In this blog series, we sit down with IIA Experts exclusively available to Research and Advisory Network clients who serve as sparring partners on key data and analytics initiatives. IIA’s RAN Expert community was founded in 2016 and currently has 150+ active practitioners and industry thought leaders eager to give back and guide data and analytics leaders on key challenges, from how best to organize their teams to measuring business value and increasing user adoption.

In our in-depth Q&A with IIA Expert Neil Bhandar, VP of Analytics at Generac Power Systems, we explore the shifts happening at the core of the C-suite due to the rising prominence of data and analytics. Bhandar proposes a provocative reordering of the corporate hierarchy, underscoring the transformative power of data not just as a business supporter but as a definitive leader. We delve into what it means to be a chief data and analytics officer (CDAO)—a role that transcends traditional data oversight to become a pivotal player in strategic corporate decision-making. Bhandar’s insights challenge us to reimagine leadership roles like the CDAO as central to an organization’s data strategy, highlighting their crucial influence over timing, responsiveness, and the integration of emerging technologies like AI. He discusses how an organization can reimagine the potential of data, aligning its power with both the operational cadence of the business and the ethical considerations of its use, where the CDAO is a cornerstone of business strategy and innovation. This conversation has been edited for length and clarity.

Neil, it’s a pleasure to have you. To kick things off, what's your take on the evolving role of the chief data and analytics officer?

Neil Bhandar: Thanks, I look forward to the discussion. Well, it's pretty crystal clear to me—the role of the CDAO is not just evolving; it will soon become a cornerstone of the C-suite. The CDAO should be working alongside the CEO, influencing every aspect of strategy and operations.

Intriguing! And what about the relationship with the chief information officer?

Neil Bhandar: Here's where I think it gets transformative for most companies, especially non-digital natives: the CIO should report to the CDAO. I know, it seems like we're flipping the world on its head but hear me out. Data is no longer a byproduct—it's the main event. It shapes decisions across the board—from finance to HR, from the way we handle our logistics to the innovations we pursue. I’ve had conversations with some global executive recruiting firms that corroborated this idea with some of their select clients.

You're saying data is the game-changer in every field?

Neil Bhandar: Absolutely. It's the heartbeat of a company. We're not just tracking past performances or making predictions anymore. Data is about creating and envisioning new futures. It's transformative. And who better to lead that charge than the CDAO?

That's a powerful perspective. Data as the driver of not just decisions, but of our future reality itself. Building on that, can you clarify the distinct roles of the CIO and CDAO?

Neil Bhandar: Certainly. The CIO's role is crucial but fundamentally enabling. They are the guardians of data, overseeing the infrastructure that supports it. Their domain is the technical aspects: security, storage, and ensuring backend systems are seamless.

So, their role is more supportive in nature?

Neil Bhandar: Exactly. They're not typically the visionaries of data, but they're essential in providing the tools for those visions to be realized. This is why I firmly believe the CIO should report to the CDAO. The CDAO is, or should be, the strategic partner to the CEO, akin to the chief strategy officer or the head of business development.

It's an interesting dynamic. By placing the CDAO in a more strategic position, you're essentially suggesting a realignment of the power structure within organizations. Looking ahead, how do you foresee the integration of the CDAO within the future corporate environment?

Neil Bhandar: Precisely. It's a strategic realignment that reflects the central role data now plays in driving business strategy and innovation. It's inevitable that the CDAO will become integral in the future. The condition is—we must avoid overcomplicating things with data. Today, there's a disconnect; the data specialists speak one language, and the rest of the business speaks another. It's like deciphering code when they mention things like Kubernetes—it's all Greek to the non-tech folks.

The challenge of bridging the communication gap?

Neil Bhandar: That's right. We need a data organization that speaks business fluently, but also translates that into data insights and strategy. This role should make the complex understandable, connecting the dots between data potential and business outcomes. The CDAO must be the translator, the interpreter who takes data's technical depths and surfaces clear, actionable business intelligence.

Your vision is quite thought-provoking. As we explore this, let's get practical for a moment. What should the job description of a chief data and analytics officer encompass?

Neil Bhandar: Great question. First and foremost, the CDAO has to be a business leader. They need a deep understanding of the company's value chain, business model, and cycles. Because here's the thing: data and analytics are universal. You can shift them from one industry to another. That adaptability is crucial, but so is the need to avoid becoming a jack-of-all-trades and master of none.

Specificity within the universal applicability of data, yeah?

Neil Bhandar: Precisely. The CDAO must be a specialist in translating data into actionable insights, tailored to the unique rhythms and requirements of their industry.

You've emphasized understanding business cycles. How critical is this for a CDAO?

Neil Bhandar: It's vital. If a CDAO doesn't grasp the ebb and flow of their business cycles, their ability to respond effectively to market changes is in jeopardy. It's not enough to just spot trends. You must be agile enough to act on them promptly. Otherwise, you risk being outpaced by the competition and that can set you spiraling out.

Timing is everything, then?

Neil Bhandar: It's paramount. A CDAO must be as adept at timing as they are at analytics. Recognizing patterns, predicting trends, and understanding the intricacies of value chains and business models—it's all part of the dance. And if I were to draft the job description for a CDAO, it would hinge on mastery of these three elements: business cycles, value chains, and business models.

Let’s pivot to the role of analytics. There's been a shift toward commoditization in the field. What are your thoughts on that?

Neil Bhandar: Without a doubt. Analytics is becoming a commodity, and this changes everything. It means analytics capabilities are more accessible, ready to be plugged in and played with. The days of highly specialized, one-off analytical models are giving way to a more standardized, product-like approach.

What does this mean for the chief data and analytics officer?

Neil Bhandar: It means the CDAO has a new priority: they need to know how to integrate these off-the-shelf analytical solutions effectively into the business. The CDAO must be the architect, not just a custodian of data strategy, adept at assembling these modular, analytical components into a cohesive, strategic framework.

Let's touch on the structure of the C-suite. What's your stance on the idea of a combined chief data officer (CDO) and chief analytics officer (CAO) role?

Neil Bhandar: The decision to combine these roles hinges on the organization's maturity and the industry context. It's not one-size-fits-all.

How does this relate to the earlier point about understanding business cycles?

Neil Bhandar: If an organization has a solid grasp of its business cycles, then fusing the CDO and CAO roles can be synergistic. The roles share a common foundation in data and analytics but approach it from different angles.

It sounds like context is king in this decision.

Neil Bhandar: Precisely. When I was with Proctor & Gamble, we viewed marketing from two distinct perspectives: the heart and mind, and the wallet and space. Similarly, data and analytics serve dual purposes in a company. The heart and mind side is about understanding and leveraging data, whereas the wallet and space is about the tactical, actionable analytics that drive revenue.

Could you elaborate on how the concepts of “heart and mind” versus “wallet and space” relate to the data and analytics roles?

Neil Bhandar: Regardless of geography, a mother's concerns and desires for her children are universal—that's the “heart and mind.” It's about resonating with core human emotions and needs, which is central to both data interpretation and analytics application. And then there's the “wallet and space”—this is where practicality kicks in. Decisions become about cost, about immediate benefits. Here's where the trade-offs happen, where the data and analytics translate into immediate consumer actions, like the thrill of an Amazon purchase.

How does this duality translate into leadership?

Neil Bhandar: Well, it creates a dynamic where these perspectives can either conflict or converge. In the best-case scenario, they come together under a leader who harmonizes the long-term impact of emotional engagement with the immediate gratification of transactional excitement. That's the kind of nuanced understanding a combined chief data and analytics officer brings to the table.

Hm, tie this back to the CDAO’s role in a more direct way.

Neil Bhandar: Sure. Take the market side of the business, for instance. It's fast-paced, focusing on pricing, promotion, merchandising—it's where quick wins dominate. Emotion, on the other hand, is about building lasting connections through compelling communication and engagement.

And how does this impact the leadership?

Neil Bhandar: This dichotomy is critical. On the one hand, you might be tempted to throw money at problems for quick fixes. On the other, you're crafting narratives that foster long-term engagement. Effective prioritization demands a single leader who understands the delicate balance between immediate actions and the slow build of emotional capital. For a CDAO, this means knowing when to leverage data for a quick impact versus when to invest in long-term analytical strategies.

So, expand on the idea that consolidating the data and analytics functions hinges on maturity.

Neil Bhandar: A mature organization understands its data lifecycle and can integrate it without friction. The analytics function might have an appetite to consume as much data as possible to enhance model performance, whereas the data function takes a more strategic, risk-aware approach.

There's a potential conflict of interest?

Neil Bhandar: Yes, there's a natural tension. For example, the analytics side might ignore the broader implications of using certain data, aiming for precision in the short term. Meanwhile, the data side is cautious, thinking long-term about issues like secondary data use and compliance, and the risks that come with it.

But when should these roles be combined?

Neil Bhandar: If an organization can navigate these complexities and has a firm grasp of data ethics and strategy—then yes, merge the roles. Otherwise, it's wise to maintain separate roles to ensure that healthy tension keeps both functions in check. It's all about balance and ensuring neither side oversteps but works in concert for the greater good.

Alright, in this ideal scenario for a mature organization, who do you see as the rightful owner of AI?

Neil Bhandar: It's a fascinating question. When we talk about AI, we need to dissect what we mean. Are we discussing the technology itself, its applications, the ethical considerations, the governance? AI is not just a single entity; it's a multifaceted discipline. From the technical infrastructure to its strategic use, AI could potentially fall under different jurisdictions. But in a mature organization, where the CDAO is the nexus of data and analytics, AI should ideally sit within this domain too. This is due to AI's inherent reliance on data and its potential to shape business strategy.

Could you give an example of how AI has been effectively positioned within an organization?

Neil Bhandar: Sure. I've seen AI thrive directly under the business umbrella, not just as a technological concept but as a value-driving tool. It's about application, not just academic discussion.

What does that look like in practice?

Neil Bhandar: Let me give you an example from JPMorgan. Because of how data and analytics leadership was positioned in the company, they integrated AI seamlessly into everyday banking operations. Consider a transcription engine that not only captures our conversation but also links keywords to news, products, or past CRM entries—making our interaction incredibly informed and efficient. JPMorgan is an excellent case study in business cases dictating the positioning of AI. AI belongs in the business because it addresses clear, immediate needs. It's a pragmatic approach. When you shift to discussing implementation—how to make it all happen—that's when the conversation involves technology, data, or analytics teams. Each brings something different to the table.

It's a complex web. The positioning of AI isn't a one-path journey. It's a decision that involves analytical methods and technology choices, and also operational considerations, like whether the AI is running on individual laptops or connected to a central engine.

What questions should organizations ask to best position AI?

Neil Bhandar: You have to consider the organization's structure, the agility of its processes, and who benefits from the AI. If it's too centralized and only a few reap the benefits, you're missing the point. AI should be about broad value creation, not isolated successes. That's why it's hard to pin AI down to one department or leader—it's about who can best align it with the organization's capacity for change and the overall value delivered.

How can a leader in data and analytics effectively advocate for the evolution of their role across enterprises?

Neil Bhandar: Advocacy starts with tangibility. The key is taking even the smallest data concept and showing its real-world impact. Whenever I get data, I make it a point to demonstrate its use, to prove its value. It's not about being a “data gopher.” It's about being integral to the organization's decision-making process.

And the significance of this approach?

Neil Bhandar: It's vital for breaking down barriers both down and up the chain of command. Leaders, especially those who've ascended the ranks over decades, might not grasp these new technologies intuitively. It's about translating these concepts into their language, showing them the concrete outcomes, not just the data points.

It sounds like for AI to be effective, its implementation must be clearly visualized and communicated within an organization. Can you explain how a leader should approach this?

Neil Bhandar: Yes, visualization is key. A successful leader must take any AI concept and paint a clear, vivid picture that the organization can easily grasp. It's about instantaneously translating abstract ideas into tangible business use cases.

And how should they convey these concepts?

Neil Bhandar: They need to integrate these concepts into the broader narrative of the business. They should break it down: explain what changes, what stays, who's responsible. It's the nitty-gritty details that make a concept believable and actionable, not undermining intelligence but providing clarity due to the novelty and associated apprehensions surrounding AI.