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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.
Unfortunately, most businesses’ data architectures currently have clear shortcomings. Seventy-two percent of organizations cite data management as one of the top challenges preventing them from scaling AI use cases. In particular, three specific data management challenges consistently rise to the surface for data leaders as they work to deploy AI.
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.
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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.
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Fortunately, it is becoming easier to eliminate data silos. Data lakehouses have become increasingly common, allowing businesses to store structured, semi-structured, and unstructured data in their original formats in a unified environment. This eliminates the need for separate pipelines and can help AI applications gain a more holistic view of data.
Still, most incumbent data processing systems were designed for structured data, making it slow and expensive to process the varied data lakehouses store. Organizations are finding that in order to decrease the cost and latency of AI applications and enable real-time use cases, they need to move beyond lakehouses and unify their entire data platform to handle all types of data.
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.
Gartner estimates poor data quality is a key reason 30% of internal AI projects are abandoned. Current methods for ensuring data quality are also inefficient, as 80% of data scientists’ time is spent accessing and preparing data. On top of that, a large percentage of this time is spent cleaning raw data.
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.
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. 
About the author: Rajan Goyal is co-founder and CEO of DataPelago, which is developing a universal data processing engine to unite big data, advanced analytics, and AI. Goyal has a proven track record of leading products from inception to multi-billion dollar revenue. With 50+ patents and expertise in pioneering DPU architecture, Rajan has held key roles at Cisco, Oracle, Cavium, and Fungible, where he served as CTO. He holds degrees from the Thapar Institute of Engineering and Technology and Stanford University.
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