Rules catch what may be wrong. Streaming DataOps fixes what is wrong, using real-world data. Carrier bills, broker filings and supplier ASNs prove the defect, and the fix lands at the source in SAP. Zero copies. Zero code.
Three short videos: why you pay twice for bad data, evidence-based validation against a carrier bill, and profiling, scoring and dedupe on a live SAP client.
Once today, as surcharges, penalties and rework. Again tomorrow, as delay in your S/4HANA, AI and supply chain programs. These numbers show the size of the leak.
Rules catch what may be wrong. Streaming DataOps fixes what is wrong, using real-world data. A rule can say a weight looks odd. Only the carrier bill can say what the box actually weighed. Streaming DataOps reads that evidence against the live SAP record, and the correction goes back to SAP through your own approval flow.
One wrong weight, one stale address, one wrong HTS code. The same root cause shows up as four different bills across the business.
| Value Driver | What Operations Needs | What Bad Master Data Delivers |
|---|---|---|
| 01 · Information CIO |
Clean, trusted master data feeding S/4HANA, AI agents and analytics, so programs go live on time. | Migrations stall on data cleanup. AI agents fed wrong master data make wrong decisions, faster. |
| 02 · Inventory COO |
Correct weights, dimensions and units of measure behind every pick, pack and ship decision. | Carrier surcharges on every affected package. Skewed packaging, slotting and freight plans. |
| 03 · Cashflow CFO |
Correct HTS codes, countries of origin and partner data, so duty and compliance costs are right the first time. | Customs penalties, overpaid duty, address correction fees and rework that never shows up in one budget line. |
| 04 · Workflow All operators |
Defects stopped at the first step, before they cascade into orders, deliveries and invoices. | Fixing a weight costs nothing at record creation. At the carrier invoice it costs $33, often thousands of times a year. |
Enter your package volume, defect rates and contracted fees. The math runs in your browser; nothing is sent anywhere.
| Defect | Fee per Package ($) | Share of Packages (%) | Packages Hit | Annual Cost |
|---|---|---|---|---|
| Bad or incomplete address | ||||
| Wrong dims or weight Additional handling |
||||
| Wrong dims Oversize or large package |
||||
| Total annual surcharge leak | ||||
leaking every year, before a single customs penalty or delayed program.
Default fees are 2026 published examples from FedEx and UPS. Additional handling and oversize fees vary by service, zone and contract; replace them with your own.
A defect that is free to fix at creation gets more expensive at every step it survives. Streaming DataOps stops it at the source, so every step downstream gets the fix.
| Stage | How the Defect Shows Up | What Streaming DataOps Does | What This Replaces |
|---|---|---|---|
| 01 · Record Created | Missing dims, zero weight, net weight above gross, wrong unit. | Scores the record against 22+ rulesets the moment it exists. Flags it to the operator. | Defect found months later, by someone else, at higher cost. |
| 02 · Order and Delivery | Wrong packaging, wrong carton, wrong freight estimate. | Surfaces the defect with the affected orders, before shipping. | Rework at the dock. Expedites to recover. |
| 03 · Carrier Invoice | Address correction, additional handling and oversize surcharges. | Reads billed dims and weight against master. Triages green, yellow, red. | Paying surcharges indefinitely. Disputes by spreadsheet. |
| 04 · Customs Entry | HTS code or country of origin filed differently than master. | Compares the broker entry summary with master. Proposes classification fixes. | Penalties and duty variances found at audit time. |
| 05 · Transformation and AI | Duplicates and defects carried into S/4HANA, Fabric and AI agents. | Dedupes and cleans pre-existing records before they move. | A cleanup workstream that delays go-live. |
Fix at the source, fix everywhere. Because the correction lands in SAP itself, not in a copy, every report, workflow and agent that reads the record gets the fix at once.
Three structural gaps, each compounding the others. They explain why governed data can still cost you money.
| # | Structural Gap | Why It Has Become Untenable |
|---|---|---|
| 01 | Rules check plausibility, not truth. A weight of 2 kg passes every rule even when the box weighs 20. |
Only outside evidence (the carrier bill, the customs filing) proves the real value. Rules alone leave the costliest defects untouched. |
| 02 | Governance at creation does not watch the workflow. Records approved years ago keep running every day. |
Pre-existing records, supplier changes and new carrier rules create defects that creation-time workflows never see. |
| 03 | Cleaning a copy does not fix the source. Extract, clean, reload projects drift out of date before they finish. |
The copy is clean, SAP is not, and the next shipment uses SAP. Every copy is also one more thing to govern. |
Proof from the largest catalog in retail. Walmart says the quality of its catalog data affects nearly everything it does. It used AI to create or improve more than 850 million catalog data points, work it estimated would have needed about 100 times the headcount by hand. The lesson: fix the data first, then scale automation. Source: Walmart Q2 FY2025 earnings call, August 2024.
Streaming DataOps closes the loop between a defect and its fix inside the SAP security model, on zero-copy live data, with your people approving every change.
| Loop Stage | What Streaming DataOps Delivers | What This Replaces |
|---|---|---|
| Profile Discovery at field level |
360-degree profiles across SAP and other sources. Business profile (min, max, median, distinct, outliers) and technical profile (type, length, nullability). | Sample-based assessments by consultants. Weeks before anyone sees a number. |
| Score Data governance register |
Statistical and stochastic rules sort every defect by severity: completeness, consistency, plausibility, impossibility, statistical outliers. | Static rule lists that flag everything equally. Teams drown in noise. |
| Dedupe AI fuzzy match |
Flags likely duplicate materials for consolidation. Runs in your tenant with no pay-per-use AI. A person approves every merge. | Exact-match checks that miss "Casing (aluminum)" vs. "Casing, aluminium". |
| Prove Real-world evidence |
Reads carrier bills, broker filings, denied party lists and supplier ASNs against master. Triage: matches, inside tolerance, outside tolerance. | Guessing which rule violations actually cost money. |
| Fix Write back at the source |
Each correction is a change request into your existing approval flow, under native SAP authorization. Fully auditable. | Tickets, spreadsheets and manual TCODE edits with no audit link. |
| Evidence | Detects | Writes Back To |
|---|---|---|
| Carrier billing file | Billed dims and weight vs. master | Material master: dims, weight, volume |
| Denied party lists (OFAC, BIS, EU) | Vendor and customer master vs. current list | Vendor and customer master: block, flag for review |
| Broker entry summary | HTS code and origin filed vs. master | Material master: classification, country of origin, tariff rate |
| Supplier ASN or remittance | Delivered quantity and UoM vs. master | Material and vendor master: UoM, packaging |
Zero-code visual rules. 22+ rulesets and 61 operator templates out of the box. Each module runs on USB4SAP: zero-copy, SAP-certified, live data, native SAP authorizations.
Field-level business and technical profiles across SAP, non-SAP databases and cloud apps.
Statistical and stochastic rules score each record and rank findings from critical to review.
AI fuzzy matching flags likely duplicates for review and consolidation, in your tenant.
Compare master data with carrier, customs, compliance and supplier evidence, then correct it at the source.
| Module | Works When | Fails When |
|---|---|---|
| Discovery and Profiling | You do not know the true state of your master data, or you are scoping an S/4HANA or AI program. | Master data is small, recently rebuilt, and already fully profiled. |
| Governance Register | Defects are known to exist but nobody can say which ones matter most. | A mature MDG process already blocks every defect class at creation and no legacy records remain. |
| Dedupe and Consolidate | Years of acquisitions, plants or legacy migrations have left near-duplicate materials or partners. | A single, recently cleansed catalog with strict creation controls. |
| Validate and Write Back | You ship parcels, import goods or pay surcharges and penalties you suspect come from master data. | You have no external evidence feed (no carrier, broker or supplier files) to compare against. |
Master data governs. Governance polices. Streaming DataOps filters. Each layer does one job, on the same live SAP data, with no new copy to manage.
SAP MDG, or your MDG of choice, controls workflows at record creation. Streaming DataOps routes its corrections back through it.
Purview keeps the catalog and quality scores. Streaming DataOps supplies the evidence-backed findings behind them.
Live rules at the source, through every step of the workflow. The operator controls the action. Results in Excel and Microsoft Fabric.
Live access to 1.3M+ SAP objects: 450,000+ business-named semantic objects and 850,000+ tables and views with delta. Runs on SAP ECC, S/4HANA, non-SAP databases and cloud apps.
Certifications: SAP NetWeaver Certified · Microsoft Certified (Excel, Power BI, Fabric) · USPTO Patent · Azure Marketplace Published · In Production since 2021
Results in the first week. No configuration project for STREAM Now. No data leaves your environment under STREAM Activate.
Pre-built Streaming DataOps rulesets and operator templates on demand. SaaS, on-premise, or customer cloud.
For: Operations and master data teams who want findings this week.
STREAM Engine in your cloud or on-premise. Data never leaves your environment. 100% native SAP application authorizations.
For: Regulated industries and enterprises with strong data sovereignty requirements.
SIs and enterprise teams add their own rulesets, evidence sources and templates on STREAM Engine.
For: SIs building data quality and S/4HANA readiness practices on live SAP.
The same root cause (unproven master data) shows up differently by function. Start with the question your executive will ask.
| Persona | Their Leak | Streaming DataOps Entry Point | Ask Them |
|---|---|---|---|
| CIO | S/4HANA and AI programs slowed by data cleanup. Another tool, another copy, another integration project. | Discovery and Profiling + Governance Register. Zero copy, native SAP security, results in week one. | How much of your S/4HANA or AI timeline is really data cleanup? |
| COO | Carrier surcharges, dock rework and freight plans built on wrong weights and dimensions. | Validate and Write Back on carrier billing files. | How many packages last year were billed at a different weight than your master says? |
| CFO | Surcharges, customs penalties and duty variances spread across budgets nobody owns. | Surcharge leak calculator + evidence validation on carrier and broker files. | What did master data defects cost you in surcharges and penalties last year? |
| CDO | Quality scores that look fine while operations keep paying for defects. | Governance Register + Dedupe, feeding Purview and Fabric. | Which of your quality scores are backed by real-world evidence? |
Streaming DataOps runs on SAP-certified, zero-copy streaming. These are the concerns we hear most from IT, master data and audit teams, with direct answers.
No. SAP MDG governs records at creation. Streaming DataOps checks existing records against real-world evidence across the workflow and routes every correction back through your MDG or existing approval process. It makes your MDG investment pay off downstream.
No. Streaming DataOps reads SAP live via zero-copy streaming, under native SAP authorizations. There is no extract, no shadow database to clean, and your data stays in your tenant.
Your people. Each correction is raised as a change request into your existing approval flow, under native SAP authorization, and lands in the SAP audit trail. Nothing is written without that approval.
Typically within the first week. Ops-ready business objects, 22+ rulesets and 61 operator templates are available out of the box, and rules are built visually with zero code.
No. Fuzzy matching runs in your tenant with no pay-per-use AI fees, and your data is not sent to an outside model.
Carrier billing files, denied party lists (OFAC, BIS, EU), broker entry summaries, and supplier ASNs or remittances. Each is compared against the matching SAP master record and triaged against your tolerance.
More questions about data governance, platform architecture, or security? See the full FAQ or talk to our technical team.
Pilot Streaming DataOps on your own master data and your own carrier bills. In the 500,000-package example, recovering two thirds of the surcharge leak is worth about $1.47M a year.