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From Data Analysis to Data Mining
May 19, 2016
FMI PD Day – Understanding the New Era of Leveraging Internal Control
Who am I? What do I do?
I’m a Data Analyst
Internal Control - PIC
Internal control (contrôle interne):
Is generally recognized as a set of means that organizations put in place to mitigate risks and
provide reasonable assurance in the following broad categories:
• The effectiveness and efficiency of programs, operations and resource management,
including safeguarding of assets;
• The reliability of financial reporting; and
• Compliance with legislation, regulations, policies and delegated authorities.
In practice, the set of means that represent internal controls can include those elements of an
organization such as its resources, systems, processes, culture, structure and tasks that, taken
together, support people in managing risks in order to achieve an organization's objectives.
3 pre-requisites to Data Analytics & Data Mining
Access to data: This sounds so easy but is by far the most difficult, time consuming and
frustrating step. As an auditor we have access to all data, as defined by the internal audit
policy, audit charters, and supported by the Privacy Act. But in reality we have zero data by
default. We own no data and have no reporting access to systems.
Buy-in from management: Management within your sector needs to support this. Given how
scarce resources are in today’s environment of cost containment, it can be a tough sell to
dedicate resources to something new where the value is not proven, which means time away
from other things that were being done in the past with known value.
Tools/Skills: ACL, Excel, problem solving skills, creativity & innovation, inquisitive mind,
attention to detail, and communication & presentation skills.
Applications of Data Analysis
Inform risk assessments, both on an annual basis and a project basis. This is applicable for
audits but can be applied to an internal control environment just the same.
Provide an organizational level overview of risk indicators based on materiality or volume,
complexity, and variability by entity (region, portfolio, vendor, payment method, cost ctr, etc…)
• Volume: Total expenses, total revenue, total assets
• Variability: Amount of discretionary expenses (travel, professional services, other O&M), # of
JV entries, # of reversals…
• Complexity: # of funds, # of GLs, # of currencies, # of doc types…
Produce a management dashboard and business intelligence reports to inform decision making. Some
other departments have implemented data visualization software like tableau.
Reconciling & Matching – make sure two systems have the same data, or ensure they do not.
Application of Data Mining
Multiple sources of data
Credit card transactions
Deep dives
Patterns, links, correlations
Sample selection
Challenges & Lessons learned
Challenges & Lessons learned (II)
Communication & simplicity
- Show your analysis to colleagues who aren’t familiar with it
Accuracy and confidence, quality of data
- Double check your methodology to be sure the analysis is correct and have confidence in the results
Access to data
- An ongoing challenge
False positives
- Know your department & data
- With yourself and others