Financial services organisations operate in a complex, highly regulated environment where data is a critical asset. They deal with vast amounts of data daily, including customer information, transaction records, market data, and regulatory information. For years, Master Data Management (MDM) has been the go-to solution for achieving data unification and ensuring data quality within the industry. However, as technology evolves and data challenges become more complex, the traditional MDM approach shows signs of obsolescence.
Traditional Master Data Management
Many regard MDM as the gold standard for managing and unifying data across an organisation — but that’s only because there hasn’t been a better solution until now. The core idea behind MDM is to create a centralised repository of master data, which includes essential information about customers, products, and other critical entities. This centralised repository is meant to serve as a single source of truth, ensuring consistency and accuracy of data across various systems and applications.
MDM typically involves creating data models, defining rules, and implementing data quality processes. It also requires significant upfront investments in technology and resources. While MDM has served its purpose for many years, several factors are making it increasingly obsolete in today’s financial services landscape.
The Challenges with MDM
Financial services organisations face several challenges when it comes to MDM. Firstly, they are grappling with unprecedented volumes of data, and the velocity at which this data is generated and processed has surged dramatically. This rapid pace and sheer volume of data pose significant challenges for traditional MDM systems, originally designed for a slower-paced data environment.
Secondly, the variety of data in the financial sector is vast, ranging from structured financial data to unstructured text originating from customer interactions. Traditional MDM systems are ill-equipped to handle this diverse range of data effectively.
Thirdly, financial data often exhibits complexity, being multidimensional in nature. MDM models, characterised by rigidity, hierarchy, and manual management, struggle to accurately represent this intricate complexity.
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Fourthly, modern financial operations demand real-time data access and processing capabilities. However, MDM systems tend to operate in batch-oriented modes, making it difficult to meet these real-time requirements.
Finally, while MDM provides a foundational framework for data governance, it may not fully satisfy the rigorous regulatory and compliance demands inherent to the financial services industry.
The Emergence of Alternative Approaches
Recognising the limitations of MDM, financial services organisations are exploring alternative approaches to data unification. These approaches leverage cutting-edge technologies and innovative strategies to address the evolving data landscape.
Financial services organisations are increasingly turning to AI and machine learning (ML) technologies to master their data. These technologies play a pivotal role in automatically identifying, cleansing, and integrating data from diverse sources, yielding a wide array of benefits.
AI and ML facilitate data integration and cleansing, effectively identifying and cleaning data from a variety of sources, including both structured and unstructured data. They leverage Natural Language Processing (NLP) techniques to extract insights from text-based data, such as customer emails and social media interactions.
Furthermore, ML algorithms excel in data matching and deduplication, a critical component of data quality management. Whether it involves recognising duplicate customer records or consolidating data from different systems, ML models efficiently handle these tasks.
Anomaly detection is another strength of ML models, helping pinpoint anomalies and outliers in data. This capability is particularly vital for identifying potential errors, fraudulent activities, or unusual market behaviours, contributing significantly to risk management and fraud detection.
Moreover, AI can predict data quality issues by analysing historical data patterns. For example, it can anticipate instances where specific data fields are likely to be missing or inaccurate, providing proactive measures for data enhancement.
AI and ML also streamline data preparation through automation, handling tasks like data transformation and feature engineering. Feature engineering involves selecting, modifying, or creating new features from raw data to enhance the performance of ML models. This automation reduces the time and effort required for data preparation, enabling data scientists to focus on deriving valuable insights.
In addition to data preparation, AI and ML contribute to data quality and enrichment efforts by identifying missing data elements, enriching existing data with external sources, and ensuring that data remains up-to-date and relevant.
Another arguably better, innovative approach involves using data products—consumption-ready sets of high-quality, trustworthy, and consumable data. Data products are designed for use across an organisation to address various business challenges. They leverage AI-driven efficiency with human oversight to ensure data quality and reliability, making them a valuable addition to the toolkit of financial services organisations seeking robust data unification solutions.
Benefits of using data products
High-quality, trustworthy data is at the core of data products, and it undergoes rigorous quality checks and cleansing processes to guarantee accuracy, consistency, and reliability. This stringent approach helps mitigate the risk of making erroneous decisions based on faulty data. Data products are designed with accessibility and usability in mind, catering to easy consumption throughout the organisation. They typically feature user-friendly interfaces and seamless integration into existing workflows and systems.
One of the strengths of data products lies in their diverse range of use cases. They are versatile tools capable of addressing a wide array of business challenges, whether it’s customer segmentation, risk assessment, or compliance reporting. Data products provide the essential data foundation for various applications within the financial services industry.
These products are not static but rather subject to continuous updates. This ensures that the data remains relevant and up-to-date, aligning with the dynamic nature of the financial services sector.
AI contributes significantly to the efficiency of data products, with machine learning algorithms automating data processing tasks. Human oversight complements this by ensuring that AI-driven processes maintain data accuracy and reliability. This collaborative approach harnesses the strengths of both technology and human expertise.
Data products are scalable, adapting to meet the growing data demands of financial services organizations as data volumes increase. This scalability is achieved without compromising data quality, maintaining the integrity of the information provided.
The financial services industry is witnessing an undeniable shift from traditional MDM approaches to innovative, technology-driven alternatives. While MDM has served its purpose, the growing complexity, variety, and volume of data demands a more flexible, scalable, and efficient solution. Data products, along with their AI-driven data mastering capabilities, hold the promise of addressing these evolving challenges, offering automated, reliable, and high-quality data unification. As these approaches continue to mature, they are poised to redefine the landscape of data management in financial services, fostering data-driven decision-making, operational efficiency, and regulatory compliance.
Anthony Deighton is Data Product General Manager at Tamr