I sat down with Teresa Tung to be taught extra in regards to the altering nature of information and its worth to an AI technique.
AI success will depend on a number of components, however the important thing to innovation is the standard and accessibility of a corporation’s proprietary knowledge.
I sat down with Teresa Tung to debate the alternatives of proprietary knowledge and why it’s so important to worth creation with AI. Tung is a researcher whose work spans breakthrough cloud applied sciences, together with the convergence of AI, knowledge and computing capability. She’s a prolific inventor, holding over 225 patents and purposes. 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 knowledge 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 taken with AI
Susan Etlinger (SE): In your current article, “The brand new knowledge necessities,” you laid out the notion that proprietary knowledge is a company’s aggressive benefit. Would you elaborate?
Teresa Tung (TT): Till now, knowledge has been handled as a venture. 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 workforce has bandwidth limitations or finances constraints, much more time is required.
“As an alternative of treating it as a venture—an afterthought—proprietary knowledge must be handled as a core aggressive benefit.”
Generative AI fashions are pre-trained on an present corpus of internet-scale knowledge, which makes it simple to start on day one. However they don’t know your enterprise, folks, merchandise or processes and, with out that proprietary knowledge, fashions will ship the identical outcomes to you as they do your opponents.
Corporations make investments day by day in merchandise based mostly solely on their alternative. We all know the chance of information and AI—improved determination making, diminished threat, new paths to monetization—so shouldn’t we take into consideration investing in knowledge equally?
SE: Since a lot of an organization’s proprietary data sits inside unstructured knowledge, are you able to discuss its significance?
TT: Sure, most companies run on structured knowledge—knowledge in tabular type. However most knowledge is unstructured. From voice messages to photographs to video, unstructured knowledge is excessive constancy. It captures nuance. Right here’s an instance: if a buyer calls buyer help and leaves a product evaluate, that knowledge could possibly 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 an entire and correct image of that transaction.
Unstructured knowledge has traditionally been difficult to work with, however generative AI excels at it. It truly wants unstructured knowledge’s wealthy context to be educated. It’s so essential within the age of generative AI.
SE: We hear loads about artificial knowledge as of late. How do you consider it?
TT: Artificial knowledge is critical to fill in knowledge gaps. It allows firms to discover a number of eventualities with out the intensive prices or dangers related to actual knowledge assortment.
Promoting businesses can run numerous marketing campaign pictures to forecast viewers reactions, for instance. For automotive producers coaching self-driving vehicles, pushing vehicles into harmful conditions isn’t an choice. Artificial knowledge teaches AI—and subsequently 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. Should you’re utilizing the method to create knowledge with a bigger language mannequin—let’s say, a 13-billion-parameter mannequin—that knowledge can be utilized to advantageous tune a smaller mannequin, making the smaller mannequin extra environment friendly, value efficient, or deployable to a smaller machine.
AI is so hungry. It wants consultant knowledge units of excellent eventualities, edge circumstances, and every part in between to be related. That’s the potential of artificial knowledge.
SE: Unstructured knowledge is mostly knowledge that human beings generate, so it’s typically case-specific. Are you able to share extra about why context is so essential?
TT: Context is essential. We are able to seize it in a semantic layer or a site data graph. It’s the which means behind the information.
Take into consideration each area skilled in a office. If an organization runs a 360-degree buyer knowledge report that spans domains and even techniques, one area skilled will analyze it for potential prospects, one other for customer support and help, and one other for buyer billing. Every of those specialists needs to see all the information however for their very own goal. Realizing developments inside buyer help might affect a advertising and marketing marketing campaign strategy, for instance.
Phrases typically have totally different meanings, as nicely. 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 correct info on the proper time to the correct area skilled.
SE: Given the tempo and energy of clever applied sciences, knowledge 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 really easy to make use of, it makes everyone a knowledge employee. That’s the chance and the chance.
As a result of it’s simple, generative AI embedded in apps can result in unintended knowledge leakage. For that reason, it’s important to suppose by means of all of the implications of generative AI apps to cut back the chance that they inadvertently reveal confidential info.
We have to rethink knowledge governance and safety. Everybody in a corporation 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 knowledge readiness. Are you able to elaborate on that?
TT: Positive. Generative AI wants your knowledge, however it could possibly additionally assist your knowledge.
By making use of it to your present knowledge and processes, generative AI can construct a extra dynamic knowledge provide chain, from seize and curation to consumption. It could possibly classify and tag metadata, and it could possibly 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 knowledge 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 knowledge, map relationships throughout knowledge and ideas, and even write this system together with the testing and documentation.
Generative AI adjustments what we do with knowledge. 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 knowledge into structured codecs by doing extra with unstructured knowledge.
SE: Lastly, what recommendation would you give to enterprise and expertise leaders who wish to construct aggressive benefit with knowledge?
TT: Begin now or get left behind.
We’ve woken as much as the potential AI can convey, however its potential can solely be reached along with your group’s proprietary knowledge. With out that enter, your end result would be the similar as everybody else’s or, worse, inaccurate.
I encourage organizations to concentrate on getting their digital core AI-ready. A trendy digital core is the expertise functionality to drive knowledge in AI-led reinvention. It’s your group’s mixture of cloud infrastructure, knowledge and AI capabilities, and purposes and platforms, with safety designed into each stage. Your knowledge basis—as a part of your digital core—is crucial for housing, cleaning and securing your knowledge, making certain it’s prime 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 fingers to behave.
Your knowledge 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 makes a speciality of bridging enterprise wants with breakthrough applied sciences.
Study extra about tips on how to get your knowledge AI-ready:
- Learn to develop an clever knowledge 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 tips on how to extract essentially the most worth from knowledge to distinguish from competitors. Study new methods of defining knowledge that can assist drive your AI technique, the significance of making ready your “digital core” upfront of AI, and tips on how to rethink knowledge governance and safety within the AI period.
Go to Azure Innovation Insights for extra govt perspective and steerage on tips on how to remodel your enterprise with cloud.