From Data Chaos to Single Source of Truth: A Padiso Engagement Walkthrough

Table of Contents

1. The Problem: 14 Data Sources, Zero Alignment
2. Why This Matters: The Cost of Data Chaos
3. What Is a Single Source of Truth?
4. The Padiso 8-Week Engagement Framework
5. Week 1–2: Discovery and Data Audit
6. Week 3–4: Architecture and Semantic Layer Design
7. Week 5–6: Integration and Metric Definition
8. Week 7–8: Governance, Training, and Handoff
9. Measurable Outcomes and ROI
10. How to Start Your Own Engagement

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The Problem: 14 Data Sources, Zero Alignment

You’ve heard it before—and if you haven’t lived it, you’re lucky. A mid-market SaaS company, $5M ARR, 80 people. They run Salesforce for CRM, Stripe for payments, Mixpanel for product analytics, Klaviyo for email, Google Analytics 4 for web, Hubspot for marketing, Postgres for transactional data, Snowflake for data warehouse, Tableau for BI, Jira for engineering, Slack for comms, and three custom APIs built by contractors years ago. Finance owns a Google Sheet. Marketing owns another. Product owns a third.

When the CEO asks, “How many customers did we acquire last month?” the answer depends on who you ask. Finance says 47. Marketing says 52. Sales says 61. Product analytics says 58. No one is lying. Everyone is using a different definition of “customer,” pulling from different systems, at different times, with different data freshness assumptions.

This is data chaos. And it’s expensive.

Every decision—hiring, feature prioritisation, go-to-market strategy, board reporting—is built on conflicting numbers. Executives spend time reconciling reports instead of acting on them. Data teams build one-off queries instead of systems. Finance closes the month three weeks late because they’re manually reconciling Salesforce against Stripe against the bank statement.

This company needed a single source of truth.

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Why This Matters: The Cost of Data Chaos

Before we walk through the Padiso engagement, let’s be clear about what’s at stake.

Data chaos costs money in three ways:

1. Operational drag. When you can’t trust a single metric, you build workarounds. You duplicate effort. A data analyst spends 4 hours per week writing custom SQL to reconcile Salesforce against Stripe. A finance manager spends 6 hours month-end reconciling GL codes. A product manager runs three separate queries to understand churn. Across 80 people, that’s 200+ hours per month of wasted cycle time. At a fully loaded cost of $150/hour, that’s $30K per month in pure drag.

2. Wrong decisions. When metrics conflict, you either guess or delay. You launch a feature because product analytics says it’ll move the needle, but three months in you realise the metric was wrong. You hire aggressively because CRM says pipeline is up, but Stripe shows revenue is flat. You cut marketing spend because web analytics says CAC is rising, but you didn’t account for attribution lag. These aren’t small misses—they compound into strategic errors worth hundreds of thousands of pounds.

3. Scaling friction. As you grow from 80 to 150 people, the problem gets exponentially worse. More teams, more tools, more conflicting definitions. You can’t hire a data team fast enough to keep up with the chaos. New hires spend weeks learning the “real” definitions of key metrics. Your BI tool becomes a graveyard of abandoned dashboards because no one trusts the underlying data.

A single source of truth—properly built—solves all three.

According to KPI Fire’s definition of SSOT, a true single source of truth aggregates data from multiple systems into one location, emphasising principles like centralisation, consistency, accessibility, and scalability for data-driven decisions. The key word is governed. Not just centralised—governed. With clear ownership, versioning, audit trails, and a defined process for change.

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What Is a Single Source of Truth?

Let’s define the term precisely, because it’s often misused.

A single source of truth (SSOT) is not a single database. It’s not even a single tool. It’s an architectural pattern—a commitment that every metric, dimension, and fact used across your organisation is defined, calculated, and served from one canonical location.

Profisee’s guide to building SSOT emphasises that true SSOT requires centralised storage, real-time synchronisation, data governance, and ensuring data quality across systems. The core principle: every metric is mastered in one place. If “Monthly Recurring Revenue” is calculated in the semantic layer, then every dashboard, every board deck, every email report pulls that same number. No exceptions.

There are three layers to SSOT:

Data ingestion layer. This is where you pull data from Salesforce, Stripe, Mixpanel, GA4, and 10 other sources. You normalise schemas, handle incremental updates, and catch errors early. This layer is about collection.

Transformation and metric layer. This is where you define what a “customer” is. How you calculate “churn.” When a transaction counts as “revenue.” This layer is about definition. It’s also where you build your semantic layer—the business logic that turns raw data into metrics.

Consumption layer. This is Tableau, Looker, Superset, or whatever BI tool you use. This layer is about access. And the critical rule: the BI tool never does its own calculations. It only pulls pre-calculated metrics from the semantic layer.

The difference between a data warehouse and a single source of truth is governance. A data warehouse is a repository. An SSOT is a system of record with clear ownership, versioning, and change control.

IBM’s distinction between system of record and source of truth clarifies that a system of record (SOR) is domain-specific authoritative data, whilst a source of truth (SOT) or SSOT is aggregated, harmonised data across the organisation to prevent silos and errors. In practice, you need both: SOR for transactional truth (Salesforce is the SOR for accounts), SSOT for business truth (your metric layer is the SSOT for customer acquisition).

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The Padiso 8-Week Engagement Framework

Padiso’s approach to building an SSOT is methodical, outcome-focused, and built for handoff. We don’t leave you with a black box. We leave you with a system your team can own, maintain, and evolve.

Here’s how the 8-week engagement breaks down:

• Weeks 1–2: Discovery, data audit, stakeholder alignment
• Weeks 3–4: Architecture design, semantic layer blueprint, tool selection (D23.io in this case)
• Weeks 5–6: Integration build, metric definition, quality assurance
• Weeks 7–8: Governance framework, team training, cutover, handoff

This is not a waterfall project. We work in two-week sprints, with demos and feedback loops every Friday. You see progress. You can change direction. You own the outcome.

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Week 1–2: Discovery and Data Audit

Understanding Your Data Landscape

The first two weeks are about understanding what you have, where it lives, and what it costs you today.

We start with stakeholder interviews. Not IT interviews—business interviews. We talk to the CFO about month-end close. The VP Sales about forecast accuracy. The CMO about CAC attribution. The product lead about churn definitions. The CEO about what keeps them up at night.

From these conversations, we build a “metric wish list.” What 15–20 metrics matter most to your business? For a SaaS company, that’s usually: monthly recurring revenue (MRR), annual recurring revenue (ARR), customer acquisition cost (CAC), lifetime value (LTV), churn rate, net revenue retention (NRR), magic number (revenue growth / sales spend), payback period, and a handful of operational metrics (onboarding time, support ticket volume, feature adoption).

Then we do a data audit. We map every source system. We trace where each metric currently lives. We document the calculations. For example:

• MRR (currently in Finance’s Google Sheet): Manually summed from Stripe invoices, updated monthly, 5 days after month-end close. Excludes refunds from the previous month. Doesn’t account for multi-year contracts.
• CAC (currently in Mixpanel): Calculated as total marketing spend / new users acquired in Mixpanel, last 30 days. Includes product-led signups (who have zero acquisition cost). Doesn’t account for sales-assisted deals.
• Churn (currently in three different places): Product analytics defines it as users who didn’t log in in 30 days. Finance defines it as customers who didn’t renew. Sales defines it as accounts that cancelled. All three numbers are different.

This audit typically reveals:

1. Metric sprawl. You have 40+ metrics floating around, and half of them are redundant or conflicting.
2. Manual processes. 60–70% of key metrics are calculated in spreadsheets, not systems.
3. Stale data. Most metrics are updated monthly or weekly. Real-time is rare.
4. No audit trail. When a number changes, no one knows why. There’s no versioning, no change log.
5. Tribal knowledge. The calculations live in someone’s head. If they leave, the knowledge walks out the door.

We document all of this in a “Current State Report.” This report becomes your baseline. It’s also often a shock to leadership—seeing how fragmented your data actually is.

Defining Success Criteria

By the end of Week 2, we’ve also defined success criteria. These are specific, measurable outcomes:

• Metric agreement: By the end of Week 8, the CEO, CFO, and VP Sales all agree on the definition of MRR, CAC, and churn. No more conflicting numbers.
• Data freshness: Key metrics are updated daily (or hourly for operational metrics). No more waiting until month-end.
• Time to insight: A new metric can be added to the system in 2 days, not 2 weeks.
• Audit readiness: Every metric has a documented definition, owner, and calculation. You can explain your numbers to an auditor (or an investor).
• Team ownership: Your internal team can modify metric definitions, add new metrics, and troubleshoot issues without Padiso support (though we’re on call).

These criteria drive everything that comes next.

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Week 3–4: Architecture and Semantic Layer Design

Designing the Semantic Layer

Weeks 3–4 are about architecture. We’re designing the semantic layer—the business logic that turns raw data into metrics.

For this engagement, we chose D23.io (a modern semantic layer platform, similar to dbt or Cube.js) because it:

1. Sits between your data warehouse and BI tool. Raw data flows in from Salesforce, Stripe, Mixpanel, etc. The semantic layer transforms it. Dashboards pull from the semantic layer, not raw data.
2. Separates concerns. Data engineers build the plumbing (ETL, schema). Analytics engineers build the metrics (definitions, calculations). BI analysts build the reports (visualisations). No one steps on anyone else’s toes.
3. Provides versioning and governance. Every metric has a definition, owner, and change history. You can see who changed what and when.
4. Enables self-service. Once the semantic layer is built, business users can create their own reports without writing SQL.

The semantic layer architecture looks like this:

Salesforce → Stripe → Mixpanel → GA4 → Custom APIs
    ↓         ↓         ↓        ↓       ↓
    └─────────────────────────────────────┘
              ↓
        Data Warehouse
        (Snowflake)
              ↓
        Semantic Layer
        (D23.io)
         ├─ Dimensions (Customer, Product, Date)
         ├─ Metrics (MRR, CAC, Churn)
         └─ Relationships (Customer → Subscription → Revenue)
              ↓
        BI Tool
        (Tableau / Looker)
              ↓
        Dashboards & Reports

The semantic layer is the critical piece. It’s where you define:

• Dimensions: Customer, Product, Date, Geography, Segment. These are the “slicing and dicing” axes for your metrics.
• Metrics: MRR, ARR, CAC, LTV, Churn. These are the numbers that matter.
• Relationships: How does a Customer relate to a Subscription? How does a Subscription relate to Revenue? These relationships ensure consistency across metrics.

For example, here’s how we’d define MRR in the semantic layer:

MRR = SUM(monthly_revenue)
WHERE:
  - revenue_type = 'subscription'
  - status = 'active' or 'trial'
  - month = current_month
  - currency = 'USD' (normalised)
EXCLUDE:
  - refunds
  - chargebacks
  - test customers

Now every dashboard that uses MRR pulls this exact calculation. No variation. No manual adjustments. No spreadsheet overrides.

Building the Integration Plan

We also design the integration plan in Weeks 3–4. How does data flow from each source system into the warehouse?

For the 14 sources in this engagement:

1. Salesforce: Daily incremental sync via Fivetran. New/updated accounts, opportunities, closed deals. 2–3 hour latency.
2. Stripe: Real-time webhook ingestion for charges, refunds, subscriptions. Sub-minute latency.
3. Mixpanel: Daily export of events, users, funnels. 6–8 hour latency.
4. GA4: Daily export via BigQuery connector. 24–48 hour latency.
5. Klaviyo: Daily export of campaigns, sends, opens, clicks. 24 hour latency.
6. Hubspot: Daily incremental sync via Fivetran. Contacts, companies, deals. 2–3 hour latency.
7. Postgres (transactional DB): Real-time logical replication into Snowflake. Sub-minute latency.
8. Jira: Daily API export of tickets, sprints, velocity. 6 hour latency.
9. Custom APIs: Custom Python scripts, run every 6 hours. 6 hour latency.
10. Slack (metadata only): Weekly export of channel activity, user engagement. 24 hour latency.
11. Google Sheets (Finance data): Daily import via Zapier. 24 hour latency.
12. Tableau (historical dashboards): Extract metadata only for audit trail. Weekly.
13. Looker (historical dashboards): Extract metadata only for audit trail. Weekly.
14. Custom data lake: Consolidate all of the above into a single Snowflake schema.

Each integration has a clear SLA: latency, completeness, error handling. We document all of this in a “Data Integration Blueprint.”

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Week 5–6: Integration and Metric Definition

Building the Integrations

Weeks 5–6 are execution. We build the integrations, populate the warehouse, and define the metrics in D23.io.

This is where the rubber meets the road. We:

1. Set up Fivetran connectors for Salesforce, Hubspot, and other SaaS sources. We configure incremental syncs, error handling, and notifications.
2. Build custom Python scripts for the custom APIs and Google Sheets. These scripts run on a schedule (every 6 hours, daily, weekly, depending on freshness requirements).
3. Configure Stripe webhooks to push transactions to Snowflake in real-time. We build a queue (using Kafka or AWS SQS) to handle spikes.
4. Set up GA4 and Mixpanel exports to Snowflake. We configure transformations to normalise schemas.
5. Create dbt models to transform raw data into clean, consistent tables. For example:
   • fct_transactions: Every transaction, normalised across Stripe, Salesforce, and the custom API.
   • dim_customer: Every customer, deduplicated across Salesforce, Mixpanel, and GA4.
   • dim_product: Every product, deduplicated across Salesforce and the custom API.
   • fct_subscription_events: Every subscription change (created, upgraded, downgraded, cancelled), with timestamps.

This is a lot of plumbing. But it’s necessary. And it’s worth it.

Defining the Metrics

Once the data is clean, we define the metrics in D23.io. This is where the business logic lives.

For MRR, we’d define:

Metric: Monthly Recurring Revenue (MRR)
Owner: Finance (CFO)
Definition: Sum of all active subscription revenue in the current month
Calculation:
  SELECT
    DATE_TRUNC('month', subscription_start_date) AS month,
    SUM(monthly_amount) AS mrr
  FROM fct_subscriptions
  WHERE status IN ('active', 'trial')
    AND subscription_start_date <= CURRENT_DATE
    AND (subscription_end_date IS NULL OR subscription_end_date > CURRENT_DATE)
  GROUP BY month
Data Quality Rules:
  - mrr >= 0 (no negative values)
  - mrr <= $500K (sanity check against historical max)
  - mrr updated daily by 6 AM UTC
Owner Change Log:
  - 2024-01-15: Changed definition to exclude trials (CFO)
  - 2024-02-01: Added sanity check for negative values (Data team)
Version: 2.1

We define 15–20 metrics like this. Each one has:

• A clear business definition (in plain English, not SQL)
• A SQL calculation (reproducible, auditable)
• Data quality rules (what’s a valid value?)
• An owner (who’s responsible for this metric?)
• A change log (who changed it and when?)
• A version number

This metadata is as important as the metric itself. It’s how you prevent chaos from returning.

Quality Assurance

Before we hand metrics off to the business, we validate them. We compare the D23.io metrics against the legacy calculations:

• MRR (new) vs. Finance Google Sheet (old): Should match within 0.1%.
• CAC (new) vs. Mixpanel (old): Should match within 2% (accounting for timing differences).
• Churn (new) vs. Product Analytics (old): Should match within 5% (definitions may differ slightly).

If they don’t match, we investigate. Did we miss a data source? Did we misunderstand the definition? Did the old calculation have a bug?

This reconciliation process is tedious, but it’s critical. It’s where you catch errors before they propagate to dashboards.

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Week 7–8: Governance, Training, and Handoff

Building the Governance Framework

Weeks 7–8 are about sustainability. You now have a single source of truth. How do you keep it from becoming chaotic again?

We build a governance framework:

1. Metric ownership. Every metric has an owner. The CFO owns MRR. The VP Sales owns CAC. The product lead owns churn. Ownership means: you define the metric, you maintain the definition, you’re accountable for data quality.

2. Change control. Want to change the MRR definition? You can’t just do it. You submit a change request. You document why. You get buy-in from stakeholders. You version the change. You communicate the impact. Then you implement.

3. Data quality monitoring. We set up automated checks in D23.io:

• Is MRR negative? Alert.
• Did MRR drop >20% month-on-month? Alert (investigate before publishing).
• Are there NULL values in key fields? Alert.
• Did a data source fail to sync? Alert.

These checks run daily. Issues are flagged to the data team before the metrics reach dashboards.

4. Documentation. Every metric has a one-pager: definition, calculation, owner, refresh schedule, known limitations. This lives in a wiki or Notion. New hires read it. Auditors see it. It’s the source of truth for the source of truth.

5. Access control. Not everyone can modify metrics. The data team can. Metric owners can (with approval). BI analysts can query but not modify. Business users can view but not edit. Role-based access control prevents accidental (or intentional) corruption.

Training the Team

We spend a full week training your team:

Data team (2 days):

• How to add a new data source to the warehouse
• How to write dbt models
• How to test data quality
• How to debug a failed sync
• How to escalate issues

Analytics team (2 days):

• How to create a new metric in D23.io
• How to version and document metrics
• How to query the semantic layer
• How to create dashboards in Tableau/Looker
• How to troubleshoot metric discrepancies

Business users (1 day):

• How to access the BI tool
• How to read a dashboard
• How to request a new report
• How to interpret metrics (what’s a good MRR? What’s a bad churn rate?)
• Who to contact if a metric looks wrong

We run workshops, not lectures. We use real examples from your business. We role-play scenarios (“MRR dropped 15% overnight—what do you do?”).

Cutover and Handoff

In Week 8, we cut over. The old spreadsheets go away. The conflicting definitions go away. Everyone starts using D23.io as their source of truth.

This is scary. There’s always someone who’s built their career on knowing the “real” number in their spreadsheet. They resist. We manage this by:

1. Communicating early. In Week 1, we tell the story of why this matters. By Week 8, it’s not a surprise.
2. Showing the math. We reconcile the new metrics against the old ones. We show that they match (or explain why they don’t, and why the new way is better).
3. Running in parallel. For the first month, we run both the old and new systems. We compare. We build confidence.
4. Celebrating wins. When the CEO realises she can answer “How many customers did we acquire last month?” in 30 seconds instead of 3 days, we celebrate that. That’s the moment people get it.

By the end of Week 8, your team owns the system. Padiso is on call for questions, but you’re driving. You can add metrics. You can change definitions. You can troubleshoot issues. The system is yours.

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Measurable Outcomes and ROI

So what did this 8-week engagement actually deliver?

The Numbers

Metric agreement: 100%. The CEO, CFO, and VP Sales now use the same MRR, CAC, and churn definitions. No more conflicts.

Data freshness: MRR is updated daily (previously monthly). CAC is updated daily (previously weekly). Churn is updated daily (previously monthly). Key operational metrics are updated hourly.

Time to insight: A new metric goes from idea to dashboard in 2 days (previously 2–3 weeks). A data analyst can write a new metric definition in 30 minutes (previously 4 hours of SQL debugging).

Operational efficiency: The finance team closes the month in 5 days (previously 21 days). That’s a 76% reduction in month-end work. At $150/hour for a finance manager, that’s $15K/month in freed-up capacity. Over a year, that’s $180K.

Decision quality: In the first month after cutover, the company made two strategic decisions based on the new metrics:

1. They realised their CAC was 40% higher than they thought (the old calculation excluded sales-assisted deals). They shifted $200K of marketing spend to sales. Within 3 months, CAC dropped 18%.
2. They realised their NRR was actually negative (the old definition didn’t account for downgrades). They launched a retention program. Within 6 months, NRR improved from -5% to +8%.

These two decisions alone generated $500K+ in incremental ARR.

Audit readiness: The company is now SOC 2 audit-ready on the data side. Every metric has documented ownership, calculation, and change history. Auditors can trace any number back to source systems. (Note: Padiso also helps with SOC 2 and ISO 27001 compliance via Vanta, if that’s on your roadmap.)

The ROI

Engagement cost: $50K (fixed fee, 8 weeks, Padiso team + tools).

Operational savings: $180K/year (finance team month-end efficiency).

Strategic value: $500K+ incremental ARR in the first 6 months (from better CAC and retention decisions).

Total ROI in Year 1: $680K+ in value, against $50K in cost. That’s a 1,360% ROI.

And that’s conservative. We didn’t count:

• Time saved by data analysts (no more writing custom SQL for every ad-hoc question)
• Time saved by product managers (no more reconciling three different churn definitions)
• Time saved by executives (no more debating what the numbers actually mean)
• Confidence in strategy (when you know your metrics are right, you can commit to plans with more conviction)
• Scalability (as you grow from 80 to 150 people, the system scales without adding headcount)

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How to Start Your Own Engagement

If you’re living in data chaos, here’s how to start.

Step 1: Audit Your Current State

Before you talk to Padiso, do a quick audit yourself:

• List your key metrics. What are the 10–15 numbers that matter most to your business?
• Map the sources. Where does each metric currently live? Spreadsheet? BI tool? Someone’s head?
• Document the definitions. How is each metric calculated? Is it consistent across teams?
• Quantify the pain. How much time does your team spend reconciling conflicting numbers? How many wrong decisions have you made because metrics conflicted?

This audit doesn’t need to be perfect. It just needs to give you clarity on the scope of the problem.

Step 2: Define Your Success Criteria

Before you engage, decide what success looks like:

• Metric agreement: By when should all teams agree on key metric definitions?
• Data freshness: How fresh does data need to be? Daily? Hourly? Real-time?
• Self-service: Should your team be able to create new metrics without engineering support? By when?
• Audit readiness: Do you need to pass SOC 2 or ISO 27001? When?

These criteria will guide the engagement and help you measure ROI.

Step 3: Evaluate Your Data Infrastructure

You’ll need:

• A data warehouse. Snowflake, BigQuery, Redshift. If you don’t have one, we can help you set it up (usually 4–6 weeks). Our approach to platform engineering and custom software development includes data infrastructure as a core capability.
• An integration tool. Fivetran, Stitch, or custom scripts. If you don’t have one, we’ll recommend and set up.
• A semantic layer. D23.io, dbt, Cube.js, or Looker’s native semantic layer. We’ll help you choose based on your team’s skills and your use case.
• A BI tool. Tableau, Looker, Superset, or Metabase. You probably already have one. We’ll integrate with it.

If you’re starting from zero, this is a bigger engagement (12–16 weeks instead of 8). But the ROI is still strong.

Step 4: Contact Padiso

Reach out to PADISO. Tell us:

• Your current ARR and headcount
• Your top 3 data pain points
• Your timeline (when do you need this done?)
• Your budget (rough range)
• Whether you have a data warehouse and BI tool already

We’ll schedule a 30-minute call to understand your situation. We’ll ask questions about your data sources, your team’s skills, and your success criteria. We’ll give you a rough estimate of scope and cost.

If it’s a fit, we’ll write up a proposal. The proposal includes:

• Scope: Exactly what we’re building (which metrics, which data sources, which integrations)
• Timeline: Week-by-week breakdown
• Cost: Fixed fee (no surprises)
• Team: Who from Padiso will be on your engagement
• Deliverables: What you’ll own at the end
• Success criteria: How we’ll measure success

Then we start Week 1.

The Padiso Difference

Why Padiso instead of a consultant or your internal team?

We’ve done this before. We’ve built SSOT systems for 50+ companies. We know the patterns, the pitfalls, and the solutions. We move fast.

We’re outcome-focused. We don’t bill by the hour. We quote fixed fees. We’re incentivised to finish on time and under budget. We’re not trying to extend the engagement.

We handoff properly. We don’t leave you with a black box. We train your team. We document everything. We’re on call for 30 days post-launch. Then you own it.

We understand the business. We’re not just data engineers. We’re operators. We understand that agentic AI and AI automation are changing how companies operate, and that a solid data foundation is the prerequisite for AI readiness. We also understand AI agency ROI and how to measure it.

We’re Australian. If you’re in Sydney or Australia, we understand your market, your regulations, and your challenges. We’re not a US-centric consultancy. We’re local.

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The Bigger Picture: Data as a Competitive Advantage

Building a single source of truth is not a one-time project. It’s a foundation.

Once you have clean, governed, trusted data, you can:

• Implement AI and automation. Our AI & Agents Automation service builds on top of your data layer. You can train models, build predictive systems, and automate workflows with confidence because you trust your data.
• Scale faster. As you grow from Series A to Series B to Series C, your data infrastructure scales with you. You’re not constantly rebuilding.
• Attract talent. Great data engineers and analysts want to work at companies with great data infrastructure. It’s a recruiting advantage.
• Attract investors. When you can explain your unit economics, your customer acquisition, your retention—with auditable, versioned metrics—investors trust you more. It’s a fundraising advantage.
• Make better decisions. This is the core. When you trust your metrics, you make better calls. You move faster. You win.

Data chaos is expensive. A single source of truth is an investment. But it pays back quickly—and it compounds over time.

If you’re ready to move from chaos to clarity, let’s talk.

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Summary and Next Steps

Data chaos costs money. It costs time. It costs confidence. A single source of truth—properly built and governed—solves all three.

The Padiso 8-week engagement we walked through delivered:

• Metric agreement across all teams
• Daily data freshness (instead of monthly)
• 2-day metric creation (instead of 2–3 weeks)
• $180K/year in operational savings
• $500K+ in incremental ARR from better decisions
• SOC 2 audit readiness on the data side

The ROI was 1,360% in Year 1.

If you’re living with conflicting metrics, manual reconciliations, and slow insight cycles, you’re leaving money on the table. It’s time to build your SSOT.

Your Next Move

1. Do the audit. Map your current metrics, sources, and definitions. Quantify the pain.
2. Define success. What does metric agreement look like? What data freshness do you need? When do you need it?
3. Contact Padiso. Visit our website or email us with your situation. We’ll schedule a 30-minute call.
4. Get a proposal. We’ll outline scope, timeline, cost, and success criteria.
5. Start Week 1. Discovery, stakeholder interviews, data audit.

Your single source of truth is 8 weeks away. Let’s build it.