The Single Source of Truth is a Myth

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Stop arguing over data. This week’s premium subscriber kit is the Silo Mapping Workshop Kit: a complete package to facilitate a 90-minute session that will map your data silos, align with your team, and end the futile chase for a Single Source of Truth. It includes a facilitation guide, a pre-built Miro board, a case study, and email templates. Currently, the Data Letter Premium Tier is available for $5 per month or $50 for the entire year.

Here’s how to find your data silos.

The quarterly business review is hitting a familiar rough patch. The Head of Marketing is pointing to a sleek dashboard from their automation platform, proudly showing a high lead conversion rate. The VP of Sales scoffs, pulling up a report directly from the CRM that tells a very different story. Meanwhile, Finance shares a spreadsheet - the final numbers - that doesn’t match either. The conversation devolves into a circular argument about which system is the real source of truth.

Sound Familiar?

For years, we’ve chased the holy grail of data: the Single Source of Truth (SSOT). We invest in massive data warehouses, expensive BI tools, and complex integrations, hoping to squash these debates finally, but the debates continue. Why?

Because the Single Source of Truth is a myth. It’s a technical solution to a human problem.

At its core, the problem stems from misalignment, not a faulty database. Today, we’re not going to talk about another technical fix. We’re going to talk about the human-centric process that actually works: mapping your data silos to create a Shared Source of Context.

Why the Single Source of Truth is a Fantasy

The idea is seductive: one pristine, perfectly modeled database that everyone in the company uses. Every metric is defined the same way, every report tells the same story, and harmony reigns.

The reality is a tangled web of overlapping systems and makeshift solutions:

Your CRM (like Salesforce) is supposed to be the central record, but Marketing uses it differently from Sales. Finance relies on its native ERP, but someone’s always exporting data to a spreadsheet to “make it make sense.” Support has its own platform, and somewhere, a critical business process is running on a spreadsheet saved to a desktop that only one person is aware of.

This isn’t incompetence; it’s inertia. Each department has different goals, so they adopt best-in-class tools that create natural, functional silos. Within those silos, they develop their own processes and, crucially, their own definitions of key metrics.

What is an active user? What defines a qualified lead? When is revenue actually recognized? The answers vary by department. The data team can’t model a single truth because the business can’t agree on what truth is.

The cost is wasted time arguing over data instead of acting on it. Poor strategic decisions based on flawed or incomplete pictures. And perhaps most damagingly, a deep-seated mistrust between teams that are supposed to be on the same side.

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Stop Building, Start Facilitating

The path forward is to stop trying to eliminate silos and start trying to understand them. Your goal isn’t a single source of truth, but a single source of alignment.

This happens through a structured, facilitated conversation I call the Silo Mapping Workshop. It’s not about blaming anyone or forcing agreement. It’s about creating a map of your actual data landscape and uncovering the why behind the discrepancies.

A Peek Inside the Workshop: Mapping Your Real Data Landscape

You gather the key players: the sales ops manager, the marketing ops specialist, the data analyst, and the finance business partner. You’re not bringing the department heads; you’re bringing the people who know how the data actually gets made.

You focus on one burning metric. Let’s say Monthly Recurring Revenue (MRR):

Together, you map:

1. System Silos: Where does this data live? (Salesforce, Stripe, the finance ERP, a spreadsheet for manual adjustments).

2. Definition Silos: How is it defined in each place? (e.g. Sales includes pending upgrades, Finance subtracts churned customers daily, The spreadsheet adds back one-time fees)

3. Process Silos: What human actions affect it? (e.g. An AE manually tags an account as upgraded, Finance runs a reconciliation script on the 1st of every month.)

The outcome isn’t a magical, unified number. It’s something better: clarity. Everyone leaves the room understanding why the numbers are different. From this shared context, you can then build sane, agreed-upon rules for reporting and decision-making.

Why This is a Job for Humans, Not Just AI

An AI can process millions of data points to find a correlation, but it cannot:

• Navigate the political tension between a sales leader and a marketing leader.

• Build empathy between a skeptical finance manager and a frustrated data engineer.

• Facilitate a negotiation to arrive at a common definition of ‘customer.’

• Get people to buy into a process because they were part of creating it.

AI is a powerful tool for analysis, but it is useless without alignment. The last mile problem of data isn’t technological; it’s human. This workshop solves the human problem.

This framework is your first step toward turning data chaos into clarity, but knowing what to do is only half the battle. Knowing how to do it effectively (how to facilitate, how to get buy-in, how to handle objections) is what separates a productive session from a wasted hour.