Demonstrations use synthetic business data.

See How We Solve Real Data Problems

Explore how we review supplier CSVs, Shopify catalogs, purchase orders and invoices, ecommerce migrations, and inventory feeds.

See What We Can Fix

SELECTED SYNTHETIC EXTRACT

Supplier CSV Cleanup & Repair

200 rows3 values normalized5 fields flagged

The problem. Supplier records mix formatted prices and missing or unclear fields.

What we checked. We compared the source and reviewed files by record and field, retaining ambiguous values for confirmation.

Before

Record	Field	Original
R0010	sku	—
R0020	sku	SKU-0019
R0030	price	—
R0040	price	unknown
R0050	stock	—
R0060	price	$65.25
R0070	price	$75.25
R0080	price	$85.25

After · review output

Record	Field	Result	Status
R0010	sku	—	Review
R0020	sku	SKU-0019	Review
R0030	price	—	Review
R0040	price	unknown	Review
R0050	stock	—	Review
R0060	price	65.25	Normalized
R0070	price	75.25	Normalized
R0080	price	85.25	Normalized

Findings. Three price representations were normalized. Five fields remain flagged; no missing SKU, price or stock was guessed.

Deliverables. Reviewed CSV and an exception list with record references.

supplier_before.csv supplier_after.csv supplier_exceptions.csv

SELECTED SYNTHETIC EXTRACT

Shopify Product Catalog Validation

200 rows reviewed4 field alerts

The problem. A catalog contains duplicate SKUs and missing product information.

What we checked. We checked 200 records for duplicate nonempty SKUs and missing SKU, name and price fields.

Before

Record	SKU	Product	Price
R0003	SKU-0003	Ceramic Coffee Mug - Small / 003	8.25
R0004	SKU-0003	Canvas Tote Bag - Small / 004	9.25
R0015	SKU-0015	Ceramic Coffee Mug - Medium / 015	—
R0025	SKU-0025	—	30.25

After · review output

Record	Field	Value	Reason	Status
R0003	sku	SKU-0003	Duplicate SKU: confirm the intended identifier	Needs review
R0004	sku	SKU-0003	Duplicate SKU: confirm the intended identifier	Needs review
R0015	price	—	Missing price: confirm amount	Needs review
R0025	name	—	Missing name: confirm product title	Needs review

Findings. Both rows sharing the SKU are flagged; missing values remain unresolved. This is a partial data review, not proof of Shopify import compatibility.

Deliverables. Catalog extract and an issue list for human review.

shopify_review.csv shopify_flags.csv

SELECTED SYNTHETIC EXTRACT

Purchase Order vs Supplier Invoice Review

60 PO lines60 invoice lines3 differences

The problem. The invoice differs from the purchase order on price or quantity.

What we checked. We matched lines by SKU and calculated each line delta as invoice quantity × invoice unit price minus PO quantity × PO unit price.

Before

SKU	PO qty	PO price
ITEM-005	10	15
ITEM-010	7	20
ITEM-020	9	10

After · review output

SKU	PO qty	Invoice qty	PO price	Invoice price	Delta USD	Type
ITEM-005	10	10	15	19	40	Price
ITEM-010	7	15	20	20	160	Quantity
ITEM-020	9	2	10	10	-70	Quantity

Findings. Three line differences total a signed net variation of $130. Differences require confirmation; this is not a confirmed loss or recovery.

Deliverables. Matched line comparison and a list of exceptions for investigation.

po.csv invoice.csv po_invoice_differences.csv

SELECTED SYNTHETIC EXTRACT

Ecommerce Migration Data Rescue

200 source records200 destination records5 findings

The problem. A migrated catalog differs from the source.

What we checked. We matched records by stable example ID and compared SKU, price and stock, retaining unmatched records.

Before

Record	Field	Source
R0010	price	15.25
R0020	stock	23
R0030	sku	SKU-0030
R0050	record	Present
R0501	record	Not present

After · review output

Record	Field	Destination	Action
R0010	price	99.99	Review change
R0020	stock	0	Review change
R0030	sku	—	Review change
R0050	record	Not present	Confirm
R0501	record	Present	Confirm

Findings. Three records changed, one is missing and one extra destination record is preserved for a human decision.

Deliverables. Source-to-destination findings and both original synthetic files.

migration_source.csv migration_destination.csv migration_findings.csv

SELECTED SYNTHETIC EXTRACT

Inventory & Supplier Data Standardization

3 source files200 input records200 retained

The problem. Three suppliers provide the same kinds of fields under different headings.

What we checked. We mapped the original column names into a common schema and compared source and output records.

Before

Source	Original heading	Standard heading
Supplier 1	ref	id
Supplier 1	supplier_sku	sku
Supplier 1	name	name
Supplier 1	unit_price	price
Supplier 1	units	stock
Supplier 2	row_id	id
Supplier 2	item_code	sku
Supplier 2	description	name
Supplier 2	price_usd	price
Supplier 2	available	stock
Supplier 3	record	id
Supplier 3	part_number	sku
Supplier 3	product	name
Supplier 3	cost	price
Supplier 3	qty	stock

After · review output

ID	SKU	Product	Price	Stock	Source
R0001	SKU-0001	Organic Cotton T-Shirt - Small / 001	6.25	4	1
R0004	SKU-0004	Canvas Tote Bag - Small / 004	9.25	7	1
R0002	SKU-0002	Stainless Steel Water Bottle - Small / 002	7.25	5	2
R0005	SKU-0005	Bamboo Cutting Board - Small / 005	10.25	8	2
R0003	SKU-0003	Ceramic Coffee Mug - Small / 003	8.25	6	3
R0006	SKU-0006	Insulated Lunch Bag - Small / 006	11.25	9	3

Findings. All 200 source records are retained in the consolidated file. The example does not establish that every business conflict has been resolved.

Deliverables. Three source files and a consolidated CSV retaining the source reference.

supplier_feed_1.csv supplier_feed_2.csv supplier_feed_3.csv consolidated.csv

All examples use synthetic data. Tables show selected records; download the files to inspect the complete data.