I sat down with Teresa Tung to be taught extra concerning the altering nature of knowledge 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 company’s proprietary knowledge.
I sat down with Teresa Tung to debate the alternatives of proprietary knowledge 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, knowledge and computing capability. She’s a prolific inventor, holding over 225 patents and purposes. And as Accenture’s International Lead of Knowledge 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 subjects, together with Teresa’s six insights.
Lastly, we concluded with Teresa’s Recommendation for enterprise leaders utilizing or serious about 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 probably 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 staff has bandwidth limitations or finances constraints, much more time is required.
“As a substitute of treating it as a undertaking—an afterthought—proprietary knowledge ought 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 small business, folks, merchandise or processes and, with out that proprietary knowledge, fashions will ship the identical outcomes to you as they do your opponents.
Firms make investments each day in merchandise primarily based solely on their alternative. We all know the chance of knowledge and AI—improved determination making, lowered 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 speak about 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 help and leaves a product overview, 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 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 necessary within the age of generative AI.
SE: We hear so much about artificial knowledge as of late. How do you consider it?
TT: Artificial knowledge is important to fill in knowledge gaps. It allows firms to discover a number of eventualities with out the in depth prices or dangers related to actual knowledge assortment.
Promoting companies can run numerous marketing campaign photographs 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 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 thought of information distillation. For those who’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 tremendous tune a smaller mannequin, making the smaller mannequin extra environment friendly, price efficient, or deployable to a smaller system.
AI is so hungry. It wants consultant knowledge units of excellent eventualities, edge circumstances, and every little thing 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 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 knowledgeable in a office. If an organization runs a 360-degree buyer knowledge report that spans domains and even programs, one area knowledgeable will analyze it for potential prospects, one other for customer support and help, and one other for buyer billing. Every of those consultants needs to see all the info however for their very own function. Figuring out traits inside buyer help might affect a advertising and marketing marketing campaign method, for instance.
Phrases typically have totally different meanings, as effectively. If I say, “that’s scorching for summer time,” context will decide whether or not I used to be implying temperature or pattern.
Generative AI helps floor the appropriate info on the proper time to the appropriate area knowledgeable.
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 really 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. Because of this, it’s crucial to assume 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 company 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 might be run inside a safe enclave.
SE: You’ve stated generative AI can jumpstart knowledge readiness. Are you able to elaborate on that?
TT: Certain. Generative AI wants your knowledge, however it might probably 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 could possibly classify and tag metadata, and it might probably generate design paperwork and deployment scripts.
It could possibly additionally help 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 might probably make it easier 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 could possibly 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 together with your group’s proprietary knowledge. With out that enter, your outcome would be the similar 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 purposes and platforms, with safety designed into each degree. Your knowledge basis—as a part of your digital core—is important for housing, cleaning and securing your knowledge, making certain it’s prime quality, ruled and prepared for AI.
With out a sturdy 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 International Knowledge 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 the right 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.
Go to Azure Innovation Insights for extra government perspective and steering on the right way to rework your small business with cloud.