I sat down with Teresa Tung to study extra in regards to the altering nature of knowledge and its worth to an AI technique.
AI success is determined by a number of components, however the important thing to innovation is the standard and accessibility of a company’s proprietary information.
I sat down with Teresa Tung to debate the alternatives of proprietary information and why it’s so crucial to worth creation with AI. Tung is a researcher whose work spans breakthrough cloud applied sciences, together with the convergence of AI, information and computing capability. She’s a prolific inventor, holding over 225 patents and functions. And as Accenture’s International Lead of Information Functionality, Tung leads the imaginative and prescient and technique that ensures the corporate is ready for ever-changing information developments.
We mentioned a bunch of matters, together with Teresa’s six insights.
Lastly, we concluded with Teresa’s Recommendation for enterprise leaders utilizing or fascinated with AI
Susan Etlinger (SE): In your current article, “The brand new information necessities,” you laid out the notion that proprietary information is a company’s aggressive benefit. Would you elaborate?
Teresa Tung (TT): Till now, information has been handled as a mission. When new insights are wanted, it might take months to supply the info, entry it, analyze it, and publish insights. If these insights spur new questions, that course of should be repeated. And if the info workforce has bandwidth limitations or price range constraints, much more time is required.
“As an alternative of treating it as a mission—an afterthought—proprietary information ought to be handled as a core aggressive benefit.”
Generative AI fashions are pre-trained on an present corpus of internet-scale information, which makes it straightforward to start on day one. However they don’t know your enterprise, individuals, merchandise or processes and, with out that proprietary information, fashions will ship the identical outcomes to you as they do your opponents.
Firms make investments day by day in merchandise primarily based solely on their alternative. We all know the chance of knowledge and AI—improved resolution making, diminished threat, new paths to monetization—so shouldn’t we take into consideration investing in information equally?
SE: Since a lot of an organization’s proprietary data sits inside unstructured information, are you able to discuss its significance?
TT: Sure, most companies run on structured information—information in tabular type. However most information is unstructured. From voice messages to pictures to video, unstructured information is excessive constancy. It captures nuance. Right here’s an instance: if a buyer calls buyer help and leaves a product evaluate, that information might be extracted by its parts and transferred to a desk. However with out nuanced inputs just like the buyer’s tone of voice and even curse phrases, there isn’t a whole and correct image of that transaction.
Unstructured information has traditionally been difficult to work with, however generative AI excels at it. It really wants unstructured information’s wealthy context to be educated. It’s so necessary within the age of generative AI.
SE: We hear loads about artificial information nowadays. How do you consider it?
TT: Artificial information is critical to fill in information gaps. It permits firms to discover a number of eventualities with out the in depth prices or dangers related to actual information assortment.
Promoting businesses can run varied marketing campaign pictures to forecast viewers reactions, for instance. For automotive producers coaching self-driving automobiles, pushing automobiles into harmful conditions isn’t an choice. Artificial information teaches AI—and due to this fact the automobile—what to do in edge conditions, together with heavy rain or a shock pedestrian crossing.
Then there’s the concept of information distillation. When you’re utilizing the approach to create information with a bigger language mannequin—let’s say, a 13-billion-parameter mannequin—that information can be utilized to positive tune a smaller mannequin, making the smaller mannequin extra environment friendly, value efficient, or deployable to a smaller system.
AI is so hungry. It wants consultant information units of fine eventualities, edge situations, and every part in between to be related. That’s the potential of artificial information.
SE: Unstructured information is mostly information that human beings generate, so it’s usually case-specific. Are you able to share extra about why context is so necessary?
TT: Context is essential. We are able to seize it in a semantic layer or a website data graph. It’s the that means behind the info.
Take into consideration each area skilled in a office. If an organization runs a 360-degree buyer information report that spans domains and even methods, one area skilled will analyze it for potential clients, one other for customer support and help, and one other for buyer billing. Every of those specialists needs to see all the info however for their very own goal. Understanding developments inside buyer help might affect a advertising marketing campaign method, for instance.
Phrases usually have totally different meanings, as properly. If I say, “that’s scorching for summer time,” context will decide whether or not I used to be implying temperature or development.
Generative AI helps floor the precise data on the proper time to the precise area skilled.
SE: Given the tempo and energy of clever applied sciences, information and AI governance and safety are prime of thoughts. What developments are you noticing or forecasting?
TT: New alternatives include new dangers. Generative AI is very easy to make use of, it makes all people a knowledge employee. That’s the chance and the chance.
As a result of it’s straightforward, generative AI embedded in apps can result in unintended information leakage. Because of this, it’s crucial to suppose by way of all of the implications of generative AI apps to cut back the chance that they inadvertently reveal confidential data.
We have to rethink information governance and safety. Everybody in a company wants to concentrate on the dangers and of what they’re doing. We additionally want to consider new tooling like watermarking and confidential compute, the place generative AI algorithms may be run inside a safe enclave.
SE: You’ve mentioned generative AI can jumpstart information readiness. Are you able to elaborate on that?
TT: Certain. Generative AI wants your information, however it might additionally assist your information.
By making use of it to your present information and processes, generative AI can construct a extra dynamic information provide chain, from seize and curation to consumption. It could possibly classify and tag metadata, and it might generate design paperwork and deployment scripts.
It could possibly additionally help the reverse engineering of an present system previous to migration and modernization. It’s widespread to suppose information can’t be used as a result of it’s in an outdated system that isn’t but cloud enabled. However generative AI can jumpstart the method; it might assist you perceive information, map relationships throughout information and ideas, and even write this system together with the testing and documentation.
Generative AI adjustments what we do with information. It could possibly simplify and pace up the method by changing one-off dashboards with interactivity, like a chat interface. We must always spend much less time wrangling information into structured codecs by doing extra with unstructured information.
SE: Lastly, what recommendation would you give to enterprise and expertise leaders who need to construct aggressive benefit with information?
TT: Begin now or get left behind.
We’ve woken as much as the potential AI can deliver, however its potential can solely be reached along with your group’s proprietary information. With out that enter, your consequence would be the similar as everybody else’s or, worse, inaccurate.
I encourage organizations to give attention to getting their digital core AI-ready. A fashionable digital core is the expertise functionality to drive information in AI-led reinvention. It’s your group’s mixture of cloud infrastructure, information and AI capabilities, and functions and platforms, with safety designed into each degree. Your information basis—as a part of your digital core—is important for housing, cleaning and securing your information, guaranteeing it’s top quality, ruled and prepared for AI.
With out a sturdy digital core, you don’t have the proverbial eyes to see, mind to suppose, or fingers to behave.
Your information is your aggressive differentiator within the period of generative AI.
Teresa Tung, Ph.D. is International Information Functionality Lead at Accenture. A prolific inventor with over 225 patents, Tung focuses on bridging enterprise wants with breakthrough applied sciences.
Be taught extra about easy methods to get your information AI-ready:
- Learn to develop an clever information technique that endures within the period of AI with the downloadable e-book.
- Watch this on-demand webinar to listen to Susan and Teresa go deeper on easy methods to extract essentially the most worth from information to distinguish from competitors. Find out about new methods of defining information that may assist drive your AI technique, the significance of making ready your “digital core” upfront of AI, and easy methods to rethink information governance and safety within the AI period.
Go to Azure Innovation Insights for extra govt perspective and steering on easy methods to remodel your enterprise with cloud.
