Why Business Intelligence Projects Fail During Implementation

BI projects have high failure rates due to the poor estimation of underlying risks and lack of communication.

While a widespread term, Business Intelligence (BI) is not yet correctly understood and used in organizations. A recent report from Gartner shows that 87% of organizations  have low BI indexes and that 60% of the implementation initiatives fail. 

Ilya Kirillov, CEO of InData Labs, comments:
BI implementation can be unclear, take considerable time, and require expert assistance. Not so many organizations have a clear strategy in place. Project planning and problem anticipation are the core tasks that seasoned BI professionals can help tackle.

So far, the research pinpointed two main reasons for this: the first is the failure to grasp the real needs and challenges of the business, and the second is a communication clash between the IT and other departments. Apart from these, there are many more reasons these projects hardly get traction, which we discuss in the following sections. 

Legacy Technology 

As recent as five years ago, the cloud was not yet a go-to solution for large corporations. Many still have on-premise solutions due to privacy and security concerns, which are no longer aligned with the current standards and requirements related to processing data in near real time. 

For example, if you are an online retailer and you don’t use BI tools that would be capable of assessing customers’ needs and offering dynamic pricing (think flight tickets and hotel bookings), you are missing out on business growth opportunities. 

Poor Planning and Lack of Project Management

Most BI projects are new initiatives, and this means there is no previous reference to what might go wrong and how long it can take to fix it. Sometimes technical difficulties and time needed to set up the system and train employees can delay the project by months. 

To minimize the failure rate of your BI project, it is a good idea to assign a dedicated project manager for the BI implementation instead of relying on the fact that everybody will do their job independently and without supervision.

No C-Suite Support  

 A BI initiative will benefit from the full support of the C-suite team. It takes high-level vision, strategy, and planning as well as some backup measures in place to ensure everything goes to plan. 

As most BI tools are not cheap, it also means allocating the right resources for hardware, software, and training. The final selection should be made in line with the organization’s long-term plans, which are the prerogative of the top management. 

Ignoring the End User 

Although BI tools need to solve certain problems for the organization, they also need to be user-friendly. For example, if the BI supports dashboards, these should be mobile-friendly and easy to learn in a few intuitive iterations. Most end users will not necessarily be tech-savvy. They can be clerks, sales reps, ticketing agents, call center operator, nurses, and so on, whose jobs are to focus on the customer’s needs, not the system. If the solution is challenging to use, it will only create frustration and a bounce back to the previous way of doing things.

No Synchronization between KPIs and ROI 

A BI system is just another tool to reach business goals. This means it has to rely on clear measurement of ROI compared to the system cost. The BI solution should also include the right KPIs to help its users answer revenue-critical questions on the spot. 

A common mistake is to include too many KPIs in a BI dashboard “just in case.” The best solution is to narrow down the number of KPIs to the necessary minimum and to group them together in drill-down menus if anything else is needed. 

Low-quality Data

A BI tool runs on the information it receives from integrated data sources and customers. Since analytics can only be performed on reliable and high-quality data, it makes sense to clean input details up before passing them on for analysis. 

Also, when it comes to data, a BI platform needs to be ready for integration with all your input feeds, which can vary from manually operated scanners to social media scrapers and optical character recognition tools. 

The advice here would be to start assessing the data sources you intend to use, as well as necessary integrations to be made.

Underestimated Project Scale 

Somewhat counterintuitively, it has been repeatedly proven that more massive projects, both in terms of budget and coverage, are prone to failing more often than those of a smaller scale. This happens because there is less ownership of each aspect, and it is harder to check it for consistency and compliance with the original roadmap. 

In this case, it would be best to start a BI initiative with a pilot project to let the staff members get used to the way of working and then to move on to larger implementations. Even if you have a big project underway, it is better to split it into several separate stages and assign a manager to each one to keep them on budget and within the initial timeline.  

Wrap-up

These are some of the most common reasons why BI project in particular and IT projects in general fail to meet expectations and deliver the results that companies hope for. Most of the times, it is not due to a faulty solution; it is poor implementation fueled by insufficient planning and lack of communication. 

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