AI-Powered Data Quality & Observability Services
Transform your data from a liability into your most valuable asset.Establish enterprise-wide trust with continuous validation, anomaly detection, and complete data lineage. Achieve Data Certainty
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Is Your Data a Strategic Asset or a Ticking Time Bomb?
In today's data-driven economy, poor data quality isn't just an inconvenience; it's a direct threat to your bottom line. Decisions based on flawed data lead to missed opportunities, operational inefficiencies, and a loss of customer trust. Without a clear view into your data's health and lineage, you're flying blind. It's time to move from reactive data fire-fighting to proactive, enterprise-wide data certainty.
Why CIS for Enterprise Data Quality & Observability?
We don't just identify data problems; we build the foundational trust and reliability your enterprise needs to innovate with confidence. Our AI-enabled approach ensures your data ecosystem is not only clean but also transparent, resilient, and ready for the future.
AI-Powered Anomaly Detection
Our machine learning models go beyond simple rule-based checks to proactively identify subtle data drifts, schema changes, and quality issues before they impact your business operations or analytics.
End-to-End Data Lineage
Gain complete visibility into your data's journey. We map data flows from source to consumption, enabling impact analysis, simplifying root cause diagnostics, and ensuring regulatory compliance.
Proactive Data Governance
We embed data quality directly into your governance framework. Our solutions automate policy enforcement, manage metadata, and provide a single source of truth for your data assets.
Continuous Reliability Monitoring
Treat your data like a product. We implement "data SLAs" and real-time monitoring dashboards to track freshness, volume, and quality, ensuring your data pipelines are always reliable.
Cross-Functional Collaboration
Our platforms break down data silos. We provide a unified view of data health that empowers data engineers, analysts, and business stakeholders to collaborate on data quality improvement.
Measurable Business Impact
We connect data quality metrics directly to business outcomes. By quantifying the cost of poor data and the ROI of quality initiatives, we help you build a compelling business case for data trust.
Scalable, Platform-Agnostic Solutions
Whether your data resides in cloud data warehouses, data lakes, or on-premise systems, our solutions are designed to integrate seamlessly and scale with your data volume and complexity.
Secure by Design
With certifications like CMMI Level 5, SOC 2, and ISO 27001, we build data quality solutions where security and compliance are not afterthoughts but core architectural principles.
Future-Ready Architecture
We help you build a data observability foundation that supports emerging use cases, from real-time analytics and Generative AI to complex data mesh and data fabric architectures.
Our Comprehensive Data Quality & Observability Services
We offer a full spectrum of services to build and maintain a culture of data trust across your organization. From foundational assessments to advanced AI-driven monitoring, we tailor our solutions to your specific needs.
Data Quality Framework & Strategy Development
Before fixing the data, you need a blueprint for success. We partner with you to establish a comprehensive, enterprise-wide strategy that defines data quality standards, roles and responsibilities (data stewardship), and the key metrics that align with your business objectives. This framework becomes the foundation for a sustainable data quality culture.
Outcomes:
- A clear roadmap for all data quality initiatives, ensuring alignment and preventing wasted effort.
- Defined ownership and accountability for critical data assets across the organization.
- Standardized data quality rules and metrics that provide a consistent measure of data health.
Automated Data Profiling and Discovery
You can't fix what you can't see. Our services use automated tools to scan your data sources, uncovering the true state of your data. We analyze patterns, value distributions, and relationships to identify inconsistencies, duplicates, and outliers, providing a detailed baseline assessment of your data landscape.
Outcomes:
- Comprehensive understanding of your data's structure, content, and interdependencies.
- Rapid identification of hidden data quality issues without manual effort.
- An empirical foundation for prioritizing data cleansing and enrichment efforts.
Automated Data Validation and Rule Engine Implementation
We build and deploy robust, automated data validation pipelines. By defining and implementing business rules, integrity constraints, and quality checks directly within your data workflows (e.g., ETL/ELT processes), we prevent bad data from entering your systems in the first place, shifting from reactive cleanup to proactive prevention.
Outcomes:
- Significant reduction in data errors propagating to downstream analytics and applications.
- Increased confidence in data used for critical business reporting and decision-making.
- Improved operational efficiency by automating manual data verification tasks.
End-to-End Data Lineage and Impact Analysis
We provide a "GPS for your data." Our solutions automatically map the complete journey of your data from its origin to every report, dashboard, and application it touches. This complete visibility allows you to perform instant impact analysis for any proposed changes and trace the root cause of any data issue in minutes, not weeks.
Outcomes:
- Accelerated root cause analysis, drastically reducing time to resolve data incidents.
- Simplified compliance and auditing for regulations like GDPR and CCPA.
- Risk mitigation by understanding the full downstream impact of data or schema changes.
AI-Powered Anomaly Detection for Data Pipelines
Traditional rules can miss novel issues. We deploy machine learning models that learn the normal behavior of your data pipelines—volume, freshness, schema, and distribution. These models then automatically flag unexpected deviations, alerting you to silent data failures like incomplete data loads or upstream API changes before they corrupt your analytics.
Outcomes:
- Proactive detection of "unknown unknowns" that fixed rules cannot catch.
- Increased trust in automated data pipelines and reduced need for manual oversight.
- Early warnings of data freshness or completeness issues that affect business operations.
Data Observability Platform Implementation
We help you implement a centralized platform that unifies data quality, lineage, and monitoring into a single pane of glass. This provides a holistic view of your data ecosystem's health, enabling data teams to monitor, troubleshoot, and resolve issues efficiently. We work with leading tools like Monte Carlo, Databand, and Soda, or build custom solutions tailored to your stack.
Outcomes:
- A single source of truth for data health, accessible to all data stakeholders.
- Reduced "data downtime" and faster incident resolution through unified tooling.
- Improved data team productivity by eliminating the need to switch between multiple monitoring tools.
Data Reconciliation and Auditing Solutions
For critical financial, regulatory, and operational processes, data must be perfectly aligned between systems. We build automated reconciliation frameworks that compare datasets across different sources (e.g., ERP vs. CRM, trading system vs. general ledger), flagging discrepancies and providing a clear audit trail to ensure data consistency and integrity.
Outcomes:
- Guaranteed consistency of critical data across disparate enterprise systems.
- Streamlined auditing processes and simplified regulatory reporting.
- Prevention of financial losses and operational errors caused by data mismatches.
Master Data Management (MDM) Integration
High-quality master data (e.g., customer, product, supplier) is the bedrock of a reliable enterprise. We integrate your data quality and observability solutions with your MDM platform. This ensures that your "golden records" are continuously monitored for quality and that data quality rules are enforced at the point of master data creation and maintenance.
Outcomes:
- A trusted, single view of your most critical business entities.
- Improved accuracy of analytics, marketing personalization, and supply chain operations.
- Reduced data redundancy and maintenance costs across the enterprise.
Active Data Catalog and Metadata Management
A data catalog is only useful if it's trusted and up-to-date. We enrich your data catalog with active metadata from our observability tools, including data quality scores, popularity metrics, and lineage information. This transforms your catalog from a static inventory into a dynamic, intelligent guide for data discovery and usage.
Outcomes:
- Increased data democratization by helping users find and trust the right data.
- Improved productivity for data analysts and scientists.
- Enhanced data governance through a centralized, context-rich metadata repository.
Data Reliability Engineering (DRE) Practices
We help you adopt DevOps principles for your data pipelines. By implementing Data Reliability Engineering, we help you define Service Level Objectives (SLOs) for your data products, create error budgets, and establish on-call rotations and incident management processes. This brings engineering rigor to your data operations, ensuring maximum uptime and reliability.
Outcomes:
- A proactive, engineering-driven approach to managing data pipeline health.
- Clear, objective measures of data product performance and reliability.
- Improved collaboration between data engineering and data consumer teams.
Data Governance and Compliance Integration
Data quality is a cornerstone of effective governance. We integrate observability findings directly into your governance workflows. This includes automating the tagging of sensitive data (PII), monitoring data access patterns for anomalies, and generating the reports needed to demonstrate compliance with regulations like GDPR, HIPAA, and SOX.
Outcomes:
- Automated evidence gathering for compliance audits, reducing manual effort.
- Proactive identification of data privacy and security risks.
- A stronger, more defensible data governance posture for the entire organization.
Cloud Data Quality and Observability (Snowflake, Databricks, BigQuery)
The cloud introduces new complexities. We specialize in implementing data quality and observability solutions tailored for modern cloud data platforms. We leverage platform-native features and best-of-breed tools to ensure data trust and reliability within your Snowflake, Databricks, or Google BigQuery environment.
Outcomes:
- Optimized data quality monitoring that leverages the power and scalability of the cloud.
- Cost control by identifying and eliminating inefficient data processing jobs.
- Seamless integration with your existing cloud data stack and CI/CD processes.
Data Quality Assurance for AI and Machine Learning
Your AI is only as good as your data. We provide specialized services to ensure the quality and integrity of your training and production data for AI/ML models. This includes feature validation, drift detection, and monitoring for data bias, ensuring your models are accurate, fair, and reliable over time.
Outcomes:
- Improved model performance and prediction accuracy.
- Reduced risk of biased or unethical AI outcomes.
- A robust MLOps foundation for deploying and maintaining trustworthy AI systems.
Managed Data Quality as a Service (DQaaS)
Focus on insights, not infrastructure. Our managed service provides a turnkey solution for data quality monitoring and management. We handle the tool setup, rule configuration, alert monitoring, and reporting, providing you with regular data health reports and actionable recommendations from our team of experts, all for a predictable monthly cost.
Outcomes:
- Access to expert data quality skills without the need for in-house hiring.
- Reduced total cost of ownership for data quality tooling and operations.
- Continuous improvement of data quality guided by seasoned professionals.
FinOps for Data Quality: Cost-Benefit Analysis
Data quality initiatives must be cost-effective. We apply FinOps principles to your data observability strategy, helping you understand the cost of your data pipelines and the financial impact of data downtime. We identify redundant processing and inefficient queries, helping you optimize your cloud data spend while maximizing data reliability.
Outcomes:
- Clear visibility into the cost of data quality and the ROI of your investments.
- Optimization of cloud compute and storage costs related to data processing.
- A data-driven framework for making budget decisions about your data platform.
Real-World Impact: From Data Chaos to Competitive Advantage
See how we've helped enterprises like yours turn data reliability into a strategic asset.
Fintech: Ensuring Transactional Integrity for a Payments Platform
Industry: Financial Technology (Fintech)
Client Overview: A rapidly growing payment processing company handling millions of daily transactions. Their analytics team was struggling with inconsistent data from multiple upstream sources, leading to reconciliation errors and delays in regulatory reporting, which threatened both their financial stability and their operating license.
The Problem: The client's data warehouse was plagued by silent data failures. Incomplete data feeds and unexpected schema changes from partners were not being detected, causing cascading errors in financial reports. Their manual reconciliation process was slow, error-prone, and could not keep up with the transaction volume.
Key Challenges:
- Reconciling data across three different core processing systems in near real-time.
- Detecting data freshness issues (delayed files) before they impacted end-of-day reporting.
- Ensuring data quality for strict anti-money laundering (AML) compliance checks.
- Providing a verifiable audit trail for all data transformations.
Our Solution:
CIS implemented a comprehensive data observability solution focused on transactional integrity. We deployed an automated reconciliation engine to continuously compare critical financial data points across systems. AI-powered monitors were established to track data volume and freshness, triggering alerts on any deviation from learned patterns. We also mapped the complete data lineage for key regulatory reports, allowing for instant root cause analysis.
- Deployed automated data validation rules at the ingestion layer to block malformed data.
- Established a centralized dashboard for monitoring the health of all critical data pipelines.
- Integrated alerting with the client's incident management system (PagerDuty) for rapid response.
- Generated automated data quality reports to simplify the auditing process.
Positive Outcomes:
E-commerce: Powering Personalization with Trusted Customer Data
Industry: Retail & E-commerce
Client Overview: A large online retailer with a strategic goal to increase customer lifetime value through hyper-personalization. Their marketing and data science teams were hindered by a fragmented and unreliable customer data platform (CDP), leading to ineffective campaigns and a poor customer experience.
The Problem: Duplicate customer profiles, incomplete behavioral data, and incorrect product catalog associations were rampant. This resulted in customers receiving irrelevant recommendations and marketing emails, leading to high unsubscribe rates and low conversion on personalized offers.
Key Challenges:
- Merging customer identities from web, mobile, and in-store channels accurately.
- Ensuring product recommendation models were trained on clean and complete interaction data.
- Validating the accuracy of customer segments before launching marketing campaigns.
- Monitoring the health of dozens of data feeds from third-party marketing tools.
Our Solution:
We architected and implemented a Data Quality & Observability layer on top of their existing CDP. The solution began with advanced data profiling to identify the root causes of data duplication and inconsistency. We then deployed an MDM-lite framework to create a "golden record" for each customer. Automated monitors were set up to validate the integrity of clickstream data and ensure product attributes were consistent across all systems.
- Implemented probabilistic and deterministic matching rules to de-duplicate millions of customer profiles.
- Created a data quality dashboard specifically for the marketing team to vet audience segments.
- Used anomaly detection to identify sudden drops in event data volume from their mobile app.
- Established data lineage to show exactly how customer attributes were being used in personalization models.
Positive Outcomes:
Healthcare: Guaranteeing Data Reliability for Clinical Analytics
Industry: Healthcare & Life Sciences
Client Overview: A leading healthcare provider whose research division relies on a massive data lake of Electronic Health Records (EHR) for clinical trials analysis and population health studies. The integrity of this data was paramount, as errors could compromise research findings and patient safety.
The Problem: The data lake ingested data from hundreds of different provider systems, each with its own format and standards. This led to critical issues like incorrect patient-record linkages, inconsistent medical coding, and missing data points, making the data unusable for reliable analysis without extensive manual cleaning.
Key Challenges:
- Ensuring HIPAA compliance and data privacy throughout the quality monitoring process.
- Validating complex medical coding systems (e.g., ICD-10, SNOMED) for consistency.
- Tracking the lineage of data to ensure full reproducibility of research findings.
- Monitoring for data drift in patient vitals and lab results that could indicate systemic data capture errors.
Our Solution:
CIS designed and built a HIPAA-compliant data observability framework specifically for their healthcare data lake. We implemented a series of domain-specific data quality checks to validate medical codes and ensure referential integrity between patient, visit, and treatment records. Anomaly detection models were trained to monitor distributions of clinical data, flagging outliers that could represent measurement errors. Crucially, all data quality metrics were generated on de-identified data to maintain patient privacy.
- Developed a custom data profiler to handle the complexity of EHR data structures.
- Created a "Data Trust Score" for each data source, helping researchers select the most reliable datasets.
- Implemented end-to-end lineage, allowing any data point in a final report to be traced back to its source file.
- Automated the process of quarantining bad data for review by data stewards.
Positive Outcomes:
Our Technology & Platform Expertise
We leverage a best-of-breed ecosystem of tools and platforms to build robust, scalable, and effective data quality and observability solutions tailored to your environment.
Our Proven Delivery Process
We follow a structured, collaborative methodology to ensure your data quality and observability initiatives deliver tangible business value quickly and sustainably.
1. Discover & Assess
We begin with a deep dive into your data landscape, business objectives, and pain points. Through stakeholder interviews and automated data profiling, we establish a baseline of your current data health and identify the most critical areas for improvement.
2. Strategize & Design
Based on the assessment, we co-create a strategic roadmap and design a target-state architecture. This includes defining data quality rules, selecting the right tools, establishing governance roles, and creating a phased implementation plan with clear milestones.
3. Implement & Automate
Our expert engineers build and deploy the solution. We configure observability platforms, implement automated validation checks in your data pipelines, and build out data lineage maps. We work in agile sprints to deliver value incrementally and gather feedback early.
4. Monitor & Remediate
With the solution live, we shift focus to continuous monitoring and improvement. We establish dashboards and alerting to track data health in real-time. Our team helps you create efficient workflows for investigating and remediating data issues as they are detected.
5. Govern & Scale
We help you embed data quality into the fabric of your organization. This involves training data stewards, integrating quality metrics into business reports, and creating a center of excellence to scale best practices across the entire enterprise, fostering a true culture of data trust.
What Our Clients Say
"CIS transformed our data operations. Before, we were constantly fighting fires and questioning our own reports. Now, with the observability platform they built, we have a level of trust in our data that allows us to make decisions faster and with much greater confidence. Their expertise in both the technology and the business process was invaluable."
"The data lineage solution from CIS was a game-changer for our compliance team. What used to take weeks of manual tracing for audits now takes minutes. We can demonstrate the full lifecycle of our data, which has been critical for satisfying regulators. They are true partners, not just vendors."
"We engaged CIS to improve the quality of data feeding our machine learning models. The results were immediate and dramatic. By implementing their automated validation and anomaly detection, our model accuracy improved by over 10%, directly impacting our product's core functionality. I highly recommend their team."
"The 'Data Trust Score' concept CIS introduced has been revolutionary for us. It has gamified data quality and created a sense of ownership among our business teams. For the first time, we have a clear, simple metric to rally around, and it's driving real improvements across the board."
"Our migration to Snowflake was complex, and we were concerned about maintaining data integrity. CIS's cloud data quality framework was essential to our success. They helped us build pre- and post-migration validation checks that ensured a seamless transition with zero data loss or corruption."
"The managed DQaaS offering is perfect for our team. We get the benefit of a world-class observability platform and expert oversight without the overhead of managing it ourselves. The weekly health reports are incredibly insightful and have helped us prioritize our data engineering backlog effectively."
Flexible Engagement Models
We offer a range of engagement models designed to provide the right level of support for your organization's needs and maturity.
Strategic Consulting & Assessment
A fixed-scope engagement to assess your current data landscape, identify critical quality gaps, and deliver a strategic roadmap and business case for improvement.
- Ideal for organizations starting their data quality journey.
- Deliverables include a detailed assessment report and implementation plan.
Dedicated POD (Team Augmentation)
We provide a dedicated "Pod" of data quality engineers, analysts, and architects who integrate seamlessly with your existing teams to accelerate your projects.
- Full-time, expert resources working under your direction.
- Flexible team composition that can scale up or down as needed.
Managed Services (DQaaS)
An ongoing, subscription-based service where we take full responsibility for monitoring, managing, and reporting on your data quality, allowing your team to focus on core business.
- Predictable monthly cost for a complete data quality solution.
- Includes tooling, expert oversight, and regular health reports.
Frequently Asked Questions
Think of it this way: Data Quality is about the *state* of your data—is it accurate, complete, consistent? Data Observability is about the *health of the system* that delivers that data. Observability uses monitoring, alerting, and lineage to proactively detect when data pipelines are broken or data is unreliable, often before traditional quality checks would catch it. You need both: observability to ensure the pipes are working, and quality checks to ensure what comes out of the pipes is clean.
You can see initial results surprisingly quickly. Our assessment and profiling phase, typically lasting 2-4 weeks, often uncovers critical "quick wins" that can be addressed immediately. A foundational implementation of automated monitoring on a critical data pipeline can start providing value within the first quarter. The journey to a mature, enterprise-wide data quality culture is ongoing, but tangible business impact is phased in from the very beginning.
We are tool-agnostic and focus on the right solution for your specific stack and needs. We have deep expertise with leading commercial platforms like Monte Carlo, Databand (IBM), and Bigeye, as well as open-source frameworks like Great Expectations and Soda Core. Our decision process is collaborative, weighing factors like your existing technology, budget, and specific use cases to recommend the best-fit platform.
We measure ROI by connecting data quality metrics to tangible business outcomes. This can include: 1) **Cost Reduction:** Calculating the hours saved by automating manual reconciliation or reducing data-related support tickets. 2) **Revenue Growth:** Attributing lift in marketing campaign conversions or sales effectiveness to improved data accuracy. 3) **Risk Mitigation:** Quantifying the potential cost of compliance fines or bad decisions avoided. We work with you to build a business case that resonates with financial stakeholders.
Absolutely. This is a core part of our initial Strategic Consulting & Assessment engagement. We help you identify a high-impact, low-complexity pilot project and quantify its potential ROI. By demonstrating clear value on a smaller scale, we help you secure the buy-in and budget needed for a broader, enterprise-wide data trust initiative.
Security and compliance are foundational to our approach. We are certified in SOC 2 and ISO 27001. Our solutions are designed to work on metadata wherever possible and can operate on de-identified or masked data in production environments to ensure privacy (e.g., for HIPAA). Data lineage mapping is also a critical tool we use to help you track the usage of sensitive data and demonstrate compliance with regulations like GDPR.
Ready to Build Unshakeable Trust in Your Data?
Stop letting bad data dictate your business outcomes. Let's build a future where every decision is powered by reliable, transparent, and observable data. Schedule a free consultation with our data experts today.