From “information” to an operational resource

Data becomes economically meaningful when it is applied to measurable outcomes, such as:

• Operational efficiency: reducing cycle time, waste, downtime, and rework through real-time visibility

• Risk control: detecting fraud, anomalies, and emerging threats earlier and more accurately

• Customer and market insight: improving service design, personalization, and retention

• Product and service innovation: enabling data-driven features, predictive services, and outcome-based models

• Compliance and assurance: proving controls, traceability, and responsible handling across value chains

These outcomes depend less on advanced analytics and more on the basics: quality, consistency, access discipline, and trust.

The full lifecycle is where value is won or lost

Organizations often invest heavily at the analytics end and underinvest at the beginning of the lifecycle. A practical data lifecycle includes:

Collection and capture
Data enters through transactions, sensors, customer interactions, public systems, and partner exchanges. Good practice starts with clarity on purpose, consent where required, and minimum necessary data.

Organization and quality management
Data must be cleaned, standardized, deduplicated, and linked to common identifiers. Without this, analytics and AI become unreliable and operational teams stop trusting outputs.

Storage and retention
Storage choices affect cost, security, performance, and compliance. Retention and deletion rules are as important as collection, especially for regulated data and cross-border operations.

Access and use
The primary governance question is not “who owns the data” in a conceptual sense, but “who can access it, for what purpose, under what controls, and how is usage audited.”

Sharing and exchange
Value grows when data can be exchanged responsibly with partners—within supply chains, across industries, and across borders—using agreed technical and assurance mechanisms.

Continuous improvement
Data maturity is operational. Quality, security, and usefulness require monitoring, metrics, and ongoing stewardship, not one-time projects.

Different data types require different handling

Not all data should be governed the same way. A practical approach distinguishes between:

• Personal data: information linked to individuals, requiring strong privacy protections and lawful processing

• Enterprise and operational data: production, logistics, performance, and internal business data, often central to competitiveness

• Public-sector and societal data: data held by public institutions or used for public services, often requiring additional transparency and accountability

• High-risk or sensitive data: data that, if exposed or misused, could cause material harm (security, safety, or significant economic risk)

Clear categorization reduces confusion and supports consistent controls across teams and partners.

Data governance is a business capability, not a compliance exercise

Effective data governance is how organizations make data reliable and safe to use at scale. It typically includes:

Accountability and stewardship
Assigning clear roles for data domains (e.g., customer, payments, logistics, product), including business stewardship and technical ownership.

Policies that are practical and enforceable
Rules for collection, classification, access, retention, sharing, and acceptable use. Policies must be implementable in workflows and systems—otherwise they are ignored.

Controls and auditability
Identity and access management, logging, encryption, and monitoring. Organizations need to be able to answer, confidently and quickly, “who accessed what, when, and why.”

Quality management with measurable standards
Defining what “good data” means for each domain and tracking quality metrics that matter operationally: completeness, timeliness, accuracy, consistency, and lineage.

Third-party governance
Data risks often enter through vendors, partners, and platforms. Procurement standards and ongoing assurance are essential for secure data ecosystems.

When governance is working, it reduces friction. Teams spend less time arguing over versions of truth, manual reconciliations, and unclear permissions—and more time delivering outcomes.

Responsible sharing is where modern scale comes from

Many high-impact use cases require data exchange across organizations: digital trade documentation, supply-chain traceability, payments and fraud prevention, logistics visibility, health coordination, and public service delivery.

Responsible data sharing depends on three elements:

Interoperability
Common data formats, shared identifiers, and reliable exchange interfaces reduce integration cost and increase adoption. Interoperability is a competitiveness lever, not just an IT preference.

Assurance mechanisms
Organizations need confidence that shared data will be protected and used appropriately. This includes contractual commitments, technical safeguards, and verifiable controls.

Purpose clarity and proportionality
Sharing should be purpose-driven. Clear use cases and defined boundaries build trust and prevent over-collection and misuse.

Well-designed data-sharing arrangements expand markets, accelerate innovation, and lower transaction costs—particularly for SMEs.

Cross-border data is a strategic and governance issue

As trade and services become more digital, cross-border data movement increasingly determines how efficiently organizations can operate. Cross-border operations introduce additional requirements around jurisdiction, lawful processing, security expectations, and partner accountability.

Organizations that succeed internationally typically:

• Design data architectures that can meet different jurisdictional requirements without excessive fragmentation

• Build strong internal discipline on classification, access control, and auditability

• Use trusted partners and clear assurance arrangements for shared data and outsourced processing

• Treat privacy, security, and compliance as enabling conditions for scale, not as afterthoughts

Strong cross-border data practices improve both competitiveness and trust.

Common barriers that slow progress

• Data scattered across isolated systems with inconsistent definitions and ownership

• Low trust in data quality, leading teams to rebuild reports manually and ignore analytics outputs

• Access granted by convenience rather than purpose and accountability

• Over-reliance on one-off projects instead of operational stewardship

• Security and privacy treated as blockers, rather than integrated design requirements

• Partner integrations built without shared standards, creating long-term cost and fragility

These issues are solvable, but only when data is treated as an operating discipline.

In a mature digital economy environment, organizations can:

• Produce reliable, consistent reporting without repeated reconciliation

• Deploy analytics and AI with confidence because inputs are governed and auditable

• Exchange data with partners efficiently using interoperable mechanisms and shared assurance

• Demonstrate compliance and accountability without slowing business operations

• Innovate faster by building new services on trusted data foundations

Data is not valuable because it is abundant. It is valuable when it is trusted, governed, and used to improve outcomes—within organizations and across ecosystems.