Larger CompaniesRegulatory Reporting and Reconciliation to GL – for a large Bank
FreeSight automatically imports and joins dozens of files, highlights discrepancies for immediate action, and exports a report into a templated format for distribution.Financial Reporting – for a large international Bank
Integrates over 180 source data files, normalizes them, applies complex processing rules and currency conversions, and exports summarized data into a report template – all with one click of a button.Database Querying, Charting and Reporting – for a large Law Firm
Just because one has the data doesn’t mean it’s easy to access and understand. FreeSight takes that burden away, allowing this customer to spend time reviewing and analyzing the data, not just creating reports.Data Integration of Multiple Sales Files – for a large Telecommunications Company
It takes one click and less than five minutes to import over 100 files into one clean data table for analysis and charting.
Medium Sized CompaniesMutual Fund Performance Reporting – for a mid-sized Mutual Fund Company
Every quarter, mutual fund companies have to report and chart the performance of all of their mutual funds. For our customer, FreeSight has automated this huge and onerous task to the click of a button.Accounting Forecast Revisions and Comparisons – for a mid-sized Non-Profit Organization
FreeSight reduces data manipulations and ad hoc queries from taking days per month, to only minutes.Cleaning Dirty Data for Analysis – for a mid-sized Manufacturing Company
A 70,000-row messy text file is cleaned automatically and formatted for analysis by just dropping it into FreeSight.Data Cleansing for Research Study – for a top Canadian University, Academic Research Paper
Three weeks was budgeted for data integration and cleansing for a research project. It took a total of three hours using FreeSight.
Smaller OrganizationsProduct and Customer Analysis – for a small Medical Products Company
How did a non-technical Marketing person become the company’s data expert?Combining and Cleaning Customer Data from three Systems – for a small Non-Profit Organization
Three sets of customer (donor) data from three different database systems, containing duplicates, inconsistent spelling, and inconsistent address formatting are merged and cleaned in hours instead of weeks.