Understanding Your Data and Best Practices in Data Analytics

Transcription

1 Understanding Your Data and Best Practices in Data Analytics CCIA Fall Conference 2017 October 5, 2017 Matt Anderson Investment advisory services are offered through CliftonLarsonAllen Wealth Advisors, LLC, an SEC registered investment advisor.

2 Learning Objectives This course will help you understand your data utilizing a proven data analytics methodology, including best practices in performing data analytics using a variety of tools. Specific learning points include: Interpreting your data to decrease time and increase effectiveness Normalizing your data for efficient analysis Robust data analytics methodology Importance of completeness in data analysis Best practices in data analytics 2

3 CBOK 2015 Practitioner Survey The use of monitoring and data analytics increased by 14% from 2006 to Use of continuous/real time auditing increased by 7% from 2006 to Only 40 percent of Chief Audit Executives (CAEs) say their use of technology is appropriate or extensive. 20 percent of CAEs say their departments rely primarily on manual testing. Source: 3

4 CBOK 2015 Practitioner Survey 80 percent of CEOs say data mining and analysis is strategically important to their organizations 53 percent of internal audit departments use a software tool for data mining or data analysis 4

5 Quick Poll How well is your team utilizing Computer Assisted Audit Techniques (CAAT)? What tools are being used for data analysis? What is keeping us from performing better data analysis? It is difficult to explain what data analysis is, but you know it when you see it. Data Excel IDEA / ACL 5

6 CLA s Robust Data Analysis Methodology 6

7 Multi faceted Team Combining Different Skills The CLA Team: The CLA Process: Technical Data Analysis Fraud and Forensic Experience Deep Industry Specialization Compliance, Internal Control Process Improvement 4. Report and Document 1. Plan and Identify Risk 3. Analyze and Interpret 2. Access Data

8 Robust Data Analysis Methodology Identify objectives, understand systems, obtain desired skill set Discuss risks, understand control environment, get input from interested users, rank risks Plan and Identify Risk Access Data Communicate with IT Understand what data is available Data extraction Completeness testing Achieve desired objective Document procedures and results Identify best practices and other useful information Report and Document Analyze and Interpret Technical analysis Interpret and Corroborate results Identify anomalies Reassess risk 8

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10 Access Data Do not settle for No The data exists, we need to find a way to obtain it Do not settle with what you get if it is not what you asked for Consider IT personnel involvement The accounting personnel may not be the best resource Save time by getting data in the correct format Perform a cost benefit analysis Excel is not always the best format 10

11 Access Data (continued) Common information types in Higher Ed include, but are not limited to: General Ledger Detail Accounts Receivable Accounts Payable Cash Disbursements Payroll Credit Cards Student Loan Course Registration Procurement Records Travel and Entertainment Expenses Along with the above noted data sets, the following information is also needed to adequately interpret the data files: Data Dictionary Chart of Accounts File Source Listing indicating from which systems and locations the data was extracted Vendor and Employee Master Files Log files Unstructured data sources 11

12 Technical Data Analysis Verify proper import (anyone) Test data for completeness (anyone) Normalize data and prepare for analysis (needs an understanding of data and types of tests to be performed) Design analytics to address identified risk and accomplish objectives (team effort) 12

13 Proper Import The second most important step in the data analytic process Document source of data (screen shots) Is your source data format ideal? Utilize field statistics Verify data field types are correct 13

14 Four Main Types of Data Character text data; this should be used for data that is not, numeric, date, or time. Also, if you are unsure whether a data field maintains the same structure throughout the entire database, character should be used as the default selection. Numeric numeric data; generally, the rule of thumb is that if you are going to use (or may use) the field as a part of a mathematical calculation or index key, it should be defined as numeric; the field should contain only numbers (Most DA software will recognize other non numeric characters, such as commas, parentheses, periods). Date date data (a consistent mask is required, such as MM/DD/YYYY ). Time time data (a consistent mask is required, such as HH:MM:SS ). 14

15 Completeness The most important step in the data analytics process Financial data Reconcile your general ledger detail, by account, to the beginning and ending trial balance Operational data Report totals System screen shots Verify and/or observe query scripts used to extract data 15

16 Data Normalization More Efficient Analysis Data Normalization Better Relational Analysis More/Less Detailed Analysis 16

17 Common Data Normalization Fields Absolute Value Month Year Year Month (YYYY_MM) Account Number Segmented Fund (parent and child fund) Fund Type and Fund Subtype Financial Statement Class Subsidiary Ledger Identifier Functional expense allocation Workday / Weekend Day of the week Days to posted Balance Sheet/Income Statement Single Audit/ UGG Compliance CFDA / CFSA Number and Description Shortened version of account number or vendor field Statistics by vendor/line item description Data Normalization Now = Significant Time Savings Later 17

18 Technical Data Analysis Design Analytics 18

19 Types of Analytics Population analytics Understand the entire population. Example test: Stratification by $10,000 increments Grouping analytics Summarize transactions into meaningful groups. Example tests: Summarization of manual journal entries by accounts, locations, departments People analytics Garner insight into who benefits from and who is responsible for a transaction. Example test: Count and sum of journal entries by inputting user 19

20 Types of Analytics (continued) Trending Analytics Show results through time. Example tests: Rolling monthly financials, sum cash disbursements by month, faculty course loads and head counts over time Transaction Analytics Isolate transactions exhibiting particular traits. Example test: Transactions of a certain type and dollar value threshold 20

21 Case Study 1: Student Loan Analysis

22 Case Study 1: Background State authority that provides students and families with resources to pursue education beyond high school through grants, scholarships and student loans Higher Ed provides student loans to borrowers at fixed rates and has over 250,000 unique loan application numbers totaling in the billions of distributions 22

23 Case Study 1: Planning Our main objective was to understand the makeup of the loan portfolio, get our arms around the transactions and run specific queries to address perceived risks 23

24 Case Study 1: Risk Assessment Risk areas we were looking for: Loan disbursement was less than the outstanding amount Multiple names with the same social security number or account ID # The loan outstanding was greater than $200K Borrowers who had 10 or more loans totaling over $100K Loans that had a high balance in FY13 but in FY14, it was either zero or a small balance Distributions by fund by month Borrowers who had 10 or more loans totaling over $100K 24

25 Total distributions by month from 2000 through

26 Distributions by fund by month Months Trends Funds 26

27 Specific Analysis that would be difficult/ impossible without Forensic Data Analysis: Total Outstanding Loans by Individual, 2014 totals compared to 2013 where difference is greater than 100k

28 Specific Analysis that would be difficult/ impossible without Forensic Data Analysis: 10 or more loans outstanding totaling greater than $100k outstanding 28

29 Case Study 2: Non investigative Non Instructional Time Analysis

30 Case Study 2: Background State college system of over a dozen campuses System internal audit group identified one campus of concern with applicability to all campuses Non investigative data analysis When contracts are issued to faculty or adjuncts, duties may not be adequately explained and documented Class schedules are clear Non instructional duties are not clear 30

31 Case Study 2: Planning What are the known risks and concerns? What data systems are they using? What data is available? Are there common identifiers or links between different data sources? 31

32 Case Study 2: Risk Assessment Departments may not be assigning extra duties or paying consistently for those duties One full time person was spending more than half of time on noninstructional duties Full time are supposed to be teaching at least 12 credit hours per term Leverage to adjuncts is inconsistent Course load and students per course variability Non instructional time Updating a class or program Department chair duties Coaching Committee Lab Advisor 32

33 Case Study 2: Data Sources Employee census/personal Compensation Job assignments (instructional and non instructional) Course/Section and enrollment Job authorization 33

34 Case Study 2: Non Inst 34

35 Case Study 2: Non Inst 35

36 Case Study 2: Non Inst 36

37 Case Study 2: Non Inst 37

38 Case Study 2: New Risk ID Data analysis is an iterative process Step 4 Interpret Results: Continuously reassess risk and improve procedures The initial data analysis allowed us to identify a new risk The data presented trends of courses with low enrollment 38

39 Case Study 2: Low Enrollment New Objectives: Identify departments with low enrollment by term Identify EEs with high credit hours per term (> 21) Some had credit hours > 100 in a given term Isolated specific EEs and departments Find EEs teaching a high number of credit hours who also had numerous low enrollment course sections Enrollment by EE by course/section by term 39

40 Case Study 2: Low Enrollment 40

41 Case Study 2: Zero Enrollment 41

42 Case Study 3: Compensation for Non Existing Courses

43 Case Study 3: Background Rural college that uses more adjuncts than faculty Investigative data analysis Identified risk of improper compensation payments to adjuncts 43

44 Case Study 3: Data Sources Course Schedule Data Compensation Detail Job Assignments and Authorizations 44

45 Case Study 3: Connecting the Dots Schedule Job Assignments Compensation 45

46 Conclusion

47 Implementation Considerations Build the right team and collaborate often! Use a consistent approach. Perform data analytics early on. Don t lose sight of the overall objective. Are you able to use analytics to replace antiquated procedures and make them more efficient and effective? Save import definitions, scripts, and commonly used formulas. Share best practices, tips, and tricks. 47

48 Questions? Matt Anderson, CPA/CFF, CFE, ASA Principal CLAconnect.com linkedin.com/company/ cliftonlarsonallen facebook.com/ cliftonlarsonallen twitter.com/claconnect 48