Choosing a BI Platform? 7 Pitfalls for Growing Companies

Quick summary

Choosing a BI platform is about far more than picking a tool. It’s a decision that can shape the quality of decision-making across your business for years to come. Companies in growth mode that underestimate this process usually run into one of seven familiar pitfalls, from a data landscape with no real foundation to a platform that needs replacing again after just two years.

• Pitfall 1: starting with the platform instead of the business question
• Pitfall 2: waiting until after go-live to fix data quality
• Pitfall 3: underestimating scalability during a growth phase
• Pitfall 4: skipping governance during setup
• Pitfall 5: leaving end users out of the selection process
• Pitfall 6: ignoring vendor lock-in as a selection criterion
• Pitfall 7: failing to budget for maintenance and model upkeep

Why BI platform selection looks different for growing companies than it does for large enterprises (Data and reporting environment)

"You don’t want a solution that ends up gathering dust. You want one that actually goes live and gets used."

— Martijn

A BI manager at a mid-sized professional services company has a problem that sounds simple on paper: leadership wants one reliable daily view of revenue, margin, and utilization. In reality, the data comes from three different systems, two departments define “revenue” differently, and the Excel files currently being shared around never quite match.

This isn’t an edge case. It’s the norm for companies that grow organically from 50 to 200 or 300 employees. The data infrastructure grows too, but usually without much planning behind it. Then, the moment the business decides it needs a better setup, an RFP for a BI platform is already on the table even though the real problem hasn’t been properly defined yet.

According to [CBS data from 2025](https://www.cbs.nl/nl-nl/nieuws/2025/09/gebruik-kunstmatige-intelligentie, ai---door-bedrijven-neemt-toe), large companies with 500 or more employees are far more likely to use AI technology than smaller organisations. A big part of that gap comes down to the maturity of the underlying data and reporting environment. Growing companies are right in the phase where that maturity still has to be built, and choosing a BI platform often marks the turning point.

The European market for Business Intelligence software was worth more than $14 billion in 2025 and is expected to keep growing strongly through 2034. More choice also means more room to make the wrong call. Below are the seven pitfalls Twentynext sees most often among growing companies in the Netherlands.

Pitfalls 1 and 2: starting too late with the fundamentals (Services)

Starting with the platform instead of the business question

The most common mistake is also the earliest one: the selection process starts with the tool, not with the decisions the business needs to make. An operations manager picks Power BI because colleagues use it too, or an IT team chooses Tableau because it looks polished and enterprise-ready. Only after implementation does it become clear that the dashboards look great, but no one actually uses them to make decisions.

Twentynext consistently works the other way around in BI projects: first define which decisions need to be made faster or better, then identify the data required to support those decisions, and only then ask which platform fits those requirements, the existing infrastructure, and the team’s technical capabilities. It sounds obvious, but in practice, tool selection is rarely left that late in the process.

Waiting until after go-live to fix data quality

A BI platform is only as reliable as the data flowing into it. In growing companies, data pipelines are rarely in good shape before a BI project begins. Duplicate customer records, inconsistent date formats across systems, revenue figures calculated slightly differently depending on the source, these are all familiar issues. Too often, they get pushed aside with the idea that “we’ll sort that out later.”

Later is too late. A dashboard that shows the wrong numbers undermines trust in the entire reporting environment, sometimes permanently. Data Engineering is not an optional step after go-live. It’s the groundwork that determines whether the platform will ever deliver value. Also read how Twentynext approaches Data Engineering as the foundation for BI projects.

What you can do now:

• Map each data source: which systems provide the data, who owns it, how current it is, and whether definitions are documented.
• Review each KPI: are there two or more sources that should produce the same number but don’t? Those should be your top Data Engineering priorities.
• Run a sample check: take one familiar report and trace every figure back to the source systems. Every point where someone has to guess is a risk.
• Make it a hard requirement: no BI platform go-live until the data pipeline for the three most critical KPIs is documented and validated.

Pitfalls 3 and 4: optimizing for the short term

Underestimating scalability during a growth phase

A platform that works perfectly for 80 users can become a real problem at 300. And it’s not just about license costs. It also affects load times, permissions, and the complexity of the data model once dozens of reports are being managed by multiple teams. For growing companies, it’s tempting to choose what works today and deal with scale later.

In practice, that often means being forced into a migration just as the business is in the middle of a major growth push and has little time or capacity to run another full selection and implementation process. The Gartner Magic Quadrant for Analytics and BI Platforms (2024) makes clear that buyers need to look beyond cloud integration and also assess governance, interoperability, and AI support when selecting a platform. Those are exactly the criteria that really start to matter at scale.

Skipping governance during setup

Governance can sound like something only large enterprises need. For growing businesses, it often feels like overhead. But without access control, report naming conventions, and clear rules about who can see what data, six months later you can end up with hundreds of reports and no one knows which version is the official one.

Twentynext sees this pattern in almost every BI project that starts without governance: the freedom feels great at first, but after a year, teams are wasting hours every week figuring out which report is correct and which one is out of date. A lightweight governance setup at the start, with clear ownership for each reporting domain, prevents that chaos.

What you can do now:

• Define at least three governance rules at the outset: who is allowed to publish reports, how reports should be named, and what the process is for archiving outdated reports.
• Assign a reporting owner for each business domain such as finance, operations, and sales, responsible for the quality of dashboards in that area.
• When comparing platforms, explicitly check what role-based access and permissions management are included out of the box and what would require customization.

Comparison table: structured approach versus ad hoc BI platform selection

Pitfalls 5 and 6: underestimating the human side

Leaving end users out of the selection process

IT selects the platform, the BI manager configures it, and the business is expected to use it. That classic sequence leads to poor adoption time and time again. End users know better than anyone which questions they need answered every day, what terminology they use, and how they actually read a report. A platform that makes perfect sense from a technical perspective but doesn’t fit the way people work simply won’t be used.

Take a sales team at a mid-sized professional services company with 150 employees. The BI platform shows revenue by quarter, region, and product group. But the sales team thinks in weekly deals and pipeline stages. If those dimensions aren’t there, the team stops opening the reporting environment after the first month. The investment has been made, but adoption is zero.

Ignoring vendor lock-in as a selection criterion

Platforms like Power BI are deeply integrated with the Microsoft ecosystem. That’s a strength for organisations that run heavily on Microsoft, but it can also become restrictive if the business later wants to integrate with a cloud platform outside that ecosystem, or if licensing costs rise sharply. Gartner’s analysis shows that platforms such as Microsoft and Oracle offer clear benefits through broad cloud integration, but organisations still need to weigh those benefits against the potential limitations in a multi-cloud environment.

If you choose a platform based only on current integrations, without looking at data portability and the openness of the API layer, you may find yourself having to choose all over again in three to five years. And migrations usually cost more than the original implementation.

What you can do now:

• Run at least two working sessions with end users before selecting a platform. Document the questions they ask daily and the terms they use for KPIs.
• Set a non-negotiable requirement: the platform must support the top five use cases for the primary user group without customization.
• Evaluate every vendor on the same points: how easy is data export, are there open APIs, and what would a migration cost in three years if your strategy changes?

Pitfall 7: treating maintenance as an afterthought

Underestimating model maintenance and data drift

A BI model is not a static product. Source systems change, KPI definitions evolve, and new data sources are added. Without active maintenance, a data model gradually drifts away from reality. Reports that were accurate six months ago can quietly start showing the wrong numbers, without anyone noticing.

This is the scenario Twentynext describes as the silent erosion of a BI environment. Technically, the platform still works fine. But confidence in the data slowly disappears. And once that trust is gone, people fall back on their own Excel sheets.

ISO-certified maintenance processes as a safeguard

Twentynext invests in ISO-certified service and maintenance processes because the period after go-live determines whether a BI environment keeps delivering value. In projects like these, maintenance includes periodic validation of data pipelines, impact analysis when source systems change, and versioning of the data model. For clients in sectors with strict continuity requirements, including financial services and healthcare, this is an explicit requirement. But the same principle applies to growing companies: if you don’t reserve a maintenance budget after go-live, you will pay for it later, usually twice over.

For organisations considering how they may want to add AI capabilities to their BI environment later on, it also makes sense to build AI governance into the platform decision from the start. That helps avoid a second selection process once the business wants to roll out AI-powered insights.

What you can do now:

• Build a maintenance line into the project budget for year one: in practice, this is often at least 15 to 20 percent of the initial implementation cost per year for active model maintenance.
• Set up a change log for the data model: every change to a source system or KPI definition should trigger a validation round.
• Schedule quarterly reviews with reporting owners: are the dashboards still current, and are they still being used for the decisions they were built for?
• Ask vendors directly: what is the SLA for model updates, and how are changes in source systems communicated?

Which approach fits your organisation?

Organisations in an early stage of digitalisation, just moving away from disconnected Excel files, have very different needs from companies that already have a BI environment but want to professionalise it for real-time insights or AI use cases.

According to CBS figures from 2026, nearly 90 percent of Dutch SMBs have now reached the basic level of digital intensity. That means most organisations are technically ready for a serious BI initiative. The question is no longer whether to act, but how.

Twentynext’s approach combines a business-first starting point with a structured project methodology (CRISP-DM), explicit attention to Data Engineering as the foundation, and a maintenance track after go-live. For clients in North Brabant and the wider Brainport region, that also means close collaboration with a team that treats source system integrations, data modelling, and reporting setup as one connected whole, not as separate projects stitched together afterward.

If you want a clearer sense of when an organisation is truly ready for a serious BI or AI use case, this article on organisational readiness for data projects offers a practical framework.

Frequently asked questions

What is the biggest risk of choosing a BI platform without proper preparation?

Data drift and poor adoption are the two most common outcomes of an unprepared BI implementation. Without a validated Data Engineering layer, dashboards gradually start showing incorrect data, and employees end up going back to their own Excel files. At that point, the platform investment is effectively lost, even if the organisation doesn’t realise it immediately.

How can Twentynext help with BI platform selection?

Twentynext supports the full process, from defining the business question and the decisions that need support, through Data Engineering setup and platform selection, all the way to post-go-live maintenance processes. The approach is based on CRISP-DM and ISO-certified service and maintenance processes, providing transparency and long-term reliability for clients across different sectors.

Does it really matter which BI platform you choose if the data isn’t in order?

Data quality determines the outcome, not the platform. No matter which solution an organisation chooses, if the source systems deliver inconsistent data and definitions are not standardised, the reports will not be reliable. That makes Data Engineering the priority before platform implementation, whether the choice is Power BI, Tableau, or another solution.

When does vendor lock-in become a real risk with BI platforms?

Vendor lock-in becomes a risk when an organisation places its data storage, transformation layer, and visualisation layer with the same vendor without open interfaces. According to the Gartner Magic Quadrant for Analytics and BI Platforms (2024), buyers should consciously weigh the benefits of ecosystem integration against the loss of flexibility. In practical terms: always test whether data can be exported easily and whether the API layer is open enough for future integrations.

How much should a growing company budget for BI maintenance after go-live?

A common rule of thumb in practice is to reserve an annual maintenance budget of roughly 15 to 20 percent of the initial implementation cost, depending on the complexity of the data model and the number of source system integrations. That covers periodic model validation, impact analysis when source systems change, and quarterly reviews with reporting owners. Companies that skip this budget often end up spending more during the next reimplementation.

Conclusion

The seven pitfalls involved in choosing a BI platform are not really technical problems. They are organisational choices made early in the process, or avoided altogether. The companies that get the best results start with the decision they want to improve, build Data Engineering as the foundation, involve end users early, and treat maintenance as part of the investment rather than an afterthought.

For clients in North Brabant and across the Netherlands, Twentynext supports this kind of initiative as an integrated partner, from the initial business analysis to ISO-certified maintenance processes after go-live. That’s the difference between a platform that has to be replaced again after two years and a reporting environment that grows with the business.