Advancement in Enterprise AI’s Early Years: The Rise of Data Strategies
Advancement in enterprise AI’s early years has largely been defined by experimentation, with businesses testing various models and seeing rapid improvements. However, as the top LLMs’ capabilities converge, AI agents become more prevalent and domain-specific small language models gain momentum, data strategies are increasingly the deciding factor driving AI success.
Managing Skyrocketing Data Volumes
Enterprise data’s growth and increasing complexity has overwhelmed traditional infrastructure and created bottlenecks that limit AI initiatives. Organizations not only need to store massive amounts of structured, semi-structured, and unstructured data, but this data also needs to be processed to be useful to AI applications and RAG workloads.
Advanced hardware like GPUs process data much faster and more cost-effectively than previously possible, and these advances have fueled AI’s breakthroughs. Yet, the CPU-based data processing software most businesses have in place can’t take advantage of these hardware advances. While these systems served their purpose for more traditional BI using structured data, they can’t keep up with today’s mountains of unstructured and semi-structured data making it very slow and expensive for enterprises to leverage the majority of their data for AI.
As AI’s data needs have become clearer, data processing advancements have begun to account for the scale and complexity of modern workloads. Successful organizations are reevaluating the systems they have in place and implementing solutions that allow them to take advantage of optimized hardware like GPUs.
Overcoming Data Silos
Structured, semi-structured, and unstructured data have historically been processed on separate pipelines that silo data, resulting in over half of enterprise data being siloed. Combining data from different pipelines and formats is complex and time-consuming, slowing real-time use cases like RAG and hindering AI applications that require a holistic view of data.
For example, a retail customer support chatbot needs to access, process, and join together data from various sources to successfully respond to customer queries. These sources include structured customer purchase information that is often stored in a data warehouse and optimized for SQL queries, and online product feedback that is stored in unstructured formats. With traditional data architectures, joining this data together is complex and expensive, requiring separate processing pipelines and specialized tools for each data type.
Ensuring Data Quality
The early thesis with LLM development was that more data equals bigger and better models, but this scaling law is increasingly being questioned. As LLM progression plateaus, a greater onus falls on the contextual data AI customers have at their own disposal.
However, ensuring this data is high-quality is a challenge. Common data quality issues include data stored in conflicting formats that confuse AI models, stale records that lead to outdated decisions, and errors in data entry that cause inaccurate outputs.
To ensure data quality for AI applications, businesses should define clear data quality metrics and standards across the organization to ensure consistency, adopt data quality dashboards and profiling tools that flag anomalies, and implement libraries that help standardize data formats and enforce consistency.
Conclusion
While AI presents businesses incredible opportunities to innovate, automate, and gain a competitive edge, success hinges on having a robust data strategy and rethinking current data architectures. By addressing the challenges of managing skyrocketing data volumes, unifying data pipelines, and ensuring data quality, organizations can lay a solid foundation for AI success.
FAQs
- What are the challenges in managing data volumes in AI initiatives?
- Managing data volumes is a significant challenge in AI initiatives, as it requires storing and processing massive amounts of structured, semi-structured, and unstructured data.
- How can organizations overcome data silos?
- Organizations can overcome data silos by implementing data lakehouses, which store structured, semi-structured, and unstructured data in a unified environment.
- What is the importance of ensuring data quality in AI applications?
- Ensuring data quality is crucial in AI applications, as it can lead to inaccurate outputs, outdated decisions, and confused AI models.
