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  • Writer's pictureDerrick Wong (Managing Director)

Popular Analytics Tests (Part 3 of 6)

Updated: May 16

Many Internal Audit team needs to prove ROI if they intend to invest on Analytics technology. The best way is to achieve quick implementation of analytics use cases, and start testing it out on their data. When it comes to analytics use cases, we always share with our customers on 2 things:

1) Analytics is only limited to your own creativity.

2) No Two Organizations are Identical, But there will be commonly use analytics which many will be using the same tests.

So to achieve quick ROI on your analytics, we have come up with 6 areas of the common and popular analytics tests that you can consider. They are:

1) Employee Spending

2) Vendor Management

3) Technology

4) GL JE Risk Scoring

5) Duplicate Payments

6) Counterparty Validation & Outliers

Do follow us on our LinkedIn or subscribe to our newsletters for updates. We have discussed about Vendor Management in Part 2 (url link) series.

For part 3, we are going to discuss about Technology. The phenomenal growth in scope and complexity of IT requires rigorous testing to ensure that your organization's data and processes are well-protected from the many threats that exist. Here are some analytics use cases in the area of Technology:

Identity Management: Terminated Employees

Use a Join to match terminated employee data from HR to the Active Directory file to identify still-active accounts after employee departures.

Segregation Of Duties (SOD)

Create a table containing each employee's pairs of duties with a many-to-many Join. Then use a matched Join to identify pairs of employee duties that are prohibited.

Event Log Analysis

Event logs tend to be unstructured. Create a "flat" file using static conditional fields to render the data in a format that can readily be analyzed.

System Level Settings

Extract system settings at regular points in time, then use the Compare command to identify any changes that may have taken place.

Data Integrity

Data should be rigorously tested prior to analysis to determine whether it is appropriate to use. There are a variety of tests that can be executed to identify potential data issues.

Data Migration

Production data flows regularly to data warehouses, where analysts from different parts of the organization can use it without jeopardizing live data. Continuous monitoring of the migration process can quickly identify migration issues before they pose a serious threat. The Join and Compare commands are essential for this purpose. When new systems are implemented, those commands can also be used to verify that the data has successfully migrated with no integrity issues.

Data Normalisation

Key fields can often be in varying formats from one system to another. To make them suitable for comparison, there are a number of functions that can be used in computed fields to normalize the data:

• SortNormalize

• Normalize

• Upper

• Lower

• Include

• Exclude

• String

• Value

• Zoned

• AllTrim

• Compact

• Split

• Substring

Do watch out for part 4 on analytics in GL, JE Risk Scoring, or reach out to our team for further discussion on your areas you will like to cover.

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