IT leaders know that the quality of their organization’s data shapes how their AI solutions perform. Their data engineers tell them so.
Yet the same data engineers often lament the lack of solid data in their organizations and fret about the work needed to cultivate it. These cohorts share the burden with their corporate chiefs.
Nearly two-thirds of CEOs cite low-quality or disconnected data due to siloed infrastructures and fragmented technology stacks as the main barriers preventing AI solutions from scaling, according to The Futurum Group and Kearney research.
Before exploring how to cultivate high-quality data to serve AI solutions it’s important to understand the potential consequences of poor data.
The garbage in, garbage out reality
Garbage in, garbage out. This phrase has achieved mainstream status with the rise of generative AI. When data included in an AI model is inaccurate or biased (garbage in), models fail to generalize, leading to errors in predictions or decisions (garbage out).
But what does this look like in practice? When you feed poor-quality data into an AI system, you’re essentially asking it to make million-dollar decisions based on corrupted information.
Consider a retail chain that deploys an AI system to optimize inventory across 500 stores. Now suppose the chain’s sales data improperly tagged item returns, causing them to appear as additional sales.
As a result, the AI system incorrectly assumes that certain products are “selling” 30% more. The result? Massive overordering of slow-moving items, millions in excess inventory and stock shortages.
Or consider the case of a major bank whose AI chatbot trained on customer service transcripts loaded with poorly kept, inaccurate data, with agents abbreviating checking account data with the terms “chk,” “checking” or even numerical account codes.
Unfortunately, the chatbot cannot reliably understand customer requests about basic banking services, which lowers customer satisfaction scores. Often the bank must hire more human agents to process the additional requests.
Imagine similar scenarios playing out across other regulated industries that balance on the razor blade between compliance and risk. Sometimes, the outcome yields reputational and financial damage; that is serious garbage out.
Curating High-Quality Data
Dell Technologies and NVIDIA have created this eBook explaining how organizations can craft their data strategy to ensure successful AI deployments. One critical aspect of facilitating an impactful data strategy includes preparing data.
High-quality data determines how well an AI model can perceive, predict and act—all critical performance criteria. Without sound data, your AI foundation will collapse. Here we cover the steps required to get your data house in order.
- Audit Your Data I Before you can get data to your preferred to-be state, you must assess its as-is state. Is your data management stack clean, organized and well maintained? Taking these steps could help you detect and remediate errors, duplicates or inconsistencies.
- Marry the data siloes. Organizational data lies scattered across lines of business, in applications and platforms. Consolidating data sources affords you a more accurate view of your data, reduces duplication and leads to more actionable insight.
- Preparing data. Over time, data becomes outdated, voluminous and Cleaning, labeling and standardizing data improves AI model performance while reducing deployment time. Establishing organizational standards for data structure, consistency and completeness helps ensure the models your business relies on are learning from the right signals.
- Implement governance and compliance. Data security and resilience are paramount. Establishing clear data lineage, security controls and compliance frameworks builds trust in AI systems. Keeping a strong data governance structure and audit trail ensures that sensitive data is used responsibly while honoring compliance.
- Modernize data infrastructure. AI solutions require modern compute, storage and networking technologies, as well as the expertise to configure and support them. Upgrading to platforms that support real-time access, scalability and integration enables your organization to meet increasing AI demands.
The path to modernizing your data infrastructure
Simplifying how data moves, how it’s processed and how it’s governed ensures your AI workloads can scale. A unified, flexible approach accelerates time to market and future-proofs your organization.
Acquiring such an approach is hard. Dell Technologies and NVIDIA created The Dell AI Factory with NVIDIA, which comprises technologies and services to accelerate your use cases, integrate your data and workflows and help you design your own AI journey.
Remember: Garbage in, garbage out. Good data creates competitive advantages, while bad data creates expensive problems at scale. The question isn’t whether you can afford to invest in data quality—it’s whether you can afford not to.
Can you afford not to?
Learn more about the Dell AI Factory with NVIDIA.
