A Framework for Prioritizing Analytics Efforts

Project prioritization in analytics is deceptively complex. Achieving "good" prioritization is critical for analytics functions, yet often remains undefined and unstructured, leading to chaos, resource overwhelm, and suboptimal ROI. A well-functioning prioritization process, on the other hand, empowers analysts, satisfies business leaders, and tracks clear returns on investment.

Leaders in analytics wrestle with what projects to prioritize and how to balance project types to meet business goals. This article proposes a simple framework to prioritize effectively, centered around clear communication with business leaders. By setting and regularly updating goals, analysts ensure focus on the right issues, fostering stronger internal partnerships, aligning with business objectives, boosting analytics ROI, and increasing employee engagement. Because this framework sits inside of existing team culture and a larger operating framework, it functions as a schema to orient the data and analytics organization toward demand-side business leaders. In IIA’s observation, shifting from a focus on analytics capability (i.e. supply) to a relentless pursuit of understanding and supporting your data consumers (i.e. demand) is a major step toward enterprise analytics maturity.

Seven Steps to Project Prioritization

1. What is the purpose of the analytics organization?

Understanding the analytics function's purpose is crucial. This framework is built on the belief that while the goal is often tackling “more valuable projects,” value transcends analytical complexity, focusing instead on business impact and improved decision-making.

Prioritization from this perspective is intricate, demanding enhanced stakeholder collaboration, strategic decisions from senior leaders, and a clear view of ROI. It may also challenge the status quo of decision-making authority and requires balancing organizational needs with analysts’ interests.

Before proceeding, it's crucial for analytics leaders to establish a consensus on the function's main goal, be it strategic support or otherwise, through candid discussions about present and future objectives. This agreement forms a united front around which stakeholders can align.

Analytics leaders must recognize the difficulty of this change for their partners. The potential for significant gains comes with the risk of disruption. They must seek trust, perhaps not wholly earned, and demonstrate empathy during this transition, which will go a long way in growing and deepening these critical relationships.

2. How can we develop a meaningful portfolio of analytical work?

Prioritization often arises when analytics teams are swamped, working from a project portfolio born of an unstructured intake process. Typically, this results in a reactive cycle of vague, context-less data requests from the business side, leading to mutual frustration over unmet needs and missed opportunities for deeper insights.

This perceived disconnect stems from analysts operating in isolation from business strategy. To bridge this gap, a business strategy review session is invaluable, ideally conducted semi-annually for each line of business (LOB). Such sessions should include:

1. A business-led deep dive on the LOB strategy and current priorities.
2. An analyst-led review of the LOB’s existing portfolio of analytical requests.
3. Alignment of the business partner’s top priorities to the existing portfolio of requests.

This routine has several benefits. First, it gives the analyst a much deeper understanding of what the partner is trying to achieve. Now she can not only provide more complete answers, but she can also recommend comprehensive solutions that span the analytics eco-system, from data and reporting to predictive modeling and optimization. It also syncs the business and analytics teams, ensuring a consensus on essential projects over optional ones.

3. How do we maintain alignment as priorities change and new requests are made?

Aligning resources to major goals helps a cross-functional team pursue long-term objectives, but this doesn't account for the unforeseen or emergent needs that arise. To keep the analytical work portfolio intact, I combine top-level priority setting with a routine portfolio management process.

I've found bi-monthly, 30-minute meetings per LOB effective, with essential stakeholders participating—this includes the managing analyst, LOB head, and anyone on their team who is making requests of the analytics team. In these meetings, we assess new requests, resolve priority conflicts, and address roadblocks.

In practice, the portfolio management routine should be led by the analyst to serve their needs—not as a business briefing. It's a space for analysts to seek clarification, highlight risks, and drive decisions, ensuring work progresses meaningfully. Interestingly, this also aligns the LOB internally, as leaders often discover misaligned requests, allowing them to refocus their team.

A business strategy review paired with portfolio management fosters ongoing alignment and boosts productivity. It's like the rocks-in-the-jar analogy: prioritize your 'large rocks'—strategic initiatives—before the 'sand' of daily tasks, and everything fits together more efficiently.

4. How do we decide what to work on, and who owns the decision-making authority?

I advocate that decision-making authority lies with the business leaders we support, but that doesn’t render my team passive. Our duty is to shape and inform these decisions, ensuring they are well-founded and reflect intelligent use of analytics resources.

When determining what projects to undertake, I focus on three questions:

1. Does the request support a top priority?
2. What's the expected ROI?
3. Is the business ready to act on the results?

These questions aren’t cut-and-dry criteria but conversation starters leading to well-reasoned choices. For instance, I was once asked to estimate the impact of a potential government shutdown. Was the request aligned to a top priority? No. What was the potential ROI of the work? Once could argue zero dollars. Was the business poised to action? In that case, yes, but no one quite know what the action would be. Nonetheless, it was clear that this work was critical to the company’s understanding of the potential risk it was facing.

On the other hand, for exploratory requests that begin with "It would be interesting to see...", I apply these questions to weigh the value of diverting resources from current projects.

Understanding the context of each request is key. If a partner has a history of non-action post-analysis, this history informs future decisions about resource allocation, always aiming to optimize overall returns.

For analysts feeling compelled to immediately fulfill every request, consider a case where an analyst, despite clear priorities, misallocated her efforts due to a misinterpretation of urgency, resulting in burnout and unmet primary objectives. It’s crucial that analysts clarify and align their actions with the true needs of the business, rather than self-determining priorities.

5. At what level should prioritization occur?

Prioritization of analytical resources should occur at three levels:

1. Within business units, where it's most common and straightforward.
2. Across business units, which is less frequent and typically arises when an opportunity demands reallocating resources temporarily based on ROI discussions.
3. At the enterprise level, which is the least frequent but crucial for reviewing business opportunities and ROI to determine resource alignment for maximum value.

For instance, at an organization in the Midwest where I was head of enterprise analytics, there was a specific month-long period each year when the analytics team was involved in intense goal-setting work for the sales team. By engaging in high-level discussions during business strategy reviews, we managed to realign resources temporarily by pausing other activities, ensuring deadlines were met.

During a strategic transformation at this organization, it was essential to shift analysts from product-focused to financial wellness tasks. This realignment was a straightforward decision among leaders, communicated transparently across the enterprise.

However, persuading a leader to reallocate resources for the broader enterprise can be challenging. Here, I plan for more upfront conversations, seek support from other stakeholders, and have a final conversation in a public forum, sometimes calling for an executive decision.

6. When is it time to add more resources?

The question of when to increase analytical resources is common—business and analytics leaders alike rarely feel their teams are large enough. Since successful analytics work tends to generate even more demand, it's impractical to staff purely based on demand. Staffing should instead be based on the business's readiness to act on analytical insights.

Value arises from action; without it, even the most complex solutions are futile. Additional resources should be considered when the business is prepared to act, supported by a business case that promises a solid ROI.

When it’s time to expand the team, a hybrid funding approach works well. Some situations require baseline support funded by central services, while others benefit from LOB-specific resources managed independently from central analytics routines. Regardless of the funding model, these resources, even if reporting to the head of analytics, serve the LOBs’ objectives.

7. How do we know the prioritization framework is working?

To evaluate if a prioritization framework is effective, I track three success indicators with my business partners:

1. Inclusion of analysts in strategic planning, beyond mere data requests, indicates advancing relationships.
2. Implementation and adoption of developed solutions by the LOB show that our analytics work is actionable and valued.
3. The actual ROI of initiatives where analytics played a role measures the tangible impact and justifies the investment in our team.

A robust framework goes beyond managing project throughput; it strengthens relationships, establishes credibility, and enhances impact. If a partner lingers on simple data requests, we need to re-examine our mutual goals and refine our operating model. Should an analytics output remain unused, we investigate why and use our findings to inform future resource allocation.

The most critical measure is the demonstrable ROI from analytics, which should at least cover the analytics team's cost. The focus is on actual, not potential, returns, aligning with the business case and overall LOB benefits, rather than isolating the contribution of individual analysts. The aim is to connect analytics work with high-impact business initiatives that deliver quantifiable organizational benefits.

Final Thoughts

Prioritization of analytical projects cannot happen in a vacuum. Instead, if the purpose of analytics is to influence and enable effective business decision making, analysts need to be steeped in business strategy and meet regularly with their business partners to ensure ongoing alignment. While the business leader ultimately determines his or her priorities, analysts should influence those decisions by seeing to it that those decisions are fully informed. To optimize the ROI of analytical talent, prioritization should happen at all levels of the organization and staffing levels should be aligned to the organization’s ability to take action.