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Growing demand for increased efficiency has boosted the popularity of business intelligence as a platform that can help management and employees in different departments assess and monitor key performance indicators and other performance metrics.
Organizations that have relied on Excel spreadsheets and static charts are getting introduced to data visualization tools and jumping into new solutions that offer slick charts and ease of use. However, some companies are beginning to regret their business intelligence initiatives.
With the onset of business intelligence (BI) choices in the marketplace, the cost of many of these solutions has decreased, and they can be inexpensively acquired, leading to hasty selections. But adopting a BI initiative without a strong understanding of all the implications and what it would require for success could cause the project to fail. Here are the most recent common causes of failed BI projects.
1. Hasty decision making
It would be a serious disservice for an organization to simply make technology decisions within a 30-second timespan. In the field, some decision-makers have admitted that, during the demos of some of the data visualization tools they have seen, they could immediately tell if the solution would be a good fit for them just by seeing some of the sample dashboards displayed during the sales presentation.
While first impressions are important, and dashboards must be able to quickly deliver visual cues that highlight critical indicators, such as whether a problem exists or not, there are other components that are equally essential to the tool's evaluation, beyond the look and feel. Those features, such as data connectors, advanced predictive functionality and drill-down capabilities, may take longer to assess in order to know if the tool will be a good fit with the organization.
2. Not analyzing the group
If an IT decision-maker is tasked with a BI project where they are required to go out and find the best data visualization tools, without considering if the organization is, in fact, ready for it, then the results may be disappointing.
An organization must be engaged in the discipline of using data to help support business decisions, the most important prerequisite before engaging in BI initiatives. Failure to do so will end up providing the company with a few pretty charts and KPIs that are occasionally used by a handful of users. The business would continue to suffer from a lack of knowing how they are performing in certain areas.
In one example, six months after rolling out a new BI tool, a small service-based company saw its sales teams stop using the tool, as they indicated they didn't see an increase in sales since the adoption. After spending time analyzing how the group was using the platform, it was clear that the sales reps never really looked at the dashboards, and simply saw them as fancy charts with no value.
While the sales leadership had monitors throughout the sales floor displaying KPIs, the reps lacked the training and understanding of how those numbers related to them and how they were being measured, leading to a failure of the BI initiative.
3. Creating flashy dashboards without substance
When a writer or a business analyst gets too excited about creating dashboards or cramming as many visuals into the dashboard screen as possible, it's a clear indication of the beginning of the end of that dashboard. Data visualizations are meant to quickly and easily reflect relevant indicators and insights to the business user.
An organization looking to roll out an analytics platform must put the emphasis on defining what it's looking to deliver and communicate to the business users who have control and can influence those metrics. Engaging end users during the dashboard design phase, while having a strong understanding of best practices and how certain data should be delivered, will help organizations avoid adoption gaps.
A designer is also responsible for carefully using the appropriate chart types, colors, fonts and indicators to make the experience as effective as possible and to make the insights stand out to the user.
4. Exporting the data to Excel
It is not uncommon to find some BI installations where end users receive the dashboards they have requested and quickly find they are exporting data to Excel in order to investigate certain data events that they might have discovered during their review of their dashboards. Unfortunately, this occurs often, especially with BI solutions that lack drill-down capabilities and self-service.
With these types of BI initiatives, companies simply end up adding more work for end users. Users are forced to take extra steps to work with the data and don't receive all the necessary information.
5. Limiting end-user training for BI initiatives
When analyzing some of the failed attempts at launching BI in organizations, one of the most common sources of trouble is the lack of end-user adoption. Some may assume that the users are not receiving adequate training on the tools, but, to the contrary, end users may be getting too much training on the software, but not enough training on how to measure, manage and react to what is provided to them in the dashboards.
For example, a marketing department hosting a product launch event is likely to benefit from understanding how measuring website traffic or twitter mentions would be good indicators as to whether people are likely to attend the event to learn more about the product. If you simply train the marketing team on how to create dashboards, without the understanding of how access to different indicators could help them, they will not be able to effectively use the BI initiatives.
BI has been known to help transform organizations by helping them gain insights into their own performance, but having the best BI platform does not always guarantee the best results. Time after time, organizations have shown that when a project is appropriately planned, and when the culture of the organization understands the importance of the measurements, then the results are likely to be more useful.
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