Copy-DATA & ANALYTICS

GROWTH & EMPOWERMENT 2025

DATA & ANALYTICS

DATA & ANALYTICS SWOT

2025 POST AMRIZE SPIN-OFF

• Existence of technically competent data and BI resources in the business and in IT.
• Data & Analytics team led by resources with many years of building materials experience.
• Pockets of business regions able to self-serve for BI and reporting.
• Upgraded platform for SAP Data Warehous (Datasphere) and Reports (SAC) implemented.
• Reliable business performance reports exist for some key functional areas.

• Empowering the business with secure means of self-serve datasets and tools.
• Data Governance to improve data quality, in turn, driving efficiencies in accessing the right data at the right time for improved decision making and business strategy.
• Cloud transformation, leveraging scalable technologies to introduce automation, orchestration and observability to lower the cost of building and maintaining data pipelines.
• Consolidation of business data into cloud data lakes to apply ML and AI.

STRENGTHS

OPPORTUNITIES

• Data and BI teams spread out across business regions and in IT leading to siloed and redundant initiatives across the organization.
• Business organization as a whole lacks a certain level of data fluency.
• Technical resources lack business understanding.
• Data pipelines lack automation, orchestration and observability leading to errors in reports.
• Lack of formal Data Governance leading to unclear data definitions and inconsistent reporting.
• Data warehouses and many systems running on legacy technologies limiting ability to scale new technologies like AI/ML.

WEAKNESSES

• Legacy contracts vendor locking us with third party software providers has exposed some of our data without strong protection (eg: HaulHub) or has limited our ability to access our data without paying more to the vendor (eg: Quadrel.)
• Decentralized business organization can drive further data silos that grow data complexities.
• Multi-Cloud and Multi-ERP ecosystem increases complexities and cost in moving data.
• No clear data retention/deletion standards exists leading us to risks in recovery and exposure of old data beyond legal requirements.

THREATS

DATA & ANALYTICS

3-YEAR ROADMAP - Key Initiatives

2027

2026

2025

Click on each year for more info.

VISION & MISSION for BI & ANALYTICS

BUSINESS VALUE FIRST WITH SPEED

VISIONAt Amrize, data and analytics fuel enterprise intelligence – both human and artificial – creating lasting competitive advantage.

STRATEGIC TARGETMonetizing data and enterprise intelligence to improve recurring EBIT by 5%.

Risk Reduction Compliance Fines Data Breaches Financial Statements

Grow the Business M&A Added Value Revenue Engineering

Cost Optimize Operational Administrative IT/OT/I&O

VISION & MISSION for BI & ANALYTICS

BUSINESS VALUE FIRST WITH SPEED

VALUES-Business value drives priorities -Deliver often and with speed-Continuously learn -Create trusted and reusable assets -Innovate where it counts, reuse where it works. -Prioritize interactions. -No black boxes. -Choose technology that delivers, not dazzles. -Empowerment requires ownership.

MISSION

BUSINESS EMPOWERMENT

MODERN DATA ARCHITECTURE

DATA & ANALYTICS CENTER OF EXCELLENCE

DATA GOVERNANCE & STEWARDSHIP

Embed data governance and stewardship as core business capabilities, balancing accessibility with control to fuel both operational excellence and competitive differentiation.

Design and evolve a modern data architecture founded on security, IT/OT interoperability, and automation, ensuring enterprise data is reliable, scalable and AI-ready.

Empower the business through self-service analytics, data literacy, and flexible access to trusted insights, enabling faster, smarter decisions at every level.

Establish a Data & Analytics Center of Excellence (CoE) to drive agile ways of working, share best practices, and accelerate innovation across all business domains.

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Internal VisionMarket Valuation

50

Billion Company

A 50 BILLION COMPANY

WHAT DOES THAT MEAN?

Nearly double the value of the company in 5 years.

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GROWTH LEVERS BUSINESS STRATEGY

BUSINESS STRATEGY

1. Operational Excellence

3. Inorganic Growth

2. Organic Growth

4. Technology & Innovation

5. Other / Emerging

“Scaling through partnerships or acquisition” Access new markets or capabilities through acquisitions, alliances, or ecosystem collaborations.

Beyond the current map” Unconventional ideas, external signals, or early-stage opportunities that don’t clearly fit — yet.

“Growing with what we have” Deepen customer relationships, expand within existing markets, and elevate commercial performance.

“Doing what we do — better” Drive efficiency, reduce friction, and improve how we deliver through smarter processes and systems.

“Creating new value through innovation” Invent, reimagine, or transform through digital tools, emerging technologies, and bold ideas.

BUSINESS VALUE CHAIN

Strategic Focus

Key Process

Nature

Maximize lifecycle value and strategic advantage through location, scale or TI

Long-term, non-liquid resources

Asset Utilization / Optimization

PLANTS & EQUIPMENT

RESERVES

LAND

ASSETS

I.P.

DATA

Market positioning, cost competitiveness & supply chain efficiency

Commodities with low differentiation

ASPHALT CEMENT

Porter’s 5 Forces

PRIMARY PRODUCTS

AGGREGATES

CEMENT

Operational performance and service levels

Value-added processing and blending

INTEGRATED PRODUCTS / SERVICE

Customer Relationships

READY-MIX

ASPHALT

CONSTRUCTION

Highly service-oriented, customized and bundled offerings

Branding, differentiation, solution engineering

Development, Marketing & Services

DATA WRAPPING

BUILDING ENVELOPE

WASTE MANAGEMENT

PIPE & PRECAST

TECHNICAL SERVICES

LOGISTICS

SOLUTIONS

DATA - INSIGHTS - ACTION

MONETIZATION MECHANICS

Data & Analytics

Insight Make sense of date and highlight where to target action

Data Understand the current situation

Data Management

Action Move from data-driving insight to targeted action to delivery impactful change.

Click on each icon to learn more.

Improve | Wrap | Sell

DATA MONETIZATION

THE CONVERSION OF DATA TO FINANCIAL RETURNS

Analyzed or contextualized data

Action prompts or task automation based on insight

New or better data without insight

Examples / Ideas

IMPROVE: Internal Data that improves processes, performance or decisions.

Analyzed and contextualized performance info.

Automated action that sends recognition for top performers.

Accessible, filterable and sortable raw business performance data.

• Data Lake initiatives to increase access to data and better reports
• Applying ML to data to make predictions
• Small scale applications applications to improve efficiency
• Automated workflows to process documents / transactions

WRAP: External Data that enhances a product or service’s value.

Monthly Nest Home Report analysis. “Nest Leaf” comparisons.

Air filter change prompts. Automated schedule adjustments.

• Eg: ConcreteDirect prompting customers on truck spacing.
• Idea: Aggregate Electronic Tickets
• Idea: Provide Product Quality Data to Purchasing Customers
• Idea: Provide Shipping Turnaround Time Data to Carriers

Data on historical climate energy use.

Click on example to zoom in.

DATA MONETIZATION

THE CONVERSION OF DATA TO FINANCIAL RETURNS

Examples / Ideas

Sell data on items scanned at the check-out. Sell data on traffic patterns.

• ???

SELL: External Data that customers or new stakeholders are willing to pay for.

Data + customer history lead to insights on dietary trends.

Automated suggestions based on trends and charge suppliers on the program.

Click on example to zoom in.

BUILDING DATA CAPABILITIES

A JOURNEY OF INITIATIVES

ADVANCED

FOUNDATIONAL

INTERMEDIATE

CAPABILITIES

MASTER DATA

• Produce reusable data assets.
• Automate data-quality processes.
• Identify data flows & lineage.
• Standard data definitions and metadata on CDEs.

CURATED DATA

• Ontology & taxonomy used to curate.
• Analyzing data & relationships.
• Depict data & relationships meaningfully and accessibly.
• Maintain depictions over time.

INTEGRATED DATA

• Integrate internal & external data.
• Map & harmonize sources.
• Standardize, match & join data.

DATA MANAGEMENT & GOVERNANCE

EXTERNAL ACCESS

• Using APIs to serve data assets to external channels.
• Certifying external users.
• Tracking platform activity.

DATA PLATFORM & ARCHITECTURE

MODERN TECHNOLOGY

• Cloud-native technologies
• DBMS tools on leading edge of data compression, storage, optimization and orchestration.
• Full observability of data pipelines.

INTERNAL ACCESS

• Using APIs to offer data and analytics services internally.
• Making access to raw data easy from any system.

Initiative

MACHINE LEARNING

• Use ML, natural language processing, image processing, etc.
• Practice feature engineering, model training & management
• Monitor AI value, compliance and reliability

STATISTICS

• Tools that use math, probability and statistics techniques
• Developing and hiring people with data science skills
• Establishing data science support units

DATA SCIENCE & ADVANCED ANALYTICS

REPORTING

• Standard approach to data presentation & BI reporting.
• Designating SSOT data sources.
• Business training on data storytelling and evidence-based decision making.

CO-CREATION

• Engaging customers in co-creation of new products, services, and
• Identifying the appropriate customers to co-create with
• Establishing terms and maximizing value

SENSEMAKING

• Capturing market and customer needs.
• Cross-functional customer journey mapping, product design, market research.
• Maintain a data / knowledge base of customer and market intelligence.

EXPERIMENTATION Hypothesis testingA/B Testing

BUSINESS & CUSTOMER UNDERSTANDING

AUTOMATION

• Customer data control policies
• Facilitating data control through automation
• Scale internal and external oversight

EXTERNAL OVERSIGHT

• Terms of use and data processing agreements
• Auditing customer / partner use of data assets

INTERNAL OVERSIGHT

• Establishing data ownership
• Training on laws, regulations, policies on data, compliance, security, privacy
• Establish approval processes and audits on data access

ETHICS & ACCEPTABLE DATA USE

BUILDING DATA CAPABILITIES

A JOURNEY OF INITIATIVES

ADVANCED

FOUNDATIONAL

INTERMEDIATE

CAPABILITIES

CURATED DATA

• Ontology & taxonomy used to curate.
• Analyzing dat & relationships.
• Depict data & relationships meaningfully and accessibly.
• Maintain depictions over time.

MASTER DATA

• Produce reusable data assets.
• Automate data-quality processes.
• Identify data flows & lineage.
• Standard data definitions and metadata on CDEs.

DATA MANAGEMENT & GOVERNANCE

INTEGRATED DATA

• Integrate internal & external data.
• Map & harmonize sources.
• Standardize, match & join data.

DATA PLATFORM & ARCHITECTURE

INTERNAL ACCESS

• Using APIs to offer data and analytics services internally.
• Making access to raw data easy from any system.

MODERN TECHNOLOGY

• Cloud-native technologies
• DBMS tools on leading edge of data compression, storage, optimization and orchestration.
• Full observability of data pipelines.

EXTERNAL ACCESS

• Using APIs to serve data assets to external channels.
• Certifying external users.
• Tracking platform activity.

Initiative

Initiative

STATISTICS

• Tools that use math, probability and statistics techniques
• Developing and hiring people with data science skills
• Establishing data science support units

REPORTING

• Standard approach to data presentation & BI reporting.
• Designating SSOT data sources.
• Business training on data storytelling and evidence-based decision making.

DATA SCIENCE & ADVANCED ANALYTICS

MACHINE LEARNING

• Use ML, natural language processing, i age processing, etc.
• Practice feature engineering, model training & management
• Monitor AI value, compliance and reliability

CO-CREATION

• Engaging customers in co-creation of new products, services, and
• Identifying the appropriate customers to co-create with
• Establishing terms and maximizing value

SENSEMAKING

• Capturing market and customer needs.
• Cross-functional customer journey mapping, product design, market research.
• Maintain a data / knowledge base of customer and market intelligence.

BUSINESS & CUSTOMER UNDERSTANDING

EXPERIMENTATION Hypothesis testingA/B Testing

INTERNAL OVERSIGHT

• Establishing data ownership
• Training on laws, regulations, policies on data, compliance, security, privacy
• Establish approval processes and audits on data access

EXTERNAL OVERSIGHT

• Terms of use and data processing agreements
• Auditing customer / partner use of data assets

ETHICS & ACCEPTABLE DATA USE

AUTOMATION

• Customer data control policies
• Facilitating data control through automation
• Scale internal and external oversight

DATA PIPELINE STRATEGY

SPEED, SCALE, SMARTS

Overarching Goals:

Leverage scalable cloud data management, storage, processing and protection capabilities.

Establish clear lineage to Single / Trusted Source of Truth.

Eliminate redundant data movement.

Strengthen ands simplify Data Governance and Access management.

Learn more

Provide full observability and orchestration of data pipelines.

Enable business self-service.

BUILD SMALL

4 TIERS OF INITIATIVES

Business Access to:

• Development Kits
• Datasets
• Interfaces
• Software
• Resources
• Tools

Scale: Cost, Time, Complexity, People, Business Scope

• Business Governance
• Business Builds
• Solutions Tailored for the Market

• Digital & Business Governance
• Technology Experts Build
• Solutions become Tools for T1 & T2

$0-$10k

$10-$50k

$10-$100k

$100k-$ M

DATA UNIFICATION

DATA & ANALYTICS TEAM

JimCIO

AnsonDir Data & Analytics

MahirEDGE Labs

.. hiring .. Mgr, Data Governance & Enablement

Alberto Sr Engineer, Analytics

AlejandroSr Mgr BI, Data & Analytics

SonuSr Expert, Reporting & Analytics

.. hiring .. Sr Dev, Data & Analytics

PrateekSr Expert, Reporting & Analytics

SaurabhSr Dev, Data & Analytics

Ishan Intern, DevOps

.. contr. .. Engineer, Analytics

.. contr. .. Engineer, Analytics

.. hiring .. Expert, Reporting & Analytics

Shivani Specialist, R2R

Jaideep ABAB Dev, R2R

.. hiring .. Admin., Projects

.. contr .. Engineer, Analytics

.. contr .. Engineer, Analytics

Samuel Intern, DevOps

DATA FABRIC & DATA LAKEHOUSE ARCHITECTURE

EMPOWER BUSINESS USERS WITH QUALITY DATA ASSETS

Empowered Business Users

ALIGNING SKILL LEVELS TO CONNECT TEAMS

COLLABORATION - BUSINESS & TECHNICAL

VALUE STREAMS

ALIGN BUSINESS WITH IT

DATA UNIFICATION

WHY DATA LAKE? ADVANCED ANALYTICS ARCHITECTURE

SEPARATE STORAGE, SCALABLE COMPUTING AND VISUALIZATIONS

ECAN KEY SUCCESSES 2024 EMPOWER P&L OWNERS TO HARNESS THE POWER OF DATA

$2.9 Million Value+($1.2 Million Cash Flow)

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Excellence in Data First

FROM VISION TO REALITY ECAN LIVE DASHBOARDS

Click on each icon to explore more.And clic on each image to zoom in.

ECAN RMX Customer 360 Dashboard

ECAN AGG Logistics Dashboard

Product Owner: Lara Yousif Director Marketing & Commercial Performance, ECAN Ready-Mix

Product Owner: Amir Sisame Senior Credit Manager, ECAN

Product Owner: Mark Lambie Director Logistics & Supply Chain Performance - ECAN Aggregates

ART OF THE POSSIBLE

EXAMPLE: ECAN CEMENT MARKET MAPPING ANALYTICS

Product Owner: Rob MacArthur, District Sales Manager Developed by: Tetiana Savych, Sales Coordinator Alberto Ramirez, BI Specialist Data Sources: SAP Central Intelligence Database Application Summary: A QlikSense analytics application that allows the user to visualize the cement market player’s location (producer and consumer) and their total volume by cement product category. User Benefit: Cuts down the time to visualize market consumption, locations and facilitates what if scenarios to plan out market share acquisition and retention strategies. How did the EDGE program contribute to this? In the Data Lake, combined SAP data with market intelligence data and provided the data visualization tools directly to the business user (Sales Coordinator). BI Specialist and Data Director modeled the data asset and coached the business user in the BI tool.

ART OF THE POSSIBLE

EXAMPLE: ECAN AGG AUTOMATED OPERATING PERFORMANCE INDICATORS

Product Owner: Rob MacArthur, District Sales Manager Developed by: Donald Will, Manufacturing Coordinator Alberto Ramirez, BI Specialist

Data Sources:Loadrite Belt Scales Google Sheets SAP

Application Summary: A QlikSense analytics application that allows the user to visualize an aggregate plant’s production and downtime metrics by plant stage to identify potential problem areas.

User Benefit: Automates the data extract from the Loadrite belt scale system and combines with data from SAP and manual entry sheets to cut the time to produce the reports. Users can view the OPIs on demand.

How did the EDGE program contribute to this? In the Data Lake, combined Loadrite, SAP and Google Sheets and provided the data visualization tools directly to the business user (Manufacturing Coordinator). BI Specialist and the data asset and coached the business user in the BI tool.

ART OF THE POSSIBLE

EXAMPLE: SALES BUDDY AI SALES ANALYST

Product Owner: Lara Yousif, Marketing Director Developed by: ECAN Advanced Analytics Team Varshini Balaji Data Sources: SAP, ConcreteDirect, Salesforce Application Summary: A web application using a Large Language Model . User Benefit: Cuts down the time to visualize market consumption, locations and facilitates what if scenarios to plan out market share acquisition and retention strategies. How did the EDGE program contribute to this? In the Data Lake, combined SAP data with market intelligence data and provided the data visualization tools directly to the business user (Sales Coordinator). BI Specialist and Data Director modeled the data asset and coached the business user in the BI tool.

THE ASK: WHAT ARE POTENTIAL INITIATIVES?

VALUE DRIVEN BUSINESS CASES

Save Time (indirect cost)

Reduce Errors / Rework

Revenue Engineering

Improved / Connected Insights

Surface Hidden Data

Reduce Direct Cost

Capture New Data

Automate Processes

Learn more

DATA MONETIZATION

THE CONVERSION OF DATA TO FINANCIAL RETURNS

IMPROVE: Internal Data that improves processes, performance or decisions.

Automated action that sends recognition for top performers.

Accessible, filterable and sortable raw business performance data.

Analyzed and contextualized performance info.

WRAP: External Data that enhances a product or service’s value.

Monthly Nest Home Report analysis. “Nest Leaf” comparisons.

Air filter change prompts. Automated schedule adjustments.

Data on historical climate energy use.

SELL: External Data that customers or new stakeholders are willing to pay for.

Sell data on items scanned at the check-out. Sell data on traffic patterns.

Automated suggestions based on trends and charge suppliers on the program.

Data + customer history lead to insights on dietary trends.

Click on the image to zoom in.

DATA & ANALYTICS ROLE

BACKGROUND & KEY RESPONSIBILITIES

Anson Lê

B.Com - McGill University-2001Marketing Research, Advertising, Information Systems Lafarge Marketing Analyst (Agg) 2001-2004 ERP Design Project (NA) 2005 Distribution Manager (Agg) 2006-2008 Sales & Service Manager (Agg) 2009 Supply Chain Manager (Agg) 2010-2014 General Manager (FBA) 2014-2016 Commercial Manager (Agg) 2016-2018 Rice Group (Vice President) 2018-2021 Roman Group (General Manager) 2021 Holcim / Lafarge NA TMS Product Owner 2022-2023 ECAN Dir. Data Transformation 2023-2025

DATA & ANALYTICS ROLE

BACKGROUND & KEY RESPONSIBILITIES

Anson Lê

Director, Data & Analytics - North America Lead Data & Analytics Strategy: Develop and execute a value chain-wide strategy for advanced analytics, data governance, and cloud transformation to drive tangible business outcomes. Build and Manage Analytics Teams: Establish a Center of Excellence (CoE) for Data Engineering, Data Science, and Visualization, fostering an agile, cloud-native approach to analytics delivery. Architect Scalable Data Solutions: Design and oversee cloud-based (AWS) data lakes, warehouses, and AI/ML platforms, integrating big data technologies like Spark, Kafka, and Python. Drive Business Value through Analytics: Partner with global business units to identify use cases, deliver predictive models, enable data-driven decision-making, and scale adoption of analytics tools and AI/ML solutions.

Continued Data Governance Expansion and Maturity

• Data Lakes and All Core Transactional Systems
• Expand data governance workflow automation

Scale ML/AI Applications on Data Lake Assets

• Predictive analytics
• Scaled ML based optimization
• GenAI supported analytics

Data Monetization Initiatives Externally (Data Wrapping)

• APIs and Data / Insights / Action platforms for customers & suppliers

Data Literacy Training Program for Business

• Foundation BI & Communicating Visually with Data

DataOps and Center of Excellence

• Introduce Enterprise Knowledge Graph / Ontology

Design & Implement Data Governance

• Building Materials Finance + Master Data
• Policies, roles, systems (SAP, SAC, Datasphere)

Implement Cloud Data Lakes & Modern ERP Data Warehouse

• SAP Datasphere
• AWS Data Lake for BM
• AWS Data Lake for BE

Consolidation & Cleanup of Siloed BI Dashboards, Reports, Data Pipelines and Self-Service Tools

• GAAP Financial Reports
• BE Commercial Performance
• BM Commercial Performance
• ACM Operational KPIs

Design & Implement DataOps and Center of Excellent Practices

• Scaled Agile Framework Product Delivery
• Orchestration, Observability, Automated ELT on Core Data Pipelines
• Data & Analytics Scorecard

Stabilize Data Governance and Expand Scope

• Building Envelope (ERPs) + Amrize HR
• Data Governance workflow automation

Expand and Stabilize Data Lakes and Stabilize ERP Data Warehouse

• AWS Data Lake for HR D
• atasphere to incorporate non-SAP ERPs

Enhancements of Dashboards, Reports, Data Pipelines and Self-Service Tools

• Self-Serve Data Assets
• Self-Serve AI Assisted BI Tools

Data Literacy Training Program for Business

• Foundation & Intermediate Data Management Concepts

Stabilize DataOps and Center of Excellence Practices

• Develop a centralized data science practice