Make the world a data-driven place with the cloud
Kim: Yeah. That’s the really amazing thing about the cloud, because once all the data is there, amazing things can be done with it, and innovation happens like crazy. And we see it now with everything that’s going on with OpenAI and ChatGPT and all that. And in Power BI, we’ve integrated a set of AI capabilities into the platform. And one important aspect of the AI capabilities that have been really, really useful are those that business users can use. So things like a natural language query where you can ask a question and get a graphical response, or a key influencer analysis where you can ask the system, “Hey, what’s influencing my cancellations? What measures influence this?” And even with our latest AI feature, we’re actually using GPT-3 to generate code for business users to write metrics to their dataset. So they can easily generate code to calculate year-over-year calculations or even more complex calculations just in natural language.
It really allows business users to dig into data like they never have before and just work with data and develop that literacy that they never had before. And among our biggest customers is a retail company where 40% of their users regularly use these features. So you have people who used to open a report, get a number and move on. Now they can do so much more and they can ask those questions themselves. Of course, it makes the company more efficient, because they don’t need data scientists to do this job. A business user can do it on their own, but man, it makes business users, and the whole industry, it opens up a whole set of possibilities that they never had before.
Laurel: And that’s a very good point. Anil, you don’t necessarily need to have data scientists to help you with what kind of insights you’ve gotten from the data. So you mentioned a number of back office operations like tax and ERP or enterprise resource planning. So how else do you see people being empowered to make decisions and not just spend less time maybe in the depths of spreadsheets, but to innovate and change the way they deliver goods and services ?
Anil: Absolutely. That’s an excellent question. And Kim’s comment on OpenAI and ChatGPT bringing a lot of thought and differentiated capabilities, changing the very roles of business users versus data scientists as part of that. How we look at some of the functional teams adopting these technologies is a multi-pronged approach, isn’t it? First, we see close collaboration with cloud service providers like Microsoft where this innovation and AI capabilities, machine learning, for example, text mining. And simple things like text mining used to be a data science experiment, we used to hypothesize, especially in healthcare. If someone wants to take a stream of text and find out, “Hey, what’s a disease? What’s a prescription and what’s a diagnosis?” It was all a machine learning model that did it.
But Microsoft has open or applied AI capabilities, you can just send this text stream and it will automatically give you an output in terms of “Hey, what is a disease?” the categorization of the disease versus the symptoms versus the medication versus the doctor, the ready-to-use class the class for you. It’s a simple innovation, I’m not even talking about OpenAI or anything like that. If you need to use some of these features, you should stay in close contact with hyperscaler vendors like Microsoft Azure who invest heavily in innovation and bring these features. And there are a lot of these technical forums. It can be a CDO (Chief Data Officer) forum, it’s a technology innovation forum, it’s focus group discussions that bring innovative capabilities that can work on any hyperscaler. This is another place we need to keep in touch with. And one last thing I would say is tactically, when we recommend an architecture designed for customers, we recommend doing a very modular architecture so that changing capabilities becomes easier. For example, changing OCR engines or translation engines or some examples where things are continuously maturing.
If you build your architecture in a very modular way, then this change would also be very easy. And at the end of the day, it all comes down to a very diverse team providing those capabilities. Encouraging training, advanced training and having this mix of diverse skills in tech companies like you talked about and mixing that up, obviously it brings new thinking to the team itself and so we can adopt some of these innovations and capabilities that come out of the market itself. That’s how I see it impacting some of the big ERP or back-office transformations like operations or even tax. We can definitely use some of those abilities there. For example, tax. For tax, there’s a whole stream of big data that comes from unstructured data, it’s PDF documents, unformatted pieces of documents that we get, how do we make sense of it? There are many AI capabilities you can plug in that can bring data into a structured format that regulators will also believe. So quite an impact.
Laurel: This sets a good example of what is possible in the back office with so many operations now that cloud platform hyperscalers like Microsoft Azure offer a number of these features. How then do enterprises create opportunities for interoperability between the cloud platform and the latest emerging technologies while remaining truly focused on data governance, especially for highly regulated industries like finance and healthcare?
Anil: You see, most companies have good data governance in place where definitions are agreed upon, and it’s in the area of regulations that this industry already takes care of. For example, if you look at the mortgage industry, someone comes to you for a loan, there are certain elements of that customer, you can disclose to other parts of the organization, there are certain elements that you cannot not disclose. So that the governance is well put in place, from a data point of view. When it comes to applied AI services, Microsoft Azure and other platforms are already considering some of the ethical aspects of AI. What can we do with analytics from a prediction perspective? What can’t we? We are therefore covered from this point of view.
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