How MDM Will Shape AI Strategy

AI is revolutionizing business strategy. Between 2023 and 2024, the global AI market jumped by USD50 billion, and 65% of businesses across all industries now report using generative AI.

GenAI can potentially revolutionize workplace productivity by expediting rote tasks and data-intensive processes. McKinsey estimates that this tool could boost business productivity by up to 2.9% annually. Given this potential, businesses seeking a competitive advantage must adopt AI; it’s a foregone conclusion. But how can they effectively leverage it?

Answering that question requires a keen understanding of data management strategy. As overall data consumption and generation increase alongside AI usage, leaders should consider a more comprehensive approach to data management — one that sets their AI solutions up for success and enables data-driven decision-making at scale.

The problem with traditional data management systems

Many historical data management systems are non-automated, so humans must manually parse and understand all data the system creates or ingests. As overall data volumes skyrocket, this approach becomes extremely challenging. Manual processes explain why 52% of all organizational data is “dark,” AKA uncategorized or non-understood.

Furthermore, lackluster data organization can create silos and inconsistencies across the organization. Just 14% of employees believe that actionable data surfaces “extremely well” in their workflow, evidencing significant hurdles persist in information-gathering.

AI exacerbates these issues because it requires accurate data to operate effectively. Therefore, data disorganization can lead to inaccurate, incomplete or conflicting AI outputs. AI models operate on the assumption that existing data is correct; if this isn’t the case, the engine’s outputs might mislead human operators.

For example, a finance leader wants to determine how much their organization spends with a particular vendor. They may rely on AI to analyze multiple data sets, including spreadsheets and RFPs, to generate a total spend number over a given period (e.g., one month). However, duplicative records or inaccurate information will culminate in a misleading or downright inaccurate number, greatly decreasing decision-making efficacy.

Additional concerns emerge regarding AI and data governance. Without proper guardrails, AI models can obscure the origins and permutations of data, creating a “black box” effect. This opaqueness can lead to concerns about transparency and accountability, especially if the model handles sensitive consumer information. Data leaders need a solution that enables data traceability and explainability. Leaders should understand how AI models interact with data.

The MDM solution

Master data management (MDM) tools create an authoritative view of all organizational data, including customers, products and locations. Critically, MDM tools store and process data of all types, enabling a more linear view of data across every department and function. Interoperability becomes especially critical for large enterprises attempting to eliminate data silos or mitigate issues associated with sprawling tech stacks.

Organizations can establish a robust data foundation for AI initiatives by implementing MDM practices and technologies. Benefits of MDM include:

• Data consolidation and standardization: MDM ensures that AI models are trained on consistent, high-quality data, enhancing the accuracy and reliability of AI applications. MDM solutions accomplish this by automating the data vetting process and automatically conducting audits that improve overall data quality. MDM tools ensure that AI operates on the most up-to-date information by deleting duplicative data and updating stale information.
• Robust data governance: MDM provides robust tracking mechanisms that create detailed audit trails and maintain data lineage. This feature is essential for regulatory compliance, allowing organizations to demonstrate how data is collected, transformed and utilized. Deploying MDM before AI is especially crucial for robust data governance because some emerging regulations demand clearer documentation for AI processes.
• Data integration and delivery: MDM facilitates quicker data integration and processing, accelerating the rollout of AI technologies and enabling more effective decision-making. This feature is incredibly important because AI requires a massive amount of data to operate. Machine learning models, in particular, require a significant amount of training data to learn and grow effectively.
• Improved security and data privacy: MDM tools implement strong data governance frameworks that include security measures like encryption, access controls and audit trails. These features are essential for complying with data protection and privacy laws, which require that sensitive information be securely managed to prevent unauthorized access and data breaches.

What will AI strategy look like in 5 years?

As more businesses deploy AI for productivity gains, superior data management will differentiate AI frontrunners from their lagging competitors. At this early stage, it is critical to take the time to understand data processes and build a robust repository for disparate data via MDM tools.

That’s right: this early stage. Remember that 65% of businesses have adopted AI — but that also means a significant 35% have yet to adopt any AI use cases. If these organizations start deploying MDM today with the intent to implement a sustainable AI strategy, they might just find themselves ahead of early AI adopters in five years’ time.

The views and opinions expressed in this article are those of the author and do not necessarily reflect those of CDOTrends. Image credit: iStockphoto/SvetaZi