Manual

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Data analysis

The data analysis helps you spot typical quality problems in imported monitoring records early. The aim is to make imprecise or technically faulty name data visible before it leads to unnecessary hits, extra review effort or distorted results in list screening. Please always observe our important notes on data quality.

The data analysis has NO effect on the list screening (monitoring) of your records. Your records are screened in easycompliance exactly as they were imported.

What is the data analysis for?

The function checks name fields against rules for irregularities that in practice often arise from exports out of ERP, CRM or HR systems. This is not an AI-based assessment but a set of comprehensible rules. That keeps the hints transparent and reproducible.

The analysis does not claim to be exhaustive. Records not listed by the analysis can still be faulty if you do not follow our important notes on data quality.

The analysis works in two modes:

  • Automatic: newly imported or manually added records are analysed daily. If anything is flagged, you receive a notification by email.
  • Manual (in the customer portal via "Analyse records now"): you can start a full analysis of the selected module at any time, for example straight after a data import. Existing hints are deleted.

Which records are flagged as "irregular"?

A record is flagged as irregular if at least one of the following rules applies:

  • Empty or unusable name (no meaningful content).
  • Single-word name (in practice, names of people or companies do not usually consist of just one word).
  • No alphabetic characters (digits/symbols only).
  • Invalid or unusual special characters, or technical patterns (e.g. script, SQL, URL, email or file path patterns).
  • Encoding artefacts (e.g. "Müller").
  • Role or addressing additions in the name field (e.g. "Inhaber", "vertreten durch", "c/o", "attn").
  • Person-related suffixes (e.g. "e.K.", "eingetragener Kaufmann", "Einzelfirma").
  • Salutations/academic titles without company context (e.g. "Herr", "Frau", "Dr.", "Mag.", "Magister", "MBA", "LL.M.").
  • Academic titles in brackets (e.g. "(LL.M.)", "(MBA)", "(MSc)", "(Mag.)") without company context.
  • Successor suffixes without company context (e.g. "Nachf.", "Nachfolge", "Nachfolger", "successors", "successeur", "sucesor", "الخلف", "继任者").
  • Placeholder, dummy or test patterns (e.g. "Mustermann", "unknown", "dummy", "n/a"; in multi-word names, repeated "test").
  • Typical ERP/CRM boilerplate such as "Musterfirma GmbH", "Testkunde", "Kreditor XY".

Important context rules

  • Where there is a clear company context (e.g. GmbH, AG, KG, UG, LLC, Ltd., Inc., S.L., S.A., SARL, SAS, 有限公司, ذ.م.م), title and suffix matches are not automatically treated as irregular.
  • "M.", for instance, only counts as a salutation without company context (e.g. "M. Jean Dupont" is flagged, whereas "M. Rainer Schmidt GmbH" is not flagged automatically).
  • Correct brackets are structurally permitted; the content inside them may still be irregular, for example an academic title such as "(LL.M.)".
  • "Ingenieurbüro" (including its international variants), for example, counts as sufficient office/company context in multi-part names.

Notable examples from practice

Flagged:

  • Frau Monika Meyer
  • Herr Werner Mayer
  • Dr. Martin Schmidt
  • Dipl. Ing. Thorsten Baum
  • Rechtsanwalt Friedrich Maler
  • Kühn Nachf. J. Pierre
  • Hans Müller Nachfolger
  • Mag. Hans Müller
  • Magister Peter Schmidt
  • Hans Peter Schmidt (LL.M.)
  • Anna Müller (MSc)
  • M. Pierre Dupont
  • Autengruber Dipl.-Ing. Georg
  • Jean Dupont successeur (FR)
  • Pedro García sucesor (ES)
  • John Miller Successors (EN)
  • أحمد علي الخلف (AR)
  • 王伟 继任者 (ZH)

Not flagged automatically (because of company context):

  • ASP Ingenieurbüro Meyer Nachf.
  • Albrecht & Juncker Nachf. GmbH Co KG
  • Müller Nachfolger GmbH
  • Smith Successors Ltd.
  • M. Hainer GmbH
  • M. Schwarz Gas-Systeme GmbH
  • Mag. Müller Steuerberatung GmbH
  • Magister Consulting GmbH
  • 王伟继任者有限公司 (ZH)
  • شركة الخلف ذ.م.م (AR)

Where to find the analysis in the customer portal

  1. Open the module you want and its monitoring list. The link "Data analysis" takes you to the "Data quality analysis" page.
  2. Reading the key figures. At the top you see four tiles:
    • "Records in monitoring" – the total number of records you monitor in this module.
    • "Records with hints" – how many records have a hint.
    • "Data quality" – the percentage of records without a hint (e.g. 98.5 %).
    • "Start analysis now" – triggers the analysis manually.
  3. Understanding the hints. Under "Explanation of the quality markers" the markers are explained. Below that, two tables list the affected records: "Typical quality problems" and "Single-word names".
  4. Restarting the analysis (after an import, for example): click "Analyse records now" and confirm the prompt. Afterwards a note appears telling you the analysis is running and that you will receive an email when it finishes.
  5. Removing a single hint: click the remove icon in the table row and confirm in the "Remove hint" dialog.
  6. Removing all hints: click "Remove all hints" at the top right and confirm the prompt.

The "Data quality analysis" page with the tiles "Records in monitoring", "Records with hints" and "Data quality", the buttons "Export CSV", "Analyse records now" and "Remove all hints", and the tables "Typical quality problems" and "Single-word names". The "Data quality analysis" page: key figures at the top, actions beside them, below that the explanation of the markers and the two hint tables.

The four markers in the "Quality hints" column summarise the rules above: Unicode (unusual characters), Boilerplate (salutations, titles, suffixes), Artefact (placeholders, empty fields, encoding remnants) and Single word.

How to deal with flagged hints

Check that the name in question is spelled correctly. Refer to our important notes on data quality. Remove the faulty record from automatic list screening and, where applicable, from the hit overview in easycompliance (deleting names, deleting hits), since faulty records carry no meaning for the screening result. Then create the record again, either via the import functions or by adding it manually in the customer portal (adding names).

Also review the flagged records in your source system (ERP, CRM and so on) and correct the cause right there (export mapping, field assignment, character encoding). That way, re-importing brings only clean records into easycompliance.

Exporting the data

Below the two tables you will find a button "Export records from table". One click exports the records identified as irregular as a CSV file. You can then open that file in Excel, Numbers or any other spreadsheet program.

Important note on removing existing hints

If you remove a hint or use "Remove all hints", the corresponding entry is removed. Should the same record be flagged again later (after a new import, for example), it can reappear as a hint if you have not corrected it in the source system. Removing the hints does not remove the records from easycompliance.

Frequently asked questions

Do the hints affect my screening? No. The data analysis has no effect on list screening. Your records are screened exactly as they were imported.

Do I delete records when I remove a hint? No. Removing a single hint or all hints deletes no records. The record stays in monitoring – you merely hide the hint.

What does the "Data quality" tile show as a percentage? The share of your records without a hint. At 98.5 %, therefore, 1.5 % of records have at least one hint. If there are no records at all, a dash (–) appears.

When should I use "Analyse records now"? Straight after an import, for instance, when you want to see the hints immediately rather than waiting for the next daily run. Note that existing hints are deleted first and then recalculated.

A record is flagged although it is correct – what should I do? The analysis is rule-based and can be wrong in individual cases, for instance with unusual but correct company names. You can simply remove the hint. If such cases pile up, please contact our customer service via the chat so that we can improve the detection.

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