‍‍‍This is the third article in a four-part series on data analytics and Internal Audit. The first part, “12 Data Analytics Pain Points to Avoid,” focused on helping teams anticipate common pitfalls. Part two, “Building a Data Analytics Program That Works, Sticks, and Scales,” highlighted the three make-or-break data analytics implementation inflection points where CAEs must lock in and lead. 

If you leave for a road trip to Disney World with your vehicle’s wheels unbalanced or only half-inflated, you probably won’t get to see Mickey.

Unfortunately, that’s the situation — metaphorically speaking — for many teams implementing data analytics in Internal Audit. Data analytics program implementation efforts often falter or fail due to shortfalls in data literacy skills, available technologies, strategic planning, process integration, or data quality, access, and validation. Sometimes, however, the breakdowns happen because one or more aspects of the data analytics program get too far ahead of (or behind) the others. For example, even the most skilled data analytics expert will fail without adequate leadership support, access to quality data, or effective analytics technology. 

Nobody tries to build data analytics vehicles that aren’t equipped for the trip toward data analytics maturity. But it happens all the time. 

Fortunately, your Internal Audit team can build a vehicle — a data analytics program — that gets you where you want to go. The main thing you need is a better understanding of the big picture (including the pillars of an operating model) needed to ensure that your data analytics program implementation is workable, valuable, and sustainable in your organization. 

When I consult on data analytics implementations, I share a simple concept to help teams understand both the big picture and the multiple pillars of data analytics program transformation: The data analytics wheel. The wheel is a simple way to understand the planning, maintenance, agility, and balance needed for a successful data analytics implementation journey. In this week’s newsletter, I’m sharing that concept with you. 

Introducing the Data Analytics Wheel

Think of your data analytics operating model as a single wheel with five key pillars: People, Process, Technology, Data, and Governance. 

Here’s the challenge for most CAEs and Data Analytics Leads: You need to balance this wheel to keep it rolling smoothly, making sure no single pillar gets too far ahead or behind the others. If any pillar gets too out of balance, you’re in for a bumpy ride. Your data analytics program will lose momentum or — even worse — stall.

For example, many Internal Audit teams make the mistake of over-inflating the technology pillar of their data analytics programs at the start of their journeys. The idea is that the best technology leads to the best results. The reality is that even the most powerful data analytics technology will fail to deliver value if your team lacks the knowledge or capacity to use it, your data isn’t of sufficient quality, analytics aren’t effectively integrated into your audit practices, or you can’t measure the value being created.

That’s why you need to right-size each pillar of your data analytics program so that it’s at a maturity level that can work alongside the other pillars, ensuring a smooth ride. 

The data analytics wheel is simply an extension of the typical people-process-technology operating model we commonly see in transformation initiatives. The data analytics wheel just adds data and governance, enhancing focus on those areas.

It also reinforces a time-tested lesson from Program Management 101: No technology-driven program succeeds based on the strength of the technology alone. Success is all about a comprehensive  operating model and effective organizational change management. 

Understanding the Five Pillars

As illustrated by the graphic, a strong data analytics program has five key pillars. Internal Audit teams need to learn to think about all of these pillars concurrently, ensuring that data analytics implementation efforts aren’t overly reliant on one pillar. Again, balance is vital. 

1. PEOPLE & RESOURCES — Define roles, provide training, and establish incentives to build digital literacy for consuming and producing analytics. As I covered in the first article, many programs face significant challenges in resourcing, upleveling skill sets, overcoming personal and organizational resistance to change, addressing misalignment with organizational strategy, and other areas. As a result, strategic, proactive planning for this pillar is essential.
2. PROCESS — Align analytical development and usage with the audit lifecycle to enhance efficiency and effectiveness. It’s incredibly difficult to drive full value from data analytics without adapting analytics practices to your department’s professional practices and guidelines. Methodologies and processes must be updated to incorporate the extra time, procedures, and protocols needed to integrate analytics effectively into Internal Audits. 
3. TECHNOLOGY — Equip staff with the right tools for extracting, transforming, and loading (ETL), analysis, reporting, automation, and enterprise integration. The ultimate goal is a technology stack that enables repeatable, reusable, and scalable data analytics activities and deliverables. As I outlined in the second article, it generally makes sense to start simple, assess value, establish foundational technologies, and iterate and refine your technology rollout accordingly.
4. DATA — Ensure timely access to high-quality, relevant information in the right format.  Limited access to high-quality data tends to be the #1 blocker to data analytics program success. Internal Audit teams need to align data sources with audit objectives, establish data handling procedures, and collaborate with data owners to ensure data consistency, accuracy, and completeness. Standardized data formats and automated pipelines can eventually reduce manual effort and accelerate insight generation.
5. GOVERNANCE — Establish strategy, policies, metrics, and communication protocols to measure and improve performance. Governance provides the direction, motivation, and structure to ensure that all pillars stay aligned and calibrated. It also supplies value realization metrics, helping your team prove (and improve) analytics’ value over time. In addition, expectations and incentives should be aligned to encourage and allow time for adoption. If the CAE is still emphasizing on-time, on-budget delivery above all, data analytics adoption may be unintentionally disincentivized.

5 Guidelines for Wheel Maintenance 

1. Continual Calibration is Crucial

Again, you’re in for an uncomfortable journey if any pillar lags or leads too much. Our ongoing theme is that a lack of balance can easily slow or stall your trip. Your team must learn to manage all of these pillars concurrently to keep the wheel rolling smoothly forward, continually calibrating each pillar to the right level as you mature your data analytics program. 

2. Each Pillar Is on Its Own Work/Stick/Scale Journey

My second article detailed the activities and considerations for making your Internal Audit team’s data analytics program work, stick, and scale. A quick review: 

• Making it work means enabling initial success in producing and consuming an initial set of  simple analytics within Internal Audit. 
• Making it stick means ensuring consistent, repeatable, and sustainable analytics practices that are integrated into your audits and adopted by a wider set of team members. 
• Making it scale means adapting and coordinating your program to integrate analytics production and consumption across more business risks/processes, increasing use case sophistication, and enhancing coordination with other organizational defense lines.

Each pillar of the data analytics wheel goes through these phases. As a result, pillars have unique milestones and requirements for achieving their target maturity states at each inflection point. Understanding each pillar’s differing requirements helps you calibrate your wheel over time. These milestones and target maturity states are key elements to document in your data analytics program strategy.

3. Your Wheel Grows as Your Program Matures

As program maturity increases, your operating model grows and evolves. My recommendation: Start with a small wheel and recalibrate over time. Expect ongoing interplay between your maturity journey and your wheel. 

What does this look like in practice? 

As you “make it work,” you’re developing baseline skills and initial practices using minimal amounts of data. That means you’re working with a simpler, smaller wheel and operating model. 

When you “make it stick,” you’re getting skills and practices more fully documented, managed, and repeatable. Your wheel grows to accommodate these needs, including efforts to capture and codify practices, uplevel data literacy across your team, establish and track value realization metrics, and update strategies, mandates, visions, and methodologies to integrate data analytics. 

As you “make it scale,” adapting your program to enhance automation, performance measurement, integration with other lines of defense, and application of analytics layers in other business processes, your wheel/operating model — now operating at a higher level of capability — grows further.

4. Make Sure You Have a Good Map

I recommend constructing a formal data analytics roadmap that includes a timeline of target states and sequence of incremental milestones for maturing each pillar. This helps teams keep pillars growing at the same rate.  

In particular, make sure governance embeds a level of accountability (e.g., federated with managers, centralized with a lead) that ensures proactive planning, regular updating, and ongoing maintenance of the different pillars. 

5. You May Need Different Drivers for Different Stages

Who should be in charge of maintaining your data analytics wheel? I’m often asked this question when I consult. CAEs are looking for a clear-cut role description that helps them put the right person in charge. The problem is that you may need different drivers at different stages. 

The leadership and attributes required to “make it work” may be different from those needed to “make it stick” or “make it scale.” It’s situational, and the right answer will vary from organization to organization. For example, during early phases, it’s important for leaders to be adept at training, coaching, and cheerleading. In middle phases, process and documentation skills become vital. Later, robust relationships across different parts of the organization become critical. I’ll explore this topic in more depth in a future newsletter (coming soon). 

Get Your Hands on the Wheel

Data analytics implementation is never “one and done.” Internal Audit teams who understand the different phases of the journey from the outset have a much better chance of making it successfully. 

The long journey requires deliberate planning, incremental investments, continual calibration, and proactive maintenance of your wheel/operating model. While we certainly want to travel fast, we also need to stay balanced to ensure the flexibility and agility needed to planfully change course as you meet each new challenge. Getting your hands on the wheel can go a long way toward ensuring a successful journey. After all, if you’re planning on going to the Magic Kingdom, why not make sure you can see those mouse ears after all? 

Looking to get your Internal Audit data analytics implementation on track? Instructor Jim Tarantino leads the Internal Audit Collective’s new data analytics program, DRIVE, designed to enable Internal Auditors of all levels to better integrate data analytics into their work. Register today.