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AI's appetite for data is changing data requirements
AI relies on high-quality real-time data. Companies that fail to modernize their data strategies risk falling behind, while those that adapt unlock AI's competitive advantage.
The promise of AI is real, with benefits including streamlining processes, automating tasks and creating avenues for innovation and differentiation. The fundamental element powering successful AI applications is your enterprise data.
Large language models (LLMs) have tremendous intelligence and reasoning to generate responses, but it's an organization's enterprise data that adds the relevant context to empower a business with unique AI-powered capabilities. Techniques such as retrieval augmented generation (RAG) are the link between using an LLM for its higher-level thinking abilities and your organization's unique insights, such as customer data, support information, marketing, financial and more.
AI's growing data demands
AI demands large volumes of data, which compels organizations to develop new approaches to data management. AI data needs differ significantly from traditional BI requirements because AI requires larger data volumes, multiple data types and real-time data processing that must remain accurate and unbiased while being properly governed. Generative AI takes in training data and produces inference responses to prompts, using its learned patterns and structures during inference to create novel content, such as text, images or music, based on a given prompt or input. AI agents typically do more than respond to prompts; they can also initiate actions, demonstrating the importance of training data to empower inference and the desired outputs.
Traditionally, businesses focused on compiling and maintaining data for analysis and reporting purposes. Relational databases store data in systematic arrangements of rows and columns. Though this approach achieves its original purpose, it falls short of AI requirements. AI algorithms primarily acquire knowledge by detecting patterns within extensive data sets and improve their accuracy and intelligence when they process larger amounts of data. AI algorithms become less prone to giving inaccurate responses and hallucinating when trained on extensive data sets.
Data volume matters, but data quality plays an equally important role. AI thrives on diversity, requiring both structured data, such as customer demographics and sales figures, and unstructured data, such as text, images, audio and video content. For instance, sentiment analysis models that analyze customer reviews need text data to understand customer opinions, while self-driving vehicles depend on visual and sensor inputs to find their way along streets. Diverse data types enrich AI models, leading to more accurate predictions, but the wide range of data types necessitates systems expansion to handle multiple data formats and modalities.
Real-time data ingestion is essential for AI systems in many applications. Fraud detection systems rely on instant transaction analysis to spot suspicious activities as they occur, while personalized recommendation engines must continuously adjust to user behavior. These applications function effectively using an uninterrupted flow of the most current data. It is essential to develop data pipelines that ingest and process data quickly to deliver it with low latency. Many AI applications find traditional batch processing methods too slow. Businesses must develop technological solutions to analyze primary insights from continuous data streams.
Areas of consideration for organizations include the following:
- Invest in modern and robust data platforms. Organizations should invest in scalable platforms that support data, AI and analytics with storage, database and high-performance computing technologies to support its operations and AI use cases.
- Embrace data governance. The importance of data governance rises as data becomes larger and more varied, empowering AI systems. Organizations must create defined standards and processes to maintain data quality and ensure security and privacy. They should also plan for AI governance, which focuses on the outputs of AI.
- Develop data literacy. Employees across the organization must understand how to work with data, interpret insights and contribute to a data-driven culture.
- Prioritize data quality. Data quality directly affects AI performance. Organizations must establish stringent data quality controls and procedures.
The insatiable need for data by AI systems is reshaping the data landscape. Organizations that adhere to old-fashioned data approaches risk losing their competitive position. By using larger data quantities, diverse data formats and real-time data processing, organizations can maximize AI capabilities, leading to innovation enhancement and strategic advantage.
Stephen Catanzano is a senior analyst at Enterprise Strategy Group, now part of Omdia, where he covers data management and analytics.
Enterprise Strategy Group is part of Omdia. Its analysts have business relationships with technology vendors.
