I sat down with Teresa Tung to study extra concerning the altering nature of knowledge and its worth to an AI technique.
AI success depends upon 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 purposes. And as Accenture’s World Lead of Knowledge Functionality, Tung leads the imaginative and prescient and technique that ensures the corporate is ready for ever-changing information developments.
We mentioned a number of matters, together with Teresa’s six insights.
Lastly, we concluded with Teresa’s Recommendation for enterprise leaders utilizing or keen on AI
Susan Etlinger (SE): In your latest 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 challenge. When new insights are wanted, it could possibly take months to supply the information, entry it, analyze it, and publish insights. If these insights spur new questions, that course of should be repeated. And if the information crew has bandwidth limitations or finances constraints, much more time is required.
“As an alternative of treating it as a challenge—an afterthought—proprietary information must be handled as a core aggressive benefit.”
Generative AI fashions are pre-trained on an present corpus of internet-scale information, which makes it simple to start on day one. However they don’t know your online business, 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-after-day in merchandise based mostly solely on their alternative. We all know the chance of knowledge and AI—improved resolution making, diminished danger, new paths to monetization—so shouldn’t we take into consideration investing in information equally?
SE: Since a lot of an organization’s proprietary information sits inside unstructured information, are you able to speak about 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 assist and leaves a product evaluation, that information might be extracted by its elements 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 skilled. It’s so necessary within the age of generative AI.
SE: We hear lots about artificial information lately. How do you consider it?
TT: Artificial information is important to fill in information gaps. It allows firms to discover a number of situations with out the intensive prices or dangers related to actual information assortment.
Promoting companies can run varied marketing campaign photos to forecast viewers reactions, for instance. For automotive producers coaching self-driving vehicles, pushing vehicles 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 thought of data distillation. In case you’re utilizing the method to create information with a bigger language mannequin—let’s say, a 13-billion-parameter mannequin—that information can be utilized to advantageous 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 excellent situations, edge situations, and every thing 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 typically case-specific. Are you able to share extra about why context is so necessary?
TT: Context is vital. We will seize it in a semantic layer or a site information graph. It’s the that means behind the information.
Take into consideration each area professional in a office. If an organization runs a 360-degree buyer information report that spans domains and even methods, one area professional will analyze it for potential clients, one other for customer support and assist, and one other for buyer billing. Every of those consultants needs to see all the information however for their very own function. Realizing tendencies inside buyer assist could affect a advertising marketing campaign method, for instance.
Phrases typically have completely different meanings, as properly. If I say, “that’s sizzling for summer season,” context will decide whether or not I used to be implying temperature or development.
Generative AI helps floor the proper info on the proper time to the proper area professional.
SE: Given the tempo and energy of clever applied sciences, information and AI governance and safety are prime of thoughts. What tendencies are you noticing or forecasting?
TT: New alternatives include new dangers. Generative AI is very easy to make use of, it makes all people an information employee. That’s the chance and the danger.
As a result of it’s simple, generative AI embedded in apps can result in unintended information leakage. For that reason, it’s crucial to suppose by all of the implications of generative AI apps to scale back the danger that they inadvertently reveal confidential info.
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 could be run inside a safe enclave.
SE: You’ve stated generative AI can jumpstart information readiness. Are you able to elaborate on that?
TT: Positive. Generative AI wants your information, however it could possibly 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 might probably classify and tag metadata, and it could possibly generate design paperwork and deployment scripts.
It might probably additionally assist 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 previous system that isn’t but cloud enabled. However generative AI can jumpstart the method; it could possibly assist you to 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 might probably simplify and velocity 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 know-how leaders who wish 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 end result would be the identical as everybody else’s or, worse, inaccurate.
I encourage organizations to deal with getting their digital core AI-ready. A trendy digital core is the know-how functionality to drive information in AI-led reinvention. It’s your group’s mixture of cloud infrastructure, information and AI capabilities, and purposes 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 robust digital core, you don’t have the proverbial eyes to see, mind to suppose, or arms to behave.
Your information is your aggressive differentiator within the period of generative AI.
Teresa Tung, Ph.D. is World Knowledge 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 probably 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 getting 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 online business with cloud.
