Organizations today have access to unprecedented data spanning every facet of their operations, from online customer engagement and transactional histories to real-time IoT telemetry and financial performance metrics. Yet, this explosive data growth has not always translated into faster decision-making.

In many cases, the sheer volume of information overwhelms executives, creating bottlenecks in strategic decision-making. A recent scoping review by Shahrzadi et al. (2024) found that information overload can significantly reduce decision efficiency, even in data-rich environments. This underscores the need to structure analytics systems and decision frameworks around decision clarity, not just data availability.

Where business analytics falls short

Organizations have developed increasingly sophisticated analytical models and dashboards that allow users to slice and dice data in countless ways. In parallel, new performance metrics are continuously introduced to capture nuances in every dimension of the business. However, data storytelling is often treated as a post-hoc formatting exercise rather than an integral part of the analytics solution stack.

Data scientists and analysts often focus on building the most advanced models. However, they often overlook the importance of positioning their work to enable executive decisions. As a result, executives frequently find it challenging to gain useful insights from the overwhelming volume of data and metrics. Despite the technical depth of modern analytics, decision paralysis persists, and insights often fall short of translating into tangible actions.

At its core, this challenge reflects an insight-to-impact disconnect in today’s business analytics environment. Many teams mistakenly assume that model complexity and output sophistication will inherently lead to business impact. But without a clear narrative, context, and alignment with decision-making needs, even the most advanced models risk irrelevance.

In my work advising both Fortune 500 companies and early-stage startups, the insight-to-impact disconnect in business analytics can be broadly attributed to four recurring challenges:

1. Lack of clear problem statements or business context

Analytical models are often seen as technical or exploratory exercises, rather than as solutions designed to address specific business decisions. Without a well-defined problem statement aligned with strategic objectives, even the most robust analyses risk being irrelevant.

2. Overemphasis on technical complexity with limited focus on decision levers

Increased precision is valuable only when it informs clear choices. Analytics teams often prioritize building cutting-edge models and analytical methodologies. However, these advances will not translate into tangible impact if they do not address concrete decision levers, such as resource allocation, customer segmentation, or go-to-market incentives.

3. Optimization without real-world constraints

Many models are built to optimize a singular objective, such as maximizing revenue or minimizing cost, while overlooking constraints that are difficult to quantify but critical to decision-making. For example, in budget allocation models, factors such as employee morale, organizational complexity, or operational feasibility are often excluded, resulting in outputs that may be technically correct but practically unusable.

4. Black-box models with low interpretability

Advanced methodologies like ensemble models and AI-driven analytics can generate accurate results, but they often lack transparency. When executives cannot contextualize or explain model recommendations, trust reduces and decision-making slows. A recent study found that while AI-generated explanations can affect user trust, they do not consistently improve decision accuracy. This finding further highlights the importance of model confidence and interpretability in supporting executive judgment.

Build your analytics stack for success

To address these challenges, it is essential to design the analytics stack holistically with executive decision makers in mind. Business analytics should not simply generate outputs, instead, it should support decision-making under real-world constraints and ambiguity.

When it comes to business decision-makers, here is what they typically look for in analytics outputs:

• A clear problem statement: What are we solving for? How does this analysis connect to our strategic priorities?
• Decision principles and boundary conditions: What constraints or non-negotiables must we operate within (e.g. budget caps, staffing limits, compliance rules)?
• Broad trends and key drivers: What signals matter most? Are we focused on meaningful movement, or getting distracted by noise?
• Confidence levels and model reliability: How much trust should we place in this output, and under what conditions does it hold?
• Contextualized insights and strategic implications: What does this tell us about the business? What trade-offs should we be weighing?
• Clear decision levers: What actions can we take now, and what outcomes can we expect if we do?

To meet these needs, analytics teams should not treat data storytelling as a feature to be layered on at the end, but as a function of how every layer of the analytics stack is designed. Storytelling should be embedded in the system architecture, not retrofitted into presentations or documents.

I propose a four-layer business analytics framework that embeds storytelling from the ground up.

A four-layer model that embeds storytelling principles into data management, analytics design, decision-making support, and narrative delivery — enabling faster and more actionable insights at scale.

1. Data layer: Ensure clean, aligned, and story-ready data foundations

• Align data governance frameworks to business logic, especially around definitions and ownership. For example, a finance team defines “customer churn” as contract cancellation, while a product team defines it as prolonged inactivity. Without aligned definitions and ownership, analytics outputs become inconsistent, eroding stakeholder trust. A clear governance structure ensures shared understanding and reliable reporting across teams.
• Tag metadata with narrative labels to surface relevance quickly. For example, sales analytics teams could tag quarterly regional performance as “trending up,” “on watch,” or “underperforming” based on deviation from forecast. This allows executives to quickly scan where attention is needed, without digging into the raw data or creating custom filters.
• Maintain a single source of truth with clearly defined, consistently applied business rules. For instance, one team might report monthly revenue based on when a deal is signed, while another team reports it based on when payment is received. This leads to different numbers being presented in executive meetings, even though they are looking at the same product. By creating a single data source with clear, shared rules everyone aligns around the same story, and confusion is eliminated.
• Encourage tight partnerships between data teams and business owners to keep analytics grounded in operational reality. Consider a workforce analytics team building a staffing forecast model. Without consulting business unit leaders, they optimize purely for utilization, missing key factors like cross-functional dependencies or team fatigue. By embedding business partners into model design, they produce outputs that reflect operational nuance and are more likely to be adopted.

2. Analytics layer: Design flexible, interpretable models with action in mind

• Design models and BI tools with story-ready outputs. Envision your end output first. Anticipate the types of questions decision-makers are likely to ask (e.g., “What happens to customer churn if we raise subscription prices by 5%?”, “How sensitive is our forecast to different levels of marketing spend?”). Then, build the model to proactively answer these questions without requiring major rework. At the same time, think about how you intend to present or visualize the results from the outset.

For example, when designing a customer clustering model, anticipate that executives will want to understand how much influence each signal (e.g., purchase frequency, tenure, engagement) has on cluster formation. Build relative influence factors into your model outputs to surface these drivers clearly.

• Build flexibility into models to enable scenario planning. Decision-making in real-world environments is rarely static. Therefore, it is essential to build models that allow easy exploration of alternative scenarios, such as using “what-if” toggles, adjustable assumptions, or narrative-driven scenario planning. For example, in a resource planning model, allowing executives to toggle assumptions like customer growth rate or churn probability directly in the dashboard can significantly increase model utility.
• Avoid monolithic models and prioritize transparency and explainability. Executive confidence in analytics is heavily influenced by the ability to understand, or at least contextualize, model outputs. Where possible, break down models into clear, explainable steps that trace the journey from input data to recommendation. In cases where black-box AI models are used, such as random forests or neural networks, support recommendations with backup hypotheses, sensitivity analyses, or secondary datasets to triangulate your findings and reinforce credibility.

3. Decision layer: Enable strategic choices through data context

• Clearly identify the decision levers that the organization can move. Not all parts of the business are controlled by management. It is important to consider boundaries in decision frameworks, including regulatory requirements and contractual obligations while also considering established precedents.
• While constructing an analytical model, it is essential to identify which levers are applicable. For example, in a workforce planning model, local employment laws can make the rapid reduction of headcount impractical. Knowing this constraint in advance keeps decision scenarios realistic and avoids suggesting unfeasible or noncompliant actions.
• Effectively leverage data visualization to highlight trends and trade-offs. Executives frequently have limited time and are unable to thoroughly examine extensive data sheets or technical reports. A well-constructed data visualization minimizes complexity and greatly improves understanding. Based on my experience working with C-suite executives, there are several practical frameworks that could greatly improve decision-making clarity:
  • Waterfall charts are a useful tool for depicting cumulative effects across projects or variables;
  • Two-by-two matrices help pinpoint crucial decision factors and identify strategic trade-offs quickly;
  • Heat maps provide insight into patterns and trends in large data sets;
  • Interactive dashboards enable decision makers to explore scenarios dynamically, adjust their assumptions, and visualize potential outcomes immediately.
• Acknowledge ambiguity and incorporate qualitative factors alongside data insights. Quantifying the exact nature of certain parts of business performance is inherently challenging. Even the most accurate models cannot accurately account for customer behavior or market trends due to macroeconomic uncertainty or cultural shifts.

Using qualitative insights enables a more thorough understanding of reality in these situations. For example, to better understand the root causes of customer churn, it is important to go beyond statistical models. Incorporating qualitative data, such as customer interviews and satisfaction surveys, adds depth and context that purely quantitative analysis cannot capture.

4. Narrative layer: Shape insights into decision-ready stories

Similar to writing a resume, it is crucial to weave data analytics insights into a coherent, persuasive narrative. Data itself should not be the sole focus of the narrative – a data “dump” rarely gets the point across. Instead, data should be selectively used to support a consistent argument and call to action.

Begin by clearly laying out the problem statement and building a strong business case for change. Here, leverage trend analysis to show how the landscape has shifted, and focus on key top-line or bottom-line impacts (e.g., “$10 million in annual revenue is at risk if current churn trends continue”).

Then transition the narrative to surface insights, which should form the core of the discussion. Focus less on model construction details, but more on the output and what specific insights they reveal, such as data trends, patterns, and anomalies. Strengthen these insights by including qualitative findings and operational context.

Conclude the narrative with a clear call to action. Articulate the decision options available, outline the support or resources required, and highlight areas where further analytical deep dives may be needed. The goal should be to move executives toward decision and action, not just awareness.

Conclusion: Treat storytelling as a feature in your analytics stack

To fully realize the value of data-driven decision-making, organizations must incorporate storytelling as an organic design element in the analytics infrastructure. Analytics without storytelling is intelligence without influence. In a world with increasing volumes of information, it is not enough to churn out models and more data.

It is essential to structure data and insights into narratives that drive action. In a true data-first organization, storytelling is not decoration, it is delivery. It is how analytics earns its seat at the decision-making table and enables organizations to move from insight to impact at scale.

About the Author:

Jiaxi Zhu is a recognized expert in data management, advanced analytics, and executive decision intelligence. He currently serves as Head of Analytics for Google’s Small and Medium Business division, where he leads initiatives that shape global analytics strategy, data governance, and AI-driven insights.

Previously, Zhu advised Fortune 500 companies and early-stage startups on data infrastructure, predictive modeling, and business intelligence optimization, including roles at McKinsey & Company and PwC. His work focuses on bridging the gap between data outputs and strategic decision-making, with innovations in predictive analytics, data-enabled decision systems, and scalable data governance frameworks.

Zhuis an active contributor to the Institute for Operations Research and the Management Sciences (INFORMS) and a frequent speaker at industry conferences on analytics innovation and data strategy. He holds degrees from the Wharton School of the University of Pennsylvania and UC Berkeley.