3 min readApr 6, 2024


Why Data Projects Fail to Deliver Real-Life Impact - Part II: There’s Value in Failure

Data Analytics
Data Analytics Series

When the data is not available and is needed on a continuous basis then usually the solution is to redesign the processes and make sure the data becomes available at the needed quality level. Redesign involves additional costs for the business; therefore, it might be tempting to cancel or postpone data projects, at least until they become feasible, though they’re seldom feasible.

Just because there’s a set of data, this doesn’t mean that there is important knowledge to be extracted from it, respectively that the investment is feasible. There’s however value in building experience in the internal resources, in identifying the challenges and the opportunities, in identifying what needs to be changed for harnessing the data. Unfortunately, organizations expect that somebody else will do the work for them instead of doing the jump by themselves, and this approach more likely will fail. It’s like expecting to get enlightened after a few theoretical sessions with a guru than walking the path by oneself.

This is reflected also in organizations’ readiness to do the required endeavors for making the jump on the maturity scale. If organizations can’t approach such topics systematically and address the assumptions, opportunities, and risks adequately, respectively to manage the various aspects, it’s hard to believe that their data journey will be positive.

A data journey shouldn’t be about politics even if some minds need to be changed in the process, at management as well as at lower level. If the leadership doesn’t recognize the importance of becoming an enabler for such initiatives, then the organization probably deserves to keep the status quo. The drive for change should come from the leadership even if we talk about data culture, data strategy, decision-making, or any critical aspect.

An organization will always need to find the balance between time, scope, cost, and quality, and this applies to operations, tactics, and strategies as well as to projects. There are hard limits and lot of uncertainty associated with data projects and the tasks involved, limits reflected in cost and time estimations (which frankly are just expert’s rough guesses that can change for the worst in the light of new information). Therefore, especially in data projects one needs to be able to compromise, to change scope and timelines as seems fit, and why not, to cancel the projects if the objectives aren’t feasible anymore, respectively if compromises can’t be reached.

An organization must be able to take the risks and invest in failure, otherwise the opportunities for growth don’t change. Being able to split a roadmap into small iterative steps that allow besides breaking down the complexity and making progress to evaluate the progress and the knowledge resulted, respectively incorporate the feedback and knowledge in the next steps, can prove to be what organizations lack in coping with the high uncertainty. Instead, organizations seem to be fascinated by the big bang, thinking that technology can automatically fill the organizational gaps.

Doing the same thing repeatedly and expecting different results is called insanity. Unfortunately, this is what organizations and service providers do in what concerns Project Management in general and data projects in particular. Building something without a foundation, without making sure that the employees have the skillset, maturity and culture to manage the data-related tasks, challenges and opportunities is pure insanity!

Bottom line, harnessing the data requires a certain maturity and it starts with recognizing and pursuing opportunities, setting goals, following roadmaps, learning to fail and getting value from failure, respectively controlling the failure. Growth or instant enlightenment without a fair amount of sweat is possible, though that’s an exception for few in sight!

See also: Part I, Part III, Part IV

Originally published at Written Apr-2024.




IT professional/blogger with more than 24 years experience in IT - Software Engineering, BI & Analytics, Data, Project, Quality, Database & Knowledge Management