Inaccurate or redundant data is estimated to cost many companies between 15 and 25 percent of revenue. The costs associated with bad data include the time required to correct errors, confirming data from other sources and cleaning up the mess created by mistakes stemming from erroneous data, as calculated by Thomas Redman, president of Data Quality Solutions. Other market analysts have also attempted to quantify the cost of bad data. For example, Gartner Inc. estimates that the average financial impact of poor data on a business is about $9.7 million annually.
The root causes of poor data quality can be attributed to inconsistencies among organizations in an enterprise, intentional fraudulent activity, or incomplete data entry. The costs of data that is not Complete, Consistent or Correct may include overpayment, time spent in hunting for data, finding and correcting errors, and searching for confirmatory sources for untrusted data.
The Cost Recovery analytics bundle focuses on eliminating duplication, identifying unusual costs, irregular or potential fraudulent activity, unauthorized purchases, overpayments, inactive accounts and contracts, and missed opportunities for savings. Cost recovery analytics works behind the scenes, allowing you to monitor expenditures, prescribe solutions early on, and predict potential overruns by automatically reviewing massive amounts of data. Potential errors are identified based on details within your payables, inventory, and receivables, combined with well-tested analytic techniques that uncover payment discrepancies.
Cost Recovery analytics will identify duplicate payments across operating groups, or the same expenses submitted by two or more employees, a vendor match inventory, among others. The Cost Recovery analytics can help you uncover and potentially recover money that could have been lost. The insights gained could have a big impact on how you manage vendors, improve supply chain visibility, standardize invoicing, reduce regulatory risk, and more.
The Cost Recovery Bundle will include analytics to:
Identify Duplicate Invoice Payments: Compare data across OUs and business units to identify situations that may indicate that duplicate payments have inadvertently been made.Vendor-Employee Match or Duplication: Analyze indicators that there may be duplicate vendors, or identify vendors who are also employees.
Inventory by Supplier Item Number: Analysis of the supply chain as it relates to matching identifying products that may be purchased under different item numbers – purchasing the same or similar items under different product numbers or perhaps by different operating units.
Ease of access for critical analytics
Tight Intigration with EBS source data
Ability to analyze the entire organization, across business groups, across operating units, across inventory organizations
Analysis may be done as often as necessary - weekly, monthly, quarterly, etc.
Full drill down to transaction-level detail
Complex analysis with knowledge of all data relationships
Identification of red flags and potential systemic issues
Out-of-the box software
Web-based architecture allows cloud installation
Finds data across multiple modules and thousands of tables and build that intelligence into a software product