apply its various data management capabilities to sources that contain documents, images and video," Petrie said. "The sooner they can do this, the better. Enterprises need to prepare all this unstructured data and feed it into vector databases so they can customize GenAI language models to address domain-specific data."

Eric Avidon is a senior news writer for TechTarget Editorial and a journalist with more than 25 years of experience. He covers analytics and data management.

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