I sat down with Teresa Tung to be taught extra in regards to the altering nature of knowledge and its worth to an AI technique.
AI success is determined by a number of elements, 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 functions. And as Accenture’s World 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 number of matters, together with Teresa’s six insights.
Lastly, we concluded with Teresa’s Recommendation for enterprise leaders utilizing or all for 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 undertaking. When new insights are wanted, it might 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 staff has bandwidth limitations or price range constraints, much more time is required.
“As a substitute of treating it as a undertaking—an afterthought—proprietary knowledge needs to be handled as a core aggressive benefit.”
Generative AI fashions are pre-trained on an present corpus of internet-scale knowledge, which makes it straightforward to start on day one. However they don’t know your corporation, 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 daily in merchandise primarily based solely on their alternative. We all know the chance of knowledge and AI—improved resolution making, decreased danger, 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 assist and leaves a product overview, that knowledge 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 knowledge has traditionally been difficult to work with, however generative AI excels at it. It really wants unstructured knowledge’s wealthy context to be skilled. It’s so vital within the age of generative AI.
SE: We hear quite a bit about artificial knowledge today. How do you consider it?
TT: Artificial knowledge is important to fill in knowledge gaps. It allows corporations to discover a number of situations with out the intensive prices or dangers related to actual knowledge assortment.
Promoting businesses can run varied marketing campaign pictures to forecast viewers reactions, for instance. For automotive producers coaching self-driving vehicles, pushing vehicles into harmful conditions isn’t an possibility. 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. In case you’re utilizing the approach to create knowledge with a bigger language mannequin—let’s say, a 13-billion-parameter mannequin—that knowledge can be utilized to nice 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 fine situations, edge situations, and all the things in between to be related. That’s the potential of artificial knowledge.
SE: Unstructured knowledge is usually knowledge that human beings generate, so it’s typically case-specific. Are you able to share extra about why context is so vital?
TT: Context is vital. We are able to seize it in a semantic layer or a site data 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 knowledge report that spans domains and even techniques, 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 desires to see all the information however for their very own objective. Understanding tendencies inside buyer assist might affect a advertising and marketing marketing campaign strategy, for instance.
Phrases typically have completely different meanings, as properly. If I say, “that’s sizzling for summer time,” context will decide whether or not I used to be implying temperature or development.
Generative AI helps floor the best info on the proper time to the best area professional.
SE: Given the tempo and energy of clever applied sciences, knowledge and AI governance and safety are high 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 straightforward, generative AI embedded in apps can result in unintended knowledge leakage. Because of this, it’s important to assume via all of the implications of generative AI apps to cut back the danger that they inadvertently reveal confidential info.
We have to rethink knowledge governance and safety. Everybody in a corporation wants to pay attention to 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 knowledge readiness. Are you able to elaborate on that?
TT: Positive. Generative AI wants your knowledge, however it might 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 might probably classify and tag metadata, and it might 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 frequent to assume 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 might show you how to perceive knowledge, map relationships throughout knowledge and ideas, and even write this system together with the testing and documentation.
Generative AI modifications what we do with knowledge. 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 knowledge into structured codecs by doing extra with unstructured knowledge.
SE: Lastly, what recommendation would you give to enterprise and know-how 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 carry, however its potential can solely be reached along with your group’s proprietary knowledge. With out that enter, your consequence 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 knowledge in AI-led reinvention. It’s your group’s mixture of cloud infrastructure, knowledge and AI capabilities, and functions and platforms, with safety designed into each degree. Your knowledge basis—as a part of your digital core—is crucial for housing, cleaning and securing your knowledge, guaranteeing it’s prime quality, ruled and prepared for AI.
With no sturdy digital core, you don’t have the proverbial eyes to see, mind to assume, or palms to behave.
Your knowledge is your aggressive differentiator within the period of generative AI.
Teresa Tung, Ph.D. is World 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 methods 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 methods to extract probably the most worth from knowledge to distinguish from competitors. Find out about new methods of defining knowledge that may assist drive your AI technique, the significance of making ready your “digital core” prematurely of AI, and methods to rethink knowledge governance and safety within the AI period.
Go to Azure Innovation Insights for extra govt perspective and steerage on methods to remodel your corporation with cloud.