Vizzi Analytics helps teams turn raw data into useful answers across audit, finance, risk, compliance, and general data analysis. Explore common ways teams use Vizzi to investigate transactions, compare datasets, automate recurring analysis, and identify results that deserve further attention.
Duplicate payments • journal entries • sampling • full-population testing
Reconciliations • AP and AR analysis • unusual transactions • financial comparisons
Clean • join • transform • summarize • identify outliers
Exception testing • monitoring • recurring controls • rule-based analysis
Apply tests across complete populations, document how results were produced, and focus reviewer time on the transactions that deserve attention.
Import a complete accounts payable population and look for payments that may have been made more than once. Compare fields such as vendor, invoice number, amount, and date to surface exact matches and potential duplicates for investigation. Where it helps, similar values can be compared to find near matches rather than only identical ones.
Unusual journal activity can be identified with filters, calculated fields, and exception tests rather than a single dedicated “journal entry” button. Teams typically look for items that fall outside expected patterns and then review the supporting detail.
Compare vendor master data with employee records to identify possible relationships that deserve follow-up. Joins, cleansing, standardization, and fuzzy matching can help align common identifiers before the comparison.
Vizzi can work with datasets beyond spreadsheet limits, so tests can be applied across the full population rather than only a sample. That is useful when completeness matters and when reviewers want to see how the entire file was treated.
When sampling is required, users can select samples as part of a repeatable workflow and keep the steps used to prepare the population. The workflow itself becomes part of the documentation: what was imported, how it was filtered, and how the sample was drawn.
Use visual workflows to reconcile files, analyze payables and receivables, and investigate unusual activity without rebuilding the same analysis every period.
Bring two files into a workflow, join them on one or more keys, and separate matched items from exceptions. Differences can be calculated in the flow so the next period is a data refresh rather than a rebuild.
AP analysis typically combines duplicates testing, vendor review, and filters for large or unusual payments. The same workflow can include payment timing and terms using calculated fields.
AR files can be summarized by customer, reviewed for unusual credits, and compared with balances from another system. Aging-related analysis can be built with dates, calculated fields, and summaries rather than a separate aging module.
Joins and comparisons help reconcile information from two systems and highlight missing, mismatched, or inconsistent records. This is the same visual join-and-filter approach used elsewhere in Vizzi, applied to two sources of the same business data.
Filters, calculations, summaries, visualizations, and exception tests help isolate transactions that deserve a closer look. The goal is a short list of items to review, with a workflow that shows how that list was produced.
Prepare, combine, and reshape data visually, then reuse the same workflow when the next file arrives.
Most analysis starts with preparation. Vizzi lets users organize source files into working tables before tests and summaries run.
Connect tables visually and perform joins without having to write SQL for every step. Experienced analysts can still inspect or use SQL when they want more control over the join logic.
Calculated fields, conditional logic, filtering, sorting, and summarizing are available as visual tasks. These steps stay visible in the workflow so others can see how the output was created.
Statistics, filters, calculated fields, and visualizations help highlight unusual records and patterns. The result is a focused set of rows to review rather than a full file dump.
Build the analysis once, refresh or replace the source data, and rerun the workflow for the next period. The same approach supports monthly processes, recurring reporting, and ongoing monitoring.
Define tests against your data, rerun them when files are refreshed, and keep the logic visible for review. Vizzi supports analytics and testing — it is not a full GRC or controls-management platform.
Build defined tests once and rerun them against refreshed data. Typical criteria include thresholds, unexpected dates, missing fields, and duplicate records.
Repeatable workflows and scheduling can support recurring, data-driven control tests. The emphasis is on running the same analytics consistently — not on managing an entire controls framework.
Scheduled or regularly rerun workflows can flag transactions that match defined risk or compliance criteria, so reviewers start from exceptions instead of the full file.
Filters, calculated fields, variables, joins, and conditional logic are used to implement testing criteria. The workflow shows which rules were applied and in what order.
Analytics can run across full datasets rather than being constrained by spreadsheet row limits, which matters when a control or policy is meant to apply to every transaction in the population.
Give more people the ability to work with data, automate repeatable analysis and turn large datasets into actionable results.