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MARA Grid Intelligence

Industry

Energy / Digital Assets

Design Category

Internship / Product Design

Timeline

Jun 2025 - Aug 2025

Team

Ayesha Khan, Elaine Zhang

Background

MARA Holdings (previously Marathon Digital Holdings) is one of the largest publicly traded bitcoin mining companies in North America. The company focuses on building and operating high-performance data centers powered by renewable and cost-efficient energy sources. Its mission is to scale bitcoin mining and digital asset compute in a way that is profitable, sustainable, and strategically aligned with the evolving energy landscape. At the core of its business, MARA invests heavily in infrastructure, technology, and partnerships that allow it to operate at global scale while reducing environmental impact. The MARA Grid Intelligence Platform was developed as an internal design sprint concept to explore how teams could improve the way they discover, evaluate, and secure new sites for data centers.

MARA Holdings (previously Marathon Digital Holdings) is one of the largest publicly traded bitcoin mining companies in North America. The company focuses on building and operating high-performance data centers powered by renewable and cost-efficient energy sources. Its mission is to scale bitcoin mining and digital asset compute in a way that is profitable, sustainable, and strategically aligned with the evolving energy landscape. At the core of its business, MARA invests heavily in infrastructure, technology, and partnerships that allow it to operate at global scale while reducing environmental impact. The MARA Grid Intelligence Platform was developed as an internal design sprint concept to explore how teams could improve the way they discover, evaluate, and secure new sites for data centers.

MARA's operating digital asset data center in Granbury, TX, purchased from Generate Capital for $178.6 million in December, 2023.

Problem

How might we give MARA’s teams a smarter, faster way to evaluate and secure sites for digital asset compute?

MARA’s site selection process is critical to its growth, but the way decisions are currently made creates systemic obstacles. Data lives in silos across spreadsheets, reports, and verbal updates, which makes it difficult to align across teams. Scoring methods varied, so a site considered “high potential” by one group could be rejected by another. Executives lack clarity, Finance lacks standardization, and Analysts are buried in manual work. The bigger problem is not just inefficiency, it's that MARA risked missing the right sites altogether because the process was not built for speed, consistency, or trust.

Delays = missed opportunities. Slow and inconsistent site/partner evaluation wastes resources, delays operations, and allows competitors to move faster.

Research

OBJECTIVE

To uncover the pain points that we need to address in our solution, in depth user research is required. The purpose of this research is to understand how MARA’s internal teams (Corporate Development, Operations, Executives, Finance) currently identify, evaluate, and prioritize potential partner sites, and to uncover the bottlenecks, gaps, and opportunities for improvement.

Primary Goals

Document current workflows

Document current workflows

Document current workflows

Document current workflows

Identify role-specific pain points

Identify role-specific pain points

Identify role-specific pain points

Identify role-specific pain points

Determine critical data needs for faster site selection

Determine critical data needs for faster site selection

Determine critical data needs for faster site selection

Determine critical data needs for faster site selection

Validate the feasibility of a centralized intelligence dashboard

Validate the feasibility of a centralized intelligence dashboard

Validate the feasibility of a centralized intelligence dashboard

Validate the feasibility of a centralized intelligence dashboard

METHODS

8 Stakeholder Interviews

Conducted 8 semi-structured interviews (2 per role: CD, Operations, Executive, Finance) to understand tools, workflows, and pain points.

8 Stakeholder Interviews

Conducted 8 semi-structured interviews (2 per role: CD, Operations, Executive, Finance) to understand tools, workflows, and pain points.

8 Stakeholder Interviews

Conducted 8 semi-structured interviews (2 per role: CD, Operations, Executive, Finance) to understand tools, workflows, and pain points.

8 Stakeholder Interviews

Conducted 8 semi-structured interviews (2 per role: CD, Operations, Executive, Finance) to understand tools, workflows, and pain points.

2 Process Observations

Observed 2 live site evaluation sessions with the CD and Ops teams to see firsthand how data was gathered and decisions were made.

2 Process Observations

Observed 2 live site evaluation sessions with the CD and Ops teams to see firsthand how data was gathered and decisions were made.

2 Process Observations

Observed 2 live site evaluation sessions with the CD and Ops teams to see firsthand how data was gathered and decisions were made.

2 Process Observations

Observed 2 live site evaluation sessions with the CD and Ops teams to see firsthand how data was gathered and decisions were made.

Journey Mapping Workshop

Held a collaborative workshop with 6 team members to map the “current state” journey for evaluating a new lead from discovery → final decision.

Journey Mapping Workshop

Held a collaborative workshop with 6 team members to map the “current state” journey for evaluating a new lead from discovery → final decision.

Journey Mapping Workshop

Held a collaborative workshop with 6 team members to map the “current state” journey for evaluating a new lead from discovery → final decision.

Journey Mapping Workshop

Held a collaborative workshop with 6 team members to map the “current state” journey for evaluating a new lead from discovery → final decision.

Pain Point Clustering

Synthesized notes into an affinity diagram to identify recurring problems and categorize them by workflow stage.

Pain Point Clustering

Synthesized notes into an affinity diagram to identify recurring problems and categorize them by workflow stage.

Pain Point Clustering

Synthesized notes into an affinity diagram to identify recurring problems and categorize them by workflow stage.

Pain Point Clustering

Synthesized notes into an affinity diagram to identify recurring problems and categorize them by workflow stage.

PERSONAS

We documented blockers that happen at various stages of scouting and evaluating a physical site for acquisition or partnership and identified key characteristics of the team members that would interface with our solution.

Prioritize high-value leads and filter out low-feasibility opportunities early.
Present compelling, data-backed pitches to prospective partners.
Build and maintain a steady pipeline of viable projects.

Key Goals

Leads tracked in scattered spreadsheets; no unified system for ranking.
Time wasted chasing prospects with poor technical or financial fit.
Lacks quick access to climate and energy cost data during early conversations.

Pain Points

Fast-moving, deadline-driven.
Comfortable with digital tools but prefers simple, actionable dashboards.

Behavioral Traits

AI-driven lead scoring to help identify top potential sites
Integrated location search with climate and grid data overlays.
A centralized platform to quickly access climate, energy, infrastructural data.

Opportunities

Corporate Development Manager

Primary User

Workflow Map

Relies on networking, industry events, cold outreach, and scattered public datasets.

Uses multiple tools (Google, LinkedIn, government reports, Excel) to assess viability.

Partner potential judged mostly on anecdotal knowledge, slow manual verification.

Sends raw or incomplete site info to Ops/Finance for further due diligence.

Manual Lead Sourcing

Time intensive

Fragmented Research

Inconsistent data quality

Ad-hoc Evaluations

Difficulty prioritizing best-fit opportunities quickly

Internal Handoff

Prioritize high-value leads and filter out low-feasibility opportunities early.
Present compelling, data-backed pitches to prospective partners.
Build and maintain a steady pipeline of viable projects.

Key Goals

Leads tracked in scattered spreadsheets; no unified system for ranking.
Time wasted chasing prospects with poor technical or financial fit.
Lacks quick access to climate and energy cost data during early conversations.

Pain Points

Fast-moving, deadline-driven.
Comfortable with digital tools but prefers simple, actionable dashboards.

Behavioral Traits

AI-driven lead scoring to help identify top potential sites
Integrated location search with climate and grid data overlays.
A centralized platform to quickly access climate, energy, infrastructural data.

Opportunities

Corporate Development Manager

Primary User

Workflow Map

Relies on networking, industry events, cold outreach, and scattered public datasets.

Uses multiple tools (Google, LinkedIn, government reports, Excel) to assess viability.

Partner potential judged mostly on anecdotal knowledge, slow manual verification.

Sends raw or incomplete site info to Ops/Finance for further due diligence.

Manual Lead Sourcing

Time intensive

Fragmented Research

Inconsistent data quality

Ad-hoc Evaluations

Difficulty prioritizing best-fit opportunities quickly

Internal Handoff

Prioritize high-value leads and filter out low-feasibility opportunities early.
Present compelling, data-backed pitches to prospective partners.
Build and maintain a steady pipeline of viable projects.

Key Goals

Leads tracked in scattered spreadsheets; no unified system for ranking.
Time wasted chasing prospects with poor technical or financial fit.
Lacks quick access to climate and energy cost data during early conversations.

Pain Points

Fast-moving, deadline-driven.
Comfortable with digital tools but prefers simple, actionable dashboards.

Behavioral Traits

AI-driven lead scoring to help identify top potential sites
Integrated location search with climate and grid data overlays.
A centralized platform to quickly access climate, energy, infrastructural data.

Opportunities

Corporate Development Manager

Primary User

Workflow Map

Relies on networking, industry events, cold outreach, and scattered public datasets.

Uses multiple tools (Google, LinkedIn, government reports, Excel) to assess viability.

Partner potential judged mostly on anecdotal knowledge, slow manual verification.

Sends raw or incomplete site info to Ops/Finance for further due diligence.

Manual Lead Sourcing

Time intensive

Fragmented Research

Inconsistent data quality

Ad-hoc Evaluations

Difficulty prioritizing best-fit opportunities quickly

Internal Handoff

Prioritize high-value leads and filter out low-feasibility opportunities early.
Present compelling, data-backed pitches to prospective partners.
Build and maintain a steady pipeline of viable projects.

Key Goals

Leads tracked in scattered spreadsheets; no unified system for ranking.
Time wasted chasing prospects with poor technical or financial fit.
Lacks quick access to climate and energy cost data during early conversations.

Pain Points

Fast-moving, deadline-driven.
Comfortable with digital tools but prefers simple, actionable dashboards.

Behavioral Traits

AI-driven lead scoring to help identify top potential sites
Integrated location search with climate and grid data overlays.
A centralized platform to quickly access climate, energy, infrastructural data.

Opportunities

Corporate Development Manager

Primary User

Workflow Map

Manual Lead Sourcing

Time intensive

Relies on networking, industry events, cold outreach, and scattered public datasets.

Fragmented Research

Inconsistent data quality

Uses multiple tools (Google, LinkedIn, government reports, Excel) to assess viability.

Partner potential judged mostly on anecdotal knowledge, slow manual verification.

Ad-hoc Evaluations

Difficulty prioritizing best-fit opportunities quickly

Sends raw or incomplete site info to Ops/Finance for further due diligence.

Internal Handoff

Access accurate, complete datasets without hunting across multiple tools.
Apply standardized feasibility scoring to improve decision consistency.
Reduce manual work in modeling heating demand, ROI, and CO₂ impact.

Key Goals

Pulls data from 5–6 unconnected systems, leading to delays and errors.
Lack of integrated GIS tools for visualizing energy needs and infrastructure.
Scoring methods vary across analysts, creating inconsistency in evaluations.

Pain Points

Highly detail-oriented, data-driven.
Comfortable with technical tools but frustrated by repetitive manual tasks.

Behavioral Traits

Centralized dashboard with API connections to climate, energy, and cost databases.
Customizable scoring models with role-based access.
Automated report generation for internal review.

Opportunities

Technical Operations Analyst

Primary User

Workflow Map

Often missing technical or energy data.

Contacts local municipalities, utilities, or partners for specs like infrastructure readiness, heating demand, and regulations.

Expensive and time-consuming trips for technical validation.

Sends findings back to Corp Dev/Finance for decision-making.

Receives Basic Site Info from Corp Dev

s

Manual Data Collection

Lack of centralized, reliable data sources

Ad-hoc Evaluations

High costs for site validation

On-site Visits

Long delays between opportunity identification and feasibility confirmation.

Access accurate, complete datasets without hunting across multiple tools.
Apply standardized feasibility scoring to improve decision consistency.
Reduce manual work in modeling heating demand, ROI, and CO₂ impact.

Key Goals

Pulls data from 5–6 unconnected systems, leading to delays and errors.
Lack of integrated GIS tools for visualizing energy needs and infrastructure.
Scoring methods vary across analysts, creating inconsistency in evaluations.

Pain Points

Highly detail-oriented, data-driven.
Comfortable with technical tools but frustrated by repetitive manual tasks.

Behavioral Traits

Centralized dashboard with API connections to climate, energy, and cost databases.
Customizable scoring models with role-based access.
Automated report generation for internal review.

Opportunities

Technical Operations Analyst

Primary User

Workflow Map

Often missing technical or energy data.

Contacts local municipalities, utilities, or partners for specs like infrastructure readiness, heating demand, and regulations.

Expensive and time-consuming trips for technical validation.

Sends findings back to Corp Dev/Finance for decision-making.

Receives Basic Site Info from Corp Dev

s

Manual Data Collection

Lack of centralized, reliable data sources

Ad-hoc Evaluations

High costs for site validation

On-site Visits

Long delays between opportunity identification and feasibility confirmation.

Access accurate, complete datasets without hunting across multiple tools.
Apply standardized feasibility scoring to improve decision consistency.
Reduce manual work in modeling heating demand, ROI, and CO₂ impact.

Key Goals

Pulls data from 5–6 unconnected systems, leading to delays and errors.
Lack of integrated GIS tools for visualizing energy needs and infrastructure.
Scoring methods vary across analysts, creating inconsistency in evaluations.

Pain Points

Highly detail-oriented, data-driven.
Comfortable with technical tools but frustrated by repetitive manual tasks.

Behavioral Traits

Centralized dashboard with API connections to climate, energy, and cost databases.
Customizable scoring models with role-based access.
Automated report generation for internal review.

Opportunities

Technical Operations Analyst

Primary User

Workflow Map

Often missing technical or energy data.

Contacts local municipalities, utilities, or partners for specs like infrastructure readiness, heating demand, and regulations.

Expensive and time-consuming trips for technical validation.

Sends findings back to Corp Dev/Finance for decision-making.

Receives Basic Site Info from Corp Dev

s

Manual Data Collection

Lack of centralized, reliable data sources

Ad-hoc Evaluations

High costs for site validation

On-site Visits

Long delays between opportunity identification and feasibility confirmation.

Access accurate, complete datasets without hunting across multiple tools.
Apply standardized feasibility scoring to improve decision consistency.
Reduce manual work in modeling heating demand, ROI, and CO₂ impact.

Key Goals

Pulls data from 5–6 unconnected systems, leading to delays and errors.
Lack of integrated GIS tools for visualizing energy needs and infrastructure.
Scoring methods vary across analysts, creating inconsistency in evaluations.

Pain Points

Highly detail-oriented, data-driven.
Comfortable with technical tools but frustrated by repetitive manual tasks.

Behavioral Traits

Centralized dashboard with API connections to climate, energy, and cost databases.
Customizable scoring models with role-based access.
Automated report generation for internal review.

Opportunities

Technical Operations Analyst

Primary User

Workflow Map

Receives Basic Site Info from Corp Dev

s

Often missing technical or energy data.

Manual Data Collection

Lack of centralized, reliable data sources

Contacts local municipalities, utilities, or partners for specs like infrastructure readiness, heating demand, and regulations.

Expensive and time-consuming trips for technical validation.

Ad-hoc Evaluations

High costs for site validation

Sends findings back to Corp Dev/Finance for decision-making.

On-site Visits

Long delays between opportunity identification and feasibility confirmation.

Receive complete, standardized financial data for all sites under review.
Accurately calculate payback periods and cost savings for potential partners.
Deliver consistent investment cases to funding committees.

Key Goals

Site evaluation data often arrives incomplete or in inconsistent formats.
Manual re-formatting for finance reviews wastes hours per project.
Lack of integrated tool for calculating and visualizing payback and ROI.

Pain Points

Risk-conscious, detail-focused.
Relies heavily on consistent metrics and structured reporting.

Behavioral Traits

Auto-generated financial summaries based on standardized formulas.
Pre-formatted report templates for investment committees.
Integration with MARA’s existing finance software.

Opportunities

Financial Analyst

Secondary User

Workflow Map

Often without early access to technical/market data.

Builds ROI models using static spreadsheets and outdated benchmarks.

Hard to model multiple financial outcomes quickly.

Slows down the deal-closing process.

Receives late-stage proposals

Reactive rather than proactive in evaluations.

Manual cost modeling

Limited integration between market, technical, and financial data.

Limited Scenario Testing

Delayed Budget Approval

Missed opportunities due to slow budget sign-off.

Receive complete, standardized financial data for all sites under review.
Accurately calculate payback periods and cost savings for potential partners.
Deliver consistent investment cases to funding committees.

Key Goals

Site evaluation data often arrives incomplete or in inconsistent formats.
Manual re-formatting for finance reviews wastes hours per project.
Lack of integrated tool for calculating and visualizing payback and ROI.

Pain Points

Risk-conscious, detail-focused.
Relies heavily on consistent metrics and structured reporting.

Behavioral Traits

Auto-generated financial summaries based on standardized formulas.
Pre-formatted report templates for investment committees.
Integration with MARA’s existing finance software.

Opportunities

Financial Analyst

Secondary User

Workflow Map

Often without early access to technical/market data.

Builds ROI models using static spreadsheets and outdated benchmarks.

Hard to model multiple financial outcomes quickly.

Slows down the deal-closing process.

Receives late-stage proposals

Reactive rather than proactive in evaluations.

Manual cost modeling

Limited integration between market, technical, and financial data.

Limited Scenario Testing

Delayed Budget Approval

Missed opportunities due to slow budget sign-off.

Receive complete, standardized financial data for all sites under review.
Accurately calculate payback periods and cost savings for potential partners.
Deliver consistent investment cases to funding committees.

Key Goals

Site evaluation data often arrives incomplete or in inconsistent formats.
Manual re-formatting for finance reviews wastes hours per project.
Lack of integrated tool for calculating and visualizing payback and ROI.

Pain Points

Risk-conscious, detail-focused.
Relies heavily on consistent metrics and structured reporting.

Behavioral Traits

Auto-generated financial summaries based on standardized formulas.
Pre-formatted report templates for investment committees.
Integration with MARA’s existing finance software.

Opportunities

Financial Analyst

Secondary User

Workflow Map

Often without early access to technical/market data.

Builds ROI models using static spreadsheets and outdated benchmarks.

Hard to model multiple financial outcomes quickly.

Slows down the deal-closing process.

Receives late-stage proposals

Reactive rather than proactive in evaluations.

Manual cost modeling

Limited integration between market, technical, and financial data.

Limited Scenario Testing

Delayed Budget Approval

Missed opportunities due to slow budget sign-off.

Receive complete, standardized financial data for all sites under review.
Accurately calculate payback periods and cost savings for potential partners.
Deliver consistent investment cases to funding committees.

Key Goals

Site evaluation data often arrives incomplete or in inconsistent formats.
Manual re-formatting for finance reviews wastes hours per project.
Lack of integrated tool for calculating and visualizing payback and ROI.

Pain Points

Risk-conscious, detail-focused.
Relies heavily on consistent metrics and structured reporting.

Behavioral Traits

Auto-generated financial summaries based on standardized formulas.
Pre-formatted report templates for investment committees.
Integration with MARA’s existing finance software.

Opportunities

Financial Analyst

Secondary User

Workflow Map

Receives late-stage proposals

Reactive rather than proactive in evaluations.

Often without early access to technical/market data.

Manual cost modeling

Limited integration between market, technical, and financial data.

Builds ROI models using static spreadsheets and outdated benchmarks.

Hard to model multiple financial outcomes quickly.

Limited Scenario Testing

Slows down the deal-closing process.

Delayed Budget Approval

Missed opportunities due to slow budget sign-off.

Quickly understand the viability of opportunities without wading through technical details.
Compare ROI, strategic alignment, and impact potential across multiple sites.
Make confident, timely decisions to maintain momentum in the pipeline.

Key Goals

Overwhelmed by detailed technical reports with inconsistent summaries.
Relies on verbal or ad hoc updates from teams; no real-time dashboard access.
Difficulty aligning cross-functional teams on priority opportunities.

Pain Points

Vision-oriented, time-constrained.
Prefers visual summaries over spreadsheets.

Behavioral Traits

Executive dashboard view with “Top 5 Opportunities” snapshot.
Data visualizations for ROI, cost-benefit, and environmental impact.
Exportable summaries for board presentations.

Opportunities

SVP of Corporate Development

Secondary User

Workflow Map

Data comes from multiple departments, often in different formats.

Compares incomplete data to business goals and resource constraints.

Limited visibility into real-time market trends or competitive landscape.

Weeks/months lost before greenlighting projects

Receives Fragmented Reports

No single source of truth for decision-making.

Manual Synthesis

Reliance on partial or outdated data.

Gut-based Decisions

Delayed Execution

Slow approval process reduces competitive advantage.

Quickly understand the viability of opportunities without wading through technical details.
Compare ROI, strategic alignment, and impact potential across multiple sites.
Make confident, timely decisions to maintain momentum in the pipeline.

Key Goals

Overwhelmed by detailed technical reports with inconsistent summaries.
Relies on verbal or ad hoc updates from teams; no real-time dashboard access.
Difficulty aligning cross-functional teams on priority opportunities.

Pain Points

Vision-oriented, time-constrained.
Prefers visual summaries over spreadsheets.

Behavioral Traits

Executive dashboard view with “Top 5 Opportunities” snapshot.
Data visualizations for ROI, cost-benefit, and environmental impact.
Exportable summaries for board presentations.

Opportunities

SVP of Corporate Development

Secondary User

Workflow Map

Data comes from multiple departments, often in different formats.

Compares incomplete data to business goals and resource constraints.

Limited visibility into real-time market trends or competitive landscape.

Weeks/months lost before greenlighting projects

Receives Fragmented Reports

No single source of truth for decision-making.

Manual Synthesis

Reliance on partial or outdated data.

Gut-based Decisions

Delayed Execution

Slow approval process reduces competitive advantage.

Quickly understand the viability of opportunities without wading through technical details.
Compare ROI, strategic alignment, and impact potential across multiple sites.
Make confident, timely decisions to maintain momentum in the pipeline.

Key Goals

Overwhelmed by detailed technical reports with inconsistent summaries.
Relies on verbal or ad hoc updates from teams; no real-time dashboard access.
Difficulty aligning cross-functional teams on priority opportunities.

Pain Points

Vision-oriented, time-constrained.
Prefers visual summaries over spreadsheets.

Behavioral Traits

Executive dashboard view with “Top 5 Opportunities” snapshot.
Data visualizations for ROI, cost-benefit, and environmental impact.
Exportable summaries for board presentations.

Opportunities

SVP of Corporate Development

Secondary User

Workflow Map

Data comes from multiple departments, often in different formats.

Compares incomplete data to business goals and resource constraints.

Limited visibility into real-time market trends or competitive landscape.

Weeks/months lost before greenlighting projects

Receives Fragmented Reports

No single source of truth for decision-making.

Manual Synthesis

Reliance on partial or outdated data.

Gut-based Decisions

Delayed Execution

Slow approval process reduces competitive advantage.

Quickly understand the viability of opportunities without wading through technical details.
Compare ROI, strategic alignment, and impact potential across multiple sites.
Make confident, timely decisions to maintain momentum in the pipeline.

Key Goals

Overwhelmed by detailed technical reports with inconsistent summaries.
Relies on verbal or ad hoc updates from teams; no real-time dashboard access.
Difficulty aligning cross-functional teams on priority opportunities.

Pain Points

Vision-oriented, time-constrained.
Prefers visual summaries over spreadsheets.

Behavioral Traits

Executive dashboard view with “Top 5 Opportunities” snapshot.
Data visualizations for ROI, cost-benefit, and environmental impact.
Exportable summaries for board presentations.

Opportunities

SVP of Corporate Development

Secondary User

Workflow Map

Receives Fragmented Reports

No single source of truth for decision-making.

Data comes from multiple departments, often in different formats.

Manual Synthesis

Reliance on partial or outdated data.

Compares incomplete data to business goals and resource constraints.

Limited visibility into real-time market trends or competitive landscape.

Gut-based Decisions

Weeks/months lost before greenlighting projects

Delayed Execution

Slow approval process reduces competitive advantage.

KEY INSIGHTS

Below is a side-by-side visualization showing how the user pain points we identified in research directly translate into functional opportunities for development.

User Pain Points

Data Fragmentation

Site information lives in scattered spreadsheets, reports, and tools, forcing teams to manually piece together data and slowing down analysis.

Data Fragmentation

Site information lives in scattered spreadsheets, reports, and tools, forcing teams to manually piece together data and slowing down analysis.

Data Fragmentation

Site information lives in scattered spreadsheets, reports, and tools, forcing teams to manually piece together data and slowing down analysis.

Data Fragmentation

Site information lives in scattered spreadsheets, reports, and tools, forcing teams to manually piece together data and slowing down analysis.

Inconsistency

Different teams apply varying scoring methods and ROI models, leading to mismatched evaluations and eroding confidence in site decisions.

Inconsistency

Different teams apply varying scoring methods and ROI models, leading to mismatched evaluations and eroding confidence in site decisions.

Inconsistency

Different teams apply varying scoring methods and ROI models, leading to mismatched evaluations and eroding confidence in site decisions.

Inconsistency

Different teams apply varying scoring methods and ROI models, leading to mismatched evaluations and eroding confidence in site decisions.

Delays

Manual validation and rework extend decision cycles, slowing down approvals and reducing MARA’s ability to act quickly on competitive opportunities.

Delays

Manual validation and rework extend decision cycles, slowing down approvals and reducing MARA’s ability to act quickly on competitive opportunities.

Delays

Manual validation and rework extend decision cycles, slowing down approvals and reducing MARA’s ability to act quickly on competitive opportunities.

Delays

Manual validation and rework extend decision cycles, slowing down approvals and reducing MARA’s ability to act quickly on competitive opportunities.

Lack of Transparency

Executives and Finance see incomplete or outdated data, while Corp Dev and Ops drown in raw details without a clear, shared view of site potential.

Lack of Transparency

Executives and Finance see incomplete or outdated data, while Corp Dev and Ops drown in raw details without a clear, shared view of site potential.

Lack of Transparency

Executives and Finance see incomplete or outdated data, while Corp Dev and Ops drown in raw details without a clear, shared view of site potential.

Lack of Transparency

Executives and Finance see incomplete or outdated data, while Corp Dev and Ops drown in raw details without a clear, shared view of site potential.

Feature Opportunitites

Visual Site Comparisons

Data streamlined into charts, maps, and rankings make site comparisons easier, helping executives quickly identify top opportunities and align decisions.

Visual Site Comparisons

Data streamlined into charts, maps, and rankings make site comparisons easier, helping executives quickly identify top opportunities and align decisions.

Visual Site Comparisons

Data streamlined into charts, maps, and rankings make site comparisons easier, helping executives quickly identify top opportunities and align decisions.

Visual Site Comparisons

Data streamlined into charts, maps, and rankings make site comparisons easier, helping executives quickly identify top opportunities and align decisions.

Standardized Scoring

A unified scoring model ensures consistent evaluations, builds trust across teams, and makes ROI and feasibility assessments clear and comparable.

Standardized Scoring

A unified scoring model ensures consistent evaluations, builds trust across teams, and makes ROI and feasibility assessments clear and comparable.

Standardized Scoring

A unified scoring model ensures consistent evaluations, builds trust across teams, and makes ROI and feasibility assessments clear and comparable.

Standardized Scoring

A unified scoring model ensures consistent evaluations, builds trust across teams, and makes ROI and feasibility assessments clear and comparable.

Faster Feedback Loops

Consolidating data into one platform enables real-time insights, shortens feedback loops, reduces rework, and helps MARA accelerate approvals and act quickly on opportunities.

Faster Feedback Loops

Consolidating data into one platform enables real-time insights, shortens feedback loops, reduces rework, and helps MARA accelerate approvals and act quickly on opportunities.

Faster Feedback Loops

Consolidating data into one platform enables real-time insights, shortens feedback loops, reduces rework, and helps MARA accelerate approvals and act quickly on opportunities.

Faster Feedback Loops

Consolidating data into one platform enables real-time insights, shortens feedback loops, reduces rework, and helps MARA accelerate approvals and act quickly on opportunities.

Data-backed Evaluations

Automated data collection reduces manual work, delivers reliable insights, and ensures Executives, Finance, and Analysts have transparent, up-to-date information for confident decisions.

Data-backed Evaluations

Automated data collection reduces manual work, delivers reliable insights, and ensures Executives, Finance, and Analysts have transparent, up-to-date information for confident decisions.

Data-backed Evaluations

Automated data collection reduces manual work, delivers reliable insights, and ensures Executives, Finance, and Analysts have transparent, up-to-date information for confident decisions.

Data-backed Evaluations

Automated data collection reduces manual work, delivers reliable insights, and ensures Executives, Finance, and Analysts have transparent, up-to-date information for confident decisions.

Ideation

DATA & TECHNICAL FEASIBILITY

How might we explore features like centralized data, automated scoring, GIS integration, and AI insights to solve these challenges?

Before defining features, we evaluated the data landscape and technical feasibility to ensure automated processes and AI integration could be applied responsibly and deliver real, actionable value. Click on each framework below to view in detail.

Site Scoring Framework

Data Ecosystem

Data Ecosystem

Data Ecosystem

Data Ecosystem

AI Powered

Data Pipeline

MOOD BOARD

Having established our data landscape, we then researched how to establish a unique UI direction that balances MARA’s existing brand guidelines with our vision for the platform’s look and feel.

Keywords: Data-centric, clean, tech-forward (AI-driven feel), high-contrast readability.

Geospatial site presentation

Buttons, Cards, Lists, Charts, means of Data Visualization

FEATURE DEVELOPMENT

Using the personas, we mapped out the platform’s core functions through a site map and low-fidelity sketches as a way to structure the user flow, aligning the data-driven requirements with an intuitive UI structure. This step helped us translate complex data inputs into clear workflows.

Site map generated via Relume

Sketches figuring out content placement on the UI

DESIGN TENANTS

Transparency

Every score and AI output must be traceable to trusted, verifiable data sources.

Transparency

Every score and AI output must be traceable to trusted, verifiable data sources.

Transparency

Every score and AI output must be traceable to trusted, verifiable data sources.

Transparency

Every score and AI output must be traceable to trusted, verifiable data sources.

Clarity

Tailor insights to each role, balancing executive snapshots with analyst-level detail.

Clarity

Tailor insights to each role, balancing executive snapshots with analyst-level detail.

Clarity

Tailor insights to each role, balancing executive snapshots with analyst-level detail.

Clarity

Tailor insights to each role, balancing executive snapshots with analyst-level detail.

Speed

Streamline workflows to reduce manual effort and accelerate site evaluation and decision-making.

Speed

Streamline workflows to reduce manual effort and accelerate site evaluation and decision-making.

Speed

Streamline workflows to reduce manual effort and accelerate site evaluation and decision-making.

Speed

Streamline workflows to reduce manual effort and accelerate site evaluation and decision-making.

Trust

Design for confidence in AI-driven recommendations through explainability, consistency, and accountability.

Trust

Design for confidence in AI-driven recommendations through explainability, consistency, and accountability.

Trust

Design for confidence in AI-driven recommendations through explainability, consistency, and accountability.

Trust

Design for confidence in AI-driven recommendations through explainability, consistency, and accountability.

Prototyping

WIREFRAMING

Key Points

Mapped core 3 main panels (Home, Geospatial Presentation, Data Transparency) and main user flows (login, to dashboard overview, to detailed site and region-level views).
Prioritized clarity over polish to focus on information hierarchy and layout decisions.