Strategies Vary for High-Quality Data in GenAI for Optimal Success
Take a Foray into the AI-Driven Future:
Sean Nathaniel, the brain behind DryvIQ, spearheads the Unstructured Data Management Company trusted by over a thousand organizations worldwide.
In 2025, AI adoption is at the heart of every enterprise strategy, as visibly indicated by an Intelligent Enterprise Leaders Alliance study on Enterprise Data Transformation. Here's a sneak peek:
- 55% of companies have increased their budgets for AI readiness this year.
- 85% prioritize foundational investments, such as data governance and management, while 60% focus on enhancing data security.
- A striking 75% consider aligning data initiatives with overall business goals a priority.
Despite the consensus that GenAI is the future and quality data is crucial for success, organizations grapple with the problem of obtaining the optimal data quality required for each AI use case. This challenge stems from the fact that the relevance of data varies across different AI applications. In one case, data that's relevant for an initiative might not be so for another. Just as poor data quality can derail even the broadest GenAI endeavors, a blanket approach to data quality can be similarly detrimental, especially when AI initiatives are linked to specific business objectives with high-stakes outcomes.
To achieve significant business impact with GenAI, data quality strategies must be tailored to individual use cases and in line with business objectives.
AI Readiness vs. the Nitty-Gritty:
While the executive suite yearns to accelerate AI adoption and envisions a smooth path to AI readiness, the reality is far more intricate. As per a recent Accenture report, 47% of CXOs are concerned about data readiness for generative AI. In this rush to deploy AI technologies at scale, IT teams are now grappling with the task of readying vast, disparate, and disorganized data stores, including the challenging task of managing the knowledge worker content generated daily by employees.
Managing the content produced, updated, and shared by employees presents the most significant hurdles as it contains the most pertinent information for any data-driven initiative and currently remains vastly untapped by generative AI initiatives. However, organizing and classifying this valuable data is notoriously difficult due to its content typically residing within the documents themselves rather than within the file attributes.
Worsening matters, many organizations invest in data preparation without a clear vision for the AI application. Data preparation efforts without a solid goal risk becoming aimless and inconsistent.
Why Data Quality Isn't a One-Size-Fits-All Proposition:
Applying universal rules across all data can lead to inefficiencies and missed opportunities. Different GenAI use cases possess different prerequisites, meaning datasets must be molded to suit specific purposes. For instance, the data needed to train a chatbot designed to improve employee digital experiences varies significantly from the data used to automate internal operations, and even department-specific internal initiatives may have varying data requirements. Adopting a use-case-driven approach guarantees that data relevance, organization, cleanliness, and security (ROCS) are addressed in harmony with strategic goals.
The 4 Fundamental ROCS Pillars of Data Quality:
Quality, relevant, organized, and secure data serves as the lifeblood of effective GenAI initiatives. To ensure data is primed for strategic AI initiatives, those responsible for data preparation must address four critical questions, adhering to the ROCS Framework:
Relevance: Has outdated, unnecessary, or trivial content been archived to ensure the most relevant data is accessible for the specific AI use case?
Organization: Is the data organized and classified in a manner that speeds up model training or lacks the necessary categorization to drive meaningful results?
Cleanliness: Has appropriate redaction, encryption, or anonymization been applied to safeguard the privacy and sanctity of the data according to business rules?
Security: Do robust governance and access controls exist to protect sensitive information during the training and deployment of AI models?
Strategic Methodology for AI-Ready Data Preparation:
Phase 1: Carry Out a Values Evaluation & Match AI Use Cases
What goals do you seek to achieve through AI? Conduct a values evaluation, focusing on high-impact GenAI use cases. By prioritizing use cases that align with your overall business objectives, your organization creates a foundation for data to be prepared strategically to deliver the best outcomes.
Phase 2: Scan the Inventory
Perform an audit of your unstructured data repositories to catalog the knowledge worker content across the entire organization. This will help you understand the data available to determine what's usable and how it can aid the initiatives surfaced from the values evaluation.
Phase 3: Apply the ROCS Framework to Boost Data Quality
Create top-notch, up-to-date datasets tailored to your identified use cases. Automated data management can efficiently enhance data relevance and security while maintaining diligence in cleanliness and organization.
Adopting this strategic, use-case-specific approach to data quality yields superior outcomes for generative AI models, minimizes waste by eliminating unnecessary data preparation, and encourages collaboration between IT teams and organizational leaders.
Those Who Tackle Their Data Systematically Today Shall Rule the AI Dominion Tomorrow:
As the potential (and demand) for AI to drive transformation in revenue and operational efficiencies intensifies, a general approach to data will not suffice. Achieving AI readiness involves having the right data tailored to the right purposes, not just loads of data. While this might seem like an insurmountable challenge, we collaborate with organizations that systematically tackle their data and derive better results from their AI initiatives. Those who adopt this methodology won't only stay ahead but will set the pace for the AI-driven future.
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- Sean Nathaniel, the founder of DryvIQ, reinforces the importance of tailoring data quality strategies for individual AI use cases in 2025, a year when AI adoption is pivotal for enterprise strategies according to the Intelligent Enterprise Leaders Alliance study on Enterprise Data Transformation.
- The recent Accenture report indicates that 47% of CXOs are apprehensive about data readiness for generative AI, highlighting the complexity in accelerating AI adoption and ensuring data quality for specific AI use cases.
- Adopting a use-case-driven approach to data quality can guarantee that data relevance, organization, cleanliness, and security (ROCS) align with strategic goals, enabling organizations to rule the AI-driven future by deriving better results from their AI initiatives.
