Looking at data in the correct way is crucial in solving a myriad of business and non-business matters. Data forensics turns raw data into actionable insight to optimize business decisions and performance. Data forensics coupled with Artificial Intelligence (AI) analyzes large amounts of raw data to find a "needle in a haystack" or HIDDEN DATA, thereby revealing answers to questions and insights that managers did not know to ask.

Data forensics uses multi-dimensional hierarchical reduction (dynamic queries to rapidly filter data) to eliminate irrelevant material from the discovery process and to reduce the size of the dataset. Data reduction is an important step to help to improve the efficiency and performance of machine learning algorithms.

By using dynamic queries, one can select a smaller part of a data set to use for viewing or analysis (dynamic queries allow you to restrict the data that you are viewing to the items of interest). When data is filtered, only rows and columns that meet the filter criteria will be displayed; the other rows will be hidden. Dynamic queries also enable one to remove observations that may contain errors or are undesirable for analysis. Once the smaller subset is created, the user can then drill-down, for further analysis and to hopefully uncover, hidden data.

Dynamic querying can also be used to evaluate the performance of statistical algorithms and models. The basic idea is to split up the sample into two or more groups, and to then apply the analysis independently to each group and compare the results. This kind of dynamic querying would select cases from the data at random, rather than using some rule which is based on the data. This ensures a valid comparison and is often referred to as trainingtesting, and validating.

Data forensics allows for the discovery of relationships, patterns, and trends across a dataset to be able to craft a strategic direction or solve a specific problem. Thus, providing Business Intelligence to the end user.