Extracting Actionable Information from Data
We are surrounded by systems with terabytes of data from which actionable information can be difficult to extract. If you can define what information you are interested in and differentiate it from other data, our data analysis tools and skilled Philadelphia, PA professionals can help you find it. The ability to perform data analysis procedures is limited only by defined objectives, the data, available tools, skill and creativity.
Data analytics allow you to examine a large number of transactions or records quickly rather than manually sampling a handful of transactions. Utilizing data analysis procedures can provide detailed information on an entire population of transactions and increase your efficiency.
Data analysis can be used to identify transactions meeting specified criteria, provide statistics on a population of information and identify unusual patterns of information.
APPLYING DATA ANALYTICS
Data analysis procedures can be designed to search for specific transactions, evidence of controls, indicators of fraud or other unusual patterns of activity. Data analytics can be applied to any Philadelphia, PA industry or organizational-specific data sets or transactions including the following data sets and transaction types:
• Credit Card Transactions
• Human Resources
• Insurance Claims
• Journal Entries
|• Loan Data
• Receiving Data
• Sales Commissions
• Telephone Usage
• Time & Expense
• Philadelphia Vendor Information
Data analysis procedures are often easy to oversimplify when first articulated. Consider the following description of a data analysis project as an example:
Let’s pull the data from the time and expense system and then run some procedures to determine if there are any problems with time and expense reporting, we should be done in a couple of hours.
This description does not define a clear objective and understates the complexity and steps required to obtain, understand and evaluate data as well as the performance of follow-up procedures. The chart below summarizes the key steps in a data analysis project: