Why 90% of business analytics projects fail

Shocking Statistics: Most BI Projects Never Make It to Real Impact

Every year, companies invest billions of dollars in business intelligence. Yet in practice, BI rarely becomes a true driver of change. Reports quickly become outdated, dashboards are opened a few times a year at best, and key decisions are still made intuitively and by inertia.

According to Gartner, by 2027 eight out of ten analytics projects will fail. In most cases, the root cause is a poorly defined business objective and a lack of clarity around which decisions analytics is supposed to influence.

BI affects the very logic of management: how a company works with information, how quickly it reacts to deviations, and what it bases its actions on. This systemic approach is exactly what many organizations lack. Instead, they treat analytics as a standalone technology initiative rather than an integral part of the business model.

In this article, we’ll explore why business intelligence initiatives fail, the early warning signs that appear at the start, how successful companies approach BI, and how to build a BI solution that actually works.

7 Key Reasons Why BI Projects Fail

Let’s look at the most common challenges BI initiatives face — even within experienced teams.

1. Lack of Clear Vision and Strategy

The most common reason BI projects fail is launching without a clear management question. The decision to implement analytics is often driven by a desire to “see the numbers,” without a real understanding of what should change once those numbers are available.

When it’s unclear from the start what should improve and who is responsible for using analytics, BI turns into a technical project with no business impact. As a result, the analytics system exists in isolation: teams collect data, build reports, visualize metrics — but none of it is embedded into decision-making processes.

2. Low Stakeholder Engagement

BI development often becomes overly technical: analysts integrate data sources and build dashboards while the business continues to operate as usual, barely involved in the process.

The issue is that effective analytics starts with the right questions. When stakeholders join too late — or not at all — BI ends up answering abstract or secondary questions. The data may be correct and the reports well structured, but using them for real management decisions becomes difficult.

3. Ignoring Change Management

Any new approach inevitably meets resistance or confusion. Some employees stick to familiar workflows, while others don’t fully understand how the new system affects their daily work. Without a prepared environment, BI easily becomes a formal add-on to old processes.

Change doesn’t stick automatically. Without clear communication, training, and updated rules for working with data, analytics never becomes part of everyday actions. In this scenario, BI exists, reports are available — but nothing actually changes.

4. Focusing on Technology Instead of People

When BI projects focus solely on tools, a gap quickly forms between the system and its users. Data becomes overloaded, interfaces feel complex, and metric logic is understandable only to a small group of specialists. Analytics turns into a closed expert domain.

The value of BI lies in supporting everyday management decisions. If users don’t understand what the numbers mean or how to act on them, the system won’t integrate into workflows. BI tools must be designed around real user needs.

5. Poor Communication and Lack of Collaboration

Effective BI implementation requires alignment across teams. When each department operates independently with its own reports and definitions, the analytical picture falls apart.

The situation worsens when there is no shared understanding of key metrics. The same indicator may be interpreted differently by different teams, undermining trust in data and rendering analytics useless. Without a single version of the truth, BI may look convincing but actually amplifies confusion.

6. Insufficient Budget and Resources

BI is often underestimated, despite being deep, ongoing work with data at every level. When companies expect to deliver BI in a few weeks with one person, the outcome is predictably poor.

Such projects may appear functional at first: data loads, dashboards open, something is used. But as soon as complexity grows or load increases, the system starts to break.

Underfunded BI projects are like houses built from cheap materials: they look fine until the first serious storm.

7. Rushed Launch Without Proper Planning

There’s always a temptation to “start with something” and figure things out later. In BI, this approach rarely works.

Without a solid data model, a clear understanding of future users, and defined dependencies between metrics, the system cannot scale. Small changes break report logic, and any complexity requires manual fixes. Over time, BI loses value instead of gaining it.

Warning Signs: How to Know Your BI Project Is at Risk

Most BI projects lose value gradually, but early signals often appear long before the project is officially considered a failure.

For example, new reports are regularly created but rarely used in real scenarios. Analytics exists, but it doesn’t influence actions. This usually means BI has detached from the business context and started living its own life.

Another red flag is constant disputes over numbers. If different teams see different values for the same metrics, trust in the system disappears. BI stops being a reliable source of truth and becomes a topic of debate.

You should also be concerned if every new request requires manual workarounds or temporary fixes. This indicates that the underlying data model doesn’t support growth and that migration merely moved old problems to a new platform.

Finally, a serious indicator is declining executive interest. When BI is no longer the foundation for strategic planning and is used only operationally, its influence is fading.

BI that changes nothing is like a painting in an office that has blended into the background. It exists, it was once carefully chosen, paid for, and even argued over. Then everyone got used to it — and now all it does is collect dust.

The 10% That Succeed: What Successful BI Looks Like

BI projects don’t survive on intuition or enthusiasm alone. If they succeed, it’s because the company did several fundamentally right things — most of which have nothing to do with technology.

Here are four key success factors.

1. Strong Leadership and Clear Ownership

Successful BI initiatives always have one or more owners who don’t fully delegate responsibility to IT. They clearly define why BI exists and keep it in focus not just at launch, but every day.

This is personal accountability for success. Where leadership prioritizes BI, it survives and evolves. Where it’s handed off without oversight, the project slowly stalls — even if it technically continues to exist.

2. Flexible, Iterative Approach

Many BI problems begin with trying to cover everything at once. Systems are designed as universal solutions meant to answer all business questions. In reality, this almost always leads to delays and loss of focus before any tangible result appears.

Projects that survive take a different path. They start with one specific scenario — where money is lost, transparency is lacking, or something doesn’t add up.

This approach delivers quick, visible results. Teams see that the system works, skepticism fades, and only then does BI expand — carefully, step by step, without breaking what already works. These iterations, not grand designs, create scalability.

3. Focus on Quick Wins

If the first tangible BI benefit appears only after a year, the moment is already lost. Interest fades, and the system is seen as another long, drawn-out initiative with unclear value.

Teams where BI sticks show results within 30–60 days. Not a full system — just one improvement that removes manual work, clarifies a metric, or simplifies a daily action. That’s when the sense of value emerges, and people start asking new questions themselves.

4. Continuous Measurement and Adjustment

Successful BI evolves alongside the business. What was useful a month ago may no longer be relevant — and that’s normal.

Analytics is constantly tested in practice. Teams observe what users actually use and what they ignore, refine metrics, and remove excess. When processes or priorities change, the data model changes too.

Importantly, attention is paid not only to business metrics but also to the system itself. If BI slows down, stops updating, or loses trust, it’s noticed immediately. This continuous feedback loop keeps analytics embedded in daily operations.

How to Avoid Repeating Common Mistakes: A Step-by-Step Approach

There’s no magic button for perfect BI. But there is a clear sequence of decisions that helps overcome the main challenges.

Step 1. Define a specific business problem
BI must start with a clear question — a loss, a blind spot, or an inefficiency. One problem, one BI scenario, one real change.

Step 2. Assign a business owner
The project needs an owner with influence who understands why BI is being implemented.

Step 3. Build an MVP around one micro-task
The first working use case should appear quickly — ideally within 30 days. One action or metric that becomes easier or faster. This is the first win that builds trust.

Step 4. Involve end users before launch
Users must be part of the process. Otherwise, dashboards risk being built and never used.

Step 5. Define how BI will change behavior
Every report must have a practical outcome — what is done differently now. If there’s no answer, the report has no value.

Step 6. Set up feedback and adaptation
Launch is the beginning, not the end. Regular reviews of what works and what doesn’t keep BI relevant and alive.

From Failure to Success: Real-World BI Cases

Coca-Cola Bottling Company: Structured Data, Actionable Decisions

Operating in an environment where delays quickly turn into losses, Coca-Cola Bottling Company needed up-to-date visibility across production, logistics, inventory, and sales.

Before BI, the company relied on manual reports and spreadsheets. Data came from multiple systems, updated with delays, and failed to provide a holistic view. Problems became visible only after impacting results.

BI centralized key data and shifted from static reports to real-time metrics. Managers gained direct access to insights without constant IT dependency.

As a result, response times shortened, planning became more accurate, and operations more stable. BI stopped being a reporting tool and became part of daily operations.

NYSHEX: Faster Decisions Through Accessible BI

New York Shipping Exchange operates in global logistics, where access to information directly affects efficiency. After rapid growth in 2019, data handling became a bottleneck.

Analytics was slow and closed off. Data from product and cloud services was manually consolidated in Excel, and most requests landed on engineers.

BI was implemented with a focus on accessibility: data was centralized, and tools were designed for non-technical users. Teams could explore metrics independently and get answers quickly.

Decisions accelerated, engineers focused on product development, and BI became the foundation of operational speed.

Starbucks: BI for Customer Understanding

Starbucks doesn’t rely on intuition when choosing new locations. It analyzes demographics, income levels, traffic patterns, and nearby businesses to assess real potential.

BI also powers local campaigns. During heatwaves in Memphis, cold drink promotions were launched; in areas with higher alcohol consumption, Starbucks Evenings was tested.

Loyalty data enables precise timing and personalization of offers. BI creates the feeling that the brand understands and responds to customers in real time.

American Express: Deep Behavioral Insights Through Data

AMEX leverages its closed payment system to gain a comprehensive view of transactions from both merchants and cardholders.

BI supports not only fraud detection but personalized benefits, recommendations for restaurants and events, and more effective digital marketing. This approach increased engagement while reducing reliance on costly offline campaigns.

Get an Expert Assessment of Your BI Project

As this article shows, most BI projects face the same issues — they’re just not always noticed in time. Often, analytics technically works but is barely used day-to-day. Not because of the tool, but because of how BI is embedded into processes and what it’s actually used for.

In many cases, a full rebuild isn’t necessary. It’s enough to identify what already works, what adds unnecessary complexity, and which changes could deliver real impact in your specific context.

If you want to understand where to start or what to rethink in your existing BI setup, you can get independent feedback by booking a free workshop with IWIS.