EY - Internal Audit: harnessing the power of analytics

Internal Audit: harnessing the power of analytics

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Big data is fundamentally changing the way the enterprise operates, and Internal Audit (IA) can’t afford to be left behind.

Companies everywhere are developing the technical capacity to analyze the vast amounts of information they have captured, and they’re using the insights generated from that data to build stronger customer relationships, reach new customers and improve the bottom line.

In the context of IA, analytics is the analysis of a large population of data to obtain insights and improve business performance, reduce risk and maximize business value.

The IA function must embrace analytics to keep pace with or outpace the business; it must become a natural part of the thought process. This will involve not only the adoption of new tools and techniques but also a change in mindset.

However, the results will be worth the effort: IA’s use of data analytics can help the business improve its processes and deliver even better products and services.

Properly developed, analytics can help IA provide business insights and act as a strategic advisor while holding the line on costs or even reducing them.

Using analytics, IA can examine an entire population of data and focus on potential issues. There are three stages of analytics, with the mathematical complexity growing with each step:

  • Descriptive analytics is the first stage. The idea is to report, visualize and understand what has already happened, either in real time or after the fact.
  • Predictive analytics help develop an understanding of the underlying relationship between input and outputs to understand why something happened or to predict what will happen in a given scenario or set of scenarios.
  • Prescriptive analytics are designed to determine which decision or action will produce the most effective result.

This maturity model provides a useful way to measure an organization’s progress along the analytics journey.

The IA analytics maturity model

EY chart – The IA analytics maturity model

Analytics is not a “nice to have,” but a “must have,” because IA must build the capability to analyze the vast amounts of data generated by the organization to meet its compliance mandate. Our full report presents full details on how IA function should approach the analytics journey.

Where is your IA function on its analytics journey?

Each organization and function will need to tailor the analytics strategy and delivery model to its IA vision, mandate and plan, making key decisions on the strategy, methodology, talent, knowledge and tools along the way. The questions below can help you frame a discussion on your journey and determine the best way forward:

  • How can you be sure you’re fulfilling your IA mandate in an efficient and effective manner?
  • To what extent do you rely on information and analytics from the business to identify risk?
  • What role does innovation and continuous improvement play in your IA culture?
  • How do you manage the analytical skill set in your IA function, including career paths?
  • How are you aligning your function’s capabilities and talent to keep pace with the explosion of data in your organization?
  • Do you have a defined governance model and strategy in your organization?
  • How does your IA methodology incorporate analytics?
  • To what extent are you managing data throughout the audit lifecycle?
  • What are the objectives and related benefits of analytics in your IA function?
  • What methods have you developed to measure the business benefit delivered from your use of analytics?
  • How are the results of analytics embedded in your audit reports? Do the reports include data-driven findings and visualizations to corroborate the findings?

If you need help with any of these questions, contact us today.

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