The Benefits of Data Analytics - 23566

Organizations today are loaded with various business systems that capture enormous amounts of data. The challenge is not only processing large amounts of data but sifting through these vast amounts of raw data to make decisions and identify potential anomalies to support business compliance. Hanford Mission Integrated Solutions (HMIS), a limited liability company consisting of Leidos, Centerra and Parsons operating the Hanford Mission Essential Services Contract (HMESC) for the Department of Energy (DOE) at the Hanford Site in Richland, Washington, is building a background data analytics program for this purpose. HMIS background data analytics are generated with the use of Microsoft Power BI, an interactive data visualization software product developed with a primary focus on business intelligence. The background analytics are developed to connect disparate data sets, transform and clean the data into a data model, and create a platform for sharing data that may warrant further evaluation. As in most organizations, labor charging generates a large amount of records and represents a significant cost to the company. HMIS conducts traditional reviews, such as monthly floor checks, to ensure compliance and understanding of labor charging practices. HMIS also utilizes background data analytics for identifying potentially actionable data that is not easily evaluated during traditional floor checks. Several examples include analytics that capture background data, such as the pre-loading of time, time sheet corrections, approvals of time sheets outside of an employee's organization, and save history for time sheets. This is real-time data that can be used to identify potential errors or trends, thus providing respective employees and managers the ability to correct, improve or provide additional training. In some cases, data analytics quickly identifies potential noncompliant activities. For example, if an employee was to update their daily time sheet with a training code while on overtime, a background analytic would identify a red flag immediately because this is not something HMIS typically allows. This flag would generate additional investigations to validate the time was correctly recorded as training on overtime, and that training on overtime was approved consistent with company procedures. This review could also determine the employee simply made a mistake when updating their time sheet by not identifying the proper code of account on their time sheet. The key in this example is that the anomaly is identified, reviewed, updated or corrected if necessary, so the proper actions are taken prior to invoicing the cost to the DOE, or respective customer. It is important to know some data anomalies may simply represent information outside of a pre-conceived expectation because there is not a clear indicator. In addition, because the analytic is pulling live data (i.e., data as it exists on the employee's time sheet even though the time sheet has not yet been submitted for approval), the situation is being addressed as a current event. For example, if an employee is involved in a vehicle accident with a government vehicle, they are required to submit to a drug test and charge the time associated with the drug test to administrative time (A-time). The background analytic identifies additional charges to A-time, but a quick evaluation may determine the charge is appropriate with no further action required. Background data analytics can also be utilized for non-labor related transactions. For example, analytics ensure employees with cell phone stipends have their number appropriately listed and loaded with necessary software as required by company procedures. Other examples include analytics to identify if the electronic bill of materials (eBOM) requestor and approving manager are the same individual, and background comparisons between employee names, addresses, etc., against vendor listings. As a result of using innovative processes and reports to streamline the review of data, HMIS will continue to expand background data analytics and the development of dashboards for senior management to enhance compliance-based reviews and analysis to minimize risk of invoicing unallowable costs to the government. Incorporating data analytics into critical work elements allows companies to anticipate, manage and respond to adverse events more effectively while minimizing risk. (authors)