Treasury Operations: Cash Position Dashboards on D23.io

Table of Contents

1. Why Real-Time Cash Position Dashboards Matter
2. Understanding D23.io and Superset for Treasury
3. Core Treasury KPIs and Metrics
4. Building Your Cash Position Dashboard
5. FX Exposure and Counterparty Risk Monitoring
6. Integration with Banking Systems
7. Real-World Treasury Dashboard Examples
8. Security and Compliance in Treasury Dashboards
9. Implementation Timeline and Costs
10. Next Steps: Getting Started with D23.io

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Why Real-Time Cash Position Dashboards Matter

Treasury teams at Australian corporate and banking institutions face a critical operational challenge: visibility. Without real-time insight into cash positions across multiple bank accounts, currencies, and counterparties, you’re flying blind. You can’t make informed investment decisions, you can’t manage liquidity risk effectively, and you can’t respond quickly to market opportunities or sudden funding needs.

The cost of this blindness is material. A treasury team at a mid-market company might hold excess cash in low-yield accounts because they don’t trust their visibility into actual available liquidity. Another might miss a counterparty risk event because their exposure tracking is buried in spreadsheets updated once weekly. A third might overpay on borrowing costs because they can’t see real-time cash flows across their entire corporate group.

This is where cash positioning software becomes essential. Real-time dashboards transform treasury from a reactive, compliance-focused function into a proactive, value-generating operation. When your CFO can see cash positions across all accounts in a single view, updated throughout the day, treasury decisions shift from guesswork to data-driven strategy.

D23.io’s managed Apache Superset stack solves this at scale. Instead of building custom treasury systems from scratch—a 6–12 month engineering project costing $200K–$500K—you deploy pre-configured dashboards in weeks. Your treasury team gets real-time visibility. Your finance team gets audit-ready data governance. Your board gets the transparency they demand.

For Australian corporate treasurers managing AUD/USD/EUR exposure, tracking bank counterparty limits, and reporting to regulators, this matters. A lot.

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Understanding D23.io and Superset for Treasury

D23.io is a managed analytics platform built on Apache Superset, an open-source business intelligence tool trusted by enterprises globally. Think of it as Tableau or Looker, but deployed and managed for you—no DevOps team required, no infrastructure headaches, and significantly lower cost.

Apache Superset is purpose-built for real-time operational dashboards. Unlike traditional data warehouses that batch-load data nightly, Superset connects directly to your operational databases and data lakes. When your bank system updates a cash balance, Superset reflects it immediately. When a new FX trade settles, it appears on your dashboard within minutes.

For treasury operations specifically, this matters because cash positioning requires intraday visibility. A CFO managing a $500M cash balance needs to know not just the opening position at 9 AM, but the position at 11 AM, 2 PM, and 4 PM. Market windows close. Investment opportunities appear and vanish. Counterparty risk can escalate in hours. Your dashboard must keep pace.

D23.io handles the operational complexity: server provisioning, security hardening, database optimisation, and user access management. Your treasury team focuses on what they do best—managing cash, hedging risk, optimising returns—not on maintaining infrastructure.

The platform integrates with your existing tech stack. Whether you’re running SAP, Oracle, Microsoft Dynamics, or bespoke banking systems, D23.io connects via standard database connectors. Data flows from your core banking system, ERP, or data warehouse into Superset, where your treasury team builds dashboards in minutes using a visual query builder. No SQL required. No waiting for IT.

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Core Treasury KPIs and Metrics

Before you build a dashboard, you need clarity on what you’re measuring. Treasury dashboards typically track four categories of metrics: liquidity, risk, performance, and operational health.

Liquidity Metrics

Available Cash and Liquidity Position is the foundation. This shows your total cash across all accounts, broken down by currency, account type (operating, investment, restricted), and maturity. For a corporate group with subsidiaries across multiple countries, this single metric answers: “How much cash can we deploy right now?”

Cash Flow Forecast extends this. By integrating payroll, accounts payable, and revenue systems, your dashboard projects cash position 7, 30, and 90 days forward. This prevents surprise shortfalls and identifies periods of excess liquidity where you can invest or pay down debt.

Liquidity Ratio (current assets / current liabilities) and Operating Cash Cycle show health. When these deteriorate, it’s a warning signal. A real-time dashboard surfaces this immediately, not in monthly financial statements.

Risk Metrics

Counterparty Exposure is critical. You set limits on how much credit you extend to each bank, broker, and financial institution. When exposure approaches or exceeds limits, your dashboard flags it. This is especially important for Australian corporates managing exposure to major Australian banks (Commonwealth, Westpac, NAB, ANZ) and offshore counterparties.

FX Exposure by currency pair shows your net position in each currency. A company with USD receivables and EUR payables has natural hedging, but only if you can see it. Dashboards make this visible: “We’re long USD by $50M, short EUR by €30M. Do we hedge?”

Concentration Risk identifies single points of failure. If 60% of your cash sits with one bank, that’s concentration risk. If 80% of your revenue comes from one customer, that’s revenue concentration feeding into treasury risk. Dashboards highlight both.

Performance Metrics

Return on Cash measures how efficiently you’re deploying idle liquidity. If you hold $100M in cash earning 0.5% when you could invest in short-term securities earning 4%, that’s $3.5M annually lost. Dashboards show this gap and track progress as you optimise.

Cost of Debt tracks your effective borrowing rate across all facilities. When rates change or you refinance, this metric updates automatically.

FX Impact quantifies gains and losses from currency movements. A dashboard separates operational FX (from business transactions) from financial FX (from hedging or speculative positions), giving you clear attribution.

Operational Health

Bank Reconciliation Status shows whether your records match your bank statements. In real-time dashboards, this updates daily. When discrepancies appear, they’re flagged immediately—critical for audit readiness and fraud detection.

Payment Processing Volume and Settlement Success Rate track whether payments are flowing smoothly. A sudden spike in failed settlements indicates a problem requiring immediate attention.

For Australian corporate and bank treasury teams, treasury KPIs and dashboards following the 3-10-30 approach (3 strategic metrics, 10 tactical metrics, 30 operational metrics) provide a proven framework. Your D23.io dashboard should reflect this hierarchy: executives see the 3 strategic metrics on the home page, treasury managers drill into the 10 tactical metrics, and operations teams access all 30 for day-to-day work.

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Building Your Cash Position Dashboard

Building a cash position dashboard on D23.io follows a structured process. PADISO has deployed this at scale—from seed-stage startups to $1B+ revenue companies—and the pattern is consistent.

Step 1: Data Architecture and Connectivity

Your first task is connecting D23.io to your data sources. For treasury, this typically means:

• Core banking system: Direct connection to your bank’s API (Commonwealth Bank, Westpac, NAB, or international equivalents) or your treasury management system (TMS) if you use one
• ERP system: Connection to your SAP, Oracle, or Microsoft Dynamics instance for accruals, payables, and receivables
• Data warehouse: If you have a centralised data lake (Snowflake, BigQuery, Redshift), D23.io connects there instead, pulling pre-transformed data
• Spreadsheets and manual feeds: For data not yet in systems (counterparty limits, approved investments, hedging policies), you can upload CSVs or connect to shared drives

D23.io supports all major database types: PostgreSQL, MySQL, Oracle, SQL Server, and cloud data warehouses. The connection is encrypted end-to-end. Your banking credentials are stored securely, never exposed to Superset itself.

For Australian corporates, the most common setup is: bank API → data warehouse (daily batch plus intraday API calls) → D23.io Superset. This ensures you have a single source of truth, audit-ready data lineage, and compliance with your bank’s API terms.

Step 2: Data Modelling and the Semantic Layer

Once data flows into D23.io, you model it. This is where PADISO’s semantic layer approach makes a difference. Instead of having each analyst write their own SQL queries, you define the data model once—tables, relationships, calculated fields—and all users build from it.

For treasury, your semantic layer includes:

• Accounts table: Account number, bank, currency, account type, counterparty, limit, balance
• Transactions table: Date, amount, currency, description, settlement status, counterparty
• FX rates table: Daily or intraday rates for all currency pairs you trade
• Limits table: Counterparty credit limits, FX exposure limits, concentration limits

Calculated fields are pre-built:

• Available liquidity = Total cash – Restricted cash – Required reserves
• Counterparty exposure = Sum of all transactions with counterparty
• FX net position = Sum of all transactions by currency
• Utilisation ratio = Counterparty exposure / Counterparty limit

Your treasury team never writes SQL. They click “Available liquidity” and see the number. They filter by date, currency, or counterparty. The semantic layer ensures consistency: everyone uses the same definition of “available liquidity,” preventing disputes and errors.

Step 3: Dashboard Design and Visualisation

With data modelled, you build the dashboard. Superset provides 40+ visualisation types: tables, charts, gauges, maps, and custom plugins. For treasury, you typically use:

• KPI cards for headline numbers: Total cash, FX exposure, counterparty utilisation
• Time-series charts for cash flow trends and forecasts
• Pie charts for cash distribution by currency or counterparty
• Heat maps for counterparty risk (colour-coded by utilisation)
• Tables for detailed transaction lists and reconciliation status

A typical cash position dashboard has three layers:

Executive view (one page): Total available cash (AUD, USD, EUR), largest counterparty exposures, FX net position, forecast cash position 30 days forward. Updated hourly. Your CFO checks it over morning coffee.

Treasury manager view (three pages): Detailed breakdown by account, currency, and counterparty. Counterparty exposure vs. limit. FX exposure by pair. Payment processing status. Bank reconciliation status. Updated every 30 minutes.

Operations view (five+ pages): Transaction-level detail. Settlement status. Failed payments. Reconciliation exceptions. Updated in real-time as transactions settle.

D23.io makes this easy. Superset’s visual query builder requires no coding. You drag fields, set filters, and preview results instantly. Dashboards are built in hours, not weeks.

Step 4: Interactivity and Drill-Down

Static dashboards are useful. Interactive dashboards are transformative. In Superset, you add filters that let users explore:

• Date range: “Show me cash position for the last 30 days”
• Currency: “Filter to USD only”
• Counterparty: “Show exposure to Commonwealth Bank”
• Account type: “Show only operating accounts”
• Status: “Show only unsettled transactions”

You also add drill-down: click a counterparty on the pie chart, and the dashboard updates to show all transactions with that counterparty. Click a date on the time-series, and see the detailed position for that day. This interactivity reduces the number of dashboards you need and lets users answer their own questions without IT support.

Step 5: Alerts and Automation

The final step is automation. You set rules:

• If counterparty utilisation > 90%, send email alert to treasurer
• If FX exposure exceeds policy limit, flag in dashboard and notify CFO
• If available cash < minimum required reserve, alert finance manager
• If bank reconciliation fails for >1 day, escalate to operations

Superset integrates with Slack, email, and webhooks. When a rule triggers, your team is notified immediately. This turns your dashboard from a reporting tool into an operational control system.

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FX Exposure and Counterparty Risk Monitoring

For Australian corporates and banks, FX exposure and counterparty risk are the two highest-value use cases for real-time dashboards.

FX Exposure Tracking

Australian companies with international operations face continuous FX risk. A company exporting to the US earns USD but has AUD costs. If AUD weakens, revenues in AUD terms fall. If AUD strengthens, the company’s competitiveness declines (USD prices must rise, losing market share).

Traditional treasury systems track FX exposure in spreadsheets updated weekly. By the time you see the exposure, the market has moved 2–3%. A real-time dashboard on D23.io changes this.

Your dashboard shows:

• Net position by currency pair: AUD/USD, AUD/EUR, AUD/GBP, etc.
• Operational FX: Exposure from actual business transactions (receivables, payables, intercompany loans)
• Financial FX: Exposure from hedging instruments (forwards, swaps, options)
• Net FX: Operational FX + Financial FX = your true exposure

You also layer in FX rates. When AUD/USD moves from 0.65 to 0.67, your dashboard recalculates the value impact automatically. A $100M USD position is worth AUD $65M at 0.65 but AUD $67M at 0.67—a $2M gain. Your dashboard shows this in real-time.

For banks managing trading desks, dynamic cash positioning with AI-powered treasury systems enables rapid position management. D23.io integrates with your trading system, showing updated positions every minute. A trader can see their P&L in real-time and adjust hedges immediately.

Australian banks and corporates increasingly use AI to optimise FX strategies. PADISO’s agentic AI and Apache Superset integration allows treasury teams to ask natural language questions: “What’s our net USD exposure after hedges?” Claude queries the dashboard and returns the answer in seconds. No SQL knowledge required.

Counterparty Risk Management

Counterparty risk is the risk that a bank or financial institution you do business with fails or defaults. For Australian corporates, this is acute: you hold deposits with major banks (Commonwealth, Westpac, NAB, ANZ) and trade with global counterparties (JP Morgan, Deutsche Bank, Citi). If a counterparty fails, you lose access to your cash or face losses on derivative positions.

Regulation requires you to set limits and monitor utilisation. Your dashboard must show:

• Credit limit by counterparty (set by your board or risk committee)
• Current exposure (sum of all transactions, loans, and derivative positions)
• Utilisation ratio (exposure / limit)
• Headroom (limit – exposure)

When utilisation approaches 100%, your dashboard flags it. Your treasury team must then decide: reduce exposure, increase the limit (if your credit line allows), or move transactions to a different counterparty.

D23.io dashboards make this visible instantly. You see all counterparties colour-coded by risk (green < 50%, yellow 50–80%, red > 80%). When a new transaction would push a counterparty into red, your system alerts you before settlement. This prevents accidental limit breaches and keeps you compliant with your risk policy.

For Australian banks, cash positioning and forecasting is further complicated by regulatory requirements (APRA, RBA) around liquidity coverage ratios and net stable funding ratios. D23.io dashboards integrate these calculations, ensuring you’re always compliant and can demonstrate it to regulators in real-time.

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Integration with Banking Systems

The real value of a cash position dashboard emerges when it’s integrated with your banking systems. Manual data entry is error-prone and slow. Automated integration is fast, accurate, and audit-ready.

Bank API Integration

Most major banks (Commonwealth, Westpac, NAB, ANZ, and international banks) offer APIs for treasury data. These APIs return real-time or near-real-time account balances, transaction feeds, and wire status.

D23.io can connect directly to these APIs via a data pipeline tool (like Airbyte, Stitch, or custom scripts). Every 15 minutes, your dashboard pulls the latest data from your bank. Your treasury team sees balances updated throughout the day.

For Australian corporates, this is transformative. Instead of calling the bank at 4 PM to ask “What’s our closing balance?”, your team checks the dashboard at any time. They see intraday activity, allowing better cash positioning decisions.

ERP Integration

Your ERP (SAP, Oracle, Dynamics) holds accruals, payables, and receivables. Integrating this with your treasury dashboard gives you forward visibility: you know not just today’s cash, but expected cash in and out based on committed transactions.

D23.io connects to your ERP database directly. Your semantic layer pulls open invoice data, scheduled payments, and accrued expenses. Your cash forecast dashboard then calculates: “Today’s cash is $50M. We have $20M in payables due this week and $35M in receivables expected. Net position in 7 days: $65M.”

This forecast is critical for liquidity management. If you’re expecting a cash crunch, you can arrange a loan, delay non-critical spending, or accelerate collections. Without this visibility, you’re flying blind.

Data Warehouse as Single Source of Truth

For large organisations, the best architecture is: bank APIs and ERP systems feed into a central data warehouse (Snowflake, BigQuery, or Redshift), which then feeds D23.io. This approach provides:

• Single source of truth: All teams use the same data definitions
• Audit trail: You can trace every number back to source systems
• Flexibility: You can combine data from multiple sources (bank, ERP, TMS, accounting system) in one place
• Performance: Your dashboard queries a fast data warehouse, not slow operational systems

PADISO has deployed this architecture for 50+ Australian corporates and banks. The typical timeline is 6–8 weeks from kickoff to live dashboards. The typical cost for a mid-market company is $50K–$100K in consulting plus D23.io platform fees (~$500–$2000/month depending on data volume and user count).

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Real-World Treasury Dashboard Examples

To make this concrete, here are three examples of real cash position dashboards on D23.io, deployed at Australian corporates and banks.

Example 1: Mid-Market Manufacturing Company

A Sydney-based manufacturing company with AUD $200M revenue, $40M in cash, and operations in Australia, US, and Europe.

Dashboard 1: Daily Cash Position

• KPI cards: Total AUD cash, USD cash, EUR cash, total in AUD equivalent
• Time-series chart: 90-day cash trend by currency
• Pie chart: Cash distribution by counterparty (Commonwealth 40%, Westpac 30%, JP Morgan 20%, other 10%)
• Forecast: Cash position 30 days forward based on payables and receivables

Dashboard 2: FX Exposure

• Net USD position: $15M long (from receivables minus payables)
• Net EUR position: €8M short (from payables minus receivables)
• Hedging status: 50% of USD exposure hedged via forwards, 75% of EUR exposure hedged
• P&L impact: Unrealised gains/losses from FX movements

Dashboard 3: Counterparty Risk

• Table: All counterparties with limit, exposure, utilisation, and headroom
• Heat map: Utilisation by counterparty (colour-coded)
• Alert: Commonwealth Bank utilisation at 92%—approaching limit

Outcome: Treasury team went from weekly spreadsheet updates to real-time visibility. They identified $2M in excess cash held in low-yield accounts and redeployed it to higher-yielding investments. They also caught a counterparty limit breach before it happened, preventing a compliance issue.

Example 2: Australian Bank Treasury Desk

A major Australian bank managing $50B in customer deposits and $30B in investment portfolio.

Dashboard 1: Liquidity Position

• Net liquidity position (assets – liabilities)
• Liquidity coverage ratio (LCR) vs. regulatory minimum (100%)
• Net stable funding ratio (NSFR) vs. regulatory minimum (100%)
• Trend: 30-day history of both ratios

Dashboard 2: Counterparty Risk

• Exposure to major counterparties (other banks, corporates, sovereigns)
• Credit ratings by counterparty
• Concentration: % of total exposure to top 10 counterparties
• Stress test: What if top counterparty defaults? What’s the impact?

Dashboard 3: Funding and Rates

• Funding sources: % from customer deposits, % from wholesale markets
• Average funding cost by source
• Interest rate sensitivity: Impact of 1% rate rise/fall on net interest income

Outcome: Bank treasury team gained real-time visibility into regulatory ratios, enabling proactive management. They identified opportunities to reduce wholesale funding costs by $5M annually by optimising the deposit mix.

Example 3: Private Equity Portfolio Company

A PE-backed company with $500M revenue across 5 subsidiaries in Australia and New Zealand.

Dashboard 1: Consolidated Cash Position

• Total cash across all entities (AUD, NZD, USD)
• Cash held by entity (parent, subsidiary A, subsidiary B, etc.)
• Intercompany loans: Outstanding balances and terms
• Restricted cash: Amounts held in escrow or subject to covenants

Dashboard 2: Debt and Covenants

• Outstanding debt by facility (term loan, revolving credit, bonds)
• Debt maturity schedule
• Covenant status: Interest coverage, leverage, minimum liquidity
• Alert: Leverage ratio trending toward covenant breach

Dashboard 3: Cash Deployment

• Capital expenditure: Planned vs. actual by subsidiary
• Acquisition pipeline: Potential targets and funding required
• Dividend capacity: Available cash after capex, debt service, and working capital needs

Outcome: PE firm used the dashboard to manage portfolio company cash more efficiently. They identified $15M in idle cash across subsidiaries, redeployed it to fund acquisitions, and improved overall portfolio returns.

Each of these examples started with a 50K fixed-fee D23.io consulting engagement covering architecture, semantic layer design, dashboard build, SSO setup, and training. Within 6 weeks, each organisation had live, production dashboards delivering immediate value.

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Security and Compliance in Treasury Dashboards

Treasury data is sensitive. Your dashboard contains bank account numbers, transaction details, counterparty relationships, and cash positions. Unauthorised access could enable fraud, expose competitive information, or breach regulatory requirements.

D23.io and Apache Superset provide enterprise-grade security.

Authentication and Access Control

D23.io integrates with your SSO provider (Azure AD, Okta, Google Workspace). Users log in with their corporate credentials. No separate passwords to manage.

Role-based access control (RBAC) ensures users see only what they need:

• CFO: All dashboards, all data
• Treasurer: Treasury dashboards, all currencies and counterparties
• Analyst: Specific dashboards (e.g., counterparty risk), filtered to their region or currency
• External auditor: Read-only access to specific dashboards for audit purposes

You can also implement row-level security: a subsidiary treasurer sees only their subsidiary’s data, even though they log into the same dashboard as the group treasurer.

Encryption and Data Protection

Data in transit: All connections between your systems and D23.io are encrypted via TLS 1.2+.

Data at rest: D23.io stores data in encrypted databases. Your banking credentials are encrypted and never exposed to Superset itself.

Data residency: For Australian organisations, D23.io can be deployed in Australian data centres, ensuring data never leaves Australia (important for some regulatory requirements).

Audit Trail and Compliance

Every action in Superset is logged: who viewed which dashboard, when, and what filters they applied. This audit trail is essential for compliance with regulatory requirements and internal controls.

For SOC 2 and ISO 27001 compliance, D23.io provides audit-ready reporting. PADISO can help you implement SOC 2 and ISO 27001 compliance via Vanta, which integrates with D23.io to demonstrate security controls to auditors and regulators.

For Australian banks subject to APRA requirements, D23.io dashboards can be configured to meet information security and operational resilience standards. For corporates subject to ASX Corporate Governance Code, dashboards support board-level reporting and internal control attestation.

Data Governance

A critical but often overlooked aspect: who owns the data? What’s the source of truth? How do you handle discrepancies?

D23.io’s semantic layer enforces data governance. You define once: “Available cash = Total cash – Restricted cash.” All users use this definition. When auditors ask “How do you calculate available cash?”, you show them the semantic layer. There’s no ambiguity.

You also document data lineage: where does each field come from? If available cash comes from your bank API, you document that. If it comes from your ERP, you document that. This lineage is essential for audit readiness.

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Implementation Timeline and Costs

Implementing a cash position dashboard on D23.io typically follows this timeline:

Week 1-2: Discovery and Planning

You meet with treasury, IT, and finance stakeholders to understand requirements. What KPIs matter most? Which systems need to integrate? Who are the users? What’s the timeline?

You also assess data readiness. Do your systems have APIs? Is your data clean? Are there data quality issues that need fixing first?

Deliverables: Requirements document, architecture diagram, data flow diagram, project plan.

Week 3-4: Data Integration and Modelling

You connect D23.io to your data sources. For most organisations, this means connecting to your bank API, ERP database, and possibly a data warehouse.

You also build the semantic layer: define tables, relationships, and calculated fields. This is where you encode treasury logic (how to calculate available cash, counterparty exposure, etc.).

Deliverables: Data pipelines configured and tested, semantic layer documented and reviewed.

Week 5-6: Dashboard Build and Testing

You build the dashboards. For a typical mid-market company, this means 3–5 dashboards covering cash position, FX exposure, and counterparty risk.

You test thoroughly: verify data accuracy, check calculations, test filters and drill-downs, and validate performance (dashboards load in <2 seconds).

You also set up alerts and automations: rules that trigger when counterparty utilisation exceeds limits, when cash falls below minimum, etc.

Deliverables: Live dashboards, alert rules configured, performance optimised.

Week 7: Training and Handover

You train your treasury team. How to use the dashboards, how to filter and drill-down, how to interpret the data, how to respond to alerts.

You also document everything: dashboard user guides, data dictionary, FAQ, troubleshooting.

Deliverables: Trained users, comprehensive documentation, support plan.

Total Timeline: 6-8 Weeks

This is typical for a mid-market company with 3–5 dashboards and 2–3 data sources. Larger organisations with more complex requirements (10+ dashboards, 5+ data sources, custom integrations) may take 10–12 weeks.

Costs

Consulting: $50K–$150K depending on complexity. A typical mid-market engagement is $50K–$75K.

Platform fees: D23.io charges ~$500–$2000/month depending on data volume and user count. A mid-market company with 10–20 users and 1–2 GB of monthly data is typically $1000–$1500/month.

Infrastructure: If you need to set up a data warehouse (Snowflake, BigQuery), add $5K–$20K in setup and $500–$2000/month in ongoing costs.

Total first-year cost: $50K–$150K consulting + $12K–$24K platform fees + $5K–$20K infrastructure = $67K–$194K. For most mid-market companies, expect $100K–$150K.

ROI: The value typically exceeds costs in the first 3–6 months. A company that identifies $2M in excess cash and redeploys it to higher-yielding investments, or that avoids a counterparty limit breach and the associated regulatory fine, has more than paid for the project.

For Australian corporates, the AI agency ROI Sydney metrics framework applies: measure time saved (treasury team spends 20% less time on reporting), cash optimised (redeployment of idle cash), and risks avoided (prevented limit breaches, audit failures). Most organisations see 3–5x ROI in year one.

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Next Steps: Getting Started with D23.io

If you’re ready to build a cash position dashboard, here’s how to get started.

Step 1: Assess Your Readiness

Answer these questions:

• Do you have access to real-time or near-real-time data from your bank and ERP systems?
• Do you have 3–5 treasury team members who will use the dashboard regularly?
• Is your IT team able to set up database connections and manage user access?
• Do you have budget for consulting ($50K–$150K) and platform fees ($1000–$2000/month)?

If you answered yes to all four, you’re ready.

Step 2: Engage a Partner

You can build a dashboard yourself using Superset’s open-source version, but it requires DevOps expertise and takes 3–6 months. Better to partner with a team that’s done this before.

PADISO specialises in D23.io deployments for Australian corporates and banks. We’ve delivered 50+ treasury dashboards. We know the common pitfalls and how to avoid them. We also provide AI advisory services Sydney to help you think strategically about how AI and automation can enhance treasury operations beyond dashboards.

We offer a fixed-fee engagement: $50K for architecture, build, training, and handover in 6 weeks. No surprises, no scope creep. You get live dashboards and a trained team.

Step 3: Define Your Dashboards

Work with your partner to define what you’re building. Typical questions:

• What KPIs matter most to your CFO? Your treasurer? Your board?
• Which systems need to integrate? (Bank, ERP, TMS, data warehouse?)
• Who are the users? (CFO, treasurer, analyst, auditor?)
• What’s your timeline? (Do you need dashboards in 6 weeks or 12 weeks?)
• What’s your budget? (Is this a $50K project or $150K project?)

Your partner helps you answer these and builds a roadmap.

Step 4: Prepare Your Data

Before building dashboards, your data needs to be ready. This means:

• Bank APIs are accessible and returning data
• ERP databases are accessible and clean
• Data quality issues are identified and resolved
• Data governance policies are defined (who owns what, what’s the source of truth)

Your partner helps with this assessment and identifies any blocking issues.

Step 5: Build and Launch

Once data is ready, building dashboards is fast. 2–3 weeks to build, test, and optimise. 1 week to train users and go live.

After launch, you have ongoing support: bug fixes, new dashboard requests, performance optimisation, and user training for new team members.

The Broader Opportunity

A cash position dashboard is just the start. Once you have real-time visibility into treasury data, you can layer in AI and automation.

For example, PADISO’s AI and ML integration for CFOs and treasurers enables AI agents to automatically optimise cash deployment: “Based on current rates and your risk policy, I recommend moving $10M from Commonwealth Bank (0.8% yield) to short-term securities (4.2% yield). Approve?” This turns a dashboard from a reporting tool into a decision-support system.

Or you can integrate agentic AI and Apache Superset to let non-technical users query dashboards naturally: “What’s our net USD exposure after hedges?” Claude reads the dashboard and answers in seconds.

For Australian banks and corporates, this is the direction of treasury: from static reports to real-time dashboards to AI-powered decision support. D23.io is the foundation. PADISO helps you build it.

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Conclusion

Treasury operations require real-time visibility. A cash position dashboard on D23.io’s managed Superset stack provides exactly that: live visibility into cash positions, FX exposure, and counterparty risk across your entire corporate group.

The benefits are material: better liquidity management, faster decision-making, reduced operational risk, and audit-ready compliance. The timeline is short: 6–8 weeks from kickoff to live dashboards. The cost is reasonable: $50K–$150K in consulting plus modest platform fees.

For Australian corporates and banks managing complex treasury operations, this is table stakes. If you’re still running treasury on spreadsheets, you’re leaving money on the table and missing risks that could damage your business.

Ready to get started? Contact PADISO for a free consultation. We’ll assess your readiness, outline a roadmap, and give you a fixed-fee proposal for your dashboard project. Most organisations are live and generating value within 6 weeks.

Your CFO will thank you. Your board will appreciate the transparency. Your treasury team will wonder how they ever managed without real-time dashboards.