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Out with the Rules: Why MDM Never Delivers on its Promise

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By Anthony Deighton, CEO at Tamr.

Unifying and managing data across an entire organisation is a battle against siloed systems and inconsistent data standards that reduce operational efficiencies, impair decision-making and compromise customer experiences among many other business impacts. That’s why the promise of Master Data Management (MDM) was so attractive when it first emerged. With the ability to bring together customer, product or supplier data into a single and accurate golden record using rules-based approaches, MDM was hailed as the solution that would finally bring uniformity to enterprise data.

However, this has not materialised. MDM simply cannot deliver on its promise. Writing, modifying and maintaining rules is time-consuming for data teams because as data changes, the rules must change, too. And that requires significant manual human effort, making it difficult for these solutions to scale. That’s why companies who invest in MDM fail to realise the promise of the golden record.

Case in point: falling short in enhancing customer experience

To understand the significance of these issues, consider the use of data in corporate banking. Customer experience is paramount in corporate banking, where personalised service and efficient transaction processing can significantly impact client retention and business development. For corporate clients, who often deal with complex financial products and services, the stakes for a seamless experience are particularly high.

Data is crucial to deliver this level of personalised service to corporate clients. Success depends on the bank’s ability to quickly and accurately integrate data from diverse sources, such as transaction histories, account information and corporate financial statements. However, traditional MDM systems, with their rigid rules and manual processes, fall short in this dynamic environment. This leads to slow and error-prone operations that result in data inconsistencies and missed opportunities to tailor services and enhance client interactions.

This situation highlights the need for a different approach, one that can automate the data mastering process to improve the speed and accuracy of data integration, unification and management. Only with such capabilities can a bank truly understand its corporate client’s needs and preferences, allowing for the customisation of financial solutions and proactive service.

Calling time on subpar solutions

AI-driven data mastering addresses the challenge of fragmented data effectively. AI-powered solutions can continuously analyse vast amounts of customer data, enhancing data quality for more accurate insights that drive personalised customer experiences. For example, by evaluating a customer’s transaction patterns and financial health in real time, a bank can provide customised financial advice, risk management solutions and tailored investment opportunities. This not only enhances the customer experience but also strengthens client relationships and bolsters competitive advantage in the corporate banking sector.

Out with the rules

It’s clear that when we delve into the comparison between an AI-powered approach to data management and traditional rules-based MDM, the advantages of the former become more pronounced. AI-powered solutions stand out by offering a seamless way to manage the vast amounts of customer data that banks and other institutions rely on.

The efficiency of AI is unmatched, especially when contrasted with the protracted timelines and high costs associated with MDM, which gets bogged down in manual processes that can stretch over months or even years. In contrast, AI-driven solutions can compile and refine golden records within days. This stark difference not only accelerates time to value, but also slashes both project and operational costs, thanks to the automation and efficiency inherent in AI-powered systems.

Data accuracy is a non-negotiable for business decision making. AI excels in data accuracy by prioritising the cleaning and matching of data, ensuring that entities are resolved effectively, and that the data is dependable for decision making. Automation significantly cuts down on the labour required for data curation, by as much as 90%, which not only boosts accuracy but also frees essential resources for other strategic tasks. On the other hand, the manual efforts required by MDM are fraught with inconsistencies and errors, compromising the reliability of the data.

AI provides a comprehensive view by aligning all data sources and enhancing them with unique attributes and third-party data enrichments. This approach ensures the data is not only complete, but also possesses the depth and context required for nuanced insights. Contrast this with MDM, where developing and maintaining data quality and matching logic is a cumbersome manual task that often falls short in achieving a unified data panorama.

The sustainability of golden records over time is crucial for maintaining their integrity and relevance. AI-powered solutions leverage real-time API integrations for a dynamic connection with source systems, ensuring the golden records evolve alongside the data landscape. This adaptability, coupled with user-friendly interfaces, supports ongoing data management by allowing for easy reviews, updates and validations. In comparison, the durability of golden records managed by MDM is hampered by their slower adaptation to new data sources and reliance on manual updates, which can quickly render the data obsolete.

It’s clear that when we delve into a comparison between AI and traditional rules-based MDM, AI-powered solutions stand out by offering a seamless way to manage the vast amounts of customer data that organisations rely on. Data is continuously updated and enhanced in quality, leading to more accurate insights that underpin operational efficiencies and personalised customer experiences.

Human refinement in AI-powered solutions

While AI excels at processing vast amounts of data rapidly, identifying patterns and automating repetitive tasks, it occasionally encounters data that is noisy, ambiguous or exceptionally complex. In such cases, the nuanced judgement and domain expertise of humans become invaluable. Human refinement in AI-powered solutions involves a collaborative effort where human experts review, refine and enhance the AI’s output. This could mean correcting errors that the AI couldn’t resolve, making judgement calls on complex cases, or adding contextual insights that enhance the data’s accuracy and reliability.

This human-AI collaboration is not about replacing one with the other but leveraging the strengths of both. AI provides a robust foundation by handling the bulk of data processing, ensuring efficiency and scalability. Human experts then build upon this foundation, applying their understanding and insights to refine the data further. This dual approach ensures that the golden records are not just comprehensive and up to date, but also accurate and reflective of real-world complexities.

The process of human refinement is essential for maintaining the highest quality standards in golden records. It acknowledges that while AI can dramatically improve data management processes, the nuanced understanding and adaptability of humans are irreplaceable. By combining AI’s computational power with human insight, organisations can achieve a level of data accuracy, reliability and depth that neither could accomplish alone.

In with the new: fulfilling the promise of golden records

The journey from traditional MDM to AI-powered golden records represents a significant evolution in data management. This transition is not merely a technological upgrade but a complete shift that embraces the complexities and dynamism of today’s data landscape. AI-powered solutions, enhanced with human refinement, offer an unparalleled ability to manage, integrate, and utilise data effectively, ensuring that it becomes a strategic asset rather than a challenge to overcome.

The implications of this shift are profound, especially because the accuracy, completeness and timeliness of data can dramatically influence decision-making, customer experience and competitive positioning. By adopting AI-powered golden records, organisations can achieve faster time to value, reduce operational costs, enhance data accuracy and ensure the comprehensiveness and durability of their data assets.

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