Manager of Data & Analytics

Manager of Data & Analytics

The Manager of Data & Analytics will serve as the senior leader accountable for ISCO's enterprise data strategy, governance program, analytics capabilities, and AI/ML roadmap. This role owns the end-to-end data value chain ensuring data is governed, trustworthy, accessible, and actively leveraged to drive operational excellence, strategic decision-making, and competitive advantage.

ISCO is at the early stages of its data maturity journey. The Manager will be expected to stand up foundational governance and data quality capabilities while simultaneously charting the longer-term vision for analytics, AI, and data-driven transformation. This requires a leader who can operate at both the strategic and tactical levels someone who can present a data strategy to the executive team and also roll up their sleeves to define metadata standards, select tooling, and work through data quality issues on the plant floor.

As a midsize organization, ISCO requires this leader to combine the strategic oversight of a data executive with the hands-on capabilities of a governance architect and lead data steward, particularly in the program's early phases. As the team and program mature, the Manager will shift increasingly toward strategy, stakeholder management, and organizational leadership.

Scope of Accountability

The Manager of Data & Analytics has enterprise-wide accountability spanning:

• Enterprise Data Strategy: Setting the vision, roadmap, and investment priorities for data, analytics, and AI across ISCO.
• Data Governance Program: Owning the governance operating model, policy framework, stewardship network, and metadata standards across all priority domains.
• Master Data Domains: Product, Customer, Supplier, Item/Material, Facilities/Fleet, and Quote data.
• Operational & Manufacturing Data: Fabrication, labor tracking, work orders, Bills of Materials (BOMs), quality management data (QMDs), and OT/IT integration.
• Analytics & AI: Business intelligence, advanced analytics, predictive modeling, and AI/ML initiatives enterprise-wide.
• Cross-Functional Data Integration: Data flowing across operations, sales, quality, finance, and manufacturing systems (ERP, Pipeline, Excel, fabrication systems).
• Team & Capability Building: The Data & Analytics function including data engineers, analysts, stewards, architects, and data scientists.

Key Responsibilities

1. Enterprise Data Strategy & Vision
• Define and own ISCO's enterprise data strategy, aligning data investments with business objectives, the Target Operating Model, and the company's multiyear transformation roadmap.
• Establish a clear, prioritized, and funded multi-year roadmap for data governance, architecture, analytics, and AI with measurable milestones and business outcomes.
• Serve as the executive voice for data across the organization articulating the value of data to the leadership team and building enterprise-wide commitment to data-driven decision-making.
• Identify and evaluate emerging technologies, methodologies, and industry trends (e.g., data mesh, data products, generative AI) for applicability to ISCO's context.
• Develop business cases and ROI frameworks for data investments, ensuring initiatives are tied to measurable value creation.
2. Establish and Lead ISCO's Enterprise Data Governance Program
• Launch and mature foundational governance capabilities including:
• Identifying authoritative "single source of truth" domains.
• Establishing a data ownership and stewardship model.
• Implementing data quality controls and a quality framework.
• Defining governance roles, processes, metadata requirements, and Critical Data Element (CDE) selection.
• Stand up enterprise-wide policies for data lineage, definitions, data ethics, privacy, security, retention, and lifecycle oversight.
• Introduce a structured governance operating model spanning Product, Customer, Supplier, Facilities/Fleet, and other critical domains.
• Develop and maintain a governance policy library, including clear procedures for policy creation, interpretation, enactment, and exception handling.
• Establish and chair (or co-chair) an enterprise Data & Analytics Governance Board, setting cadence, membership, decision-rights, and escalation paths.
3. Design and Manage Metadata Frameworks & Knowledge Organization
• Create and maintain a categorization framework for data assets including taxonomies, ontologies, business glossaries, and controlled vocabularies to maximize accessibility and reusability across the enterprise.
• Structure business metadata in a logical and coherent manner, establishing procedures for updating and modifying definitions and information models in a controlled way.
• Set standards for the onboarding and linking of technical data assets to business metadata using metadata management solutions.
• Ensure alignment between business concepts, data models, and technical assets so that information retrieval and data sharing are consistent and reliable.
• As the team grows, transition hands-on metadata architecture work to a dedicated Governance Architect while retaining strategic oversight and quality assurance of the framework.
4. Coordinate and Lead Data Stewardship Activities
• Build and lead the enterprise stewardship network, establishing standard processes for how stewards execute their activities (work steps, tools, communication cadences).
• Mentor and guide data stewards in stewardship activities including data quality remediation, metadata capture, and business definition maintenance.
• Interpret governance policies and translate them into actionable guidance for stewards and business users.
• Provide consolidated reporting on stewardship activities, data quality status, and policy compliance to the governance board and executive leadership.
• As the program matures, recruit and develop a Lead Data Steward to assume day-to-day stewardship coordination while retaining program-level accountability.
5. Mature Data Quality & Master Data Management (MDM)
• Lead MDM/MDG initiatives to improve consistency of product, customer, item/material, quote, and facility data.
• Address systemic data quality issues identified in operations and manufacturing, such as:
• Inconsistent data entry causing manual cleanup and undermining repeatability.
• Fragmented data sources causing discrepancies in labor hours and planning decisions.
• Lack of accurate labor tracking impacting variance analysis and costing.
• Drive implementation of enterprise-grade Data Catalog & Data Quality tools (such as Collibra, Alation, Informatica, Atlan, Monte Carlo, Soda) for metadata management and automated quality monitoring.
• Establish a continuous improvement model for data quality moving from reactive cleanup to proactive prevention through root-cause analysis, process redesign, and automated controls.
6. Enable Modern Data Architecture & Data Integration
• Own the design and evolution of ISCO's enterprise data architecture, ensuring scalable, reliable data systems that align business strategy with IT architecture and future ERP.
• Identify and prioritize data integration needs across operations, sales, quality, and finance.
• Drive harmonization of data sources (ERP, Pipeline, Excel, QMD, fabrication systems, etc.) to reduce manual reconciliation and improve accuracy.
• Provide data architecture leadership for ISCO's ERP modernization initiative, ensuring governance, quality, and integration requirements are embedded in the program from the outset.
• Evaluate and guide architectural decisions around cloud data platforms, data lakehouse patterns, real-time streaming, and API-based integration.
7. Build and Lead Analytics, BI, and AI Capabilities
• Own the enterprise analytics and AI roadmap, including forecasting, predictive quality, anomaly detection, SKU/production optimization, and operational intelligence.
• Drive modernization of ISCO's BI environment establishing self-service analytics capabilities, standardized reporting frameworks, and governed data products that business users can trust.
• Lead real-time manufacturing reporting and alerting through integrated OT/IT data (QMDs, fabrication, work orders).
• Drive AI/ML initiatives aligned to business needs such as:
• Demand forecasting and inventory optimization
• Predictive maintenance and predictive quality
• Automated process efficiencies
• Sales/Customer analytics and digital experiences
• Digital twin and simulation capabilities
• Establish an AI governance framework including model validation, bias monitoring, explainability standards, and responsible AI practices ensuring AI initiatives are trustworthy and aligned with organizational values.
• Identify and execute quick-win analytics projects that demonstrate value early and build organizational appetite for advanced capabilities.
8. Organizational Leadership & Team Building
• Build, lead, and develop a high-performing Data & Analytics function, including data engineers, analysts, data stewards, governance architects, and (over time) data scientists.
• Define the organizational structure, hiring plan, and capability