From Readiness to Results: Unlocking AI's Value in Asset Management with Databricks and DataArt Unlocking AI's Value in Asset Management with Databricks and DataArt Executive Summary: Why Readiness Is No Longer Enough Asset managers have run isolated AI pilots for years, yet most struggle to move from experimentation to enterprise impact. Boards and regulators now demand results over proofs of concept. The next phase of AI adoption requires a trusted, scalable, unified data foundation. Models deliver consistent performance only when data quality, governance, and architecture work together. Databricks and DataArt combine technology leadership and implementation expertise to help asset managers achieve real AI results: · Faster reporting and real-time insights · Proactive risk and compliance management · Reduced operational overhead · Personalized client engagement Value Pressure Boards Results Trusted Data on Databricks From Proof of Concept to Proven ROI Clients Transparency Regulators Compliance The AI Readiness Gap in Asset Management Despite heavy investment, most firms remain stuck in the pilot trap due to: · Data fragmentation ­ Market, portfolio, and client data scattered across legacy systems · Compliance complexity ­ Regulatory expectations (SEC, FCA, ESMA) demand full data lineage and explainability · Low trust in data ­ According to Gartner, poor data quality costs firms an average of \$12.9M annually · Siloed AI initiatives ­ Disconnected from operational pipelines and governance frameworks Key Insight: AI initiatives fall short not because of inadequate algorithms, but due to inconsistent data, lag, and governance challenges. Overcoming these gaps is key to success. What Firms Aim For vs. What They Experience Aim Experience Predictive AI Data Silos Faster Reporting Pilot Failures Alpha Insights Compliance Bottlenecks dataart.com Unlocking AI's Value in Asset Management with Databricks and DataArt Trusted Data Foundations on Databricks Databricks provides the unified platform asset managers need to bridge data engineering, analytics, and machine learning. With DataArt'xs financial-domain accelerators and proven methodologies, asset managers can rapidly implement Databricks solutions and achieve business benefits more quickly. 1. Modern Data Platforms DataArt helps firms build modern systems on Databricks that bring together all types of business data, including trading, research, risk, and client information. Benefits: · Democratized access to reliable, governed data · Native integration of streaming, AI/ML, and BI workloads · Scalability and security tailored to financial environments Three Pillars of Trusted Data 1 Modern Data Platform 2 Data Quality & Governance 3 Responsible AI 2. Data Governance and Quality AI-driven finance demands traceability. DataArt implements governance frameworks directly within Databricks, ensuring every data element is discoverable, auditable, and explainable. Key features: · Automated data lineage · Embedded validation rules and anomaly detection · Compliance-grade audit trails\` · Secure data zones for regulatory reporting Ingestion Raw Quality Control Trusted Governance Layer Outcome: reliable data pipelines that regulators, boards, and clients can trust. AI Ready AI Insights dataart.com Unlocking AI's Value in Asset Management with Databricks and DataArt 3. Real-Time Intelligence and AI Enablement By leveraging both Databricks and DataArt, asset managers unlock continual, high-impact analytics across the asset management lifecycle, achieving tangible enterprise benefits. Example Use Cases: Use Case Description Business Impact Investment Research Automation AI models to aggregate, verify, and present data on companies from the watchlist. Faster and more accurate decision-making Portfolio Performance Attribution ML models explain and decompose drivers of portfolio performance. Enhanced transparency and investor confidence. Portfolio Risk Optimization Dynamic rebalancing using correlation analysis and predictive AIML. Minimized downside exposure, optimized returns. Client Engagement Personalization AI-powered analytics for tailored reporting and recommendations. Stronger loyalty, higher wallet share. Scaling from Pilot to Enterprise Value DataArt applies a proven four-stage methodology for asset managers adopting Databricks: 1. Readiness Assessment ­ Evaluate data landscape, identify modernization priorities. 2. Pilot ­ Launch a focused use case (e.g., investment research or portfolio analytics) within 4­6 weeks. 3. Scale ­ Expand architecture to production workloads, automate quality checks, and embed governance. 4. Outcomes ­ Deliver measurable ROI and continuous innovation. Staircase to Scale Pilot Readiness Scale Outcomes Fast Time-to-Value. End-to-End Support dataart.com Unlocking AI's Value in Asset Management with Databricks and DataArt Why Databricks + DataArt Databricks + DataArt: A Strategic Alliance for Intelligent Orchestration in Asset Management What We Offer You Gain 4­6 week value-focused pilots Rapid proof of ROI with production-ready assets End-to-end data + AI implementation Future-proofed data architecture built on Databricks Native Databricks engineering & MLOps AI models that scale safely across investment functions Modular accelerators for governance, lineage, quality Compliance-grade lineage, auditability, and explainability Financial-domain expertise & proven delivery models Lower operational cost, faster reporting, and higher trust in data Conclusion and Next Steps AI Readiness is a prerequisite ­ now is the time for measurable results. Together, Databricks and DataArt enable asset managers to modernize their data foundations, scale AI across business functions, and deliver transparent, trustworthy insights to clients and regulators alike. Next Step: Join our AI Value Diagnostic Workshop to identify your organization's strongest business opportunities for AI with Databricks and define a strategic, results-driven adoption roadmap. Book your session: sales@dataart.com dataart.com