‍By Jim Tarantino, CISA, CRISC, ACDA

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Myth: “Just hire a data scientist.” It will solve your data analytics challenges.

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Reality: Data analytics success is a people and resourcing challenge, not merely a technology challenge.

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My last article focused on how data analytics operating models need to maintain a carefully calibrated balance across the five pillars of people, process, technology, data, and governance. Letting one pillar get too far ahead or behind the others throws everything off balance. It’s a lot to manage all at once, but crucial to get right.

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This article zooms in on the people pillar. Why? Because many data analytics program failures stem from poorly timed resourcing decisions: overskilling too early (so experts get bored or underutilized), underskilling too late (missing coaching and integration support), or misaligning leadership styles with program maturity. The result? Programs fizzle, teams disengage, and leadership loses faith.

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Here’s some good news: You have more control over your data analytics people challenges than you may think. 

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Overcoming these challenges is primarily about being intentional with staffing and skills development. Program leadership and skills needs change as your program matures, which means you may need different leadership styles and behaviors at different stages. Unfortunately, Internal Audit leaders often overskill during early stages and underskill in later stages, leaving them without the technical, process, training, coaching, or relationship skills needed to advance maturity. 

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You can avoid these mistakes. My experience consulting on data analytics implementations has shown me that there are clear themes and leading practices for how leadership styles, technical capacities, and team member roles need to evolve across the three maturity phases of data analytics implementation. 

Phase 1 “Make It Work”

To review, teams at this stage are leveraging their baseline skills, technology, and data to produce and consume simple, quick-win data analytics within Internal Audit. These early-stage results help prove out analytics’ value and determine where it makes sense to invest more resources. 

Program Leadership Needs: Enablement and Experimentation

In the early stages of your program, success depends less on the data analytics leader’s deep technical expertise and more on leadership that can unlock curiosity, build confidence, and help the team get moving. Your program lead needs to be hands-on and approachable — someone who can coach and train others, champion quick wins, and create space for experimentation without fear of failure. At this point, the goal isn’t perfection — it’s momentum. The right leader helps the team explore what’s possible, learn from early efforts, and lay the groundwork for scalable, integrated analytics later on.

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Characteristics to look for and cultivate in program leaders to “make it work”:

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• Audit and analytics fluency: Understands core audit and risk concepts alongside foundational data tools.
• Hands-on and facilitative: Comfortable experimenting, coaching others, and working directly with early use cases.
• Inspiring and influential: Sparks curiosity, builds buy-in, and rallies the team around small wins.
• Quick-win-oriented: Focused on demonstrating early value to build momentum and justify further investment.
• Psychological safety mindset: Fosters an environment where learning, iteration, and occasional missteps are safe and encouraged.

Technical Skills Needs: Foundational Data Literacy and Ongoing Learning

Most Internal Auditors engage with data analytics in one of two roles:

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• Consumers use analytics in their planning, fieldwork, and reporting, asking better questions, interpreting results, and integrating insights into their audit work. 
• Producers create the analytics, including designing queries, preparing data, building visualizations, and testing controls. 

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Both roles are critical, but each requires different skills, expectations, and levels of support as your program matures. Accordingly, it’s helpful to view technical skills through these lenses, tracking how each role evolves through the different maturity stages. 

Consumers

At this stage, Internal Auditors aren’t yet producing or consuming data analytics at scale. Most are still consumers, coming up with ideas for business process areas where the data analytics producers can begin experimenting with proofs of concept and quick wins. This helps both roles develop foundational data analytics literacy and experience to support ongoing skills development. Consumers are also helping to evaluate early prototypes and proofs of concept, enabling the team to learn from and build on its early wins.

Producers

You likely don’t need a data science superstar at this point — it’s an expensive skill set that you won’t be able to make the most of until your program is further along (i.e., better data, more advanced technologies, more integration across the business). Plus, an advanced data scientist is likely to get bored with this stage’s elementary use cases. 

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One or more producers (including the data analytics lead) start with developing simple analytics that use in-place technologies and reliable pre-existing data. However, producers should be intentional about expanding and maturing their baseline skills. For example, based on the team’s initial successes, is there a specific area where it makes sense to invest in leveling up producers’ skills, developing analytics routines, or investing in enabling technologies? 

Implementation Tips

• Smaller teams should likely focus on upskilling existing team members with targeted analytics training (e.g., Microsoft Excel, basic SQL, Power BI, Tableau, Microsoft Copilot). 
• Larger teams may want to embed a part-time or dedicated data analyst(s) to drive pilots and build quick but reusable solutions. 
• Teams of any size may consider hiring or contracting external advisors or trainers to accelerate capability building and mentor internal resources.

Phase 2 “Make It Stick”

At this stage, data analytics are becoming a standard part of audit planning, execution, and reporting. You’re building on phase one’s baseline to broaden literacy across more of the team and ensure consistent, repeatable, and sustainable analytics that are integrated into professional practices.

Program Leadership Needs: Vision, Change Management, and Coaching

This phase calls for a data analytics lead who can do more than coach — they must guide the team through a shift toward scale and consistency. The ideal leader has strong process and documentation capabilities, along with change management skills to embed analytics into daily audit work. Just as importantly, they must articulate a clear vision and strategic roadmap for how analytics will enhance coverage, drive insight, and deliver measurable value over time.

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Characteristics to look for and cultivate in program leaders to “make it stick”:

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• Vision setting: Clearly articulates how analytics enhances assurance coverage and audit insight.
• Change management: Guides the team through adoption, helping embed analytics into daily audit practices.
• Coaching and accountability: Provides clear expectations, feedback, and support to drive consistent execution and delivery.
• Technical credibility: Understands how to interpret and communicate analytics outputs in a business-relevant way.
• Documentation and institutionalization: Formalizes analytics within audit templates, methodologies, and workpapers.
• Value realization focus: Refines performance measures to demonstrate impact and continuously improve outcomes.

Technical Skills Needs: Advanced Data Literacy and Process Integration 

Consumers

Most team members remain consumers of analytics in this phase — but now, the priority is to help them become more confident, capable, and consistent users. This includes not only making targeted data requests and interpreting outputs accurately, but also actively using results to inform audit decisions and recommendations. Consumers should collaborate closely with producers to clarify needs, challenge findings constructively (e.g., “I asked for X, but this looks like Y”), and explore alternative approaches that add value. Strengthening these skills helps embed analytics as a standard, repeatable part of the audit process and build credibility and trust with auditees and stakeholders.

Producers

Producers at this stage should demonstrate intermediate proficiency in data wrangling, quantitative and qualitative analysis, and visualization, alongside a solid grasp of controls testing and analytic automation. Their role at this stage extends beyond building analyses to operationalizing them — embedding repeatable, risk-aligned analytics within core audit activities. This requires close collaboration with consumers to tailor outputs to business context and support effective use of results. By producing transparent, well-documented analytics, producers help build confidence among auditors, auditees, and stakeholders, fostering broader acceptance and sustained integration of analytics into professional practice.

Implementation Tips

• Perform structured walkthroughs of data analytics results during audit planning and fieldwork to build familiarity.
• Create rotational data-focused auditor roles to spread capability across the team while ensuring deep specialization in core data analytics resources.

Phase 3 “Make It Scale”

At this stage, your data analytics program becomes a strategic enabler for Internal Audit’s assurance, advisory, and continuous risk monitoring work. This requires the integration of advanced analytics, automation, and AI and machine learning (ML) capabilities — not just to enhance individual audits, but to scale insights, streamline coverage, and support risk identification across the enterprise. The focus shifts to fine-tuning your team structure, tooling, and governance model to ensure long-term flexibility, scalability, and value realization across Internal Audit and beyond.

Program Leadership Needs: Strategy, Relationship Building, and Collaboration

To extend adoption and unlock enterprise-wide value, your data analytics leader at this stage must be a strategic thinker with strong technical fluency and exceptional relationship-building skills. Success at this stage depends on forging deep, sustained partnerships across functions — including IT, Data Science, and Compliance — while maintaining alignment with Internal Audit’s mission. The ability to anticipate emerging trends and actively shape how analytics is used to deliver forward-looking assurance becomes essential.

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Characteristics to look for and cultivate in program leaders to “make it scale”:

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• Strategic and innovation-minded: Shapes long-term vision and builds a scalable analytics operating model.
• Multidisciplinary leadership: Assembles and leads cross-functional analytics teams with diverse skill sets.
• Enterprise integration: Drives alignment with enterprise data platforms, governance, and BI ecosystems.
• Cross-functional collaboration: Partners with IT, Data Science, and business stakeholders to co-create impactful solutions.
• External scanning: Actively monitors emerging technologies and practices to inform future investments.
• Stakeholder engagement: Builds credibility and influence with senior leadership and control partners to sustain momentum.

Technical Skills Needs: Advanced Capabilities Driving “Intelligent” Assurance 

Consumers

At this stage, consumers should be fluent users of analytics — able to interpret increasingly complex outputs and apply them to continuous risk monitoring, root-cause analysis, and dynamic scoping decisions. Their role evolves from simply using results to actively shaping them: asking more sophisticated questions, collaborating with producers during solution design, and helping ensure analytics outputs are relevant, explainable, and actionable. As the use of automation and AI grows, consumers must also be equipped to confidently communicate insights to stakeholders and reinforce the credibility of analytics-driven findings.

Producers

Producers in this phase include a broader mix of advanced data professionals. While some will focus on more traditional data science work (e.g., statistical modeling, segmentation, predictive analytics), others may specialize in ML engineering, model deployment, or AI solution integration. The right mix depends on data quality, organizational risk appetite, and the types of problems Internal Audit is solving. Producers should also have the ability to work across functions, embed repeatable solutions into enterprise tools, and support governance standards (e.g., model transparency, version control, ethical AI). Collaboration with IT, enterprise data teams, and AI/ML partners becomes essential for building scalable, high-impact analytics assets that extend beyond point-in-time audit work.

Implementation Tips

• Provide advanced training in data science and ML topics such as supervised/unsupervised modeling, model validation, feature engineering, and explainable AI.
• Upskill producers in deployment practices and tools (e.g., cloud platforms, automation frameworks, ML Ops) to operationalize analytics at scale.
• Coach consumers on how to engage with and challenge more sophisticated analytics outputs, building comfort in interpreting AI-driven insights and articulating results to stakeholders.
• Encourage cross-team immersion (e.g., rotate auditors into analytics projects and vice versa) to build mutual understanding and break down silos between technical experts and audit practitioners.

Cross-Phase Resourcing Strategies

Some considerations and leading practices apply at every maturity stage. Teams of any size can benefit from the following strategies. 

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Be open to the idea that different stages may require different leaders:

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• Rotate leadership or evolve the role as the program matures to help ensure the right focus, energy, and expertise at each stage. As we’ve detailed, the skills needed to launch a program are not always the same as those required to operationalize it or scale analytics across the enterprise. Factors like program complexity, desired analytic sophistication, organizational structure, timeline pressures, and available internal support also shape what kind of leadership is most effective at a given point. 

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Rethink talent attraction, onboarding, retention, and development to support analytics strategy:

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• Update roles, expectations, and job descriptions. For example, evolve job profiles and role descriptions to include analytics, engineering, and automation competencies. Embed stakeholder engagement and communication skills into all data analytics technical role expectations to ensure outputs are risk-relevant and actionable.
• Be open to different candidates. Look beyond traditional auditor profiles when hiring, contracting, or borrowing talent. Prioritize diversity of backgrounds and thought. Blending operational, financial, IT, and data science expertise will provide richer insights.
• Look for and cultivate “triple threat” auditors. Revise hiring practices to assess technical proficiency, business translation capability, and cultural fit within an assurance environment. Future-proof by investing in cross-training data analytics specialists in AI/ML fundamentals, automation, and emerging AI-enhanced audit methodologies. Enhance performance management frameworks to recognize data analytics contributions, knowledge sharing, and innovation efforts. Establish dual career paths for analytics specialists within Internal Audit to retain talent and build succession pipelines. 

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Build resourcing strategies that make sense for your team’s size, make-up, and priorities:

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• Large teams will benefit from proactively developing layered data analytics functions that include data scientists, engineers, ML specialists, and analytics translators or product owners to connect business needs with technical solutions.
• Small teams should consider partnering with enterprise data science teams for advanced use cases while building their internal interpretation capability.

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Create a culture that prioritizes, supports, and rewards data analytics success:

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• Set the tone from the top to build and reinforce an analytics-enabled culture. The goal is a culture where data-driven insights are core to assurance, advisory, and risk management activities. For example, Internal Audit leaders should model data curiosity and usage in their own decision-making. They should also encourage auditors to ask better questions of data outputs, focusing on insights over technical details. 
• Put analytics in the spotlight. Host data analytics showcases or “demo days” to highlight capabilities and inspire teams. Recognize and celebrate data analytics successes, however small, to build momentum. 
• Engage at key make-or-break inflection points. The make it work/stick/scale article offers detailed guidance on when, how, and why CAEs need to be hands-on at key junctures of the data analytics maturity journey. 

Get Your Data Analytics People Strategy on Track 

Data analytics implementation is a leadership and people journey as much as it is a technical one. It’s not as simple as putting one person in charge and handing them the keys. However, Internal Audit leaders can reliably improve outcomes and control over data analytics-related people issues by ensuring that program leaders, consumers, and producers have the right skills at the right times. To clear the path for advancing data analytics maturity in your organization, make sure you’re intentional in building, resourcing, and supporting your team.

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

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