Lack of Clear Objectives: The Number One Killer of DSS
It’s shocking how often organizations neglect to define clear objectives for their DSS. Without specific, measurable goals, it’s virtually impossible to determine whether the system is meeting expectations.
This phenomenon is supported by a Gartner study, which found that up to 50% of organizations fail to establish clear business objectives for their analytics. Consequently, these organizations are unlikely to achieve the desired ROI from their analytics.
Don’t fall into this trap – ensure your organization’s vision is clearly communicated, and the objectives for the DSS are aligned with that vision.
Data Overload: A Cautionary Tale
Data is the lifeblood of DSS, but an excessive amount of data can stifle a system’s effectiveness. The phrase “analysis paralysis” aptly describes this pitfall. In some cases, an DSS user may be overwhelmed by the volume of available data.
According to one study, 80% of organizations use less than 20% of their available data for analytics. Don’t be another statistic.
When designing a DSS, focus on presenting relevant and timely data.
Inadequate Maintenance: Why DSS Go Off the Rails
Once a DSS is implemented, many organizations adopt a “set-it-and-forget-it” mentality. Unfortunately, this results in stagnant data and irrelevant analytics, reducing the effectiveness of the DSS.
A survey conducted by Gartner revealed that 66% of analytics teams reported concerns about the reliability of their data.
The User Experience Conundrum
Your DSS may contain the best algorithms and vast quantities of data, but if users find the system difficult to navigate or the insights unhelpful, it will fail miserably. As illustrated in a McKinsey report: “The use of data analytics is the most significant differentiator within organizations that were classified as analytics leaders.”
Given the benefits of DSS, poor user experience can no longer be considered an afterthought. Design an interface that presents insights clearly and helps the end-user make better decisions.
Lessons For The Future
The success of DSS depends, in no small part, on the lessons learned from DSS pitfalls. First, define clear business objectives for the DSS. Then, ensure data quality is emphasized over data quantity, as that means that the data presented to the end users is the most important and timely for the decision-making process.
Once implemented, regular maintenance will keep your DSS on track. Lastly, DSS need a well-designed user interface which will push the adoption by the end-users increasing the overall success of the project.
Failure Lessons from Decision Support Systems - Time to Speak Up
Have you experienced any of the common pitfalls associated with Decision Support Systems? Do you have suggestions for how organizations can improve the implementation of DSS?
Leave a comment below to help spread your experience, and keep an open discussion that will be of great benefit to our community.