Business intelligence (BI) software sounded like a good idea, at first. You were looking forward to using it to analyze business data and present insights in a user-friendly manner. However, now that you’re beginning implementation, you’re experiencing many challenges.

If this describes your situation, it might be time to start taking those red flags seriously.

Today, we’re taking a look at some of the main factors that lead to business intelligence failure. We’ll also share how your company can overcome these obstacles to leverage everything BI technology can offer.

6 Reasons for Business Intelligence Failure

1. Lack of Executive Support and Direction

The role of BI in business has evolved in recent years. BI is no longer seen as just a tool for reporting and analysis. It is now seen as a strategic asset that can be used to guide business decisions, drive innovation, and gain competitive advantage.

As a result, it’s more important than ever for BI initiatives to have the support of senior executives.

Engaged executives can provide the strategic direction you need for successful software selection. They can help you define a BI strategy to ensure the new business intelligence solution aligns with the organization’s goals as well as its existing enterprise software.

A Failed Payroll System Implementation

Panorama’s Expert Witness team was retained to provide a forensic analysis and written report to the court regarding the failed implementation of a major software developer’s ERP/payroll system.

2. Too-Long Timelines, Too-Little Engagement

There are many steps required to complete a successful BI project. Initial phases, such as gathering requirements and specifying technical and functional features, are just the beginning.

After a while, your project teams may begin to grow weary and disengaged. It might seem as though the time between planning the effort and actually delivering something tangible is enormous.

This is especially the case if you approach the implementation as a waterfall project, only beginning the next phase when the preceding one is complete.

If possible, try to take an agile approach, instead. This involves dividing up the project into smaller phases, which helps keep stakeholders engaged.

Testing and development activities occur concurrently in an agile project, which can facilitate ongoing communication among developers, managers, testers, and customers.

3. Unnecessary Dashboards and KPIs

As you configure your BI system, it can be tempting to create a bevy of dashboards and key performance indicators (KPIs) just because you can.

Instead, consider how your business or department defines success, and look for ways to use BI to measure these criteria.

You don’t want to overcrowd your dashboards and scorecards with ambiguous performance indicators that overwhelm and confuse your employees, rather than help them.

4. Lack of User Adoption

Implementing business intelligence solutions is one thing. Convincing your workforce to use them is another.

These systems are advanced, and often intimidating for first-time users. This is true for even the most user-friendly data visualization tools. Most business users aren’t data science experts, so knowing how to interpret insights can be challenging.

We recommend developing a change management plan to make sure employees are involved in the project from the beginning. This means participating in requirements gathering, training sessions, and more.

5. Data Visualization Without Direction

A dashboard can be filled with all kinds of data points, but above all else, it should tell a story. Where were you before, and where are you now? What happened along the way?

If your BI technology doesn’t tell a story, you’ll struggle to understand where business successes and failures occurred, impeding your ability to make data-driven decisions.

6. Poor Data Quality

The availability of data has increased dramatically in recent years. This means that businesses now have more data to work with, but it also means that data is more complex and challenging to manage.

As a result, it’s more important than ever for businesses to have a strong data governance program in place to ensure data quality.

Your BI development team needs data that is clean and ready to use. Otherwise, they’ll move ahead based on existing data, which may not be completely accurate. This spells business intelligence failure.

As we’ve seen in recent examples like the Gannett AI fiasco and the American Express chatbot controversy, even sophisticated tools can go astray if proper attention is not paid to data quality ethical considerations.

According to our business process improvement consultants, one of the best ways to ensure ongoing data quality is to optimize your cross-functional processes for seamless integration across departments.

Business Intelligence Today

The business intelligence landscape is constantly evolving, and businesses need to adapt in order to stay ahead of the curve. Some current BI trends include:

• Cloud-based BI solutions: These solutions are becoming increasingly popular as they offer a number of advantages over traditional on-premises solutions. This includes scalability, flexibility, and cost-effectiveness.

• Artificial intelligence (AI) in BI: AI is rapidly transforming the BI landscape by automating tasks, providing deeper insights, and making predictions. For example, AI-powered predictive analytics tools can help businesses predict future scenarios, such as customer churn or increased product demand.

• Increased focus on security and privacy in BI: As businesses collect and store more data, it’s more important than ever to ensure that this data is secure. Strong authentication methods and ongoing employee education are essential.

The Road to Success

Companies that successfully adopt BI will be more competitive, agile, and responsive than those that don’t.

To learn more about how to avoid business intelligence failure, contact our ERP project recovery consultants below.

About the author

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As Director of Panorama’s Expert Witness Practice, Bill oversees all expert witness engagements. In addition, he concurrently provides oversight on a number of ERP selection and implementation projects for manufacturing, distribution, healthcare, and public sector clients.

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