Jumping on the Data Analytics Bandwagon - cdn.ymaws.com · • Recognize the differences between...
Transcript of Jumping on the Data Analytics Bandwagon - cdn.ymaws.com · • Recognize the differences between...
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Presenting Today
Elisa Gilbertson, CPAManager, Weaver Analytics
More than 10 years of experience Practice emphasis in data analytics, auditing and consulting for a
variety of industries including nonprofit organizations and higher education.
Reema Parappilly, CISAPartner, IT Advisory Services
More than 13 years of experience Practice emphasis in IT audits, data analytics, Sarbanes-Oxley
Compliance, and System and Organization Controls (SOC) reporting.
Objectives
• Recognize the differences between data and information
• Understand the process of analysis used to turn data into information
• Provide examples of analytics that may be useful for you
What is Data Analysis?
The process of drawing inferences fromlarge sets of data to help identify trends,preferences, hidden patterns, and more.
Data vs Information
Data:• Raw, unorganized facts
that need processing
• No interpretation or analysis
• Inefficient and ineffective for communicating
• Cannot be wrong (but isn’t always right)
Information:• Data that is
processed, organized, structured, and/or presented in a given context to make it useful
• Meaningful to the user
• Can be wrong
Data: Journal Entries (as part of a
general ledger detail)
Vendor Lists (with no comparative figures)
Employee Master Files
Student AR Subledger Detail
Information: Subset of Journal Entries posted
on a holiday
Period-by-period comparison of Vendor purchases
Period-by-period comparison of Employee gross pay
Student AR Aging
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Data vs Information
Garbage In – Garbage Out
• Computers operate by logical processes– Will process unintended or nonsensical inputs– Result in undesired and nonsensical outputs
• Fields not used as intended– Non-unique or non-required Purchase Order numbers– Transaction Dates vs Posting Dates
• Processes not occurring as intended– Attendance not taken in every class– Manual adjustments used instead of subledger entry
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Phases of AnalysisIdentify a
problem or question
Create a hypothesis
Gather the data
Develop and run a
model
Prepare the final product
Adjust as needed
Data to Information
1. Identify a problem or question– Be as specific as reasonable– Make a problem “hierarchy” if needed
– Example: Why was there such a large return of Federal Student Financial Aid (SFA) funds during the Spring-18 semester?
2. Create a hypothesis– Be as specific as possible– Utilize knowledge of others to provide direction
– Example: There was an increase in SFA recipient withdrawals during SP-18.
Data to Information
3. Gather the data– Start broad, but stay on target– Check completeness and accuracy– Verify data validity
– Example: Obtain multiple semester’s worth of student enrollment data (including withdrawal and financial aid information)**
4. Create a “draft” model & run analysis– Identify analysis parameters – Review results against hypothesis – do they agree?
– Example: SP-18 student withdrawals went DOWN
Data to Information
5. Make adjustments and repeat (as necessary) – Hypothesis – was the original disproved?– Data – did it contain garbage?– Analysis – did it return useless information?
– Example: New hypothesis that SP-18 withdrawals occurred EARLIER, resulting in greater R2T4 returns.
6. Prepare product for presentation – Present analysis in clear, succinct manner
– Example: Comparative bar chart showing R2T4 days
• Aid Register– Over-limit
disbursements– Satisfactory
Academic Performance compliance
• Return to Title IV– Recalculation of
refunds– Completeness
(through review of enrollment data)
• Enrollment– Successful students– Success of curriculum– Attendance
Financial Aid & Enrollment
• Check Register– Check number gaps– Check number dups– Duplicate payments– Unapproved vendors– Employee payments– Threshold avoidance
• Vendor Master File– Lacking proper fields– Vendor duplicates– Employee matches– Address irregularities
Accounts Payable
VendorID VendorName CheckAmount #Of Checks VendorAddr1 VendorCity VendorState1 Company A 7,896.29 3 PO BOX 8 Anywhere TX2 Company A 74,199.82 16 PO BOX 1 Anywhere TX
56 Company B 756.00 4 PO BOX 3 Anywhere TX77 Company B 186.00 1 PO BOX 3 Anywhere TX
9879 Company C 57.00 1 1100 Main Here TX23 Company C 675.00 2 PO BOX 3 Here TX
345 Company D 5,372.00 3 PO BOX 141 Here TX456 Company D 11,743.00 1 PO BOX 149 Here TX234 Company E 1,500.00 1 PO BOX 32 Here TX
8421 Company E 52,185.14 9 101 15 Here TX215 Company F 574.80 2 PO BOX 30 Anywhere TX
8753 Company F 30,314.65 19 PO BOX 26 Elsewhere WA
Check Register GapsGap_Start_Check
Gap_End_Check
Number_Items _Missing
178140 178142 1178142 178144 1178260 178262 1178386 178388 1178422 178424 1178466 178468 1178472 178474 1178506 178523 16
1782523 178661 137
Multiple Vendor Records for 1 Vendor
• Invoice Register– Duplicate payments– Duplicate billing– Threshold avoidance
Accounts Payable
– Split-transactions– Invoice number irregularities– Comparative analysis
Potential duplicate billings – same amounts, same dates, different invoice numbers
• Payroll Register– Payments after termination– Employees not in master file– Comparative analysis– Overtime appropriateness– Vacation/Benefit analysis
Payroll
• Employee Master File– Lacking proper fields– Employee duplicates– Address irregularities
Employees with duplicate addresses
• P-Card Charges– Duplicate payments– Out-of-policy purchases– Credits/returns
P-Cards
– Split-transactions– Off-time purchases– MCC analysis– Benford Analysis
Data analysis can be a daunting and stressful process;
however, with good data and focused analysis,the process can
add value to your operations.
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Let’s Connect
@weavercpas
facebook.com/weavercpas
linkedin.com/company/weavercpas
youtube.com/weavercpas
Insights blog – weaver.com
Questions?
Elisa Gilbertson, CPAManager, Weaver Analytics
Reema Parappilly, CISAPartner, IT Advisory Services