Using	Data	Analytics	to	Enhance	Spend	Management	Control	in	the	Public	Sector8 December 2010Public Sector Finance World 20...
Agenda1.   Analysing how data analytics will provide greater spend      management control2.   Exploring different data an...
Data	Analytics• What is Data Analytics?  • Wikipedia:  “Analysis of data is a process of inspecting, cleaning, transformin...
Data	Analytics    “Making sense out of         nonsense!”
Data	Analytics	– Spend	Control• Public sector finance professionals – guardians of public   money• Value‐for‐money  • Econ...
Data	Analytics	– Spend	Control• ERP systems provide wealth of information…• If you can access it!• Typically requires IT d...
Data	Analytics	Tools• You already have them!• Data analysis software  • Key characteristics  • Slice and dice to what you ...
Data	Analytics	Tools• Caseware IDEA ‐ Data analysis or generalised audit software  • Need to understand data generated.  •...
Data	Analytics	Tools	– Case	Study• Audit of Staff Claims   • Medical Claims  • Transport Claims• Why review using data ana...
Use	of	IDEA	in	Audit	of	Staff	Claims
Use	of	IDEA	in	Audit	of	Staff	Claims	(Medical)• Perform Analysis  Audit Observation #1                                 • ...
Use	of	IDEA	in	Audit	of	Staff	Claims	(Medical)• Perform Analysis  Audit Observations #2                                  ...
Use	of	IDEA	in	Audit	of	Staff	Claims	(Transport)• Audit Observations #1  • Non‐deduction of Normal Travel Expenses from Of...
Use	of	IDEA	in	Audit	of	Staff	Claims	(Transport)• Audit Observations #3  • Unusual Multiple Journeys Within the Same Day b...
Questions	&	Answers    Yoong Ee Chuan CPA CIA CISA CISM    Email: yekker@gmail.com
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Using Data Analytics To Enhance Spend Management Control In The Public Sector Yoong Ee Chuan

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Using Data Analytics To Enhance Spend Management Control In The Public Sector

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Using Data Analytics To Enhance Spend Management Control In The Public Sector Yoong Ee Chuan

  1. 1. Using Data Analytics to Enhance Spend Management Control in the Public Sector8 December 2010Public Sector Finance World 2010Yoong Ee Chuan, CPA CIA CISA CISM
  2. 2. Agenda1. Analysing how data analytics will provide greater spend  management control2. Exploring different data analytics and computer assisted  audit tools and techniques3. Questions and answers 
  3. 3. Data Analytics• What is Data Analytics? • Wikipedia: “Analysis of data is a process of inspecting, cleaning, transforming,  and modeling data with the goal of highlighting useful information,  suggesting conclusions, and supporting decision making. Data  analysis has multiple facets and approaches, encompassing diverse  techniques under a variety of names, in different business, science,  and social science domains.” • Some examples • Data mining • Business intelligence • Statistical applications
  4. 4. Data Analytics “Making sense out of  nonsense!”
  5. 5. Data Analytics – Spend Control• Public sector finance professionals – guardians of public  money• Value‐for‐money • Economy (Less is more) • Efficiency (Input vs output) • Effectiveness (Output vs outcome) “If you know the enemy and know yourself you need not fear  the results of a hundred battles. “ Sun Tzu• Enemy = Enterprise Resource Planning (ERP) systems +  information overload + tools to understand your data
  6. 6. Data Analytics – Spend Control• ERP systems provide wealth of information…• If you can access it!• Typically requires IT dept + Finance + Operations to get  reports and analysis they want• ERP  Worksheets  Info for decision making• Data analytics tools and techniques make user (Finance or  Operations etc) make the data talk to them• How…..?  Let’s find out!
  7. 7. Data Analytics Tools• You already have them!• Data analysis software • Key characteristics • Slice and dice to what you want • Filters, sort, summarise, total, count, chart, pivot • Microsoft Excel, OpenOffice Calc, Google Docs, etc • IDEA, ACL, SPSS, etc. • Concept is that the data analysis tools help you make sense out of  the (non)sense of data flooding your organisation • There is no “one perfect tool” • Experiment and use what suits you
  8. 8. Data Analytics Tools• Caseware IDEA ‐ Data analysis or generalised audit software • Need to understand data generated. • Need to know what is the audit issue/business problem. • Need to define that data needed and apply the right analysis to  derive the answers. • How to report it in a meaningful way.
  9. 9. Data Analytics Tools – Case Study• Audit of Staff Claims  • Medical Claims • Transport Claims• Why review using data analytics? • Detect non‐compliances and help organisation save $ • Review ALL (100%) of transactions vs sample 30 claims• How to use? • Step 1: Import data from ERP system i.e. Excel or flat files • Step 2: Define field definition (text, numeric, date) • Step 3: Run analysis i.e. exceptions, duplicates, patterns • Step 4: Report exceptions, anomalies, patterns
  10. 10. Use of IDEA in Audit of Staff Claims
  11. 11. Use of IDEA in Audit of Staff Claims (Medical)• Perform Analysis  Audit Observation #1 • Duplicate Receipt Numbers • From same staff ID
  12. 12. Use of IDEA in Audit of Staff Claims (Medical)• Perform Analysis  Audit Observations #2 • Double‐payment detected  by IDEA
  13. 13. Use of IDEA in Audit of Staff Claims (Transport)• Audit Observations #1 • Non‐deduction of Normal Travel Expenses from Office to Home  for journeys Starting or Ending from Home • IDEA analysis • Extract FROM = “Home”, FROM_TO_HOME = “N” and OFF_DAY = “N” • Do similar for TO = “Home” etc. • Review exceptions• Audit Observations #2 • Possible Duplicate Taxi Claims and Claims without Valid Taxi  Receipt Numbers • IDEA analysis • Extract  data where “RECEIPT_NO” is not “” and test for duplicates • Extraction exception conditions where business rules are relatively  clear
  14. 14. Use of IDEA in Audit of Staff Claims (Transport)• Audit Observations #3 • Unusual Multiple Journeys Within the Same Day by Same Officer • IDEA analysis • Summarise by RECEIPT DATE and ID • Sort by NO_OF_RECS • Identify those staff who make many trips on same day• IA Approach • Do Field Statistics – get the big picture of data • Analyse for exceptions to business rules e.g. transport claim rules • Review different scenarios where controls may be circumvented  e.g. duplicate claims, high or frequent transactions by same  individual
  15. 15. Questions & Answers Yoong Ee Chuan CPA CIA CISA CISM Email: yekker@gmail.com
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