The success of GenAI models lies in your data management strategy
The rise of generative AI (GenAI) felt like a watershed moment for enterprises looking to drive exponential growth with its transformative potential. However, this enthusiasm may be tempered by a host of challenges and risks stemming from scaling GenAI. As the technology subsists on data, customer trust and their confidential information are at stake—and enterprises cannot afford to overlook its pitfalls.
Yet, it is the quality of the data that will determine how efficient and valuable GenAI initiatives will be for organizations. For these data to be utilized effectively, the right mix of skills, budget, and resources is necessary to derive the best outcomes. Such data also has to be placed in environments, be it private or public clouds, that can meet both business requirements and technical needs.
In light of these considerations, it has become a growing imperative for business and IT teams to collaborate and align their business priorities for AI use. How will organizations wield AI to seize greater opportunities, engage employees, and drive secure access without compromising data integrity and compliance? These are vital concerns that companies must address and communicate across every level of the business.
While it may sound simplistic, the first step towards managing high-quality data and right-sizing AI is defining the GenAI use cases for your business. Depending on your needs, large language models (LLMs) may not be necessary for your operations, since they are trained on massive amounts of text and are largely for general use. As a result, they may not be the most cost-efficient AI model to adopt, as they can be extremely compute-intensive.
Conversely, smaller models, such as domain- or enterprise-specific ones, may deliver more value at a much lower cost, while offering more accurate, context-specific insights than LLMs.
Optimizing GenAI with data management
More than ever, businesses need to mitigate these risks while discovering the best approach to data management. That’s why many enterprises are adopting a two-pronged approach to GenAI. The first is to experiment with tactical deployments to learn more about the technology and data use. This is known as data preparation, a short-term measure that identifies data sets and defines data requirements. These data will be cleansed, labelled, and anonymized, with data pipelines built to integrate them within an AI model.
The data preparation process should take place alongside a long-term strategy built around GenAI use cases, such as content creation, digital assistants, and code generation. Known as data engineering, this involves setting up a data lake or lakehouse, with their data integrated with GenAI models. On top of extending the capabilities of the GenAI data repository, such a data lake should support organizations in enhancing their data management to establish the most suitable posture for GenAI.
Choosing the right infrastructure for your data
One of the most crucial decisions business leaders can make is choosing the right infrastructure to support their data management strategy. Computational requirements, such as the type of GenAI models, number of users, and data storage capacity, will affect this choice.
Look for a holistic, end-to-end approach that will allow enterprises to easily adopt and deploy GenAI, from the endpoint to the data center, by building a powerful data operation. An example is Dell Technologies Enterprise Data Management. This includes Dell Data Lakehouse for AI, a data platform built upon Dell’s AI-optimized hardware, and a full-stack software suite for discovering, querying, and processing enterprise data. From eliminating data silos to offering data teams self-service access for crafting high-quality data products, the Dell Data Lakehouse can help businesses accelerate their AI outcomes.
But achieving breakthrough innovations with AI is only possible with unlocking the value of data. This is where data solutions like Dell AI-Ready Data Platform come in handy. Purpose-built for running AI at any scale, it unlocks the value of unstructured data so enterprises can access, prepare, train, and fine-tune their AI efficiently—on-premises, at the edge, or in any cloud—through a single point of data access and at peak performance.
In particular, Dell PowerScale provides a scalable storage platform for driving faster AI innovations. By offering an energy-efficient storage foundation for running AI workloads at high performance, enterprises can get swift business insights alongside multicloud agility, built-in federal-grade security, and storage efficiency. We see this in McLaren Racing, which successfully translated data into speed through AI. The company has boosted its car performance and speed via real-time data analyses of at least 100,000 parameters from more than 300 onboard sensors.
Find out more about effective data management for your GenAI deployments.
