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 depends upon a number of elements, however the important thing to innovation is the standard and accessibility of a company’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 bunch of matters, together with Teresa’s six insights.
Lastly, we concluded with Teresa’s Recommendation for enterprise leaders utilizing or interested by AI
Susan Etlinger (SE): In your latest 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 could actually 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 funds 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 current corpus of internet-scale knowledge, 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 knowledge, fashions will ship the identical outcomes to you as they do your rivals.
Firms make investments every single day in merchandise based mostly solely on their alternative. We all know the chance of knowledge and AI—improved choice making, decreased 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 kind. However most knowledge is unstructured. From voice messages to pictures 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 assessment, that knowledge could possibly 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 educated. It’s so essential within the age of generative AI.
SE: We hear rather a lot about artificial knowledge as of late. How do you consider it?
TT: Artificial knowledge is critical 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 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 possibility. Artificial knowledge teaches AI—and due to this fact the automotive—what to do in edge conditions, together with heavy rain or a shock pedestrian crossing.
Then there’s the concept of data distillation. If you happen to’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 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 knowledge units of excellent situations, edge situations, and every little thing 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 usually 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 website 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 methods, one area professional will analyze it for potential prospects, 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 objective. Figuring out traits inside buyer assist could affect a advertising marketing campaign method, for instance.
Phrases usually have totally different meanings, as nicely. 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 data 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 traits 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 chance.
As a result of it’s straightforward, generative AI embedded in apps can result in unintended knowledge leakage. For that reason, it’s important to assume by means of all of the implications of generative AI apps to scale back the chance that they inadvertently reveal confidential data.
We have to rethink knowledge 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 knowledge readiness. Are you able to elaborate on that?
TT: Positive. Generative AI wants your knowledge, however it could actually additionally assist your knowledge.
By making use of it to your current knowledge and processes, generative AI can construct a extra dynamic knowledge provide chain, from seize and curation to consumption. It may well classify and tag metadata, and it could actually generate design paperwork and deployment scripts.
It may well additionally assist the reverse engineering of an current 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 could actually enable 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 may well 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 need to construct aggressive benefit with knowledge?
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 knowledge. With out that enter, your outcome would be the identical 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 functions 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 no robust digital core, you don’t have the proverbial eyes to see, mind to assume, or arms 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 focuses on bridging enterprise wants with breakthrough applied sciences.
Be taught extra about the way 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 the way 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” prematurely of AI, and the way to rethink knowledge governance and safety within the AI period.
Go to Azure Innovation Insights for extra government perspective and steerage on the way to remodel your enterprise with cloud.
