Expert IDEA users share their experiences and advice for building and sustaining a successful data analytics program.
Sourced from IDEA Innovations Conference Adopting Analytics Roundtable Session Moderators: Angel Butler, Chevron & Rubik Yeriazarian, Briggs & Veselka
We caught up with two experienced auditors who shared some of the challenges, and benefits, they’ve discovered in adopting and integrating data analytics into their work.
Angel Butler has used data analytics as both a consultant and corporate auditor. In her current role with a global oil and gas corporation, her team leads the design and delivery of advanced analytic solutions for corporate audit initiatives and projects. Rubik Yeriazarian works in forensic accounting and relies on data analytics when providing litigation support and handling big cases. Here are some words of wisdom from their experiences in using data analytics, and encouraging others to use it as well.
Building and sustaining a successful data analytics program requires three things: people, processes and tools. More than ever, audit departments are investing in resources and technology to help analyze the immense amount of data available. However, even the most sophisticated data analytics technology doesn’t guarantee an audit department will have what it needs to be successful.
The use of data analytics has to be a decision driven from the top down. Management needs to make it a priority, back it with budget and understand that it will take more than a couple of weeks to implement…and months to standardize processes.
Not everyone will buy in at once. Start by determining who within the organization could benefit from data analytics. Determine how you help clients and key stakeholders achieve their goals, then start there and share those victories, even if they are small.
Training – Power users are important, but be sure you’re building staff skills as you go in the event a power user leaves or is moved into a new role. Power users or champions are a great way to cross-train staff, serve as go-to resources for questions and advice, and help expand the use of IDEA within the organization.
Resources – Are there subject matter experts you can collaborate with?
Manual vs. Automated – How could you “sell” the idea of automating practices/processes to within the organization? Is someone performing manual tasks that could be automated to save time? They will become an instant “fan” of data analytics if you can save them time and effort.
Collaborate – Is there a technology and/or innovation committee you can approach and educate about the benefits of using data analytics? Where else could it be used in the organization to add value?
Management Involvement – Are there exceptions that can be reviewed by management to help free auditors to focus on other high-risk areas? For example, using an automated process to review payroll and accounts payable data.
The use of IDEA must be built into the structure of the audit plan and it needs to be viewed as a key strategy for the team and department. With that in mind, determine how you plan to take IDEA as a resource and make the most of it.
It’s not effective to look at the entire audit universe, even if you have the resources to do so. Where do you start? Data analytics can be used throughout the entire audit lifecycle. IDEA should be part of the risk assessment process to look materiality for the original scope and see the “big picture” of the organization. It can also be used for audit planning to focus your staff and determine where technology can be used to save time and effort. IDEA can help you look at the entire process (100% of transactions or create a smart sample), so you can focus on specific areas to scope what you want to audit. IDEA’s visualization feature can be used for the executive summary of the audit report to turn findings into a pictorial representation of your work.
Start Small – If you’ve just started using IDEA, identify the processes that make sense, such as financial data (payables and procurement). Find out what financial and accounting systems are used and start there. Build a case study repository to share successes and encourage the use of data analytics in other areas.
Red Flags – While you may be using IDEA to look for “red flags,” sometimes they may not lead to a multi-million-dollar fraud. Red flags can be just as valuable to management. For example, you may find weekend postings that turn out to be valid, but perhaps they alert management to staff having to work overtime due to heavy workloads. Blank entries may indicate a bad process that needs to be fixed. Management may be interested in the findings even if they don’t impact revenues.
Scripting Processes – Are there areas of the business that need regular oversight? For example, IDEAScripts can be used to automate the review of payables data to check employee files against the vendor master file and compare addresses. Performed on a global basis, it might identify duplicates or fraud. Think about areas of the business that would benefit from more regular reviews and use IDEA to establish an ongoing audit of available data.
Some organizations are not capturing data yet, which creates unique situations and challenges for how data analytics can be used. For example, many organizations use QuickBooks, which can be imported into IDEA. Take time to clean the data and implement the use of IDEA where it makes sense.
Start by learning the tools and normalizing the data, then move to repeatable and standardized processes. How can/do you get the data? ODBC? Rely on IT? ERP systems? Are there tools available to help you get direct access, such as SmartExporter for SAP data extractions?
Tests – It’s not enough to develop tests and use the same ones every time. The sophistication of your analytics has to keep up with ever-changing fraud schemes and the pace of business. Keep looking at the data to see what potential patterns emerge. IDEA can be used to look at the data in different ways. For example, round dollar spending and amounts just under authority limits often go undetected, but may indicate fraud. Some fraudsters know you might use Benford’s Law and stay away from first and second digits. Learn new ways to test – don’t coast.
Scripts – Once you work towards an automated approach, you may consider having a process for making changes to scripts, or a sign-off process. One suggestion is to use feedback surveys sent to those using the reports to see what needs to be improved or modified.
Standardize – Many of the larger firms have adopted (SmartAnalyzer) Financial, and IDEA add-on that can be used to standardize the data acquisition process, testing and reporting to increase consistency.
Read more here.
One final piece of advice is to approach the use of data analytics as a non-negotiable. It’s not an option, it’s a choice. While it may take time to learn and absorb, you will eventually get there, and it will be worth the journey. Build metrics into performance reports and consider it an investment in the department. Think of the reduction in fieldwork (and travel) where advanced planning saves time. Think about the amount of “busy work” that will be reduced, allowing you to focus on more value add work including analyzing results.
What are some people/processes/tools you’ve used that have proven successful in building a data analytics program?