Why is Phase 0 critical for data product vision development?

Phase 0 serves as the foundational planning stage where organizations define project scope and establish the groundwork for successful implementation. During this phase, setting a clear data product vision ensures proper alignment with business objectives before technical implementation begins, reducing risks and increasing chances of achieving measurable outcomes.

What are the key components of an effective data product vision?

The five essential components include: Purpose and Value Proposition (defining how data products achieve business objectives), Quality Standards and Service Levels (establishing SLAs for accuracy and reliability), Accessibility and Discoverability (ensuring easy access through catalogs and APIs), Ownership and Accountability (assigning clear domain ownership), and Governance and Compliance Framework.

How does data product vision align with IFS Cloud implementation goals?

Data product vision connects directly with strategic IFS Cloud objectives including operational efficiency improvements, cost reductions, and enhanced customer satisfaction. For example, manufacturing organizations can create data products supporting 30% productivity increases through workflow automation.

What is the difference between traditional data management and data mesh approach?

Traditional centralized data architectures suffer from lack of domain knowledge and complex governance. Data mesh addresses these issues through decentralized domain ownership, treating data as products, self-serve infrastructure platforms, and federated governance, enabling faster time to market and better business alignment.

Crafting a Data Product Vision

IFS-ERP CRIMS customization of IFS Cloud

TL;DR: For CIOs & ERP Managers

The Challenge: Many ERP implementations fail to deliver the promised 414% ROI because data is treated as a byproduct, not an asset.

The Phase 0 Solution: Before technical deployment, you must define a Data Product Vision. This means shifting from centralized data lakes to domain-oriented ownership (Manufacturing, Finance, Asset Mgmt) where data is packaged, managed, and served like a product.

The Outcome: By establishing governance, SLAs, and ownership early, organizations unlock specific IFS Cloud benefits: 15% cost reduction in maintenance, 50% faster decision-making, and an 11-month payback period.

Phase 0: The Foundation of Data Mesh Success

Setting a clear data product vision in Phase 0 forms the foundation for successful IFS Cloud Data Mesh implementation. This foundational phase ensures data gets treated as a product while aligning with decentralized, domain-oriented data mesh principles and supporting specific IFS Cloud business objectives.

A data product vision defines the purpose, value, and expectations for data products within an organization. It transforms thinking from viewing data as a byproduct of business operations to recognizing it as a valuable asset that drives decision making, innovation, and operational efficiency.

Key Entities: IFS Cloud, Data Mesh Architecture, Phase 0 Implementation, Data Product Management, Enterprise Data Strategy.

Core Components of Data Product Vision

Purpose & Value Proposition

Define how data products achieve business objectives. Organizations implementing IFS Cloud typically target 414% three-year ROI and $5.5 million average annual benefits.

Manufacturing: Real-time shop floor visibility tracking OEE metrics to achieve 15% cost reduction via predictive maintenance.
Asset Management: Tracking asset health indicators in offshore equipment to enable 50% faster outage resolution.

Quality Standards & SLAs

Establish measurable expectations. Data products must meet specific Service Level Agreements (SLAs) to remain trustworthy.

Project Management: 99.5% uptime for project cost tracking and sub-second refresh rates for resource dashboards.
Supply Chain: Inventory data updates within 15 minutes of transactions to support AI-driven production planning.

Governance, Access & Ownership

Accessibility

Make data products discoverable via catalogs and APIs.

Example: Global manufacturers use role-based catalogs where plant managers see local metrics while executives see consolidated dashboards.

Ownership

Assign domain accountability.

Production: Owns Scheduling Optimization (MSO) data.
Maintenance: Owns anomaly detection models.
Finance: Owns project profitability data.

Compliance

Security and Audit trails.

Pharma Example: Formula-based modules require data products with complete lot traceability and batch tracking for FDA compliance.

Aligning Vision with IFS Cloud Goals

Connect the data product vision directly with strategic business objectives through measurable outcomes.

Operational Efficiency Support 30% productivity increase through workflow automation (e.g., automated production planning).

Financial Performance Target 25% downtime reduction and 20% cost savings in Year 1 via predictive maintenance.

Customer Satisfaction Improve «Available-to-Promise» accuracy and real-time order tracking.

Implementation Best Practices

Structured Data: Implement JSON-LD schema markup for Organization and Product types to improve AI search discoverability.
Entity Optimization: Clearly define relationships between IFS Cloud components and Data Mesh principles.
Technical SEO: Ensure crawlability with semantic HTML and fast-loading pages.

Measuring Success

Track data product adoption rates and business impact:

414%

3‑Year ROI

50%

Faster Decisions

$2.5M

Staff Efficiency

11 Mo

Payback Period

Frequently Asked Questions

A data product vision defines the purpose, value, and expectations for data products. It shifts thinking from viewing data as a byproduct to recognizing it as a valuable asset that drives decision making, innovation, and operational efficiency while supporting specific IFS Cloud business objectives like achieving 414% three-year ROI.

Phase 0 is the foundational planning stage. Setting a clear vision here ensures alignment with business objectives before technical implementation begins, reducing risks and increasing the chances of achieving measurable outcomes like the 11-month payback period typical of successful IFS Cloud implementations.

The five essential components are:

Purpose and Value Proposition: Defining how it achieves business goals.
Quality Standards: Establishing SLAs for accuracy/reliability.
Accessibility: Ensuring easy access via catalogs/APIs.
Ownership: Assigning clear domain accountability.
Governance: Maintaining data integrity and security.

It connects data directly to strategic objectives. For example, manufacturing organizations can create data products supporting 30% productivity increases through workflow automation, while asset-intensive industries can target 50% faster equipment outage resolution through predictive maintenance data products.

Traditional centralized data architectures often suffer from a lack of domain knowledge and complex governance (the «bottleneck» effect). Data Mesh addresses this via decentralized domain ownership, treating data as products, providing self-serve infrastructure platforms, and federated governance, enabling faster time to market.

IFS.ai capabilities enhance data mesh implementations by enabling predictive maintenance, automated production planning, and intelligent resource optimization. Data products can leverage AI for anomaly detection and demand forecasting, directly supporting the ROI benefits of the platform.