Data Designed to Deliver: 6 Ways to Better Understand Data and AI
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As developments in artificial intelligence (AI) continue to capture the hearts and minds of executives, practitioners and consumers alike, businesses are finding AI-powered solutions increasingly complex and costly to test and implement. This is especially true for businesses that have yet to invest in a holistic data strategy.
Despite numerous reports of digital transformation success in recent years, from an IT perspective, most organizations still lack the ability to fully realize AI's many game-changing promises.
One explanation lies in the fact that harnessing the power of large language models (LLMs) and other AI solutions for enterprise use requires a certain level of high-performance architecture (HPA). We're not just talking about the GPUs, CPUs and assortment of networking, storage and memory arrays in your data center.
Rather, our concept of HPA merges the traditionally segmented workflows of high-performance computing (HPC) and AI/ML app development into a single framework with the infrastructure components required to meet the data demands of today's AI-powered solutions. These components should be purposefully designed and expertly deployed into an organization's IT environment with the goal of achieving a specific business outcome.
The relationship between data, AI and the infrastructure required to maximize outcomes can be confusing, especially in the shadow of market pressures to adopt enterprise AI solutions as quickly as possible. We've found that reframing the pursuit of quick AI success into a more purpose-driven journey to data maturity can bring a sense of clarity and simplification to the confusion.
With simplicity in mind, let's examine six useful ways to think about the relationship between data and AI.
There's no avoiding it. Data is vital. It has become a foundational building block of modern business success. The lifeblood coursing through the digital veins of the Internet as well as the dreams of its future iterations.
Data fundamentals and best practices, including a robust strategy and investment in infrastructure modernization, should be addressed by IT and business leaders long before they allow their heads to be turned by the promise of all-knowing chatbots and endlessly automated operations.
In our digital age, data is increasingly instrumental to an organization's ability to make more strategic business decisions, identify and mitigate risks, unlock new revenue streams, improve customer and employee experiences, and much more. Yet the efficacy of any AI-powered solution ultimately boils down to data quality, data governance, data management and the ability of your architecture to deliver the results you're expecting.
If your organization treats data as anything but vital to its ongoing success, we strongly encourage rethinking that position.
As both the actual substance and flow of modern IT business operations, data shares many properties with the concept of fluidity.
You've likely heard someone use the analogy "data is the new oil." The crux of this concept is that data and oil are similar commodities that, while not particularly valuable in their raw or unrefined states, become incredibly valuable when properly extracted, refined, stored and manipulated to achieve a discrete purpose.
For oil, that purpose usually relates to the production of fuel or other useful chemicals and derivative products. For data, that purpose usually involves deriving actionable insights that drive the business forward.
The concept of fluidity highlights data's malleable nature, allowing it to flow with ease between properly connected environments. By thinking of data as a valuable-yet-unrefined fluid present just below the surface of business operations, organizations might better appreciate the orchestrated processes and procedures that must be in place to extract the full value from this inherently latent commodity.
Critical mass and alignment, from business and IT perspectives, are both needed to capitalize on the next generation of data-driven solutions. In other words, organizations need to generate the appropriate momentum behind their data and AI initiatives to succeed.
Momentum is even more urgent for organizations wishing to pursue the most ambitious of data initiatives, particularly those around enterprise-wide generative AI. Data can propel your business to new heights. But to gain the speed needed to tip the scales toward actionable insights and real-world outcomes, data stakeholders and IT environments must flow together toward a unified goal.
So how can you generate the right momentum to achieve your data and AI goals?
It starts with aligning your data strategy with your AI strategy. By breaking down the strategy development process into a series of incremental steps, you'll be able to fully examine your data flows — from source to actionable insight — while ensuring that the right stakeholders are bought in and up to speed.
Ultimately, momentum will come from aligning your data and AI use cases with your business objectives through purpose-built technology.
The amount of new data generated each year continues to multiply, with no end in sight. Moreover, part of the excitement propelling interest in emerging AI solutions is their perceived ability to act as force multipliers for business results across industries.
Exponential then, as a shared characteristic of data and AI, symbolizes a particular intrinsic quality of these exciting technology advancements we hope to harness. However, doing so is easier said than done.
Organizations must strategically invest in the right people, processes and technology related to establishing a sustainable foundation of clean, actionable and reliable data if they hope to harness the exponential potential of AI to transform their business. They also need to validate their long-term plans for storing and managing the escalating amounts of data their business will inevitably depend on year over year.
If you can think it, data can probably help you do it. Whether your aim is to drive operational efficiencies and savings through automation; achieve gains in sales, scalability or productivity; improve customer, patient and employee experiences; enable smoother regulatory compliance; make progress toward sustainability goals; or unleash an organization's untapped potential for innovation through AI-powered solutions — data is going to play a key role.
It is AI's capacity to inspire — to ignite an endless array of possibilities in the mind's eye — that tends to excite people most. It's easy to get carried away by focusing on the possibilities, both good and bad. That's why our proven approach to AI, what we call Practical AI, focuses on getting to achievable outcomes faster through:
- Upfront goal setting to identify the specific business objectives you hope to achieve with data and AI.
- Assessing your current data and AI capabilities and deficiencies across a range of related technologies.
- Modernizing IT infrastructure (as needed) to ensure reliability, handle growing data demands and enhance scalability.
- Establishing the visibility needed to accurately forecast your organization's changing data demands over time.
When designed right, data represents an opportunity to drive your business wherever you want it to go. Just remember, the road to possibility is best traveled with a tested roadmap.
True intelligence may be the pinnacle of our hopes and dreams for AI.
At the micro level, we want AI to give us easier insights and enable smarter decision-making through products and strategies designed to spur productivity and efficiency. At the macro level, many believe solving the hard problem of Artificial General Intelligence (AGI) — the still-theoretical stage of AI maturity that involves a machine's ability to closely mimic the complexity and plasticity of human thought — is the true aim of this work.
Whether you're inspired by the practical applications of AI or its philosophical implications, it's important to recognize data's facilitatory role in each path. Not only is data vital to enabling smarter decision-making, but it also represents the singular launch pad from which organizations can confidently and securely explore any and all future advances in AI.
Even without AI in the picture, investing in the tools and methodologies of data intelligence can allow organizations to better understand the data points they routinely collect, store and manipulate to enhance their products and services.
There is a good chance that data is inextricably woven into the very fabric of your business. In fact, your organization has probably made significant gains in recent years in its data capabilities, perhaps simply in terms of the volume of data collected to survive the day-to-day realities of our post-pandemic digital age.
"Data designed to deliver" captures how WWT thinks about the symbiotic relationship between data and AI.
Wherever you are in the process of testing and implementing transformative AI solutions, we hope this article has helped you realize the importance of data maturity in reaching your goals. If you need any assistance in reframing the pursuit of quick AI success into a more impactful journey to data maturity, reach out. We're here to help.