Success Requires an Organization-Wide Emphasis on Quality Data

AI is set to reach mainstream adoption this year, according to recent research. Establishing tangible AI goals and success metrics is an important first step in creating the internal buy-in to get projects off the ground. However, implementing those projects requires another foundational element—availability of quality data. In fact, it is critical all the way through a deployment as data quality issues are among the most frequent causes of AI failures (along with project costs and challenges integrating with existing systems and processes). In fact, a third of organizations are developing data management capabilities in the next year. 

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Defining Quality

The first step to improving data quality, though, is defining it. Data is everywhere, but actionable insights and valid GPT outputs only come from models that have been fed quality inputs. Otherwise, AI projects are at risk of producing inaccurate modeling and unreliable insights—or stalling completely. 

Quality data must be: 

• Accurate: Factual errors, misleading statements and inconsistencies are obvious data flaws and culprits that will negatively affect AI outputs. However, spelling, grammar, and context also fall into this category, as systematic inaccuracies can prevent models from understanding linguistic patterns and skew results accordingly.
• Complete: Data inputs must also have appropriate breadth and depth. Texts must span multiple genres, domains and writing styles, or they can lead to outdated results. Also, similar to ensuring accuracy with linguistic patterns, data must also come from varied dialects and languages when applicable.
• Consistent: Uniformity in formatting and labeling of data—and metadata—is another important consideration. If formatting and labeling are not consistent and coherent, data will not process the same way twice, meaning results will vary. 
• Relevant: Timeliness and alignment to intended use cases are also critical components of data quality. Extensive amounts of data are only helpful if the information is current enough not to skew results with knowledge that is outdated or only marginally relevant.
• Diverse: Taking users into account and reflecting various cultural, linguistic, and demographic backgrounds is another important consideration when ensuring the quality of data. This diversity is critical to enabling the model to understand and respond appropriately to different users.
• Compliant: Finally, data must be gathered, stored, and used in ways that adhere to local and international data privacy laws—whether they are geography based (e.g. GDPR) or industry-based (e.g. HIPAA). This challenge is most visible to multinational enterprises, but with widespread use of cloud services, organizations of all sizes in all locations must ensure compliance to a constantly evolving landscape of data privacy laws. 

Potential Pitfalls

While the legality threshold is one standard for data best practices, AI governance, risk and compliance (GRC) policies expand further, into responsibility and ethical use. Organizations must do more than just maintain line of sight into where their data is located—they must dictate it to ensure it is stored and processed according to local regulations as well as understanding how it flows. Only then can they be confident they will remain compliant with data in transit. 

However, data regulations and best practices also vary by industry. For example, highly regulated industries such as Banking, Financial Services and Insurance or Healthcare all deal in sensitive personally identifiable information (PII), which require an oversized emphasis on security and privacy. Conversely, retail and manufacturing require more emphasis on accuracy and real-time availability data for visibility into inventory, customer experience, and supply chain. 

Finally, most AI best practices include guardrails with some form of a “human in the loop” to continuously quality check the machine outputs. However, available talent may be scarce or lacking in the necessary diversity. Just as data inputs need to be representative across demographics, talent must be as well. Otherwise, organizations will still run the risk of their AI projects producing skewed results. 

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Next Steps for CIOs

While these pitfalls may seem daunting for tech leaders, with the right focus, the next steps are fairly clear-cut. First and foremost, CIOs must prioritize data quality and best practices—beginning with ensuring legality and compliance to local and industry regulations. 

Next, they must invest in solutions that can scale to support AI workloads without sprawling and that meet data privacy and security needs—whether on-premises, in the cloud or in a hybrid environment. These standards will vary, but the common link is the critical need for visibility into where data resides and how to apply the relevant safeguards.

Finally, tech leadership must ensure this data-first mindset they establish permeates the entire organization. Generating buy-in for an AI deployment based on projected returns is one thing, but success requires leadership across the organization to remain committed to providing and aligning all the right resources—including the workforce. 

Enterprises no longer need to be convinced of the transformative potential of AI. The discussion has shifted away from simply the AI technology itself and now leans more heavily toward the execution required to achieve the proposed business outcomes. Concurrently, spending is anticipated to shift toward GenAI in 2025 with a greater focus on operational AI use cases—particularly in IT. With this shift toward GenAI, organizations must embody best practices and emphasize quality data across business functions. Only then are they positioned for AI success. 

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