Accelerating Data Modernization for a Global Credit Bureau
How Tavant Modernized Legacy Data Systems and Powered Scalable, Powerful Analytics
Mastering Data Archival Techniques: A Comprehensive Guide
In today’s data-driven business landscape, managing vast amounts of information efficiently is critical to maintaining optimal system performance, regulatory compliance, and cost-effectiveness. Data archival, the process of storing inactive data for long-term retention, is a fundamental practice for organizations, particularly those utilizing platforms like Salesforce. Understanding the nuances of data archival techniques is pivotal to ensuring seamless operations and future-proofing your organization’s data management strategy. The Essence of Data Tiering & Tiering Pyramid Data tiering is the practice of categorizing data based on its frequency of use and importance to the organization. This categorization allows for optimized storage and retrieval, enhancing system performance. The tiering pyramid is a conceptual framework that classifies data into different tiers: Tier 1: Operational Data (Full Search & Reporting) Tier 1 encompasses real-time operational data actively used for day-to-day business processes. This data must be readily accessible for immediate search, reporting, and decision-making. Salesforce’s platform is an ideal repository for this tier due to its quick access capabilities and seamless integration with operational processes. Tier 2: Historical Data (Limited Search & Reporting) As data ages, its frequency of access decreases. Tier 2 holds historical data that is still relevant but requires limited search and reporting functionalities. This data is essential for trend analysis and long-term business strategies. Leveraging Salesforce’s platform for this tier may be feasible, albeit with specific optimizations, to effectively manage the reduced search and reporting requirements. Tier 3: Archived Data (External Platform) Archived data, while no longer actively used, holds immense value for regulatory compliance, legal requirements, and potential future references. Tier 3 involves moving this data to an external platform, such as a data lake, allowing for cost-efficient storage and controlled API access for retrieval. Exploring Archival Approaches Effective data archival demands carefully considering the platform’s capabilities and the organization’s needs. Here are three key approaches to data archival within the Salesforce ecosystem: Approach 1 – Archiving on Platform (Using Record Archiving Indicator) Salesforce offers a built-in mechanism for archiving data using the Record Archiving Indicator. This approach involves flagging records as archived within standard or custom objects. While this keeps data within the Salesforce environment, it may impact performance due to increased data volume. Effective data partitioning and indexing are essential to ensure smooth operations. Approach 2 – Archiving on Platform (Big Objects) Salesforce’s Big Objects provide a specialized storage mechanism for large volumes of data with infrequent access requirements. This approach suits Tier 2 and Tier 3 data, allowing seamless integration with existing Salesforce processes while maintaining scalability and performance. Approach 3 – Archiving off Salesforce Platform (Data Replication to a Data Lake) For Tier 3 data, where long-term retention is essential, archiving of the Salesforce platform is a pragmatic choice. Replicating data to a data lake offers cost-effective storage and control over API access. This approach minimizes the impact on Salesforce performance and aligns with the concept of data tiering. Crafting Your Data Archival Strategy Devising an effective data archival strategy involves deeply understanding your organization’s needs, compliance requirements, and the platform’s technical capabilities. Here’s a roadmap to guide your strategy: Assessment: Analyze your data landscape to determine what data falls into each tier and its associated requirements. Platform Optimization: Optimize your Salesforce platform depending on the chosen archival approach. Implement data partitioning, indexing, and leverage platform features like Big Objects. Archival Policy: Define a clear archival policy that outlines when data transitions between tiers and when it’s eligible for archiving. Implementation: Based on your chosen approach, implement the necessary processes and tools for data archival, whether within the Salesforce platform or an external data lake. Testing and Monitoring: Rigorously test the archival processes and set up monitoring to ensure that data is being archived correctly and can be retrieved when needed. Documentation and Training: Document your archival strategy and provide training to relevant teams. This ensures consistency in data management practices across the organization. Continuous Refinement: Regularly revisit your data archival strategy to adapt to evolving business needs, compliance regulations, and technological advancements. When to Archive Data Instead of Migrating Choosing between archiving and migrating data is a crucial decision in data management. Here’s when archiving is the preferred option: Compliance and Legal Obligations: Archiving keeps data accessible for compliance and legal purposes without complex migrations. Historical Analysis: Data needed for historical analysis or reference is best archived to preserve insights and minimize disruption. Cost-Efficiency: Archiving is often more cost-effective than data migration, saving resources and technology investments. Minimizing Disruption: Archiving has minimal impact on daily operations compared to potentially disruptive migrations. Long-Term Retention: Archiving suits data retention over extended periods, as it’s designed for long-term storage. Data Tiering Alignment: Align archiving with data tiering to maintain efficient practices. Scalability: Archiving helps manage data growth gracefully, especially when dealing with large volumes. Data archival is not just about storage; it’s a strategic practice that impacts your organization’s efficiency, compliance, and future readiness. Mastering the art of data tiering and choosing the right archival approach is your key to unlocking optimal performance and data governance. By implementing a well-thought-out data archival strategy, you position your organization as a thought leader in efficient data management and set the stage for continued success in the dynamic world of business technology.
Why Data Modernization Should be a Priority for all Lenders and Bankers
Before the pandemic, mortgage companies were already under attack from fintech and nontraditional lending organizations. Most businesses didn’t have much to say about how the changing competitive landscape would impact their lending programs, so it seemed like the mortgage and banking industries were happy with how things were going. They mainly relied on the relationships they had built with their customers. However, in a post-COVID world, digital technology has completely transformed the financial industry. Mobile banking has gradually replaced brick-and-mortar banking. The cadence of transaction processing has shifted from periodic batching to real-time processing, posing a significant challenge to financial institutions; legacy IT systems are obsolete and incapable of providing a real-time digital banking and lending experience. This is challenging because customer expectations are not just sky-high – they are stratospheric. The demand for consistent real-time digital lending has made the financial services industry more competitive, and banks and fintech mortgage lenders are struggling to meet the needs of modern customers with their legacy, rigid IT systems. Data Modernization: The Foundation for Digital Transformation Data modernization, a process of migrating siloed data from legacy databases to modern cloud-based databases, enables organizations to be more agile by eliminating the inefficiencies, bottlenecks, and unnecessary complexities associated with legacy systems. Until recently, the processes for implementing loan origination hadn’t changed for decades. In many organizations, the process is still “informal” and carried out manually, often with paper documentation sent from department to department. The pandemic has revealed flaws in nearly every company’s data management practices. Organizations have recognized the urgent and critical need for a modern data infrastructure that manages data to make it highly accessible, practical, compliant, and valuable. Fintech mortgage lenders benefit from near-term cost savings and powerful analytics that extend personalization and optimize forecasting when they have a modern data backbone and digital mortgage solutions. As a result, mortgage companies have started shifting their focus from optional to critical digital transformation. The first step toward modernization is to create automated flows for this overall process, employing RPA, artificial intelligence (AI), or machine learning (ML) technologies to reduce human involvement, reduce errors, and automate adjustments where necessary, all in support of human activity where desired. Second, reviewing the data needs for this process and making the data better and more complete can help people make better decisions and grow the credit market. Data becomes even more powerful when it is smartly combined with intelligent process automation (a combination of Robotics Process Automation (RPA) and Artificial Intelligence (AI), or more precisely, a mix of tools and techniques such as OCR, speech recognition, Machine Learning, and Natural Language Processing (NLP) techniques. Loan forgiveness and mortgage forbearance are not new elements of loan servicing, but those areas have reached a scale hitherto unknown in the mortgage industry. Therefore, additional data and analytics are needed to make better decisions about loan modifications and their potential impact on the business’s risk and capital. AI analytics will only be helpful if the mortgage companies have the additional data to make a meaningful decision. A real-world example of the value of alternative data came from 2016 when severe flooding affected homes owned by a regional bank. Rather than waiting for homeowners to default on flooded or destroyed homes, the bank enlisted the assistance of a mapping and analytics firm to confirm flood-stricken homes against the bank’s mortgages. As a consequence of this, the bank was able to make use of the data and achieve a significantly improved comprehension of the threat that this event posed to its portfolio. And the organization was given the tools it needed to proactively reach out to the customer to arrange forbearance or provide other assistance to the homeowner. Why is Data Modernization an opportunity for lenders and bankers? Process efficiency: Reducing the “time to yes” The underwriting process’s inefficiency occurs in preparing the credit proposal, outlining detail for the credit committee what all these risks are, and calculating their likelihood and impact. Automation, data insights and analytics, and underwriting platform-based digital mortgage solutions are key levers that significantly impact the underwriting value chain. These technologies influence risk assessment and proactive risk monitoring and thus aid in risk prevention. Next-gen modern technologies automate manual processes and integrate legacy applications, such as policy administration systems, to eliminate information duplication. Other interventions, such as agent/customer portals, intelligent workflow, and real-time process visibility, allow agents and underwriters to work closely together. Subsequently, it reduces sales cycles, bringing “time to yes” down to five minutes. Raising the standard: transparency, consistency, and auditability Modern loan origination systems help standardize the credit underwriting process by ensuring that the best method for managing operations is used. Individual lending organization differs in some details, but most follow a consistent pattern in credit underwriting, and this process can be improved if everyone involved uses the same platform. Having instant shared access to the information required to complete the underwriting process improves efficiency while also increasing transparency and lowering the operational risk of critical information remaining in the hands of a few key personnel. It’s all about the data Banks and financial organizations generate massive amounts of data, and the vast majority of them are terrible at managing it. Data can now be found everywhere. Data modernization allows for more informed decision-making by reliably extracting data from various disparate systems. It facilitates the identification of high-value data combinations and integrations. It also enables people to identify opportunities at the moment quickly, allowing them to capitalize on something that would otherwise have gone unnoticed, eventually generating more revenue. Furthermore, data modernization reduces the risks associated with data security and privacy compliance. In its process, it looks for sensitive information so that you can limit user access to data in a precise and more efficient way. How can your organization take advantage of digital lending modernization? Consumers have shifted dramatically toward online channels during the pandemic, and businesses and industries have responded in kind. To help financial institutions achieve operational efficiencies, credit process optimization and automation of low-end credit processes to
Payment Tokenization to Reduce PCI DSS Scope
Don’t want the risk of handling or storing sensitive payment data on hosted servers? Want to achieve and maintain payment security (Payment Card Industry (PCI)) certification faster and easier? If these are your concerns, then Payment Tokenization is the way to go. It is a great way to reduce the scope of PCI Data Security Standard (DSS). Eliminating the payment data from your network is the only way to ensure that your customers’ sensitive personal information is not compromised during a security breach. Tokenization is the replacement of sensitive data with a unique identifier that cannot be mathematically reversed. In a transactional environment, tokens take the place of sensitive credit card data. Typically, the token will retain the last four digits of the card as a means of accurately matching the token to the payment card owner. The remaining numbers are generated using proprietary tokenization algorithms. How It Works To make a purchase on a website, the customers will enter their payment card information into the designated payment fields on the order page. When the customer submits the form, the card data is immediately transmitted directly to Card processors like CyberSource for storing, processing, and token generation. The card data never has to get stored in your environment even though you need the card for recurring processing. There are 2 main flavors of tokenization namely Silent Order POST (SOP) and Hosted Order Page (HOP)). Card processors return the result by substituting the PAN data with a uniquely generated token, which one can call subscription ID. You store the token in your database for future transactions or chargeback resolution on that account. For your recurring transactions, you just have to pass that token or subscription ID to the card processor. Customer service representatives can easily verify customers, as the custom token will retain the last four digits of the original PAN. Benefits of Tokenization Reduces PCI DSS Scope Renders payment card data meaningless to hackers Chargeback and payment reconciliation can take place without handling payment data Not mathematically reversible The format fits legacy payment card data fields Integrates with Account Updater to automatically update payment data for fewer failures The interesting part is that, whether you are starting with an e-commerce system of your own or an already existing one, you can easily use or switch to tokenization. If you are starting new, you will get all your cards tokenized but, if you already have cards, you can get them ‘ONE time tokenized’ using some batch process and then you will be able to switch to tokenization for all future orders. In the continuing next part of the series, we will look more deeply into the Flavors of Tokenization.
