Data Blind-Spot Assessment

You have hundreds of stores.
How many are you actually watching?

Ten questions, one at a time. Each one paired with the industry data behind it. A score out of 100 on how well your team tracks and uses retail data. Plus a fix roadmap emailed to you.

Start the assessment ~3 minutes · Score emailed instantly · No deck

01 · Foundation

Are the systems unified?

POS, inventory, ERP, finance, labor. If they don’t join, you can’t reason across them.

02 · Detection & diagnosis

Do you catch drift early, with cause?

An alert without a root cause is a backlog item. We grade both.

03 · Action & learning

Does the loop close?

If interventions never get measured, the next ones don’t get smarter.

Question 1 of 11 · Foundation Score: 0 / 100

Foundation · Question 1 of 10

How many of these systems flow into a single source of truth?

POS, inventory, ERP, finance, labor, e-comm, vendor.

0–1 systems

Mostly siloed. Reports live in their own tools.

2–3 systems

Two or three feeds into a warehouse. The rest are exports.

4–5 systems

Most operational systems unified. Some gaps.

6+ unified

Full operational data layer in place.

What your answer means

Why we ask this

Why unification matters

78%

of analytics leaders cite siloed data as the #1 blocker to AI adoption.

Source: Salesforce, State of Data & Analytics, 2023

When systems don’t join, you can’t reason across them. Every cross-system question becomes a project, so most never get asked.

Foundation · Question 2 of 10

How often does someone actually look at store-level KPIs?

Not how often dashboards refresh. How often human eyes land.

Quarterly post-mortem

Reviewed when the QBR deck gets built.

Monthly

Standard monthly ops review.

Weekly

Weekly KPI cadence with the team.

Daily / real-time

KPIs on screens. Drift noticed within hours.

What your answer means

Why we ask this

The cost of slow eyes

$1.77T

lost to global retail stockouts every year.

Source: IHL Group, 2023

How often you look at store-level KPIs is how often the bleed gets noticed. Quarterly review means three months of compounding before someone sees it.

Detection · Question 3 of 10

Of your stores, how many do you understand well enough to spot a 1% margin drift?

The long tail is where the quiet bleed lives.

Top 5–10

The biggest stores get the attention. The rest are aggregates.

Top 25

A focused set. The long tail is reviewed quarterly.

Top 100

Most of the chain. A handful of small stores fall through.

All stores

Every store has eyes on it. No silent stragglers.

What your answer means

Why we ask this

The long-tail problem

40–60%

of total shrink in chains over 100 stores typically sits in the long tail.

Source: NRF, National Retail Security Survey

The biggest stores get the attention. The other 90% is where the slow-bleed problems hide, and rarely gets the same eyes.

Detection · Question 4 of 10

Who notices first when a store deviates from plan?

A manager calls it in

People-driven. By the time you hear it, it has been days.

Someone runs a manual report

Caught on the next scheduled report.

A dashboard threshold trips

Threshold-based alerts wired into a BI tool.

Automated alert to the right person

Anomaly detection routes to the owner of the metric.

What your answer means

Why we ask this

Time to detect

30–60d

average anomaly time-to-discovery when detection runs through people.

Source: Industry consensus (multiple sources)

By the time a regional manager calls something in, it has been days or weeks. Automated detection routed to the metric owner cuts this to hours.

Diagnosis · Question 5 of 10

When a KPI moves, do you know why within the same day?

Rarely, we guess

Theories get debated in the next meeting.

Sometimes, we run a query

Analysts pull threads when something looks off.

Usually, analysts dig in

A team can attribute most movements within a day.

Always, with automated attribution

Cause-level explanation arrives with the alert.

What your answer means

Why we ask this

Where the time goes

70%

of an analyst’s time goes to finding and prepping data, not analyzing it.

Source: Forrester

Most retail teams have the analytical muscle. They don’t have the joined data. The bottleneck is rarely “we need smarter analysts.”

Diagnosis · Question 6 of 10

Can someone ask “did the Cat 7 promo affect labor utilization?” and get an answer today?

Cross-system, ad-hoc, joined across POS + labor + finance.

No, different teams own that

POS and labor live in different worlds.

In 1–2 weeks

Needs a custom report, scheduled in.

Within a few days

Analysts can join the data with effort.

Same day or instant

Cross-system queries are a normal workflow.

What your answer means

Why we ask this

The hardest question

2–3 yrs

median “time to capability” that retailers cite for cross-system ad-hoc queries.

Source: NRF, IT Priorities, 2023

POS, labor, and finance live in different worlds. The questions that span them are the most valuable. They are also the rarest answered.

Action · Question 7 of 10

Do you measure whether an intervention actually moved the number?

Promo paused, planogram fixed, vendor escalated. Did it work?

Never tracked

Actions taken. Outcomes assumed.

Sometimes, manually

Tracked for the big ones, in someone’s notes.

Yes, in spreadsheets

Logged in a tracker the team maintains.

Closed-loop in the system

Action, outcome, attribution. All linked.

What your answer means

Why we ask this

The closed loop

2.5x

higher ROI on data spend at companies with closed-loop analytics.

Source: McKinsey

Most teams measure inputs (questions asked, dashboards built) instead of outputs (interventions that worked). The compounding lives in the closed loop.

Action · Question 8 of 10

From a new question being asked to a credible answer in hand:

Weeks

Goes into the analyst queue.

Days

Same-week turnaround if pushed.

Hours

Self-serve BI handles most.

Minutes

NLQ or instant query against unified data.

What your answer means

Why we ask this

The asking tax

6+ days

median ad-hoc question turnaround at large retailers.

Source: Forrester

When asking is expensive, fewer questions get asked. The hidden cost of slow analysis is curiosity that never makes it to the team.

Learning · Question 9 of 10

How is AI used in your operational data stack today?

None / exploratory only

No production AI in the operational stack.

A POC running somewhere

One pilot. Not yet driving decisions.

Limited prod (forecast, etc)

A model or two in production for specific tasks.

Embedded across ops

AI runs in the loop on operational data.

What your answer means

Why we ask this

Production AI in retail

~70%

of AI projects fail to deliver measurable business impact. Most stall at POC.

Source: MIT Sloan Management Review

Most retailers are running pilots. Few have production AI driving operational decisions. The gap between POC and production is where most of the spend dies.

Learning · Question 10 of 10

If a number on the KPI deck gets questioned, can you reproduce how it was computed?

Probably not

The original analyst may or may not still be around.

With effort

A few people can trace it back.

Mostly yes

Definitions live in a metrics layer.

One click

Lineage and query signed and reproducible.

What your answer means

Why we ask this

The reproducibility problem

~67%

of finance leaders are uncomfortable defending their numbers under audit-level scrutiny.

Source: Deloitte CFO Survey

If you can’t reproduce a number on demand, you can’t defend it in front of the board, a regulator, or an acquirer. Lineage is becoming table stakes.

Last step

Where should we send your score?

You’ll see your score on the next screen. The full breakdown with a fix plan goes to your inbox.

Full name

Work email

Company

Your role (optional)

Number of stores

Vertical

We don’t sell your data. By submitting you agree to receive your assessment results and follow-up from Ward.

What you’ll get

Your full diagnostic, in your inbox.

A score out of 100 with a tier classification. Flying Blind, Reactive, Aware, or Watching.
Per-category breakdown showing where the gaps are concentrated.
Your three biggest gaps. The questions you scored lowest on.
A three-step fix plan keyed to your tier and weakest dimensions.

Sent immediately. No deck. No follow-up sequence.

Your data maturity score

/ 100

Tier

Full breakdown sent to your inbox.

Per-question scores, your three biggest gaps, and what to do about each. Check your email in the next minute.

Walk through the results with us → See Ward advisory →

Why we built this

We ran 400+ retail locations before we built Ward. The questions in this assessment are the ones we wished someone had asked us at $40M, $80M, $120M. Score yourself honestly. We’ll meet you where you are.

Ward getward.ai

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You have hundreds of stores.How many are you actually watching?

Are the systems unified?

Do you catch drift early, with cause?

Does the loop close?

How many of these systems flow into a single source of truth?

How often does someone actually look at store-level KPIs?

Of your stores, how many do you understand well enough to spot a 1% margin drift?

Who notices first when a store deviates from plan?

When a KPI moves, do you know why within the same day?

Can someone ask “did the Cat 7 promo affect labor utilization?” and get an answer today?

Do you measure whether an intervention actually moved the number?

From a new question being asked to a credible answer in hand:

How is AI used in your operational data stack today?

If a number on the KPI deck gets questioned, can you reproduce how it was computed?

Where should we send your score?

You have hundreds of stores.
How many are you actually watching?