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SAP master data quality · Customer · Material · Sales Area

Every SAP record checked overnight. One number on the desk by eight.

Ixia DQ runs SAP-specific data quality rules against your Customer, Material and Sales Area master data every night, writes the results to SQL, and puts them in front of the business as a Power BI dashboard and a daily email. Nothing is installed on SAP. Live in days.

Request a walkthrough See the daily update

Pre-built rules for the three SAP master data domains. Your own conventions added in the Rule Builder, without code.

3SAP master data domains: Customer, Material and Sales Area
55pre-built SAP rules, plus a no-code Rule Builder for your own
8Power BI report pages, from executive summary to failed records
Nightlyevery record checked after the SAP extract lands; failure files follow
08:00daily update in every data owner's inbox, no login needed
The daily update

The whole position in one email, every morning.

Sent at 08:00 to the people who own the data and the people who feel the errors: master data, finance and operations leads. It reads top-down in under a minute and names the handful of rules behind most failures, so the morning conversation starts with what to fix.

Current positionOverall and per-domain failure rate against the report thresholds, amber from 1% and red from 5%, with the 30-day trend.
Top rules by domainThe three rules with the most failures per domain and the change since the previous run. The same figures as the dashboard's Top 10 rules visual.
Last four weeks in reviewDay by day: failing records per run, what each run added or removed, and whether fixes are sticking.
DQ AI Daily Update – 27 Mar 2026 – 0.6% failure rate, 3,781 failures in latest run
From Ixia DQ AI
Sent Fri 27 Mar 2026 08:00
To Master data, finance and operations leads
Data quality run · 27/03/26
0.6%Good
overall failure rate · 3,781 failures across 612,400 records
failure files of 27/03/26
Latest run: 3,781 failures
▼ 64 (1.7%) vs previous run
▼ 49% vs 30 days ago

In this email: 1 Current position · 2 Top rules by domain · 3 Last 4 weeks in review

1Current position

What it shows: the overall and per-domain failure rate from the latest failure files, against the report thresholds (amber from 1%, red from 5%), and the number of failing records in each nightly run over the last 30 days.
Why it matters: records that fail a rule cause errors further down the line in orders, pricing and deliveries. Any domain in red needs an action owner.
  • Data quality improved in the last run.
  • 0.6% failure rate across 612,400 records.
  • Material accounts for 79% of failures.
  • 10 rules flagged data issues, 8 of them high severity.
Records vs failures – files of 27/03/26
Customer
74 failures · 41,220 records · Good
0.2%
Material
2,988 failures · 512,640 records · Good
0.6%
Sales
719 failures · 58,540 records · Watch
1.2%
Total
3,781 failures · 612,400 records · Good
0.6%
under 1%1 – 5%over 5%
Failing records – 30-day trend
Latest run3,781
vs previous▼ 64
30-day avg5,180
30-day range3,781 – 7,420

Failing records per nightly run, 2 – 27 Mar (latest run darker).

2Top rules by domain

What it shows: the three rules with the most failures in the latest run for each domain, with the change since the previous run.
Why it matters: a handful of rules account for most failures. Fixing these gives the biggest improvement for the least effort.
Material · 2,988 failures in latest run (79%)▼ 10 vs prev
IXIA014Duplicate EAN/GTIN across materials2,214▲ 7
IXIA012EAN/GTIN not maintained536▼ 12
IXIA013EAN/GTIN check digit invalid184– 0

Top 3 rules = 98% of material failures in the latest run.

Sales · 719 failures in latest run (19%)▼ 56 vs prev
IXIA015Delivery plant is blank on sales record402▼ 31
IXIA016Distribution channel is blank236▼ 9
IXIA017Division is blank81▼ 16

Top 3 rules = 100% of sales failures in the latest run.

Sales failures by sales organisation – files of 27/03/26
Sales orgSales areasFailures% fail
1000 Wholesale14,1204022.8%
2000 Retail9,8601181.2%
3000 Export2,410612.5%
12 other sales orgs32,1501380.4%

Wholesale (1000): 402 failures against 14,120 sales areas (2.8%), 56% of all sales failures.

Customer · 74 failures in latest run (2%)▲ 2 vs prev
IXIA006Account group (KTOKD) is blank41– 0
IXIA007Region is blank21▲ 3
IXIA005Duplicate Tax/VAT number12▼ 1

Top 3 rules = 100% of customer failures in the latest run.

3Last 4 weeks in review

What it shows: day by day, the number of failing records in each nightly run and how many it added or removed compared with the run before.
Why it matters: shows whether fixes are sticking, when big clean-ups happened, and whether failures creep back between clean-ups.

Failing records fell from 6,840 to 3,781 over the last 4 weeks. The biggest drops were 11 Mar (▼ 1,120) and 6 Mar (▼ 731). 14 runs improved, 9 got worse and 3 were unchanged.

▼ improved— no change▲ got worseShaded: a move of 100 or more. Outlined: latest run.
The Power BI dashboard

The same numbers, with a filter on everything.

The dashboard reads the same run and result tables the email is built from, so the two never disagree. The Executive Summary answers the morning's questions on one screen. Filter by run, domain, rule, SAP table, sales organisation or distribution channel and every visual follows; the Failed Records page goes down to the record keys.

Four cards, four questionsCurrent risk exposure (failures in the latest run), the direction we're on (change against the previous run), immediate risk (high-severity rules impacted) and a noise filter (the latest run against the average of the last seven).
Domain rates against thresholdsOverall and per-domain failure rate with the records and failures behind each, coloured amber from 1% and red from 5%.
30-day trendFailing records per nightly run with the 30-day average, the range, and the change against 30 days ago. A clean-up and a relapse are both visible the next morning.
Top 10 failures by ruleShare of total and change since the previous run, each rule coloured improving, unchanged or worsening, with its business name from the rule catalogue.
Your tenantPublished in your own Power BI tenant with the Microsoft sign-in people already have. No extra accounts, no data leaving your estate.
Request a dashboard demo
Ixia DQ AI ReportsData Quality Run: 27/03/26
Executive SummaryDashboard10-DayTrendsGridSplitRules by SAP tableFailed Records
Domain AllSales org AllDist channel All
Failures latest run3,781▼ 2%vs 3,845 previous runCurrent risk exposure
Change vs previous run64▼ fewer failuresFailures decreased vs previous runDirection we're on
High-severity rules impacted8▶ 0%vs 8 previous runImmediate risk
Failures vs 7-run average3,781▼ 464 belowvs 4,245 avg of last 7 runsContext / noise filter
0.6%overall failure rate
3,781 failures across 612,400 records
Customer
0.2%74 failures · 41,220 records
Material
0.6%2,988 failures · 512,640 records
Sales
1.2%719 failures · 58,540 records
under 1%1 – 5%over 5%
Latest run · 27 Mar3,781
vs previous run▼ 64 (−1.7%)
vs 30 days ago▼ 49%
30-day avg5,180
30-day range3,781 – 7,420
Top 10 failures by rule · failures, % of total, vs previous run
IXIA014
2,214 59%▲ 7Duplicate EAN/GTIN across materials
IXIA012
536 14%▼ 12EAN/GTIN not maintained
IXIA015
402 11%▼ 31Delivery plant is blank on sales record
IXIA016
236 6%▼ 9Distribution channel is blank
IXIA013
184 5%–EAN/GTIN check digit invalid
IXIA017
81 2%▼ 16Division is blank
IXIA008
54 1%▼ 5Material description is blank
IXIA006
41 1%–Account group (KTOKD) is blank
IXIA007
21 1%▲ 3Region is blank
IXIA005
12 0%▼ 1Duplicate Tax/VAT number
improvingunchangedworsening
Leading drivers
  • Data quality improved in the last run.
  • 0.6% failure rate across 612,400 records.
  • A total of 3,781 data quality failures were recorded.
  • Material domain accounts for 79% of failures.
  • 10 rules flagged data issues.
  • 8 high-severity rules flagged data issues.

The Executive Summary page, recreated with illustrative example data. The live report is built in Power BI on the Ixia DQ result tables.

Executive SummaryThe four cards, domain rates, 30-day trend, Top 10 rules and leading drivers for the latest run.
DashboardOne run in depth: rules impacted, failures by domain, by rule, by SAP table and field, by sales organisation, with the business reason behind each rule.
10-DayEvery rule by domain across the last ten runs, an arrow in each cell showing whether it moved up, down or held.
TrendsOne small chart per rule over the last ten runs, so a rule creeping back is seen before it matters.
GridRun-level list of failures by rule with the failure reason, domain, SAP table and SAP field. Export-ready.
SplitShare of failures by rule for the selected run, with drill-through to the failed records behind any slice.
Rules by SAP tableFailures per rule and a daily stacked view by SAP table, so the master data team sees which tables need work.
Failed RecordsRecord level: rule, severity, sales organisation, distribution channel, division and the field that failed, filtered by domain.
How it works

A nightly loop that ends in SAP, not in a spreadsheet.

Ixia DQ sits beside SAP, reads from SQL views your BI team already maintains, and writes every result to a run log and a result table. Everything the business sees comes from those two tables.

01 · SOURCE

SAP extracts to SQL views

One view per domain: Customer (KNA1, KNB1, KNVV), Material (MARA, MARC, MVKE, MAKT) and Sales Area. Ixia DQ never queries SAP tables directly.

02 · NIGHTLY

Rules run against every record

The full rule pack across the three domains, each rule with a severity, a reason and a fix. Inactive clients, plants and test accounts are excluded by agreed lists.

03 · GRAIN

Failures counted at business grain

Denormalised views carry many rows per material or customer. Every rule declares its reporting grain, so a failure is one material, not eleven plant rows.

04 · RESULTS

Run log and result table

Each run gets a RunID in dq.DQ_Run; every failure lands in dq.DQ_Result with its record key, rule, severity and reason.

05 · OUTPUTS

Dashboard, email, failure files

Power BI and the 08:00 email read the result tables. A failure file per domain gives the key, failed field, current value, expected value and how to fix it.

06 · FIX IN SAP

Correct the master, confirm overnight

Owners fix records in SAP (XD02, MM02, VD02). The next run shows what was resolved, what persists and what is new.

Declared, never inferred

Reporting grain, severity and exclusions are written into the rule, not guessed from the data. A missing grain column stops the rule and says so.

Blocked is not passed

If a view lacks a column a rule needs, the run writes a blocker row naming the column. Dashboards exclude blockers from the failure count and list them as gaps to close.

One set of numbers

The email, the dashboard and the failure files are all built from the same run. When the business asks why two reports disagree, the answer is that they cannot.

The rule library

55 SAP rules on day one. Yours added without code.

The generic pack covers the checks every SAP site needs: completeness of the fields that drive tax, logistics and pricing, GS1-valid barcodes, duplicates across the master. A customer pack adds your own conventions, written in the Rule Builder with a logic type, a severity, why it is flagged and how to fix it.

Customer

KNA1 · KNB1 · KNVV · general, company code and sales data
  • IXIA001
    Customer name (NAME1) is blankCannot be identified, billed or contacted
  • IXIA004
    Country is blankCountry drives tax, legal and logistics decisions
  • IXIA005
    Duplicate Tax/VAT numberOne legal entity, several customer records
  • IXIA006
    Account group (KTOKD) is blankControls field selection for the customer

Material

MARA · MARC · MVKE · MAKT · basic, plant and sales data
  • IXIA014
    Duplicate EAN/GTIN across materialsTwo products, one barcode, wrong pick
  • IXIA012
    EAN/GTIN not maintainedCannot trade via EDI, GS1 or retailer systems
  • IXIA013
    EAN/GTIN check digit invalidFails scanning at every touchpoint
  • IXIA009
    Base unit of measure is blankNo quantity-based transaction can post

Sales Area

KNVV · sales organisation, channel and division
  • IXIA015
    Delivery plant is blank on sales recordNo supplying location, order creation fails
  • IXIA016
    Distribution channel is blankSales area key incomplete
  • IXIA017
    Division is blankSales area key incomplete
  • Yours
    Delivery priority, order combination, transport zoneSite conventions added in the Rule Builder

Logic types in the Rule Builder

Most rules are a form, not a program. Pick the type, the field and the values; the engine does the rest. Complex checks drop to a Python function.

blank_checknot_blankvalue_in_listvalue_not_in_listvalue_not_equalsregex_matchconditional_blank_checkconditional_value_equalsduplicate_keygtin_valid_lengthgtin_valid_check_digitpython_function

Every rule is in exactly one state

Rule status is governed in one place and reported everywhere, so a quiet rule is never mistaken for a clean one.

ENABLEDRuns nightly; failures persist to the result table.
DISABLEDSwitched off with a written reason, on the data owner's confirmation. Reported as disabled.
RETIREDNo longer applicable. Kept in the catalogue so history still reads.
BLOCKEDEnabled, but a required column is missing from the view. One blocker row per night names it.
AI in the workspace

Findings explained in SAP terms, with the transaction to fix them.

After a run, the workspace hands the structured findings to the model: domain, record counts, the top failing rules and their trend. The narrative that comes back names rule codes, counts and the SAP transaction to use, so it can be forwarded as is.

Executive summary

Where the failures are, which domain owns them and whether the position improved since the last run. Written for the steering committee, not the DBA.

Material carries 79% of failures. IXIA014 (duplicate EAN/GTIN) is 2,214 of them and rose by 7. Sales improved by 56 after the delivery-plant clean-up.

Root cause and fix plan

Patterns across rules, ranked by effort against impact, each with the SAP transaction and the field to correct.

Fix plan 1: resolve 2,214 duplicate GTINs in MM02, Basic Data 1, field EAN/UPC. Top 3 material rules are 98% of the domain's failures.

Profiling and detection

Upload a file or point at a view and the workspace classifies the columns, recognises the SAP dataset type and profiles nulls, distinct values and samples before any rule runs.

Detected: Customer master (KNA1 + KNVV), confidence 0.94. 41,220 rows, 42 columns, 3 columns over 50% blank.
Why Ixia DQ

Built for SAP programmes in South African retail and pharma.

Live in days, not months

A first deployment goes from SQL views to rules, result tables, dashboard and daily email in about two weeks. No SAP add-on, no agent on the SAP host, no new logins for the business.

Rules written against real SAP data

The library grew from a live SAP deployment, not a whiteboard: GTIN duplicates, sales-area completeness, plant determination, account groups. The rules a migration programme actually needs.

Governance, not just reporting

Rules change only on the data owner's written confirmation, and the reason travels with the rule. Exclusion lists, disabled rules and blockers are visible in the outputs, so a clean number can be trusted.

Your tenant, your data

Runs on your SQL Server, reports through your Power BI tenant, emails from your mail system. Credentials live in environment variables, never in code.

Next step

See your own master data in the dashboard within two weeks.

Bring one domain view. We run the generic rule pack against it, show you the dashboard and the morning email on your numbers, and agree the thresholds with your data owners.

Ixia Consulting · Cape Town
info@ixiaconsulting.co.za
+27 21 403 6581

Ixia Data Quality AI 2.1 · SAP-aware data observability, profiling, rules and AI insights.

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