00:00:03 Dillon Caldwell
Hello, welcome to Getting Started With, where our job is to make your job easier.
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On this episode, we're getting started with data analytics basics and why it's becoming one of the most important tools for internal auditors today.
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By the end of this video, you'll understand what data analytics is, why it matters in internal auditing, simple types of analytics you can use, and some practical tips.
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So let's get started.
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Think of data analytics like using a flashlight in a dark room.
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If you only look at a handful of transactions, it's like shining your light on one corner.
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You'll see a little, but most of the room stays dark.
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When you use data analytics, it's like flipping the light on in the room.
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Suddenly you can spot the patterns, the outliers, and anything that looks unusual.
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Data analytics uses data to identify trends, patterns, and anomalies that support audit conclusions.
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In other words, it's going beyond looking at a handful of transactions and instead using the data to tell a bigger story.
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Why does this matter in internal audit?
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Analytics make your work more efficient and more powerful.
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Instead of looking at a small sample, you can review an entire population of transactions.
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Analytics also strengthen your evidence and can provide insights management finds valuable long after the audit is complete.
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Analytics isn't just for you.
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Supervisors use them to validate your results.
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Management may rely on them to make decisions.
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And audit committees may look at analytics for big picture insights.
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The work you do with analytics often has value far beyond your immediate audit.
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Here's a pro tip.
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Good analytics don't just confirm what you already know.
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They help you ask better questions.
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If the data shows a spike in travel expenses for one department, that's a signal to dig deeper.
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Quality analytics rests on three building blocks.
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First, clean data.
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You need accurate and complete information.
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Second, a clear question.
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What exactly are you trying to test?
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And third, the right tool.
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Often Excel is enough to get started.
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Keep it simple, and you'll build stronger analytics.
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Speaking of tools, here's a useful one.
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You don't need anything fancy to begin with analytics.
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Most auditors already have Excel, and that's enough to start exploring data.
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Learn a few key features like pivot tables, filters, and formulas, and you'll be able to answer powerful questions right away.
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As you grow, more advanced tools can help you handle larger data sets and create visuals that make results easier to understand.
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There are a few common types of analytics auditors use.
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Descriptive, what happened?
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For example, how much did each department spend last quarter?
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Diagnostic,
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Why did it happen?
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For example, were the higher expenses due to delayed approvals?
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Predictive or prescriptive?
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These are more advanced and not always needed at the start, but can forecast what may happen.
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or suggest actions to take.
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Let's look at a simple example.
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Using data analytics, you can scan for duplicate vendor payments.
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Same invoice number, same amount, but paid on different dates.
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That's a red flag for control gaps or possible fraud.
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Another classic tool is Benford's Law, which looks at whether numbers and data sets follow natural patterns.
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If they don't, it could mean the data was manipulated.
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Even a simple test like this can give you valuable leads.
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Here's a bright idea.
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Start small.
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Pick one data set and one question you want to answer.
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Run a simple test, like looking for duplicate entries or unusual timing.
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You'll be surprised how quickly you can uncover insights.
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Watch out for some common mistakes when starting with analytics.
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One is misinterpreting the data.
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Just because two things happen together doesn't mean one caused the other.
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Another is using messy data without checking its quality.
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And finally, don't run complicated tests without first understanding the business process.
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Analytics should support your audit, not distract from it.
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So how do you avoid these mistakes?
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Start by combining your analytics with context.
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Always connect what the data shows back to the process you're auditing.
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Next, check the quality of your data before you start.
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Are there duplicates, blanks, or errors that could throw off your results?
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And finally, keep it simple.
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Begin with basic tests and grow from there.
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Analytics works best when it's clear and focused.
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Let's look at a real-world example.
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One audit team used analytics to review travel expenses.
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By scanning all transactions, they spotted dozens of duplicate claims that manual testing had completely missed.
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That's the power of analytics, finding insights you'd never catch by looking at just a few items.
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Here's a bright idea.
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Once you've built a foundation with data analytics, there are even bigger opportunities ahead.
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You can apply analytics to detect fraud red flags, use continuous auditing to monitor risks in real time, or create dashboards that visualize results for stakeholders.
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And as artificial intelligence becomes more integrated into auditing, it works hand in hand with data analytics, helping you automate testing, spot subtle risks earlier, and spend more time on judgment and insight.
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Starting small now prepares you for these advanced applications later.
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Thanks for watching Getting Started with Data Analytics Basics.
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Next, be sure to check out our episodes on audit sampling and documentation to see how analytics supports testing and evidence gathering.
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You can find these episodes and other helpful resources, including tools, podcasts, and training at the links below.
