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First Steps in Building a Data Analytics Program for Your Internal Audit Team

Start by agreeing on what you want to get from the internal audit program, then work out how to staff it and what type of technology you want to invest in

Collecting and analyzing data could be taken as a fundamental job description of any auditor but, in recent years, internal audit teams have become a lot more interested in the discipline of “data analytics,” which is essentially the use of software – from the simple to the sophisticated – to find important trends in large amounts of data.

This analysis can be risk-focused (such as finding indications of fraud, waste, abuse, or policy or regulatory noncompliance) or performance–focused (such as reasons for increased sales, decreased costs, or improved profitability). As all managers learn to become a lot more comfortable with technology and the production and management of data (especially when it comes to “big data”), this interest in data analytics programs will only increase.

Nearly two-thirds of audit departments recently made or are planning to make significant investments in data analytics, according to CEB data.

Getting Started

Audit teams thinking about starting a formal analytics program, should do two basic things. First, define your analytic objectives and, second, evaluate what talent and technology will be required to achieve those goals. Spending time on these two steps will help a rollout of the program run smoothly, and form a strong foundation from which audit teams can build a business case for more investment later.

  1. Define your objectives: When deciding on your goals, it helps to see what your peers are focusing on. The primary goals of data analytics for chief audit executives are more efficient work from their teams and stronger monitoring capabilities in the business.

    Most commonly, auditors use data analytics for fieldwork and engagement planning, and use the results to identify anomalies and to test controls. However, there are lots of other ways you can take advantage of data analytics (see chart 1). The number of possible ways to make use of an analytics program makes it that much more important to clearly define and refine what you want from it.

    Common objectives of a data analytics program

    Chart 1: Common objectives of a data analytics program  Source: CEB analysis

    Click chart to expand

    You should make sure you’re within the scope of your audit department’s overarching goals and desired activities, and should leave this stage with a prioritized list of analytic objectives and a good understanding of how to apply data analytics within the audit function. The template in chart 2 will help.

    Template for defining the objectives of a data analytics program

    Chart 2: Template for defining the objectives of a data analytics program  Source: CEB analysis

  2. Pressure-Test Your Plans: Before moving forward with more investments in team time and resources, check that the objectives you laid out are realistic based on team skill sets and on your firm’s technological capabilities.


For each analytic objective, you should identify the staff skills required to achieve it. Sometimes auditors are actually prepared to handle analytic concepts, but are intimidated by the idea of “analytics.”

To combat this problem, the internal audit team at one global bank in our network of audit professionals uses familiar tools to introduce auditors to analytic techniques. This educational approach increases their early adoption and dispels fears about developing a new competency.

Once you gain a good idea of what you’re working with, you can consider different ways to staff the team based on the resources you have, and what your goals are (see chart 3). Create a list of skills your team needs and come to an agreement on the staffing plan that makes the most sense based on current capabilities and training capacity.

Different staffing models for data analytics teams

Chart 3: Different staffing models for data analytics teams  Source: CEB analysis

Click chart to expand


Many internal audit teams are yet to adopt more sophisticated data analytics technologies. Most teams still rely primarily on spreadsheet-based tools and applications; ACL is the second-most frequently used tool. Use chart 4 to compare the different sets of analytic tools out there to figure out what works best for you.

Examples of data analytics tools

Chart 4: Examples of data analytics tools  Source: CEB analysis

Click chart to expand

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