From Data Silos to Dashboards – Mars’ Voyage of Data Foundations Shaping Today’s Decisions for a Better Tomorrow

Adopt a unified external platform to replace scattered silos. With a single source for a million records, the business gains faster decision-making and clearer risk signals across functions. This shift empowers leaders to prioritize work with confidence and allocate resources where impact is highest.

Define a common model and a compact set of methods for data integration, quality checks, and lineage. Map core sources, assign owners, and address issues through a shared cadence. In early pilots, replacing manual pulls with dashboards yielded 20–30% faster decision-making cycles and a notable drop in rework.

Scale to vast streams of events from internal apps and external feeds. This enables a cross-disciplinary view on customer interactions, supply chain, and risk. In practice, jamadagni led a group replacing some siloed reports with interactive dashboards accessible to anyone. The result: fewer manual pulls, better visibility, and faster course corrections, even during halloween planning cycles when demand spikes.

Encourage teams and individuals to embrace dashboards as practical decision aids, addressing issues head-on. Provide role-based access, institutional memory, and a feedback loop to refine the platform و model. Training should mix live scenarios and simulated events for decision-making confidence across business units.

بعض practical actions include appointing a data product owner, scheduling monthly reviews of data quality, and publishing a dashboard scorecard across teams. Each step lowers issues, accelerates decision-making, and builds a common language that aligns external signals with internal priorities.

From silos to visible dashboards, Mars reinforces a data foundation that informs better planning for a brighter tomorrow. Continue refining data cleaning, cross-platform alignment, and a clear ownership map. By adopting these changes, organizations realize improved data quality and convert raw data into concrete actions with measurable impact through innovations in analytics.

Turn Data Silos into Actionable Dashboards for Real-Time Decisions

Consolidate all data sources into a single platform that ingests streaming telemetry and batch records, then build dashboards that support real-time decisions.

View these silos as opportunities to drive innovation, assign data owners, create data products, and enable self-service for teams across services and worlds, able to act on insights.

Develop a catalog and data lineage so datasets have context; ensure access is allowed by policy and auditable, enabling teams to view trusted sources and move from insight to action.

Design the pipeline by building a unified view: ingest and normalize, enrich with domain models, then push the result to the view layer and dashboards, delivering a clear path from data to decision.

Set objective metrics for each dashboard: latency targets, data freshness, and anomaly alerts; include adjustments to thresholds as conditions change, then adapt rapidly.

Create cross-functional teams to own data products; these teams work with clients to translate insights into actions, like automated alerts, using services that offer reusable patterns across missions, well aligned to workflows.

In a case study, combine Mars climate data with rover telemetry to predict resource needs and risky conditions, delivering a single view that speeds operations and improves outcomes across worlds and missions.

Plan a modernization roadmap: first unify data sources, then deploy dashboards, then scale to new domains; this approach helps teams thrive and allows clients to modernize workflows while generating improvements and significant results.

Next steps include collecting user feedback, iterating dashboards with frequent adjustments, and tracking a significant reduction in decision cycle time across services.

Adopt AI, Cloud, Edge, and Digital Twins in Mars’ Factory of the Future

Start a 90-day pilot on four brick-and-mortar lines using AI, cloud, edge, and digital twins to prove data-led optimization. Target unplanned downtime reduced by 25%, cycle times cut 8–12%, and energy intensity lowered 5–10%. Build a partnership with a cloud provider and a systems integrator to deliver calibrated digital twin models, collect sensor data, and run real-time adjustments through edge devices on the shop floor. Create governance and code standards for data sharing across four areas of the business, including family-owned plants and key partner networks, while using wrigleys as a tangible benchmark for packaging and line performance. This approach concentrates on customers and consumers alike, delivering faster feedback and clearer visibility into outcomes while maintaining compliance and traceability.

Data-driven roadmap for Mars’ factory

• Establish a four-branch data foundation: MES, ERP, SCADA, and supply-chain systems feed into a unified data lake with a clear data dictionary and lineage tracking.
• Develop digital twins for critical lines (gum packaging, coating, and quality-control stations) to simulate takt times, throughput, and defect scenarios without interrupting live production.
• Deploy edge devices at the mill floor to capture latency-sensitive signals (vibration, temperature, pressure) and enable near-instant adjustments to speed, temperature profiles, and tooling.
• Shift analytics to the cloud for model training, governance, and long-term forecasting; run lightweight inference at the edge to preserve reaction times on the line.
• Implement AI-driven predictive maintenance to cut downtime, predict part wear, and optimize spare-part sets; measure impact with OEE, defect rate, and energy per unit as primary metrics.
• Incorporate four cross-functional pilots across packaging, coating, quality inspection, and energy management to validate the approach and build learnings for scale.
• Engage consumers and customers by surfacing product quality signals and traceability data from the line to the shelf, strengthening trust and brand reputation.
• Maintain continuous improvement loops with testing, validation, and adjustments; publish small increments to governance and code to reflect evolving requirements and security needs.

Governance, testing, and change management

1. Define a concise governance model: data ownership, access rights, model lineage, and auditing to ensure responsible AI use and compliance with supplier and consumer data rules.
2. Set up four experiment sets focused on reliability, quality, efficiency, and responsiveness; run controlled A/B tests on separate lines to isolate impact.
3. Schedule regular partner reviews with four key groups–customers, partners, suppliers, and internal teams–to align on outcomes and adjust milestones.
4. Adopt robust testing protocols: shadow testing for models, sandbox environments for code changes, and staged rollouts to minimize disruption to brick-and-mortar operations.
5. Track KPIs such as OEE, first-pass yield, scrap rate, energy intensity, and mean time between failures; require quarterly reviews to approve scale-up or plan pivots.
6. Clarify change management steps: training, documentation, and user-friendly dashboards that translate complex models into actionable floor decisions.
7. Document lessons learned and update playbooks to accelerate future deployments across other lines and brands, including wrigleys-like packaging families.
8. Ensure supplier and partner readiness with security baselines, code freezes during critical runs, and contingency plans to preserve production continuity.

Leverage GEM Insights to Track Chocolate Trends and Sustainability

Install four live GEM dashboards that integrate governance, environmental, market, and product data to spot trends and enable rapid adjustments across the chocolate portfolio. Each view pulls from supplier scorecards, sales, certifications, and production metrics, ensuring governance remains transparent with partners.

Look at the biggest shifts in consumer preferences: single-origin bars, dark vs milk formats, and sustainability-labeled products. GEM Insights surfaces signals by region, channel, and packaging, guiding decisions on product mix and pricing.

Some signals emerge after campaigns or seasonal events; others reflect longer cycles. The platform allows you to monitor these dynamics and respond with targeted adjustments that support both commerce growth and environmental goals.

Maintain mutuality with suppliers and retailers by sharing dashboards and aligning incentives, which reduces risk and speeds collaboration. The digital view across platforms provides a clear view of progress and gaps along the value chain.

Four flagship data streams to monitor

Market and behavior signals: looking at consumer preferences, purchase frequency, and price sensitivity across regions; track the biggest category shifts and potential pockets of demand for premium or sustainable lines.

Product performance: monitor products by origin, certification status, and packaging choices to identify thriving lines and those needing adjustments in formulation or messaging.

Environmental footprint: track emissions, water use, deforestation risk, and waste associated with suppliers and facilities to identify hotspots and opportunities for collaboration with partners.

Governance and mutuality: verify data quality, supplier compliance with certifications, and share progress with partners to maintain alignment and trust.

Practical steps to deploy GEM Insights

Assemble a cross-functional team spanning governance, sustainability, commerce, and IT. Define four to six core metrics for each stream and appoint data owners with clear responsibilities; establish data quality checks and metadata standards.

Develop role-based views so brand teams see consumer signals, procurement sees supplier risk, and executives see portfolio performance. Set a data-refresh cadence that fits decision cycles–some signals update weekly, others monthly–and automate where possible.

Adopt a growth mindset across teams and encourage innovation by testing hypotheses about product development and packaging. Use insights to determine when to introduce new SKUs, adjust flavors, or switch to more sustainable packaging with mutual alignment from partners.

Create a Unified Digital Integration Strategy Across Mars’ Systems

Introduce a unified digital integration strategy across Mars’ systems by deploying a data fabric that links silos across manufacturing lines, confectionery, and commerce, enabling direct mapping from shop floors to dashboards and establishing rapid reliability for decision-making.

An approach introduced last quarter formalized data ownership, truth, and governance, creating a single source of truth for sales opportunities, manufacturing performance, and emissions reporting.

Expanding access to trusted data through clearly defined avenues drives cross-functional collaboration; they see how product lines, pricing, and demand signals align, unlocking new opportunities.

Kavita and the data-ownership council will drive standards, including common taxonomies for mapping, quality checks, and security controls to sustain long-term reliability.

To ensure sustainable outcomes, align the data layer with manufacturing processes, so that lines of production feed real-time dashboards that support rapid decisions on inventory, capacity, and emissions reduction strategies.

The approach centers on a capability-first mindset: each domain owns its data, yet participates in a global view that aggregates commerce, manufacturing, and logistics lines to reveal truth about performance and risk, while protecting sensitive information.

Key pillars of the strategy

Unified fabric and mapping across silos reduce waste and الموثوقية gaps, enabling teams to see how sales opportunities flow from the plant floor to storefronts and commerce channels.

Governance that is owned by domain teams delivers including clear accountability, with explaining data lineage at every milestone and a focus on sustainable data practices.

Implementation roadmap

Establish a data catalog and API layer in the first quarter, with Kavita’s team certifying that definitions for lines, products, and dashboards are uniform across factories and markets.

Pilot the approach in confectionery and associated lines to validate rapid data delivery, الموثوقية, and emissions reporting, then scale to manufacturing hubs and commerce endpoints, including distributors and retailers.

Track progress against milestones and adapt the strategy to new avenues for data sharing, ensuring the platform remains sustainable and capable of supporting expanding data needs, from mapping to full dashboards that drive sales and operations decisions.

Track Sustainability KPIs Across Mars’ Supply Chain

Track sustainability KPIs across Mars’ supply chain in a single dashboard, appoint kavita as head of governance, and set quarterly targets for sites and wrigleys suppliers.

The program began with mapping the network: identify sites, plants, and distribution hubs, then attach data streams for energy, fuel, water, and waste. Using standardized definitions, collect data from each site and from wrigleys packaging lines to ensure comparability. The dynamic data model helps teams see how fuel use, transport modes, and production sizes drive KPI trends.

Key metrics: carbon intensity per unit, energy use per kilogram, water reuse rate, waste diversion, and supplier ESG scores. The dashboard explains how changes in routes, supplier practices, and packaging configurations affect these metrics. Track the energy mix at each site, the share of renewable power, and fuel efficiency of fleets; establish targets that push performance toward the future. sometimes data gaps appear, so we close them with source checks, particularly for high-risk suppliers, to keep the numbers trustworthy. The sizes of sites and production lines influence baselines and improvements, so tailor targets by size category and region.

To prevent misguided conclusions, implement data quality controls, assign professional data stewards, and have kavita coordinate the office’s efforts. Use standing reviews and additional validation steps to explain anomalies and align on action plans. Governance processes clarify ownership, reduce ambiguity, and support timely decisions.

Adopt avenues for continuous improvement: regular supplier events, cross-site learning, and monthly dashboards at the office; allocate additional resources for ongoing governance and data quality. The approach remains truly integrated, offering ways to reduce the footprint across the supply chain and set standing commitments that drive significant, measurable results. The future-ready plan ensures sites, including those of wrigleys, can adapt to changes and sustain improvements across sizes and modes.