Taliferro
CEO · Operational Visibility

If Nobody Trusts The Numbers, The Business Slows Down

When reports, spreadsheets, and dashboards disagree, your team stops acting and starts guessing.

The problem is rarely the report. The problem is usually the system behind the report.

Taliferro finds where visibility breaks, then gives your team a clear written diagnosis.

$999 flat Intake within 24 hours Written plan in 3 days
Executive frustrated by contradicting reports and unreliable company data
The signal

Visibility breaks before performance breaks

When leaders cannot see what is real, every decision takes longer than it should.

The real cost

Your team cannot move fast when nobody trusts the view

The meeting starts. The reports disagree. Finance has one number. Operations has another. The work pauses while everyone argues about what is real.

Why it keeps happening

The dashboard only repeats what your systems believe

If the systems disagree, the dashboard will disagree too.

The rescue

We find where visibility breaks

One focused session. A clear diagnosis. The first moves your team should make next.

When contacts, revenue, accounts, activity, or operations live across disconnected tools, every report inherits the conflict.

Where data quality breaks

Five root causes a data quality consultant looks for first

When reports contradict each other, the problem is almost always in one of these five places — not in your dashboards.

  1. Duplicate records and conflicting source systems. When the same customer, account, or transaction lives in multiple places with no single record of truth, every downstream report disagrees by design.
  2. Unclear data ownership. When no one person is accountable for the accuracy of a field or dataset, errors accumulate and nobody has the authority to fix them.
  3. Manual entry and transformation steps. Every place a human touches data before it reaches a report is a place where the data can change. These handoffs compound quickly.
  4. Inconsistent definitions. "Revenue," "active customer," and "pipeline" mean different things to different departments. If definitions aren't enforced at the data layer, reports will never match.
  5. Timing and latency mismatches. When systems update on different schedules, reports pulled at the same moment will show different states — and both will be "correct" in isolation.
Executive frustrated by contradicting reports and unreliable company data
What you get

A clear path back to one trusted view

You get the root causes, the broken handoffs, and the next moves needed to stop debating the data.

  • The 2–3 root causes blocking visibility.
  • A map of where the view breaks.
  • Specific next steps for leadership, data, or operations.
Client perspective

The value is clarity

“Taliferro quickly identified issues affecting reporting consistency and operational visibility. Their ability to diagnose the real problem fast saved us considerable time.”

— Lester Blair
Blair Capital Management

Executive frustrated by contradicting reports and unreliable company data
FAQ

Quick answers before you book

No, you usually do not need to replace every system. No, another dashboard will not fix broken visibility. Yes, a focused diagnosis can show where the problem starts.

Executive frustrated by contradicting reports and unreliable company data
Why is enterprise data quality always so poor?

Usually because data was an afterthought — systems were purchased for functionality, not for clean integration. Each system tracks slightly different versions of the same entity (customer, order, account), and there's no governance over which version is authoritative. The result is multiple conflicting truths, all technically correct within their own system.

Do you need to replace all your systems to fix data quality?

Rarely. Most data quality problems are fixable at the integration layer — how systems talk to each other — without replacing the systems themselves. A clear data contract between systems resolves the majority of inconsistency without a platform overhaul.

How long does it take to fix bad data?

The diagnostic — what you get here — happens in 3 days. The actual remediation depends on scope, but most organizations see meaningful improvement within 6–10 weeks of executing the right plan. The key is identifying the correct root cause first. Fixing symptoms without finding the source creates recurring problems.

Can AI fix our data quality problems?

AI can help with data enrichment and anomaly detection, but it cannot fix bad architecture. If your source systems are producing conflicting data, AI will confidently serve those conflicts back to you as insight. The foundation has to be correct before AI adds value.

Why does our data quality keep getting worse even when we invest in better tools?

Tools don't fix data quality problems — governance and structure do. Most organizations add dashboards, BI platforms, and data pipelines on top of a foundation that was never designed for trustworthy data. The result is more tooling that reports the same bad data faster. The real fix is upstream: source systems, ownership, and validation rules before the data moves anywhere.

How do I know if I have a data quality problem or a reporting problem?

If different people pull the same metric and get different numbers, you have a data quality problem. If everyone agrees on the number but the number isn't useful, that's a reporting problem. Both are fixable, but they require different interventions. A good diagnostic starts by identifying which category you're actually in before recommending changes.

What does a data quality consultant actually deliver?

A data quality consultant diagnoses where your data breaks, names the root cause, and gives you a clear written action plan. Taliferro's engagement is $999 flat and delivers a written diagnosis in 3 days — specific enough for your team to act on immediately, without a long consulting retainer.

How fast can data quality issues be fixed once diagnosed?

Quick wins — like removing duplicate records, fixing a broken join, or assigning clear data ownership — can show results in days. Structural fixes to source systems take longer, but they're the ones that hold. Most teams see their first measurable improvement in 2–4 weeks once the right problem is clearly named and ownership is assigned.

Same-Day Rescue

Stop guessing what is real

Book one focused session. Get a written diagnosis in 3 days.

$999