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Kim: Yes. This is the amazing thing about the cloud because once the data is gone, amazing things can be done with it and innovation is going like crazy. And what we’re seeing right now is everything with OpenAI and ChatGPT and all of this happening. And in Power BI, we’ve deployed a lot of AI capabilities in the platform. And an important aspect of AI capabilities are those that can be used by really, really useful business users. So you can ask questions like a natural language query and get answers like a chart, or ask the system, “Hey, what’s affecting my cancellation? What actions are they affecting?” And even with our new AI feature, we use GPT-3 to create code for business users to write actions on their datasets. So you can easily generate code to calculate year-by-year calculations or more complex calculations using only natural language.
This really allows business users to mine and work with data like never before and build literacy like never before. And some of our biggest customers, a retail company that we work with, 40% of their users use these features regularly. So now you have people to open a report, get a number and continue. Now you can do more with it and ask those questions yourself. Both actually make the business more efficient, because they don’t need data scientists to do this work. A business user can work on his own, but man, it makes business users, and the entire line of business, opens up a whole range of opportunities that did not exist before.
Laurel: And that is a very good point. Anil, you don’t necessarily need data scientists to understand the insights you get from the data. So you mention a lot of back office work like tax and ERP or enterprise resource planning. So how do you see people being empowered to make decisions?
Anil: Absolutely. That’s a very good question. And Kim’s comments about OpenAI and ChatGPT, bringing in a lot of different thinking and skills, changing the role of business users and the role of data scientists as part of it. It’s a multifaceted approach to how we look at some of the functional groups that are adopting these technologies, right? One, we will see close cooperation with cloud service providers like Microsoft in that AI innovation and capabilities, machine learning, for example, text mining. And simple things like text mining have been data science experiments before, especially in health care where we came up with hypotheses. If someone takes a stream of text and wants to know, “Hey, what’s a disease? What’s a prescription, and what’s a diagnosis?” All that was doing was a machine learning model.
But Microsoft has open or applied AI capabilities, just send that text stream and say, “Hey, what’s a disease?” It will automatically give you results in terms of. It assigns you the disease and the symptom and the medicine and the category of medicine with the doctor, out of the box. That’s simple innovation, I’m not even talking about OpenAI or anything like that. If you want to take advantage of some of these capabilities, you’ll need to work closely with hyperscale vendors like Microsoft Azure that are investing heavily in innovation and bringing these capabilities. And these technology platforms are many. It could be a CDO. [chief data officer] Forum is a technology innovation forum, with focus group discussions that bring new capabilities that can run on any hyperscaler. That’s another place we need to meet. And one more thing I would say is tactically, when we give clients a designed architecture, we recommend that they do a very modular architecture so that the capability switch is easy. For example, changing OCR engines or translation engines or just a few examples where things are constantly evolving.
If you build your architecture in a very modular way, that switch will also be very easy. And ultimately it all boils down to a very diverse team providing these skills. Encouraging training, providing advanced training and, as you said, and mixing that with developing the specific skills of the technology business, obviously brings new thinking to the team itself and we can embrace some of the new innovations and capabilities that come along that way. He withdrew from the market itself. That’s how I see it affecting some big ERP or back office changes like operations or tax. We could definitely use some of these skills there. For example, tax. For tax, there’s a whole big stream of data that comes from unstructured data, from PDF documents, unformatted documents that we get, how do you understand it? There are general AI capabilities that you can plug into that can bring the data into a structured format that regulators will trust. So that’s a bit of an impact.
Laurel: This provides a good example of how much work can be done in the back office, as cloud platform hyperscalers such as Microsoft Azure offer many of these capabilities. How can companies create synergies between the cloud platform and the latest technologies and focus on data management, especially in highly regulated industries such as finance and healthcare?
Anil: Look, good data governance is structured when most enterprises agree on definitions, and the industry supports it in regulations. For example, if you look at the mortgage industry, someone comes in and asks you for a loan, there are certain elements of that customer that you can describe to other parts of the organization, there are certain elements that you can’t describe. So management is well structured, in terms of information. When it comes to applied AI services, Microsoft Azure and other platforms already consider some ethical aspects of AI. What can we do with predictive analytics? What can’t we do? So we’re covered from that point of view.
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