Machine Learning in Insurance Risk Management: A Strategic Guide for CIOs

In the data-intensive world of insurance, risk is no longer a static variable—it’s a continuously evolving signal. With traditional risk models struggling to capture the complexity of today’s landscape, forward-looking Insurance CIOs are turning to Machine Learning (ML) to architect the next generation of risk intelligence.

At Artha Data Solutions, we bring a foundational belief to every transformation: Machine Learning is only as powerful as the data that fuels it. With over a decade of expertise in enterprise data management and AI implementation, we’re helping insurers reimagine risk—especially in critical areas like health insurance fraud, predictive underwriting for group health, and loan default prediction.

Why Traditional Risk Models Fall Short

In both health insurance and loan underwriting, actuarial and credit scoring models have historically relied on:

Static demographic variables,
Lagging indicators (e.g., past claims, past defaults),
Oversimplified risk categories.

However, today’s risk environments are dynamic—shaped by real-time behaviors, social determinants, lifestyle factors, and external data sources. Traditional systems can’t adapt fast enough. Machine Learning offers agility, personalization, and continual learning—but only with the right data fabric.

Machine Learning in Action: Health Insurance Fraud & Overutilization

Fraudulent claims and overutilization of medical services cost health insurers billions annually. Manual audits can’t scale, and rules-based systems are often circumvented.

Artha helps insurer implement an ML pipeline that:

Ingests structured EHR data, unstructured claim notes, and third-party data (e.g., provider reviews, drug pricing feeds),
Applies NLP models to extract procedure inconsistencies,
Uses anomaly detection algorithms (e.g., Isolation Forest, Autoencoders) to flag unusual billing patterns, such as upcoding or medically unnecessary procedures.

Architecture:

Real-time ingestion using Apache Kafka from TPA systems,
ML feature engineering via Databricks on Azure,
Integration with claims workflow tools through RESTful APIs,
Explainability via SHAP for each flagged claim.

Business Outcome:

28% reduction in fraudulent payouts within 12 months,
60% increase in audit efficiency through prioritized case queues,
Improved regulatory audit outcomes due to model traceability.

Machine Learning for Loan Underwriting: Beyond Credit Scores

Challenge:

In the lending ecosystem, traditional risk scores (like CIBIL or FICO) miss out on valuable behavioral and contextual risk indicators—especially in first-time borrowers or gig economy workers.

ML-Driven Solution:

For a digital lending platform, Artha deployed a real-time ML underwriting engine that:

Merges KYC, bank statements, mobile usage metadata, and psychometric test results,
Builds a composite risk profile using gradient boosting (XGBoost) and neural network ensembles,
Continuously retrains models using feedback loops from repayment behavior.

Data Stack:

ETL using Talend for alternate data ingestion,
Scalable data lakehouse with Apache Iceberg on AWS S3,
ML Feature Store with Feast,
MLOps pipeline on Kubeflow for model deployment and monitoring.

Business Outcome:

35% increase in approvals for thin-file customers,
22% reduction in NPA rate by predicting early warning signals (EWS),
Fully auditable model decisions compliant with Federal Banks and GDPR guidelines.

CIO Checklist: Building AI-Ready Risk Infrastructure

To unlock the full potential of ML in risk, CIOs must focus on data-first architecture. Key enablers include:

Unified Risk Data Lake

Aggregate data from claims, policy, provider networks, third-party APIs, CRM, and mobile apps.
Normalize data formats and apply semantic models for domain consistency.

ML Feature Store

Serve consistent features across models and use cases: fraud, underwriting, retention.
Enable governance, lineage, and reuse across departments.

MLOps & Compliance

Automate retraining, performance drift monitoring, and explainability reporting.
Enforce differential privacy, data minimization, and consent tracking at every stage.

AI Model Governance

Maintain a central repository for all risk models with versioning, approval workflows, and automated risk scoring of the models themselves.

Strategic Outlook: From Reactive Risk to Predictive Intelligence

The evolution of insurance is not just about digital tools—it’s about intelligent ecosystems. Machine Learning enables:

Precision pricing based on granular risk,
Fraud mitigation that learns and adapts,
Proactive care management in health insurance,
Hyper-personalized lending even for non-traditional profiles.

At Artha, we don’t just build models—we build data intelligence platforms that help insurers shift from policy administrators to risk orchestrators.

Don’t Let Dirty Data Derail Your ML Ambitions

Machine Learning doesn’t succeed in silos. It thrives on clean, governed, contextualized data—and a clear line of sight from insights to action.

As a CIO, your role is not to just “adopt AI,” but to build the AI Operating Model—integrating data pipelines, MLOps, governance, and domain-specific accelerators.

Artha Data Solutions is your strategic partner in this transformation—bringing AI and data strategy under one roof, with industry accelerators built for insurance.

Let’s Build the Future of Risk Intelligence Together.

🔗 Learn more at www.thinkartha.com
📧 Contact our Insurance AI team at hello@thinkartha.com

Reinventing Customer Identity: How ML-Based Deduplication is Transforming Banking Data Integrity

Reinventing Customer Identity: How ML-Based Deduplication is Transforming Banking Data Integrity

In today’s digitally distributed banking landscape, one truth is increasingly clear: you can’t deliver trust, compliance, or personalization on a foundation of fragmented customer identities.

For decades, banks have battled data duplication across channels — core banking, mobile apps, credit systems, and onboarding platforms — each capturing customer details slightly differently. The result? Poor KYC/AML performance, missed cross-sell opportunities, and fractured customer experiences.

But now, a new generation of ML-powered data deduplication and identity resolution is flipping the script — turning disjointed records into unified, intelligent customer profiles.

The Identity Crisis in Banking

Studies suggest that 10–14% of customer records in financial institutions are duplicated or mismatched. These issues arise from:

Legacy data from branch systems, call centers, and credit card units
Variations in data entry (e.g., “Jon Smith” vs “Jonathan Smith”)
Lack of standardization in joint accounts, addresses, and contact info

Gartner warns:

“By 2027, 75% of organizations will shift from rule-based to ML-enabled entity resolution to address the scalability and accuracy gaps in customer data quality.”
— Gartner Market Guide for Data Quality Solutions, 2024

In banking, the cost of poor identity resolution is more than operational — it’s regulatory and reputational. Inaccurate data undermines:

KYC/AML compliance
Fraud detection reliability
Credit and risk scoring models
Personalized customer engagement

The ML-Based Breakthrough: Artha’s Identity Resolution in Action

Faced with the above challenges, a leading retail bank partnered with Artha Solutions to implement a machine learning-powered deduplication and customer identity solution. The objective: unify customer records across siloed systems with compliance-grade accuracy.

Machine Learning-Based Deduplication

Artha applied intelligent similarity scoring across key attributes like:

Customer names (abbreviations, suffixes)
Address variations (unit numbers, zip mismatches)
SSNs, phone numbers, email IDs, and account metadata

Using historical data, an ML model was trained to detect match/non-match patterns far beyond traditional rule engines.

Active Learning + Human-in-the-Loop Validation

To ensure regulatory accuracy, Artha implemented a human-in-the-loop review model:

Ambiguous matches flagged for compliance validation
Resolution actions logged for full auditability
Progressive improvement of model accuracy via active learning feedback loops

Golden Customer Record Generation

Once verified, duplicate entries were merged into a single, trusted profile for:

KYC/AML screening
Cross-sell targeting
Risk and credit analysis

This unified identity became the source of truth across Salesforce Financial Services Cloud, core systems, and fraud engines.

Under the Hood: A Scalable, Cloud-Native Stack

Component	Purpose
Python + Dedupe	ML deduplication logic and feature matching
AWS Glue + Redshift	Scalable ingestion, enrichment, and storage
Apache Airflow	Orchestration and monitoring of data jobs
Streamlit UI	Human-in-the-loop validation interface
MuleSoft	API integration with banking cores and CRM

This modular architecture ensured secure scaling, pipeline observability, and seamless integration with the bank’s hybrid cloud infrastructure.

Tangible Gains: Measurable Impact on Compliance and CX

Impact Metric	Before	After	Result
Duplicate Customer Records	10–14%	<2%	↑ Trustworthy identity resolution
Onboarding Discrepancy Resolution	Hours per case	<30 minutes	↓ 75% operational effort
Fraud Detection False Positives	Frequent	Sharply reduced	↓ Manual investigations
Cross-Sell Eligibility Accuracy	Inconsistent	High precision	↑ Offer targeting ROI
AML Reporting Data Fidelity	Inconsistent	High accuracy	↑ Audit readiness & compliance
Customer Experience Friction	High	Minimal	↑ NPS and loyalty

McKinsey & Co (2025):
“Banks that implement AI-powered entity resolution see up to $1.2M annual savings in fraud loss mitigation and compliance operations — while achieving faster, more personalized customer journeys.”

Looking Ahead: From Cleanup to Continuous Identity Intelligence (2025–2030)

The shift from batch deduplication to continuous identity intelligence will define the next era of banking IT. Artha’s approach paves the way for:

Real-time identity stitching during onboarding and transaction events
Federated ML models that learn across regions while respecting data privacy
Integration with AI co-pilots for branch agents and compliance teams

As banks prepare for tighter regulatory scrutiny and rising customer expectations, identity resolution becomes not just a data task — but a strategic differentiator.

Final Thought for CIOs and CDOs

If your data quality initiatives stop at ETL and dashboards, you’re treating symptoms, not causes. The real transformation starts with clean, intelligent, real-time customer identity. And ML-powered deduplication is the new gold standard.

Artha Solutions empowers financial institutions to move beyond rule-based matching — toward trust-first data engineering, AI-readiness, and identity intelligence at scale.

Ready to unlock compliance-grade customer identity and eliminate duplicate data risk? Let’s talk. Email us at solutions@thinkartha.com

ETL Migration in 1-2-3: From Legacy Gridlock to Automated Acceleration

The Industry Problem: Legacy ETL Tools Are Holding Back Innovation

Across industries — from banking and manufacturing to retail and healthcare — enterprises are struggling with stagnant, high-maintenance ETL platforms like Informatica, DataStage, and Pentaho etc.. These tools, once reliable, now create more friction than value in a data-driven world.

Here’s why:

Rising Costs: License renewals, specialized talent, and dual-stack maintenance drain budgets.
Sluggish Delivery: Change cycles are slow, blocking cloud initiatives and AI readiness.
Vendor Lock-In: Rigid architectures limit flexibility, scalability, and modernization.
Innovation Bottlenecks: Modern data demands — real-time, governed, AI-ready — can’t be met by legacy designs.

The pressure to modernize ETL pipelines is no longer optional. But getting there isn’t easy.

The Migration Dilemma: Resource Gaps & Risky Rewrites

Once a modernization decision is made, enterprises hit the next wall: how to migrate safely and efficiently. Manual ETL migration sounds simple — until you’re deep in thousands of jobs and complex logic that’s undocumented or poorly understood.

Key pain points include:

Shortage of Legacy Experts: Aging tools mean fewer available developers with niche skills.
Logic Loss Risk: Manual rewrites risk breaking business-critical transformations.
Long Timelines: Projects often run over budget and delay data initiatives for months — or years.
Testing Overload: Without confidence in code parity, testing becomes exhaustive and error-prone.

The result? Migrations that stall, bloat, and derail digital transformation.

The Solution: Automate with B’etl — Built for Scale, Accuracy, and Speed

Artha Solutions addresses this exact gap with B’etl — an AI-powered ETL migration accelerator that automates the heavy lifting of legacy-to-modern conversion.

What B’etl does for you:

Discovery & Assessment: Quickly inventory ETL workloads, identify redundancies, and prepare for change.
Code Conversion Engine: Translates legacy logic into Talend (or other targets) with precision.
Logic Retention: Business rules and transformations are preserved and revalidated — no black-box rewrites.
QA-Ready Jobs: Accelerated testing with confidence that migrated code mirrors the original.

With B’etl you:

Cut migration time by up to 70%
Save on licensing and maintenance
Enable cloud-native analytics and AI faster

ETL Modernization doesn’t need to mean disruption. With B’etl™, it means acceleration.
Visit thinkartha.com to explore how Artha is redefining the future of ETL modernization — one automated migration at a time.

Cloud-Based Data Pipelines: Architecting the Next Decade of Retail IT

As we look ahead to 2030, the retail enterprise will not be defined by the number of stores, SKUs, or channels—but by how effectively it operationalizes data across its IT landscape. From personalized offers to inventory automation, the fuel is data. And the engine? Cloud-based data pipelines that are scalable, governable, and AI-ready from day zero.

According to Gartner, “By 2027, over 80% of data engineering tasks will be automated, and organizations without agile data pipelines will fall behind in time-to-insight and time-to-action.” For CIOs and CDOs, the message is clear: building resilient, intelligent pipelines is no longer optional—it’s foundational.

Core IT Challenges Retail CIOs Must Solve by 2030

Legacy ETL Architectures Are Bottlenecks

Most legacy data pipelines rely on brittle ETL tools or on-premise batch jobs. These are expensive to maintain, lack scalability, and are slow to adapt to schema changes.

As per McKinsey Insight (2024), Retailers that migrated from legacy ETL to cloud-native data ops reduced data downtime by 60% and TCO by 35%. It’s a clear mandate for CIO/CDOs to Migrate from static ETL workflows to event-driven, API-first pipelines built on modular cloud-native tools.

Fragmented Data Landscapes and Integration Debt

With omnichannel complexity growing—POS, mobile, ERP, eCommerce, supply chain APIs—the real challenge is not data volume, but data velocity and heterogeneity. Artha’s interoperability-first architecture comes with prebuilt adapters and a data integration fabric that unifies on-prem, multi-cloud, and edge sources into a single operational model. CIOs no longer need to manage brittle point-to-point integrations.

Data Governance Embedded in Motion

CIOs cannot afford governance to be a passive afterthought. It must be embedded in-motion, ensuring data trust, privacy, and compliance at the pipeline level.

Artha’s Approach:

Policy-driven pipelines with built-in masking, RBAC, tokenization
Lineage-aware transformations with audit trails and version control
Real-time quality checks ensuring only usable, compliant data flows downstream

“Governance must move upstream to where data originates. Static governance at the lake is too little, too late.” – Gartner Data Management Trends 2025

Operational Blind Spots and Pipeline Observability

In a distributed cloud data stack, troubleshooting latency, schema drifts, and pipeline failures can delay everything from sales reporting to AI training.

How Artha Solves It:

Built-in DataOps monitoring dashboards
Lineage visualization and anomaly detection
AI-powered health scoring to predict and prevent failures

CIOs gain mean-time-to-repair (MTTR) reductions of 40–60%, ensuring SLA adherence across analytics and operations.

AI-Readiness: From Raw Data to Reusable Intelligence

By 2030, AI won’t be a project—it will be a utility embedded in every retail function. But AI needs clean, well-structured, real-time data. As McKinsey 2025 study concluded “Retailers with AI-ready data foundations will be 2.5x more likely to achieve measurable business uplift from AI deployments by 2028.”

Artha’s AI-Ready Pipeline Blueprint:

Continuous data enrichment, labeling, and feature engineering
Integration with ML Ops platforms (e.g., SageMaker, Azure ML)
Synthetic data generation for training via governed test data environments

Artha Solutions: Future-Ready Data Engineering Platform for CIOs

Artha’s platform is purpose-built to help CIOs and CDOs industrialize data pipelines, with key capabilities including:

Capability	CIO Impact
ETL Modernization (B’etl)	90% automation in legacy job conversion
Real-Time Event Streaming	Decision latency reduced from hours to minutes
MDM-Lite + Governance Layer	Unified golden records and compliance enforcement
Data Observability Toolkit	SLA adherence with predictive monitoring
AI-Enhanced DIP Modules	Data readiness for AI/ML and analytics at scale

2025–2030 CIO Roadmap: Next Steps for Strategic Advantage

Audit your integration landscape – Identify legacy ETLs, brittle scripts, and manual data hops
Deploy a cloud-native ingestion framework – Start with high-velocity use cases like customer 360 or inventory sync
Embed governance at the transformation layer – Leverage Artha’s policy-driven pipeline modules
Operationalize AI-readiness – Partner with Artha to build AI training pipelines and automated labeling
Build a DataOps culture – Invest in observability, CI/CD for pipelines, and cross-functional data squads

Final Word for CIOs: Build the Fabric, Not Just the Flows

As the retail enterprise becomes a digital nervous system of customer signals, supply chain events, and AI triggers, the data pipeline is no longer just IT plumbing — it is the strategic foundation of operational intelligence.

Artha Solutions empowers CIOs to shift from reactive data flow management to proactive data product engineering — enabling faster transformation, reduced complexity, and future-proof scalability.

AI-Powered Production Optimization: The Future of Manufacturing

Manufacturing is entering a new era where artificial intelligence (AI) and machine learning are taking central stage to gain maximum productivity. CIOs in the manufacturing sector are excited by visions of predictive maintenance, AI optimizers tweaking processes, and autonomous systems driving efficiency gains. The strategic key to turning this vision into reality, however, lies beneath the surface: data infrastructure and governance must be rock-solid before Day Zero of any AI deployment.

In practice, that means laying a foundation of high-quality, integrated, and well-governed data even before an AI project kicks off. Skipping this groundwork can spell trouble – industry research shows that many AI initiatives falter due to data issues, not algorithm flaw.^1,2 This narrative will explore how preparing data from the outset enables AI-powered production optimization to deliver on its promise, and how Artha Data Solutions is helping manufacturers bridge IT/OT divides and unlock competitive advantages through ready-to-use, reliable data.

The Promise of AI in Manufacturing and the Data Dilemma

The potential of AI in manufacturing is no more a buzzword. From AI-driven process control that continuously tunes equipment for optimal performance, to machine learning models that predict quality issues or maintenance needs, the rewards include higher throughput, lower costs, and improved quality. For example, a Tier 2 automotive supplier doubled the throughput of a production line with an AI-based system that identified bottlenecks in real time.³ In heavy industries like cement and chemicals, AI optimizers have demonstrated double-digit performance improvements without new hardware – boosting yield and energy efficiency in mere months.^4,5 Clearly, AI can be transformative on the factory floor.

Yet, these gains are only achievable if the underlying data is trustworthy and accessible. In fact, lack of data readiness is one of the biggest roadblocks for AI in manufacturing. Surveys show up to 80% of AI projects fail to move beyond pilot stages, with poor data quality and siloed data infrastructure often to blame.^6,7 Gartner analysts predict that at least 30% of AI projects will be abandoned at proof-of-concept by 2025 due to issues like subpar data quality and inadequate data governance.¹ The message is clear: no matter how advanced the AI, it cannot fix fragmented or dirty data. As one expert aptly put it, “As soon as you think you are ready to adopt AI technology, pause and evaluate your data’s quality, structure and volume”.⁸ Before algorithms start optimizing anything, CIOs must ensure their house is in order – data from across the plant and enterprise must be clean, consistent, and unified.

Before Day Zero: Building the Data Foundation for AI

Day Zero refers to the moment an AI initiative formally begins – but the real work should start well before that. Laying a strong data foundation involves multiple strategic steps:

Data Audit and Cleaning: Begin with a thorough audit of existing data sources (sensor streams, production logs, MES/ERP databases, etc.). Identify errors, gaps, and inconsistencies. It’s crucial

to fix data quality issues upfront – AI models are infamous for “garbage in, garbage out.” By cleansing and standardizing data early, manufacturers avoid feeding AI systems with misleading information. For AI systems to function optimally, the data must be clean, consistent, and error-free. This includes aligning units, timestamps, and definitions across sources.⁹

Integration of Siloed Systems (IT/OT Convergence): Most factories have a split between operational technology (OT) on the shop floor and information technology (IT) in business Bridging this divide is essential. Integrating real-time machine data with enterprise data gives AI a holistic view of operations. In fact, 56% of manufacturers cite lack of data integration as a major barrier to digital transformation.¹⁰ An integrated data architecture (data lakes, IIoT platforms, or unified databases) should be established so that all relevant data – from PLC sensors to quality labs to inventory systems – flows into a common ecosystem. This IT/ OT convergence “creates a unified platform for data exchange and analysis”, empowering real-time production insights and resource optimization.¹¹ Manufacturers that connect their disparate systems can monitor equipment performance in context, correlate process parameters with yield, and enable AI to uncover patterns spanning the entire production value chain.

Data Governance and Master Data Management: Preparing data isn’t just a technical exercise; it’s also organizational. Companies need to institute data governance policies and roles to manage data as a strategic asset. This means defining data ownership (who is responsible for which data sets), setting up data quality KPIs, and ensuring compliance with standards (for example, calibration schedules for sensors or version control for control logic data). It also means creating master data definitions for key entities (products, materials, machine IDs, ) so that every system uses consistent terminology. Effective data governance underpins trust in data-driven decisions. As Artha Solutions emphasizes, “effective data governance and analytics help [manufacturers] make informed decisions, optimize supply chains, and drive innovation”.¹² Governance ensures the AI initiative has well-defined, reliable data to learn from, and that any insights can be traced back to quality-controlled sources.

Infrastructure and Architecture Readiness: Before deploying AI, CIOs should evaluate whether their data infrastructure can support the scale and speed required. AI for production optimization often involves streaming data (from IoT sensors or machines) and heavy computations. A modern, scalable architecture – whether cloud-based, on-premises, or hybrid – with proper data pipelines is critical. This might include establishing a data lake or warehouse that aggregates OT and IT data, deploying edge computing for low-latency processing on the shop floor, and setting up APIs or middleware to connect legacy systems. Security is also paramount: ensure a robust framework (even zero-trust architectures) to protect sensitive production data as it flows to AI systems. The goal is a resilient plumbing of data such that from Day Zero, data is continuously flowing from the factory floor to AI models and back to decision-makers, without bottlenecks or security gaps.

By addressing these areas before AI implementation begins, organizations set themselves up for success. They avoid the common pitfall of AI teams spending 80% of their time wrangling data and fighting quality issues after the project is underway. Instead, the AI team can focus on model development and analysis from Day One, because the data foundation is already in place. The payoff is dramatic: companies that put in the prep work have seen their AI projects deliver value faster and more smoothly than those that rushed in unprepared.^2,7

Bridging IT and OT: The Key to Unified Insights

One aspect deserving special attention is IT/OT convergence – the blending of information technology systems with operational technology systems. In many manufacturing environments, decades-old OT systems (SCADA, PLCs, DCS, historians) run the production processes, while modern IT systems (ERP, MES, QMS, etc.) manage business processes. AI-powered optimization demands that these layers talk to each other. When IT and OT remain isolated, data vital to optimization is trapped in silos. For example, the maintenance department might have vibration sensor readings that never get correlated with production schedules from ERP, or quality measurements in a lab database that aren’t linked to the specific machine settings that produced those batches.

Bridging IT and OT unlocks real-time, end-to-end visibility. By connecting shop-floor sensors and control systems with enterprise data, CIOs can give AI models the full context needed to make intelligent decisions. The benefits of this unified data are tangible – manufacturers gain “real-time insights into production processes, optimize resource utilization, boost productivity through automation, and reduce operational costs” through IT/OT integration.¹¹ Imagine an AI system that not only detects an anomaly in machine performance from sensor data, but also cross-references it with maintenance logs and inventory data to recommend a proactive fix and ensure spare parts are on hand. Without IT/OT integration, such holistic optimization is impossible.

Artha Data Solutions recognizes that bridging this gap is often the prerequisite of successful industrial AI. Artha specializes in data integration across the value chain, effectively linking factory floor devices with cloud analytics and enterprise apps. By deploying robust data management platforms, connectors, and IoT frameworks, Artha helps manufacturers create a single source of truth. In practice, this might involve streaming data pipelines that pull readings from PLCs on the line into a central data lake, where they are merged with MES production records and even contextual data like ambient conditions or operator shifts. The outcome is a rich dataset ready for AI consumption. Notably, manufacturers that centralize and analyze previously fragmented data can significantly improve production efficiency and quality – centralizing data improves throughput and quality while data governance ensures those insights drive smart decisions.¹² Bridging IT/OT isn’t just an IT project; it’s a strategic initiative that yields a competitive edge by enabling AI to act on complete, timely information.

Accelerating AI Adoption with Artha Data Solutions

Establishing this data foundation can be complex, which is where experts like Artha Data Solutions come in. Artha’s vision is firmly grounded in the idea that AI success starts with data readiness. As the company puts it, “Your AI vision starts with the right foundation — your data”, and they offer intelligent data management solutions to ensure data is “AI-ready” for integration.¹³ In practical terms, Artha provides services and tools that cover the end-to-end preparation of data for AI:

Data Quality & Consistency: Artha’s data readiness framework includes rigorous data auditing, cleansing, and validation steps. They help manufacturing clients identify inconsistencies across datasets and rectify errors so that AI models can train on high-quality By ensuring clean, reliable data from the outset, Artha reduces the risk of AI models producing skewed or inaccurate results due to garbage data.^8,14

Data Integration & Single Source of Truth: Artha specializes in integrating disparate data sources – whether from legacy systems, modern IoT sensors, or enterprise applications. Their expertise in master data management (MDM) and data lakes/cloud platforms allows companies to unify their information For a manufacturer, this means bridging systems

across the entire value chain: design, production, maintenance, supply chain, and distribution. The result is an integrated data backbone where AI applications can draw insights from every stage. As evidence of this approach, Artha helped a leading energy company aggregate and model data across domains, improving data quality and enabling optimized processes enterprise-wide.¹⁵ In manufacturing contexts, such integration can, for example, tie raw material quality data to production parameters to find optimal settings, or connect customer demand forecasts with factory scheduling to intelligently adjust throughput.

AI-Ready Governance & Compliance: Knowing that industrial data often spans various formats and may be subject to compliance (safety standards, regulatory reporting, etc.), Artha implements robust data governance frameworks tailored for This includes setting up data access controls, audit trails, and compliance checks before AI deployment. With Artha’s help, organizations put in place automated data monitoring and governance policies to maintain data integrity over time.^16,17 This not only keeps the AI inputs reliable as new data streams in, but also ensures that scaling AI across multiple plants or lines meets both internal and external data regulations. Secure, governed data means AI projects won’t be derailed by privacy breaches or compliance issues down the road.

IT/OT Convergence Solutions: Crucially, Artha brings deep experience in bridging operational tech with enterprise IT. Through industrial connectivity solutions, they help capture real-time shop floor data (from sensors, machines, SCADA systems) and funnel it into analytics platforms. At the same time, they integrate relevant enterprise context (like production orders or inventory levels) so that AI models see the full picture. This IT/OT bridging is core to Artha’s value proposition – effectively, they act as the architects of the digital thread that connects equipment to analytics to business outcomes. With that thread in place, manufacturers can accelerate AI adoption because the data hurdles between operations and IT have been In one example, Artha’s integration of real-time data management allowed a client to monitor equipment health live and predict maintenance needs, reducing downtime and ensuring continuous production.¹⁸

The competitive advantages of partnering with Artha are evident. Companies get to leverage Artha’s proven methodologies and accelerators (such as their Data Insights Platform and Dynamic Ingestion Framework) to jump-start their AI journey rather than reinventing the data wheel. This means faster time-to-value for AI projects and higher ROI. In fact, organizations that properly prepare their data see returns much sooner – a recent global survey found 74% of enterprises using AI (e.g., generative AI) achieved ROI within the first year.¹⁹ Artha’s services directly contribute to such rapid ROI by front- loading the heavy lifting of data preparation. When it’s time to deploy AI, everything runs smoother: models train faster, insights are more accurate, and users trust the outputs because they know the data is sound. In short, Artha Solutions acts as the bridge between IT and OT and between raw data and AI- driven value, enabling manufacturers to accelerate AI adoption with confidence.

Conclusion: Data-Driven Future for CIOs in Manufacturing

AI-powered production optimization is no longer science fiction – it’s happening now, and it is poised to define the future of manufacturing. However, the difference between AI projects that flourish and those that flounder often comes down to the unsung groundwork: data infrastructure and governance. CIOs aiming to lead their manufacturing organizations into this AI-driven future must act strategically and proactively. Before plugging in the first AI solution, ensure your data house is in order – integrate your systems, clean your data, govern it well, and engage partners who can bring best practices to this preparation phase. As we’ve discussed, the payoff from this diligence is substantial: faster

implementation, quicker ROI, and AI solutions that actually deliver on their potential for throughput and efficiency gains.

Artha Data Solutions envisions exactly this kind of data-first acceleration of AI adoption. By focusing on data quality, integration, and readiness across the value chain, they enable manufacturers to leap ahead, turning months of tentative AI experimentation into weeks of tangible results. They help bridge the perennial gap between IT and OT, ensuring that advanced analytics and AI can be applied seamlessly from the shop floor to the top floor. The competitive advantage for organizations that get this right is significant – they can respond in real time to operational issues, continuously improve processes with AI insights, and even innovate new business models (such as predictive maintenance services or mass customization) grounded in data. Meanwhile, laggards who ignore the data foundation risk struggling with AI projects that never quite reach fruition.

In essence, the journey to AI-powered production optimization should begin with a clear roadmap for data. CIOs are in a unique position to champion this effort, aligning both the technical and governance aspects needed for success. The manufacturing leaders of tomorrow will be those who invest in these capabilities today. Prepare your data before Day Zero, and you prepare your organization for a future of AI-driven efficiency and excellence. With strong data foundations and the right partners like Artha Solutions, the factory of the future – one where AI and humans work in harmony to achieve peak performance – is well within reach.

Reference:

1,2,6,7,14,19 What’s Preventing CIOs From Achieving Their AI Goals?

https://www.cio.inc/whats-preventing-cios-from-achieving-their-ai-goals-a-26640

3 Industry Insights: A3 Industry Insights | AI in the Real World: 4 Case Studies of Success in Industrial Manufacturing

https://www.automate.org/ai/industry-insights/ai-in-the-real-world-4-case-studies-of-success-in-industrial-manufacturing

4,5 AI in Production A Game Changer For Manufacturers With Heavy Assets | PDF | Artificial Intelligence | Intelligence (AI) & Semantics

https://www.scribd.com/document/424713822/AI-in-Production-a-Game-Changer-for-Manufacturers-With-Heavy-Assets

8 The AI-native generation is here. Do not get left behind | CIO

https://www.cio.com/article/3990726/the-ai-native-generation-is-here-dont-get-left-behind.html

9,13,16,17 Data Readiness – Artha Solutions

https://www.thinkartha.com/data-readiness/

10,11 IT/OT Convergence in Manufacturing: Steps to Achieve a Smart Factory – Matellio Inc

https://www.matellio.com/blog/it-ot-convergence-in-manufacturing/

12,18 Manufacturing – Artha Solutions

https://www.thinkartha.com/industries/manufacturing/

15 Data Governance Transformation for a Leading Canadian Energy Exporter – Artha Solutions https://www.thinkartha.com/case-studies/data-governance-transformation-for-a-leading-canadian-exporter-in-the-energy- sectors-journey/

From Fragmented Care to Connected Healing: Powering Healthcare Interoperability with Trusted Data

In today’s healthcare landscape, data is the lifeblood of care delivery, yet in many organizations, it flows in silos. Patient information is fragmented across EHRs, labs, pharmacies, and insurance systems—leading to missed diagnoses, repeated tests, and administrative overload. This is not a technology problem alone; it’s a data problem. And the answer is interoperability.

The Data Story Behind Better Care

Consider this real-world scenario: A diabetic patient visits a cardiologist, unaware that her primary care provider recently updated her medication. Without access to that update, the cardiologist prescribes a new drug that interacts adversely with the current prescription, leading to an emergency admission.

Now flip the script: With interoperability in place, the cardiologist accesses the full, up-to-date medication list via a secure API linked to the patient’s longitudinal health record. An alert flags the interaction, and the prescription is adjusted safely. One seamless data exchange. One hospital admission avoided. One life potentially saved.

Multiply that by millions of patients, and the power of connected care becomes evident.

Why Data Services Are the Hidden Backbone

But achieving this level of care coordination doesn’t happen by just plugging in an API or adopting HL7 FHIR. It begins with trusted, clean, standardized data. That’s where data services come in:

Data profiling & discovery identify inconsistencies, duplicates, and gaps across fragmented systems.
Metadata and master data management create unified views of patients, providers, and encounters.
Data quality and normalization ensure information from various sources aligns with semantic standards like SNOMED, LOINC, and ICD.

Without these foundational services, interoperability efforts often fail—garbage in, garbage out. Poor data leads to poor decisions, even in a highly connected environment.

ThinkArtha: Your Interoperability Readiness Partner

At ThinkArtha.com, we specialize in making healthcare data interoperability-ready by unlocking value from the inside out.

Our vendor-agnostic data services help health systems cleanse, enrich, and align their data to industry standards—ensuring any interoperability layer (FHIR APIs, cloud data hubs, HIEs) has quality data to work with. We’ve helped health insurers harmonize over 10 million member records for real-time risk analytics, and enabled hospital groups to centralize lab and imaging data across legacy platforms to support unified care pathways.

Whether it’s migrating data to a cloud-native EHR, creating a single source of truth for patient identities, or powering AI-based care recommendations, clean, governed data is step one.

Making the Shift: From Silos to Synergy

Healthcare leaders must stop viewing interoperability as an integration challenge alone. It is a data strategy challenge.

To move from fragmented workflows to coordinated, patient-centered ecosystems, CIOs must:

Invest in data readiness assessments before launching exchange initiatives.
Build scalable, standards-compliant integration platforms rooted in cleansed and trusted data.
Adopt AI-driven data mapping and semantic normalization to reduce manual harmonization.

Interoperability is the future of healthcare—but without data services at its core, it’s just another far reality. With partners like ThinkArtha, that future is within reach.

Let’s stop chasing interoperability. Let’s build it—on the foundation of clean, connected, and patient-first data.

From Fragmented Care to Connected Healing: Powering Healthcare Interoperability with Trusted Data

The Data Story Behind Better Care

Multiply that by millions of patients, and the power of connected care becomes evident.

Why Data Services Are the Hidden Backbone

Data profiling & discovery identify inconsistencies, duplicates, and gaps across fragmented systems.
Metadata and master data management create unified views of patients, providers, and encounters.
Data quality and normalization ensure information from various sources aligns with semantic standards like SNOMED, LOINC, and ICD.

Without these foundational services, interoperability efforts often fail—garbage in, garbage out. Poor data leads to poor decisions, even in a highly connected environment.

Artha Solutions: Your Interoperability Readiness Partner

At Artha Solutions, we specialize in making healthcare data interoperability-ready by unlocking value from the inside out.

Whether it’s migrating data to a cloud-native EHR, creating a single source of truth for patient identities, or powering AI-based care recommendations, clean, governed data is step one.

Making the Shift: From Silos to Synergy

Healthcare leaders must stop viewing interoperability as an integration challenge alone. It is a data strategy challenge.

To move from fragmented workflows to coordinated, patient-centered ecosystems, CIOs must:

Invest in data readiness assessments before launching exchange initiatives.
Build scalable, standards-compliant integration platforms rooted in cleansed and trusted data.
Adopt AI-driven data mapping and semantic normalization to reduce manual harmonization.

Interoperability is the future of healthcare—but without data services at its core, it’s just another far reality. With partners like Artha Solutions, that future is within reach.

Let’s stop chasing interoperability. Let’s build it—on the foundation of clean, connected, and patient-first data.

Qlik Recognizes Artha Solutions as the North America Partner Customer Success Champion 2024

Artha Solutions Named Qlik North America Customer Success Champion of 2024

Recognized for Exceptional Customer Outcomes and Local Market Leadership with Qlik

Scottsdale, AZ – May 14, 2025 – Artha Solutions, a leading data and analytics consulting firm, is proud to announce that it has been recognized as the Qlik North America Customer Success Champion of 2024. This prestigious award highlights Artha’s unwavering commitment to delivering exceptional customer outcomes by enabling organizations to become AI-ready through data modernization and governance.

This recognition highlights Artha’s leadership in delivering outstanding customer outcomes through impactful data and analytics solutions tailored to the unique demands of customers across the North America market. By combining Qlik’s industry-leading analytics platform with Artha’s deep expertise in data strategy, integration, and quality, customers have been empowered to harness trusted data & Data Readiness for AI and machine learning initiatives.

The “Artha Advantage” suite of AI readiness accelerators combines deep industry expertise with a comprehensive range of services. These services cover data quality, MDM, governance, analytics, AI readiness, ETL tool conversion, SAP data migration, and SAP test data management, empowering organizations to reduce time-to-value, ensure compliance, and establish intelligent, future-ready data foundations. The Artha–Qlik partnership accelerates AI adoption and digital transformation across industries like banking, , finance, insurance, healthcare, manufacturing, and retail.

“Partners like Artha embody what makes our regional ecosystem so powerful—deep local knowledge, trusted customer relationships, and a relentless focus on delivering real results,” said David Zember, Senior Vice President, WW Channels and Alliances at Qlik. “Their ability to move quickly and solve complex challenges close to home is what drives lasting impact. We’re proud to celebrate this success and excited for what we’ll achieve together next.”

“We’re honoured to receive this recognition from Qlik,” said Srinivas Poddutoori, COO of Artha Solutions. “This award underscores our mission to help customers unlock the true potential of their data. By focusing on AI data readiness, governance, and scalable modernization frameworks, we’ve enabled our clients to move from data chaos to AI confidence.”

Jaipal Kothakapu, CEO of Artha Solutions, added: “This recognition means a great deal to us because it reflects the transformative journeys we’ve shared with our clients. Every engagement is personal—behind every dashboard is a business striving to grow, a team navigating change, and a leader making high-stakes decisions. That’s what drives us. With Qlik as our partner, we don’t just deliver insights—we turn data into real, lasting impact.”

About Artha Solutions	Media Contact
Artha Solutions is a global consulting firm specializing in data modernization, integration, governance, and analytics. Trusted by Fortune 500 companies across healthcare, finance, manufacturing, and telecom, Artha blends deep technical expertise with a business-first approach—helping organizations turn data into competitive advantage. Visit www.thinkartha.com to learn more.	Goutham Minumula Goutham.minumula@thinkartha.com +1 480 270 8480

About Qlik

Media Contact

Qlik converts complex data landscapes into actionable insights, driving strategic business outcomes. Serving over 40,000 global customers, our portfolio provides advanced, enterprise-grade AI/ML, data integration, and analytics. Our AI/ML tools, both practical and scalable, lead to better decisions, faster. We excel in data integration and governance, offering comprehensive solutions that work with diverse data sources. Intuitive analytics from Qlik uncover hidden patterns, empowering teams to address complex challenges and seize new opportunities. As strategic partners, our platform-agnostic technology and expertise make our customers more competitive.

Keith Parker
keith.parker@qlik.com
512-367-2884

More Information:

https://www.qlik.com/us/news/company/press-room/press-releases/qlik-honors-partners-powering-data-to-decision-excellence-worldwide

Think Artha Asia Pte Recognized as Qlik’s APAC Authorized Reseller of the Year 2024

Think Artha Asia Pte Named Qlik APAC Authorized Reseller of the Year

Recognized for Exceptional Customer Outcomes and Local Market Leadership with Qlik

Think Artha Asia Pte has been honored as the 2024 APAC Authorized Reseller of the Year by Qlik, recognizing its outstanding performance, regional expertise, and commitment to delivering cutting-edge data analytics solutions across Asia-Pacific.

The annual Qlik Regional Partner Awards recognize select partners for demonstrating exceptional expertise and innovation in their respective local markets. Winners deliver measurable business outcomes and strategic value, enabling customers in key regions to harness their data effectively and achieve rapid success.

“Partners like Artha Solutions embody what makes our regional ecosystem so powerful—deep local knowledge, trusted customer relationships, and a relentless focus on delivering real results,” said David Zember, Senior Vice President, WW Channels and Alliances at Qlik. “Their ability to move quickly and solve complex challenges close to home is what drives lasting impact. We’re proud to celebrate this success and excited for what we’ll achieve together next.”

Modernizing Pharma ERP with Data & AI: The Strategic Imperative for CIOs

As a pharmaceutical manufacturing CIO, you’re not just managing IT systems—you’re enabling traceability, compliance, and operational excellence in one of the most regulated and complex industries in the world.

With SAP ECC approaching end-of-life by 2027 and the global regulatory landscape tightening its grip on serialization, digital batch traceability, and product integrity, modernizing your ERP landscape is no longer optional—it’s mission-critical. And it begins with two things: Data and AI.

Let’s explore how CIOs can modernize their SAP landscape with a data-first approach, unlocking real-world AI use cases while maintaining regulatory integrity across the supply chain.

The Current State: ECC Limitations in a Regulated, AI-Driven World

SAP ECC has been the backbone of pharma operations for over two decades. But its limitations are now showing:

Fragmented master data across plants and systems
Custom-coded batch traceability that’s difficult to validate
Limited support for real-time analytics or AI applications
Gaps in native compliance with emerging global serialization mandates

These challenges are amplified when CIOs begin implementing AI-driven process optimization or integrating with serialization solutions like SAP ATTP. ECC simply wasn’t built for today’s speed, scale, or compliance needs. We have seen how pressing it could be while dealing with Covid-19 pandemic.

Why S/4HANA Matters — But Only With Clean Data

SAP S/4HANA promises much: real-time batch monitoring, embedded analytics, streamlined quality management, and a foundation for intelligent supply chains. However, the true value of S/4HANA only emerges when the data behind it is trusted, governed, and AI-ready.

In pharma, that means:

GxP-aligned master data for materials, vendors, and BOMs
Audit-ready batch records that can withstand FDA or EMA scrutiny
Traceability of data lineage to support SAP ATTP and regulatory serialization audits

According to Gartner, over 85% of AI projects in enterprise environments fail due to poor data quality. In regulated pharma, that failure isn’t just technical—it’s regulatory risk.

Pharma’s Silent Risk Factor: Data Integrity

CIOs must recognize that data quality is not just a technical problem—it’s a compliance imperative.

ECC systems typically have:

20%+ duplicated materials or vendors
Inconsistent inspection plans across manufacturing sites
Obsolete or unvalidated test definitions

These issues compromise everything from SAP ATTP serialization feeds to digital twins and AI-based demand forecasting.

Solution:

Establish Master Data Governance (MDG) with GxP alignment
Create a Data Integrity Index across key domains (Batch, BOM, Vendor)
Implement audit trails for all regulated master and transactional data

AI-Driven Requirement Gathering: Accelerate Without Compromising

One of the most overlooked areas in S/4HANA modernization is blueprinting and requirement gathering. In pharma, this phase is long, compliance-heavy, and often fragmented.

Now, CIOs are leveraging Generative AI to:

Analyze ECC transaction history to auto-generate process maps
Draft validation-ready requirement documents based on SAP best practices
Assist business users with smart conversational interfaces that document as-is and to-be states

This “AI-as-a-business-analyst” model is not just efficient—it helps standardize requirements and traceability, reducing the chance of non-compliant customizations.

SAP ATTP: Making Serialization a Core ERP Concern

Pharmaceutical CIOs are now expected to ensure end-to-end product traceability across the supply chain—from raw materials to patient delivery. SAP Advanced Track & Trace for Pharmaceuticals (ATTP) is purpose-built for this but depends heavily on ERP data being clean, structured, and integrated.

With the right foundation in S/4HANA and clean master data:

SAP ATTP can serialize every batch and unit pack reliably
AI models can predict risks in the supply chain (e.g., delayed shipments or counterfeit vulnerabilities)
Quality teams can track deviations or holds with full digital genealogy of the product

ATTP isn’t just an add-on—it’s a compliance engine. But it only works if your ERP core is modern and your data is trusted.

GenAI for Quick Wins: Where to Start

For CIOs looking to showcase quick ROI, consider deploying GenAI in areas that complement your ERP investment and are validation-friendly:

Digital SOP Assistants: AI bots that help QA teams find and summarize policies
Batch Record Summarization: GenAI reading batch logs to flag potential anomalies
Procurement Bots: Drafting vendor communication or PO summaries
Training Content Generation: Automated creation of process guides for new ERP workflows

These use cases are low-risk, business-enabling, and help build AI maturity across your teams.

The CIO Playbook: Data, Traceability, and AI Governance

As you modernize, consider this framework:

Pillar	CIO Responsibility
Data Integrity	Implement MDG, create Data Quality KPIs, enforce audit logs
AI Governance	Define use-case ownership, ensure validation where needed
Compliance by Design	Embed ALCOA principles into every ERP and AI workflow
Serialization Readiness	Integrate S/4HANA and ATTP for end-to-end traceability

Final Thoughts: From ERP Modernization to Digital Pharma Leadership

Modernizing your ERP is not just about migrating systems—it’s about transforming your enterprise into a digitally intelligent, compliance-first, AI-augmented pharma organization.

CIOs must lead this transformation not from the data center—but from the boardroom. With the right data governance, a smart AI adoption roadmap, and strategic alignment with platforms like SAP ATTP, your ERP modernization journey will unlock more than efficiency—it will unlock trust, agility, and innovation.

Let data be your competitive advantage, and let compliance be your credibility.

Need help assessing your ERP data health or building your AI roadmap?

Let’s connect for a Data Integrity & AI Readiness Assessment tailored to pharma manufacturing.