One of the most challenging issues when it comes to driving digital business transformation is curating the data that organizations rely on to drive them. Data management within most organizations is often sloppy, so it should not come as a surprise that, when optimizing a process, digital CxOs start encountering suboptimal experiences.
There is hope, however, in the form of generative artificial intelligence (AI) platforms that have the potential to dramatically improve data management. Informatica, for example, at the Informatica World 2023 conference today added two generative AI capabilities that the company claims will reduce the time spent on data management tasks by 25 to 80%.
CLAIRE GPT is a revamped version of the AI platform that Informatica created using machine learning algorithms that is now more accessible using a natural language interface. At the same time, Informatica also expanded the AI copilot capabilities to automate even more data management tasks and processes.
Collectively, these capabilities will eliminate many of the rote tasks that conspire to make managing massive volumes of data so challenging in enterprise IT environments, says Jitesh Ghai, executive vice president and chief product officer for Informatica.
In addition, the level of expertise needed to, for example, classify data will be much less, which Ghai noted will make data management more accessible to a much wider range of users. “It will surface intelligent recommendations,” he says.
The Informatica Data Management Cloud currently has more than 50,000 connections that leverage 23 petabytes of metadata that are created by processing 54 trillion monthly transactions for customers. It’s not necessarily clear what impact generative AI platforms may have on the need for data engineers, but it’s clear that any role involving the management of data is about to change and evolve as many of the manual tasks that were once required are eliminated.
The sad truth of that matter is that few organizations would be allocated a Good Housekeeping seal of approval for the way they currently manage data. This issue has become especially problematic because as organizations look to make better decisions using data, they are now spending a considerable amount of time cleaning up data that has not been well governed for years, sometimes even decades. On the plus side, however, there is now a much greater appreciation for data management that has been sorely lacking. More organizations than ever, for example, have been embracing best DataOps practices to manage data in a way that ensures quality. Adoption of those best practices, unfortunately, is far from pervasive, simply because the task of managing data has up until now required Herculean efforts.
It’s still early as far as the application of AI to data management is concerned, so there is still much to be parsed in terms of how effective AI might prove to be, but given the current state of data management, it’s arguable AI can only make things better. Not every data issue is suddenly going to be magically resolved but at the very least the number of tasks requiring engineering expertise should be reduced to a manageable few that, in terms of business value, will be well worth the effort.