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In just a few months since it became widely available, generative AI has swiftly captivated the attention of organizations across industries. In March 2023, IDC polled organizations and found that 61% were already doing something with generative AI (GenAI). By July, that number had risen to 78%, showcasing the rapid adoption rate of this transformative technology. The later survey found that GenAI not only propelled businesses toward embracing AI, but also dispelled some general skepticism. With AI and machine learning (ML) at the helm, businesses are now empowered to deliver the immersive experiences customers demand, while simultaneously streamlining backend operations to new levels of efficiency — or at least, these things are now possible for the enterprise.
But make no mistake, data obstacles are still firmly in place for many organizations. AI initiatives come with unique data challenges, particularly when data is spread out and siloed — a common state, thanks to our collective reliance on batch-oriented SaaS apps, ERP systems, and the myriad of other technologies that we’ve stacked up to get to where we are today.
While AI has been possible for quite some time (there have been models for decades), widespread adoption in everyday business operations hinges upon reaching a critical tipping point with data. This means having all your enterprise data in real-time, high-quality, and trustworthy to effectively operationalize AI models. Most organizations today possess enough data all on their own. It’s accessing and using it that creates an issue, and the great majority of AI use cases depend on real-time data. As Steward Bond, VP of Data Integration and Intelligence Software Service at IDC, puts it, “Businesses have always run in real time. It was only the technology restraints that pushed us into batch processing.”
Data streaming is the key to unlocking the potential of AI and ML to gain insights, streamline operations, and elevate customer experiences.
With traditional machine learning, the strategy revolved around aggregating vast amounts of enterprise data into a centralized data lake, followed by the painful process of building custom ML models. With batch processing, these models heavily relied on the help of manual engineering training — and were expensive and prohibitive to train.
Then, GenAI came along and changed the game. GenAI shifts the paradigm towards leveraging large language models (LLMs), which are pre-trained, reusable models powered by SaaS-based technologies like OpenAI. With GenAI, the goal is to feed the inference layer of the LLM with the most current, up-to-date insights about your business that are extracted from your various enterprise systems. Retrieval-augmented generation (RAG) is a great way to do this. This is how you deliver value, and it’s why access to proprietary, real-time data is so critical for organizations that want to embrace GenAI. You don’t want applications that use outdated data — and there are plenty of chatbots out there that were last updated in 2021.
Chatbots in general are a good use case here. One of the big drivers of moving to AI is enhancing customer experiences, and the ability for customers to self-serve is a powerful example across all kinds of companies in diverse industries. This is precisely what chatbots enable, allowing customers to effortlessly access information through natural, conversational interactions.
The realm of customer data a chatbot traffics in is heavily reliant on the availability of real-time data. Customers demand instant access to the latest information about their accounts, their purchases, their available options, etc. Whether it’s booking a flight through an airline, requiring real-time flight and pricing data, or swapping a pair of sneakers for another size, reliant on real-time inventory data, all of these things require real-time enterprise data. For this reason, it’s the proprietary, real-time enterprise data that’s critical to driving a worthwhile GenAI experience. Any real AI use case is intricately tied to the unique data ecosystem of your business, fueling tailored experiences that resonate with your customers.
Unfortunately, for most companies, data lives within a tangled, complex web within the business. There are multiple platforms, apps, and SaaS products creating, ingesting, and transforming data all day, every day. This tangled mess is a problem for data architecture in general, but when it comes to GenAI it becomes a much bigger issue. You can’t serve your AI models when your infrastructure is a mess and your data is all over the place.
Data streaming platforms are the key to turning data mess into data value. Data streaming platforms help you stream, connect, process, and govern your data however and wherever it’s needed. As data moves through the platform, data pipelines are built to shape and deliver data in real-time, creating a system of “data in motion.” This platform connects and unlocks all your enterprise data from source systems, wherever they reside (customer data, supplier data, inventory data), and serves it as continuously streamed, processed, and governed data products. These real-time data products are instantly valuable, trustworthy, and reusable, and ensure your data is used consistently, everywhere it’s needed.
Data streaming platforms are a new way of thinking about how data moves and flows through your business, bringing structure to the chaos of messy data infrastructures and creating a virtuous stream of real-time data. With access to real-time customer data, customer history, and both structured and unstructured data, teams can enrich data streams and build real-time streaming applications and pipelines.
For this reason, data-streaming platforms are the key to powering LLMs with reliable, real-time enterprise data laying the groundwork for developing impactful GenAI use cases. Data streaming enables GenAI applications to take advantage of real-time data to enable swift and informed decisions and processes. In fact, “real-time analytics” is the #1 reason people invest in streaming data, with security and governance as critical factors, as well. Organizations recognize the competitive edge afforded by timely insights into market dynamics and customer behaviors. Latency is also important to 93% of organizations, and streaming data offers a timely response for actionable insights. The de-facto standard for data streaming is Kafka — used by over 70% of the Fortune 500, and 80% of the Fortune 100.
A data-streaming platform like Confluent — which was built by the founders of Kafka and is the de facto data-streaming platform, deployed in over 70% of the Fortune 500 — can play a very different role in bringing enterprise data to your GenAI aspirations. Confluent was designed specifically for the intricacies of data streaming, not as an add-on or after-the-fact feature, and delivers on all four key capabilities that make up a data streaming platform: streaming, connecting, processing, and governing data across your entire organization.
Confluent eliminates data silos and connects all the systems and individual applications within your business while ensuring high-quality, trusted, secure data that is accessible to the teams that need it. Confluent also allows you to enrich, filter, and process data for building streamlined real-time applications and pipelines.
Right now, only 26% of organizations with data-streaming capability are actually using it effectively. Most streaming data is still being pumped into conventional data warehouses or alternative local data stores and only 19% currently finds its way into AI or ML models. Despite the great majority of organizations harboring long-term plans to integrate streaming data into AI or ML initiatives, there is still considerable ground to cover in this evolving paradigm.
We still find ourselves in a pivotal phase of change and experimentation when it comes to applying AI and ML to enterprise data. Although early days, those days are proving themselves to be fairly short. The relentless pace of innovation is speeding up, and companies equipped with access to streaming data are poised to seize its potential much sooner.
To learn more about why data streaming platforms are the backbone to powering the AI and ML revolution, and how Confluent can help, watch the on-demand webinar The Data Streaming Platform: Key to AI Initiatives. We also invite you to check out our GenAI hub for more resources.
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