IDEA’s unique Analytic Intelligent features automatically analyze and profile data to pinpoint patterns, trends, outliers and correlations that may have gone unnoticed in a table.
Whether you’re unsure about where to start searching for anomalies or wanting to take your analysis to the next level, IDEA offers features that meet all your data analytics needs:
Ribbon – Provides 100+ audit-specific commands to perform tasks such as duplicate detection, joins, gap detection and Benford’s Law tests.
Smart Samples – Saves time when calculating sample size, evaluating results and drawing statistically valid conclusions.
Discover – Identifies trends, patterns, duplicates and outliers and creates a dashboard to further refine your results and serves as a starting point for audit scoping and planning.
Advanced Fuzzy Duplicate – Identifies multiple similar records within one, two or three selected character fields, then output databases including or excluding fuzzy matches with varying degrees of similarity.
@functions – Performs complex operations such as date, arithmetic, financial and statistical calculations, and text searches.
Field Statistics – Calculates more than 100 commonly-requested statistics, making it easy to reconcile totals, inspect value ranges and identify errors.
Character Field Statistics – Breaks character fields into categories and identify records with blank character fields, which may indicate anomalies within the data such as transactions where there is no authorizer.
Explore – Uses predictive analysis tasks including Correlation, Trend Analysis and Time series to forecast and extrapolate data. These tasks can be used to detect gaps and duplicates in sequenced data such as invoice numbers.
Benford’s Law Tests – Spots irregularities by analyzing digits in numerical data to spot potential fraud. Advanced settings can be used to customize outputs and set parameters.
Top Records – Reveals risky transactions and high dollar amounts.
Direct Extraction – Allows you to extract multiple databases from a larger database using key values, or identify transaction that occurred after the year-end cutoff date.
Python – Enables outlier analysis using built-in artificial intelligence to independently identify anomalies in both small and large data sets and works with both numeric and character fields.