“Hiring” a robot for data analytics: 4 tips for public sector audit teams
For public sector audit teams, data analytics has been touted as a superpower: It speeds up calculations and computations, improves accuracy and enables organizations to tackle massive data sets and spot red flags in a single bound (or click).
In fact, data analytics is fast becoming a must-have for chief audit executives (CAEs) and their teams. As organizational risk profiles become more complex and difficult to manage and oversight expands beyond compliance and internal controls, auditors are pressed to use data analytics and other new tools to drive strategic decisions.
But the prospect of bringing on data analytics expertise can be daunting, especially given today’s focus on efficiency and cost-effectiveness. Training existing staff in this highly specialized and complex area takes time. Hiring in-demand data analytics professionals can be pricey or even impossible for audit departments without the budget for increased headcount.
This is where another tool with superhero potential comes in: automation.
Automation streamlines time-consuming and repetitive processes, like cross-referencing data or copying and pasting data between applications. What’s more, these software robots are easy to configure without extensive IT knowledge. In fact, PwC estimates that nearly half (45%) of tasks in today’s workforce have the potential to be automated.
With people and processes as the foundation, automation provides audit with the acceleration it needs to be more innovative and effective.
Read on for four ways automation can help public sector audit teams integrate data analytics into their operations, efficiently and cost-effectively.
4 robot-ready data analytics activities
Opportunities for automation exist throughout the entire analytics lifecycle. In fact, if one were to write a job description for a data analytics robot, tasks might include:
1. Gathering and cleansing data
Collecting and inputting data, validating completeness of fields, checking for duplications — all of these activities are the bread and butter of data analytics, comprising up to 80% of the work. Such tasks are slow and prone to error, not to mention tedious, when conducted via spreadsheets, emails and manual methods.
Robots transform this scenario. They’re able to extract and process large volumes of data and high-volume transactions faster and more efficiently, freeing audit and analytics teams up for higher-value work. Furthermore, by removing the risk of human error, robots increase the quality and accuracy of data. This raises stakeholders’ confidence in an audit team’s findings and recommendations.
2. Streamlining and strengthening controls testing
Always a data-intensive activity, the vast and growing array of areas that auditors oversee and analyze — AML, accounts payable, general ledger and beyond — has made controls testing a Sisyphean task.
Robots push this burdensome boulder firmly up the hill and keep it there, with their ability to process more, faster, than humanly possible.
With pre-built scripts, analytics and testing ideas stored in a central library, automated testing workflows add efficiency to the sampling and controls testing process. Furthermore, robots are able to test entire populations of data. Manual controls testing, by contrast, is typically to mere samples. More data analyzed means greater confidence in the results.
As an added benefit, automated testing enables continuous monitoring. Audit teams are able to move from periodic snapshots of their operations to round-the-clock oversight, enhancing their ability to keep on top of issues and serve as trusted advisors.
3. Accelerating reports and remediation
At the end of the data analytics process, automation enables swifter sharing of results and more immediate action when needed.
For executive and board presentations, robotics offers significant time savings. For example, reporting that used to take two days to complete using manual processes might now only take five minutes.
On the remediation side of things, automated notifications can alert CAEs and key stakeholders when critical thresholds have been reached, and automated remediation workflows can address issues like controls failures in real time.
4. Supporting internal audit’s shift to an advisory role
Looking ahead to the audit team’s expanding role, automation tools like robotics offer CAEs superpowers in two critical areas: predictive and prescriptive analytics.
Automation-powered risk assessments are one example. Here robots use predefined rules, data points and trends to classify risks. This enables audit teams to more quickly identify high-priority areas.
Then what? For the next step, audit teams can add machine learning for prescriptive analysis — diving into the data in an even more advanced fashion to unearth questions to ask and optimal courses of action.
Don’t be the CAE who hesitates
CAEs can no longer wait to implement data analytics. “Effective analysis of data must lie at the heart of audits if they are to remain relevant to stakeholders,” the Institute of Chartered Accountants of England and Wales declares.
Those who are harnessing the power of robotics and other automation technologies are already reaping the benefits. “IA departments, large and small, have already begun their journey into the world of automation by expanding their use of traditional analytics to include predictive models, RPA, and cognitive intelligence (CI),” writes Deloitte. “This is leading to quality enhancements, risk reductions and time savings — not to mention increased risk intelligence.”
Start your journey today. By automating time-consuming tasks, ACL Analytics helps public sector audit teams augment their capacity without increasing their headcount. Learn more today.
