Skip links

The Power of Data Transformation in Financial Services Sector

Jump To Section

Unlocking SSOT and Hybrid Data Mesh: Future-Proofing Finance

Recent events in the banking and financial services sector have sent shockwaves through global markets. The unexpected downfall of SVB has highlighted the importance of investing in robust data and analytic capabilities for critical insights, managing risks, and making informed decisions. Companies can improve their agility and responsiveness, drive outcomes, and identify and respond to trends with greater speed. A single source of truth (SSOT) and hybrid data mesh are important data management components in transforming the way companies manage risk and opportunity.

An SSOT is a specific method of managing core data, such as customer and transaction data, by creating a unified data set across the enterprise in a centralized location. This approach improves data quality and allows financial institutions to proactively focus on addressing opportunities and managing risks with greater consistency. By using a SSOT as the single authoritative source of core data, operational inefficiencies can be identified, customer behavior can be monitored, and strategies can be executed to drive growth.

Moreover, an SSOT provides a unified and consistent view of data across an organization, which is essential for building and deploying artificial intelligence (AI) and machine learning (ML) models. These models require large volumes of high-quality data to learn patterns and make accurate predictions. An SSOT reduces time to transform data and helps ensure data is accurate, complete, and up to date, making it easier to build and deploy AI and ML applications.

Read: AI Empowerment: Data Excellence and Leadership Vision

SSOT’s Rapid Data Analysis for Financial Advantage

The key benefit of a SSOT is its ability for a company to generate valuable data insights that can uncover patterns and trends more quickly. This can help them react faster and avoid an adverse impact to its financial performance or conversely capitalize on a market opportunity. Companies can continuously monitor its operations and vital information in real-time and pinpoint areas for improvement, as well as make proactive suggestions to customers related to their asset holdings or loans.

Hybrid Data Mesh: A Seamless Blend of Domain Power and Data Quality

A hybrid data mesh combines the benefits of a distributed architecture for domain data with the SSOT, offering increased flexibility and scalability. This ensures data uniformity and accuracy while allowing for domain independence, enhanced security, and compliance that is auditable and traceable. By distributing data across multiple systems and business functions, a hybrid data mesh enables faster and more accurate insights, while maintaining data privacy, integrity, security, and quality. This approach also allows for easier access to data and analysis by multiple teams across the enterprise that support business objectives.

Unleashing Domain-Level Data with Hybrid Data Mesh

By decentralizing data at the business unit or functional level, a hybrid data mesh empowers teams to quickly tap into domain-level data to identify emerging market trends and customer behavior, leading to more targeted product development and go-to-market strategies. Leveraging AI and ML tools, predictive models can be customized to specific products, markets, or customers. Data-driven decisions provide a more effective way to identify growth opportunities and achieve objectives in a rapidly changing competitive market. This empowers a more flexible and innovative culture infused with greater speed, and scale, and consistency.

Another benefit of a hybrid data mesh breaks down data silos that enable real-time data processing and analysis for to faster and more accurate decision-making. For instance, financial institutions can monitor transactions instantaneously and detect fraud or suspicious activities more quickly. This enhances agility, allowing companies to identify and remediate risks or make investments in initiatives that accelerate growth. It also facilitates the rapid ingestion, updating, and integration of new data into existing predictive models and tools.

Revolutionizing Financial Strategies: SSOT and Hybrid Data Mesh in Action

In conclusion, the SSOT and the hybrid data mesh will transform the way financial institutions manage risks and opportunities and comply with stringent regulatory requirements with greater consistency. By embracing these models, financial institutions can be more agile, react immediately to unexpected change, drive innovation, and implement growth strategies. As companies become more focused on building digital business, reliance on data democratization through an SSOT and hybrid data mesh will provide better insights, predictive tools, and advanced analytics with ML and AI. Data has become inextricably tied to digital business, innovation, and growth.

Picture of Raj Vattikuti

Raj Vattikuti

Raj Vattikuti is an American-Indian entrepreneur, business executive and philanthropist. He is the Founder and Chairman of Altimetrik Corp. He is also the founder of Vattikuti Foundation. through which he is involved in many charitable causes.

Latest Reads


Suggested Reading

Ready to Unlock Your Enterprise's Full Potential?

Adaptive Clinical Trial Designs: Modify trials based on interim results for faster identification of effective drugs.Identify effective drugs faster with data analytics and machine learning algorithms to analyze interim trial results and modify.
Real-World Evidence (RWE) Integration: Supplement trial data with real-world insights for drug effectiveness and safety.Supplement trial data with real-world insights for drug effectiveness and safety.
Biomarker Identification and Validation: Validate biomarkers predicting treatment response for targeted therapies.Utilize bioinformatics and computational biology to validate biomarkers predicting treatment response for targeted therapies.
Collaborative Clinical Research Networks: Establish networks for better patient recruitment and data sharing.Leverage cloud-based platforms and collaborative software to establish networks for better patient recruitment and data sharing.
Master Protocols and Basket Trials: Evaluate multiple drugs in one trial for efficient drug development.Implement electronic data capture systems and digital platforms to efficiently manage and evaluate multiple drugs or drug combinations within a single trial, enabling more streamlined drug development
Remote and Decentralized Trials: Embrace virtual trials for broader patient participation.Embrace telemedicine, virtual monitoring, and digital health tools to conduct remote and decentralized trials, allowing patients to participate from home and reducing the need for frequent in-person visits
Patient-Centric Trials: Design trials with patient needs in mind for better recruitment and retention.Develop patient-centric mobile apps and web portals that provide trial information, virtual support groups, and patient-reported outcome tracking to enhance patient engagement, recruitment, and retention
Regulatory Engagement and Expedited Review Pathways: Engage regulators early for faster approvals.Utilize digital communication tools to engage regulatory agencies early in the drug development process, enabling faster feedback and exploration of expedited review pathways for accelerated approvals
Companion Diagnostics Development: Develop diagnostics for targeted recruitment and personalized treatment.Implement bioinformatics and genomics technologies to develop companion diagnostics that can identify patient subpopulations likely to benefit from the drug, aiding in targeted recruitment and personalized treatment
Data Standardization and Interoperability: Ensure seamless data exchange among research sites.Utilize interoperable electronic health record systems and health data standards to ensure seamless data exchange among different research sites, promoting efficient data aggregation and analysis
Use of AI and Predictive Analytics: Apply AI for drug candidate identification and data analysis.Leverage AI algorithms and predictive analytics to analyze large datasets, identify potential drug candidates, optimize trial designs, and predict treatment outcomes, accelerating the drug development process
R&D Investments: Improve the drug or expand indicationsUtilize computational modelling and simulation techniques to accelerate drug discovery and optimize drug development processes