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Solution · Data quality & validation

One readiness score instead of twenty trackers.

Validation rules per object, a scorecard per site and programme, top offending rules with an owner next to each one, and a correction queue business users actually work. The data gets fixed in the source system; the steering committee gets one number and its trend. Built on SAP data; runs on any source — ERP tables, databases, flat files.

Start a 30-day PoC on your data See vise DMW →
since 2017
DQ before every rollout wave at a global brewer
1 tender
selected ahead of two global DQ vendors
5
DQ dimensions per object
vise DMW cleansing progress: clean records and clean fields per operating company
Cleansing progress per operating company — Nordic Rollout demo program

01

What usually goes wrong.

01 Twenty trackers, no number Each country keeps its own Excel of “known issues”. Nobody can say how ready the vendor master is, so the go-live date is a feeling, not a fact.
02 Errors found at load time IT discovers the bad records when the load fails, weeks after the business could have fixed them. Every iteration costs a cycle.
03 Business owners can’t see what to fix Error logs are written for developers. The person who can actually correct a payment term never sees a list they can act on.

02 · How it runs in vise DMW

The same discipline at every step.

  1. Steps 1–2 · Extract and scope

    Every site, every object, one programme.

    Extracts from SAP (or any ERP) per site, scoped to the objects in the wave: vendor, customer, material, open items, cost centres. Milestones and owners live on the project, so “who signs off Norway in 28 days” is a field, not an email.

    Programs and projects in vise DMW: Nordic Rollout with Norway and Sweden
    Program → project → site
  2. Step 3 · Validate

    Rules as code, owned by a named person.

    Format, list-of-values, conditional, custom and aggregate rules per field, each with severity, scope, documentation and an owner. AI drafts a rule from a sentence; a person switches it on. The same rule library runs for every site in the programme.

    vise DMW validation rules library filtered by site
    156 rules across 119 fields and 7 objects (demo)
  3. Step 4 · Profile

    One score, five dimensions, per object.

    Completeness, validity, conformity, consistency and uniqueness, rolled up into a scorecard per site and programme with the trend since the baseline. This is the number the steering committee gets every week.

    vise DMW DQ scorecard by dimension and object
    DQ scorecard — Norway, by dimension
  4. Correction loop

    Fix it in the source. Validate again. Watch the number move.

    Error details go to the people who own the data, by object and by rule, with the reason in plain words. In a data-quality project corrections are always made in the source system — never in DMW — then the object is re-validated and the scorecard updates. Top offending rules show where the effort pays off first.

    vise DMW top offending rules with six-week trends and owners
    Top offending rules — errors, six-week delta, owner
  5. Step 8 · Report

    The Friday page, written by the system.

    Weekly summary with movers and regressions, cleansing progress per operating company, attention points and meeting notes per project, Superset dashboards on the same validated data. No export, no slide deck.

    vise DMW weekly summary report: valid records, biggest improvers, regressions
    Weekly summary — who improved, what regressed

03 · 30-day proof of concept

What you have after 30 days.

  • One object from your system extracted, profiled and validated against a first rule set
  • A data assessment report: scorecard, top offending rules, what blocks the load
  • A working DMW project your team can keep running — not a slide deck
  • A proposal for the rule library and the correction process for the full programme
How the PoC and the licence work

In production

DQ before every wave since 2017 Global brewing group vise DMW validates and profiles master data ahead of each SAP rollout across the group’s operating companies. Loads go through the existing BODS pipeline; DMW is the readiness gate. Read more Public tender Energy infrastructure operator Selected as the data quality platform ahead of two global data-quality vendors. Rules, scorecards and correction queues, with no migration attached. Read more

04 · FAQ

Straight answers, the same in every review.

For SAP architects
01Do we have to migrate with DMW to use it for data quality?
No. Our longest-standing customer runs DMW as the readiness gate in front of BODS. vise DMW can validate, profile and report while any load tool you already have does the loading.
02Where do corrections happen?
In a data-quality project, always in the source system. DMW shows what to fix and why, the owner fixes it in SAP, DMW re-validates. There is no second copy of the truth.
03How is this different from Ataccama or Informatica?
vise DMW is built around SAP objects and migration waves: rules per object, scorecards per site and programme, a correction loop for business users, and the same platform carries on into migration and everyday changes. Priced by sites and users, not an enterprise platform licence.
04What does the AI do here?
It drafts validation rules from a sentence and tests them in a sandbox. A named person activates every rule. Nothing the AI writes touches your data unapproved.

Get one readiness score for one object in 30 days.

Pick the object that worries you most. We profile it, validate it and show you the scorecard — on your data, in a project you keep.

Start a 30-day PoC Watch the demo