BI Project Failure, and How to Prevent It

In a world of increasing opportunities to obtain and visualize data, it’s important to understand why business intelligence (BI) projects can fail in certain situations – especially when organizations have invested a great deal into resources.

Common Causes of Project Failure

1) Inability to Internally Resolve Target Goals & Deliverables, Often Due to Poor Communication, Cooperation, and Collaboration

This is where trust must be acquired. Without clarity on the client’s wants and needs, as well as lack of clear and approved goals and outcomes, the BI consultant can get left in the dark.  

2) Poor Data Quality

The data platform is the backbone of BI, and data quality is one of the most important aspects of BI that is often ignored. Limited or unavailable data sharing creates barriers in the way of meaningful insights. BI applications are only as effective as the accuracy of the data they are built on. But trying to use all available data will only make analysis more difficult without being able to move forward due to overanalyzing a situation. This is called Analysis Paralysis.

3) Analysis Paralysis

An overwhelming amount of data can lead to:

• Issues with data integration

• Hour-long data load times

• Larger storage size

• Overall load issues with too much data and/or unwanted data

This is an example of “Fatal error during data read” after a 1-hour load time and time-out due to large bulk of data being pulled through in Power BI. This leads to delays in presenting deliverables with no table fields to work with.

4) Lack of Communication

Lack of communication and continual feedback between IT and Management creates risks for a project’s success. It starts from failure to fulfill uncertain requirements involving a project. This leads to underwhelming business outcomes, resulting in a lack of trust in the workplace. The timeline for the BI project should be flexible, as issues are bound to happen. Unexpected bumps along the way are common and it’s unrealistic to expect that there won’t be delays in certain cases due to these hindering factors. This lack of conversation holds back teams from reaching  their full potential.

5) Low Adoption of End Users

There should be a focus on audience and what they need to know when presenting your insights. Instead of using BI tools to analyze the data for insights, end users tend to export the data and then perform the analysis elsewhere. This end-user resistance to innovation results in low adoption rates and unexpected usage patterns.

Straight-Forward Solutions

The most efficient ways to ensure the success of BI projects are:

• Create a solid requirement gathering process as an initial design step

• Deploy excellent project management.

• Ensure access to tools that match the task.

• Have an informative data set with smooth data flow.

• Manage clear, end-to-end communication by all involved.

Key Factor #1: Direct Communication

It's essential to have someone in charge of communications for the IT department, or at the very least enhanced support for the person in IT who handles the back-end data and its history to provide guidance. It is more likely for human error to occur than a technology error, so correcting your team’s actions will save project time and money. Assuring effective communication will allow setting quality expectations and goals to become easier. 

Key Factor #2: Data Clean

Data needs to be cleaned and/or filtered to achieve business objectives. Avoiding data quality issues and slowdowns will equally avoid schedule delays. Focusing on the target deliverables allows this process to be simplified.

Key Factor #3: Monitoring User Activity

Continuously monitoring user activity logs and user requests will help identify possible adoption issues and BI tool issues. To promote user adoption, BI teams should also aim to produce continuous functionality improvements and get them engaged.

Get the Results You’re After

The goal must be progression instead of perfection.

• Identify the high-priority project deliverables that will produce the minimum viable product (MVP) and agree on the factors of success for these deliverables with a roadmap approach.

• The combination of technology and good communication is one simple way to manage projects; rather than initially approving a giant project, use smaller steps and proof-of-concepts known as the “gated” approach to avoid over committing to a project that can lose its quality.

• Avoiding the root problems will lead to a positive relationship between the BI partner, client, and end user.

Following the solutions will lead to quality deliverables and an effective dashboard design in Power BI from the BI developer that fulfill the needs of the client for final end-user presentation.

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