Anyone who has worked in the info industry for several a long time can speak to its continued evolution. Today, the main target has shifted to artificial intelligence, emphasizing AI data cleansing and governance, as clean data is crucial for effective AI implementation.
That’s an emphasis that many have been pushing forward on this recent era. Data should be cleansed for analytics to be more reliable, in line with Nusrath Mohammed (pictured), data practice leader at Tata Consultancy Services.
Tata Consultancy Services’ Nusrath Mohammed talks to theCUBE about AI data cleansing.
“Now, due to AI, everybody desires to jump on that bandwagon. But then we realize data isn’t cleansed after which now we have to go backwards and begin cleansing that first,” Mohammed said. “Now, we will jump on that bandwagon.”
Mohammed spoke with theCUBE Research’s Dave Vellante on the CDOIQ Symposium, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed the importance of AI data cleansing and the evolving challenges in data governance.
From AI data cleansing to optimizing
There are a variety of tools available within the generative AI world immediately. But certain tools, akin to Microsoft Corp.’s Purview, still pose loads of questions, in line with Mohammed.
“It’s an attractive tool. But then I’m asking the query, that tool will be utilized only at the highest layer, once now we have the info lake has been built,” she said. “It’s at the highest layer. But what about on the layer of the info inception?”
When one investigates more about how generative AI will be utilized, the reply is that one must implement the technology in any respect levels. That clicked, in line with Mohammed.
“In the course of the data inception, when your data is coming into your source systems, then apply gen AI there,” she said. “Detect and provides us a report up there and say, ‘OK, your addresses will not be matching their accurate addresses.’”
It could even be implemented when a user is creating data, as help arrives to tell that user that they could have entered an address incorrectly. Then, what’s going down at a change level can also be going down at an interface level, in line with Mohammed.
“You bring it at the highest level. Now, you must bring your insights,” she said. “You’re going to have far more higher outputs too … that is how I see gen AI helping and supporting what we’re doing.”
Advances in technology doesn’t mean that embedded generative AI is ready to resolve every problem that an organization faces. Machine learning skills, data science and other skills are all still required after generative AI has provided feedback, in line with Mohammed.
“Perhaps I still must apply the normal tools to get to an actual solution. Those are a few of the challenges or the talk which I’m not hearing and which I would love to listen to,” she said. “It’s like, ‘This isn’t my silver bullet. That is a minimum of aiding me.’”
AI copilots might at some point be equipped to resolve very difficult problems. But for now, copilots still should learn what humans are doing, in line with Mohammed.
“The copilots might help, but not immediately,” she said. “It’s going to learn from me what I’m doing on a day-to-day basis, after which it’ll give me far more intelligent advice at the top. But I don’t think it’s giving me intelligent advice at this moment.”
Here’s the entire video interview, a part of SiliconANGLE’s and theCUBE Research’s coverage of the CDOIQ Symposium:
Photo: SiliconANGLE
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