• Your Goal: To reliably and efficiently bring diverse financial data from multiple internal and external sources into our systems for processing, analysis, and operational use.
• Typical Problems/Issues:
  • Building and maintaining data pipelines is complex, resource-intensive, and requires specialized skills.
  • Dealing with a multitude of data formats (FIX, XML, CSV, JSON, proprietary APIs) and connection protocols is challenging.
  • Ensuring pipeline reliability, scalability, and performance as data volumes and sources grow.
  • Lack of skilled personnel to dedicate to ongoing pipeline development and maintenance.
  • “Chaotic approach to trade and market data management, leading to wasted time and money.”
• What Finaprins Does:
  We design, build, and manage robust, automated ETL/ELT data pipelines tailored to your specific financial data sources, allowing you to outsource the complexity of data ingestion and focus on leveraging the data.
• How Finaprins Does It:
  • We leverage our expertise in financial data (trade, market) and our TradeView platform‘s capabilities to create efficient data ingestion workflows.
  • We implement connectors for various data sources, including market data vendors, trading systems, and internal databases.
  • Our “Market Data Sourcing & Distribution” service handles acquisition from diverse sources.
  • We automate data flow, scheduling, and monitoring, ensuring timely and complete data delivery.
• Compelling Reason to Buy:
  • Simplified operations with managed services: Offload the burden of pipeline development and maintenance.
  • Automated data workflows: Reduce manual effort, minimize errors, and accelerate data availability.
  • Focus on strategic work: Free up your internal IT/Data Engineering teams to concentrate on core business activities and innovation.
  • Cost-effective data solutions: Benefit from our expertise and shared infrastructure without large upfront investments.

• Your Goal: To ensure that data entering our systems is accurate, complete, consistent, and fit for purpose before it’s used for critical operations, analysis, or reporting.
• Typical Problems/Issues:
  • “Garbage in, garbage out” – poor data quality leads to flawed analysis, incorrect reporting, and bad business decisions.
  • Identifying and rectifying data errors late in the process is costly and time-consuming.
  • Lack of systematic data validation processes, often relying on manual checks or downstream discovery of issues.
  • Difficulty defining and implementing comprehensive validation rules for complex financial data.
• What Finaprins Does:
  We embed rigorous data quality checks and validation rules directly into your data pipelines, ensuring that you receive clean, accurate, and reliable data ready for immediate use.
• How Finaprins Does It:
  • Our “Market Data Validation” service eliminates errors, duplicates, and inconsistencies in third-party data.
  • We implement “Continuous Data Quality Monitoring” with ongoing assessments to identify and flag issues proactively.
  • We work with you to define specific validation rules based on your data types (e.g., price ranges, format conformity, referential integrity).
  • Our processes are designed to validate data as it’s ingested and transformed.
• Compelling Reason to Buy:
  • High-quality, reliable data: Trust the data underpinning your decisions, operations, and regulatory compliance.
  • Reduced operational risk: Minimize errors stemming from poor data that can impact trading, settlement, or risk calculations.
  • Increased efficiency: Spend less time cleaning data downstream and more time using it.
  • Essential for compliance and strategic planning: Ensure data meets regulatory standards and supports accurate strategic insights.

• Your Goal: To transform raw, often inconsistent and disparate, financial data into a clean, uniform, and consolidated format that simplifies analysis, reporting, and system integration.
• Typical Problems/Issues:
  • Data arrives from multiple systems in varying formats, with different naming conventions and structures.
  • “Complex data conversions due to systems speaking different ‘languages’, making integration difficult.”
  • Duplicate records create confusion and skew analytics.
  • Manual data cleansing is error-prone, time-consuming, and not scalable.
  • Inconsistent data representation hinders a unified view of business operations.
• What Finaprins Does:
  We expertly cleanse, standardize, and de-duplicate your financial data, transforming it into a consistent, high-quality asset ready for your business needs, including adherence to industry standards like FpML and CDM.
• How Finaprins Does It:
  • Our “Trade Data Transformation and Standardization” services streamline integration by creating standardized trade data.
  • The “Trade Data Standardization” subservice unifies trade data from multiple systems.
  • “Market Data Transformation” converts market data into required formats.
  • We implement “Data Models Implementation” to convert trade data into FpML and CDM.
  • Our processes involve parsing, validating, transforming, enriching, and mapping data to a target model.
• Compelling Reason to Buy:
  • Unified trade/market data models: Achieve consistent data representation across your organization.
  • Simplified analysis and reporting: Work with clean, standardized data that is easier to query and understand.
  • Streamlined system integration: Reduce complexity and costs when integrating systems or onboarding new platforms.
  • Consistent data across all platforms: Improve decision-making and reduce discrepancies between departments.

• Your Goal: To proactively identify and be alerted to data quality issues, trends, or anomalies in real-time or near real-time, allowing for swift investigation and remediation.
• Typical Problems/Issues:
  • Data quality issues often go undetected until they cause significant downstream problems (e.g., failed reports, incorrect P&L, compliance breaches).
  • Lack of visibility into the health and integrity of key data assets.
  • Reactive approach to data quality, fixing problems after they occur.
  • No clear metrics or dashboards to track data quality over time.
• What Finaprins Does:
  We implement “Continuous Data Quality Monitoring” services that track key data quality metrics, identify anomalies, and provide timely alerts to relevant stakeholders, enabling proactive data governance.
• How Finaprins Does It:
  • We establish baseline data quality metrics and thresholds based on your requirements.
  • Our systems continuously assess data against these metrics (e.g., completeness, accuracy, timeliness, uniqueness, validity).
  • We configure automated alerting mechanisms to notify your teams of critical data quality events.
  • We can provide dashboards and reports to visualize data quality trends.
• Compelling Reason to Buy:
  • Proactive issue resolution: Identify and rectify errors before they impact business processes or decisions.
  • Increased trust in data: Maintain confidence in the accuracy and reliability of your data assets.
  • Reduced operational risk: Minimize the potential for financial loss or reputational damage due to data errors.
  • Improved data governance: Gain better control and oversight over your critical data.

• Your Goal: To verify and ensure that data remains accurate, complete, and consistent after it has been moved, transformed, or integrated between different systems or data stores.
• Typical Problems/Issues:
  • Discrepancies can arise during data transfer or transformation, leading to inconsistencies between systems (e.g., trading system vs. risk system vs. general ledger).
  • Manual reconciliation is extremely time-consuming, error-prone, and often a bottleneck, especially for high-volume trade data.
  • Failure to reconcile data can lead to incorrect financial reporting, regulatory breaches, and operational inefficiencies.
  • Identifying the root cause of discrepancies can be challenging.
• What Finaprins Does:
  We implement automated “Trade Data Reconciliation” services to systematically compare data records between your source and target systems, identify discrepancies, and facilitate their resolution, ensuring data integrity across your landscape.
• How Finaprins Does It:
  • We define reconciliation rules and matching criteria based on key data fields.
  • Our processes automatically compare datasets from different systems (e.g., front-office trades vs. back-office settlements, internal positions vs. custodian statements).
  • We provide clear reports on matched records, breaks (discrepancies), and exceptions.
  • We can assist in investigating and resolving identified discrepancies.
• Compelling Reason to Buy:
  • Ensure consistency and accuracy: Maintain a single version of the truth across critical systems.
  • Reduce operational risk: Minimize errors in financial calculations, reporting, and settlements.
  • Enhance compliance: Meet regulatory requirements for data accuracy and auditability.
  • Increased efficiency: Automate a typically manual and labor-intensive process, freeing up valuable staff.

• Your Goal: To structure and organize financial data in a logical, efficient, and understandable way that supports analytical querying, reporting, system interoperability, and adherence to industry best practices.
• Typical Problems/Issues:
  • Data is often stored in disparate, poorly structured formats, making it difficult to query and analyze effectively.
  • Lack of a common data language or model leads to “systems speaking different ‘languages’,” hindering integration and consistent reporting.
  • Implementing complex industry standards like FpML (Financial products Markup Language) or CDM (Common Domain Model) requires specialized expertise.
  • Inefficient data models can lead to poor query performance and increased storage costs.
• What Finaprins Does:
  We design and implement optimized data models tailored to your financial data needs, including the conversion of your trade data into industry-standard models like FpML and CDM, ensuring your data is well-organized, accessible, and future-proof.
• How Finaprins Does It:
  • Our “Data Models Implementation” subservice specializes in converting trade data into FpML and CDM, ensuring “accurate and efficient standardization.”
  • We analyze your existing data structures and business requirements to design appropriate logical and physical data models.
  • We can implement star/snowflake schemas for data warehouses/marts to optimize analytical queries.
  • We ensure models support efficient data storage, retrieval, and integration.
• Compelling Reason to Buy:
  • Utilize industry standards effortlessly: Adopt FpML and CDM without needing in-house specialized knowledge or altering existing workflows.
  • Unified trade/market data models: Standardize diverse data into a single, unified model for easier analysis and reporting.
  • Improved data accessibility and understanding: Make it easier for analysts and business users to find and interpret data.

Enhanced interoperability: Facilitate seamless data exchange between systems and with external parties.

• Your Goal: To provide specific business units with curated, optimized, and easily accessible subsets of data relevant to their particular analytical and reporting needs, enabling faster insights and decision-making.
• Typical Problems/Issues:
  • Central data warehouses can be too large, complex, or slow for the specific, high-performance needs of individual departments.
  • Business users spend too much time sifting through irrelevant data or waiting for IT to generate custom reports.
  • Different departments may create their own data silos, leading to inconsistencies.
  • Lack of resources to build and maintain specialized data views for each domain.
• What Finaprins Does:
  We develop and maintain customized data marts that deliver pre-aggregated, domain-specific data directly to your business teams (e.g., operations, risk management, compliance, finance), empowering them with timely and relevant information.
• How Finaprins Does It:
  • As part of our “Data Engineering and Reporting Services,” we work with your business units to understand their specific data requirements.
  • We design data marts with optimized schemas (e.g., star schemas) for fast querying and reporting.
  • We build ETL/ELT processes to populate and regularly refresh these data marts from your central data stores or source systems.
  • We ensure data marts provide a consistent view of data aligned with overall data governance.
• Compelling Reason to Buy:
  • Empowered teams focusing on strategic work: Provide business users with direct access to the data they need, reducing reliance on IT for ad-hoc requests.
  • Flexible, efficient reporting: Enable faster generation of timely, relevant reports tailored to specific departmental needs.
  • Informed decision-making: Equip teams with curated data to support better and quicker decisions within their domain.
  • Improved analytical performance: Offer optimized data structures for specific analytical tasks, leading to faster query responses.

• Your Goal: To streamline and automate the often labor-intensive tasks of transforming raw data into suitable formats and deriving meaningful features for input into artificial intelligence (AI), machine learning (ML) models, and complex analytical applications.
• Typical Problems/Issues:
  • Data preparation and feature engineering can consume up to 80% of a data scientist’s time, delaying AI/ML projects.
  • These processes are often manual, repetitive, and error-prone.
  • Lack of standardized tools or processes for creating and managing features.
  • Ensuring data quality and consistency for model training and inference is challenging.
  • “Overwhelming array of technologies and a lack of skilled personnel make implementing the right solutions difficult.”
• What Finaprins Does:
  We develop and manage “AI Data Pipelines” that automate your data preparation and feature engineering workflows, delivering clean, structured, and AI-ready data so your data science teams can focus on model development and innovation.
• How Finaprins Does It:
  • Our “AI Data Pipelines” subservice is specifically designed to develop and manage data pipelines tailored for AI applications.
  • We implement automated scripts and processes for data cleaning, transformation, normalization, encoding, and feature creation.
  • We can help establish feature stores for reusability and consistency.
  • We ensure data is appropriately versioned and prepared for both model training and production inference.
• Compelling Reason to Buy:
  • Data ready for AI development: Remove hurdles of data preparation to kickstart AI projects and accelerate time-to-value.
  • Automate data pipelines: Delegate regular data processing tasks and focus resources on strategic initiatives.
  • Empowered teams focusing on strategic work: Allow data scientists to spend less time on data wrangling and more on building and refining models.
  • Improved model performance and reliability: Ensure consistent, high-quality data inputs for more accurate and robust AI/ML models.

• Your Goal: To have access to accurate, comprehensive, and consistently formatted historical market and trade data, which is crucial for reliably backtesting trading strategies and validating risk models.
• Typical Problems/Issues:
  • Sourcing, cleaning, and aligning historical data from various providers and internal systems is a massive undertaking.
  • Inconsistencies, gaps, or survivor bias in historical data can lead to flawed backtesting results and incorrect model validation.
  • Ensuring point-in-time accuracy for historical data is complex but critical.
  • Maintaining this historical data repository is an ongoing operational burden.
• What Finaprins Does:
  We provide and manage high-quality, consistent historical datasets, drawing from our “Market Data Management Solutions” and “Trade Data” services, specifically prepared and validated for robust backtesting of trading algorithms and risk models.
• How Finaprins Does It:
  • We leverage our “Market Data Sourcing & Distribution,” “Validation,” and “Transformation” capabilities to build comprehensive historical datasets.
  • We ensure data is cleansed, standardized (e.g., consistent symbology, timestamps, corporate action adjustments), and checked for common issues.
  • We can help manage and provide access to large volumes of historical tick data, bar data, and fundamental data.
  • Our processes focus on ensuring data integrity and consistency over time.
• Compelling Reason to Buy:
  • Enhanced quantitative analysis: Improve the accuracy and reliability of backtesting results with clean, standardized historical data.
  • More confident model deployment: Reduce the risk of deploying underperforming or flawed trading strategies or risk models.
  • High-quality, reliable data: Access trustworthy historical data without the internal burden of sourcing and preparing it.
  • Accelerated research and development: Speed up the iteration cycle for quantitative researchers and strategy developers.

• Your Goal: To operationalize AI/ML models by reliably feeding them with live, real-time data for immediate predictions or with large datasets for periodic batch scoring, and to monitor these pipelines for performance and health.
• Typical Problems/Issues:
  • Moving models from a research environment to a robust production setting is a significant technical challenge (“the last mile” problem).
  • Production data pipelines for AI/ML need to be highly reliable, scalable, and low-latency (for real-time).
  • Monitoring data drift, model performance degradation, and pipeline failures in production requires specialized tools and expertise.
  • Ensuring data consistency between training and inference pipelines is critical.
• What Finaprins Does:
  We deploy, manage, and monitor resilient “AI Data Pipelines” that feed your production AI/ML models with the necessary data for real-time inference or batch scoring, ensuring your AI-driven applications operate smoothly and reliably.
• How Finaprins Does It:
  • Our “AI Data Pipelines” subservice includes the operationalization of data flows for live models.
  • We design pipelines to handle real-time data streams (e.g., market data feeds, transaction flows) or large batch updates.
  • We implement monitoring for data quality, pipeline latency, throughput, and error rates.
  • We integrate with your model serving infrastructure to ensure seamless data delivery.
  • We manage scheduling, error handling, and alerting for these critical production pipelines.
• Compelling Reason to Buy:
  • Successfully operationalize AI/ML: Bridge the gap between model development and production deployment.
  • Ensure reliable model performance: Provide consistent, timely, and high-quality data to your live AI applications.
  • Delegate regular data processing tasks: Outsource the complexity of managing production data pipelines for AI.
  • Reduce operational overhead: Minimize the internal effort required to maintain and monitor AI data infrastructure.