that occurs to each mission supervisor: the stand-up assembly begins and all of a sudden a crucial ticket is blocked, a developer calls in sick, a dependency slips or a key characteristic is delayed. Right away, your fastidiously deliberate timeline begins to break down, and also you’re scrambling for options.
On this article, we’ll discover how a machine studying mannequin predicted 41% of mission delays earlier than they hit the timeline, slicing prices and lowering last-minute firefighting.
The issue: 62% of IT tasks miss their deadlines in 2025
As a Mission Supervisor working with Agile groups, I’ve usually handled delays and blockers, they rapidly grew to become a part of on a regular basis life. However after I got here throughout the 2025 Wellington State of Mission Administration examine revealing that in 2025, 62% of IT tasks miss their deadlines, it shocked me into motion. It’s a rise in comparison with the 2017 PMI Pulse of the Career examine the place it was 51% in 2017. Mission delays are reaching a crucial stage.
I do know delays are widespread, however I hadn’t imagined it might be that prime. However in the present day, we now have instruments to anticipate and higher perceive these dangers. Utilizing Python and Information Science, I constructed a mannequin to foretell mission delays earlier than they occurred.
This statistic highlights two crucial factors: delays usually stem from recurring causes, they usually carry an enormous enterprise affect. On this article, we’ll discover how data-driven approaches can uncover these causes and assist mission managers anticipate them.
With this information, we are able to select the very best plan of action.
That is the place we are able to use information science. Surprisingly, the 2020 Wellington State of Mission Administration report publicizes that solely 23% of corporations use mission administration software program to handle their tasks, regardless that these instruments generate a wealth of helpful information.
By analyzing info from mission tickets, we are able to construct predictive machine studying fashions that spotlight potential dangers earlier than they escalate.
That’s precisely what I did: I analyzed greater than 5,000 tickets, not solely from my present mission but additionally from previous initiatives.
Mission administration software program, it seems, is an unbelievable supply of knowledge ready to be leveraged.
The Information Hole in Mission Administration
In conventional mission administration, reporting performs a central position, but few studies supply a complete, detailed retrospective of the mission as a complete.
For instance, in Scrum, we monitor our velocity, observe the development of our burndown chart, and measure the variety of story factors accomplished.
Conventional reporting nonetheless fails to present us the whole image. Information science can.
As Mission Managers, we might know from expertise the place the crucial factors lie, however validating these assumptions with information makes our selections way more dependable.
Constructing the Dataset
To discover this concept, I analyzed 5,000 Jira tickets—one of many richest sources of mission information accessible.
Since actual mission information can’t all the time be shared, I generated an artificial dataset in Python that mirrors actuality, together with key variables similar to precedence, story factors, staff measurement, dependencies, and delay.
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Having constructed a sensible dataset, we are able to now discover the completely different ticket profiles it comprises. This units the stage for our exploratory information evaluation.
Most tickets are of low or medium precedence, which is in keeping with how mission backlogs are often structured. This preliminary distribution already hints at the place dangers may accumulate, some extent we’ll discover additional within the EDA.
Whereas excessive and important precedence tickets characterize a smaller share of the whole, they’re disproportionately extra prone to be delayed.
This bar plot confirms the phenomenon: high-priority tickets are strongly related to delays. Nevertheless, this will stem from two completely different dynamics:
- Excessive-priority tickets are inherently extra complicated and due to this fact at higher danger of delay.
- Some tickets solely grow to be excessive precedence as a result of they have been delayed within the first place, making a vicious cycle of escalation.
With this simulated dataset, we now have a sensible snapshot of what occurs in actual tasks: tickets differ in measurement, dependencies, and complexity and a few inevitably find yourself delayed. This displays the on a regular basis challenges mission managers face.
The subsequent step is to maneuver past easy counts and uncover the patterns hidden within the information. Via Exploratory Information Evaluation (EDA), we are able to take a look at our assumptions: do increased priorities and extra dependencies actually improve the chance of delay? Let’s discover out.
Exploratory Information Evaluation (EDA)
Earlier than shifting to modeling, it’s necessary to step again and visualize how our variables work together. Exploratory Information Evaluation (EDA) permits us to uncover patterns in:
- How delays fluctuate with precedence.
- The affect of dependencies.
- The distribution of story factors.
- The everyday staff sizes dealing with the tickets.
This chart confirms a key instinct: the upper the precedence, the higher the chance of delay.
Dependencies amplify this impact, the extra there are, the upper the possibilities of one thing slipping by way of.
As soon as a delay or danger of delay seems, escalation mechanisms push the precedence even increased, making a suggestions loop.
Lastly, ticket complexity additionally performs a job, including one other layer of uncertainty.
Most tickets fall into the medium-risk class. These demand essentially the most consideration from mission managers: whereas not crucial at first, their danger can rapidly escalate and set off delays.
Excessive-risk tickets, although fewer, carry a disproportionate affect if not managed early.
In the meantime, low-risk tickets often require lighter monitoring, permitting managers to focus their time the place it really issues.
We additionally discover that almost all tickets have small story level sizes, and groups are often round 5 members.
This means that agile practices are typically being adopted.
Now, we’ll go additional and take a look at the distribution of danger scores throughout tickets.
We see that solely a small portion of tickets carry a really excessive danger, whereas most sit within the medium zone. Which means that by focusing early on the riskiest tickets, Mission Managers might stop many delays.
To check this assumption, let’s now discover how complexity per individual and precedence work together with danger scores.
We can’t observe a transparent development right here. The chance rating doesn’t appear to strongly rely upon both ticket complexity or precedence, suggesting that different hidden components may drive delays.
Technical Deep Dive: Predictive Mannequin
The uncooked information gives a stable basis, however area information is crucial to constructing a really sturdy mannequin. To higher seize the dynamics of real-world tasks, we engineered new options that mirror mission administration realities:
- Complexity per individual = story factors/staff measurement.
- Has dependency = whether or not a ticket is determined by others (dependencies > 0).
- Precedence story factors interplay = precedence stage multiplied by story factors.
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We selected a Random Forest mannequin as a result of it may deal with non-linear relationships and gives insights into characteristic significance.
Our fundamental focus is on Recall for the optimistic class (1 = delayed). As an example, a recall of 0.6 would imply the mannequin appropriately identifies 60% of all really delayed tickets.
The target shouldn’t be good precision however early detection. In mission administration, it’s higher to flag potential delays, even with some false positives, than to overlook crucial points that would derail your complete mission.
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The mannequin achieved a recall of 0.41, which means it efficiently detected 41% of the delayed tickets.
This will likely appear modest. Nevertheless, in a mission administration context, even this stage of early warning is efficacious. It provides Mission Managers actionable indicators to anticipate dangers and put together mitigations.
With additional refinement, the mannequin might be improved to anticipate extra delays and assist stop points earlier than they materialize.
We are going to use a confusion matrix to higher perceive the mannequin’s strengths and weaknesses.
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The mannequin appropriately identifies 169 delays, however it additionally generates 373 false alarms, duties flagged as delayed that really completed on time. For a Mission Supervisor, this trade-off is appropriate as a result of it’s higher to research just a few false positives than to overlook a crucial delay. This is part of danger administration.
Nevertheless, the mannequin nonetheless misses 245 delayed tickets, which means its predictions are removed from good.
General, this mannequin works greatest as an early warning system. It gives helpful indicators however nonetheless wants additional coaching and refinement. Most significantly, it must be complemented with human experience, the judgment and expertise of Mission Managers, to make sure an entire and dependable mission overview.
Mannequin Interpretability, Scoring, Enterprise affect, Dashboard & mannequin validation
To actually perceive why the mannequin makes these predictions, we have to look below the hood. Which options drive the chance of delay essentially the most? That is the place mannequin interpretability is available in.
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We are able to observe that complexity and the priority-story factors interplay are the strongest drivers of prediction accuracy.
Scoring tickets: Figuring out What’s Actually at Danger.
Why does this matter for Mission Managers? As a result of we are able to go one step additional.
Calculate a danger rating for every ticket.
This rating highlights which duties are most in danger, permitting PMs to focus their consideration the place it issues most and take preventive motion earlier than delays escalate.
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Enterprise Affect Evaluation.
The tickets with the best danger scores verify the development: solely excessive and important precedence duties carry the best danger.
This perception issues not just for managing the mission timeline but additionally for its monetary affect on the enterprise. Delays don’t simply decelerate supply, they improve prices, scale back shopper satisfaction, and devour helpful staff sources.
To quantify this, we are able to estimate the enterprise worth of the predictions by simulating how a lot value might be prevented after we anticipate dangers and take preventive motion.
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Our baseline reveals that 27.6% of tickets are delayed. However what if Mission Managers might focus solely on the riskiest 20%? We are going to now simulate this focused intervention and see how a lot affect it makes.
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We recognized 1,021 high-risk tickets, representing about 20% of all duties. Amongst them, 516 (50.5%) are literally delayed. In different phrases, these few tickets alone drive roughly 10% of complete mission delays.
To make this extra concrete, we translate the affect into enterprise phrases with a medium-size mission valued at $100,000. By making use of preventive actions on these high-risk tickets, we are able to estimate the potential value financial savings.
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By taking early actions, we might save $9,270, almost 10% of the whole mission value. That’s not simply danger mitigation; it’s a direct enterprise benefit.
PM Dashboard
To make these insights actionable, we are able to additionally construct a Mission Administration Dashboard. It provides a real-time view of dash well being, with all the important thing KPIs wanted to trace progress, anticipate dangers, and keep an entire mission overview.
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Mannequin validation
We examined the robustness of the mannequin with a 5-fold cross-validation. Recall was chosen as the principle metric, as a result of in mission administration it’s extra necessary to catch potential delays than to maximise total accuracy.
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The recall scores throughout folds ranged from 0.39 to 0.42. This implies the mannequin is way from flawless, however it constantly flags round 40% of delays, a helpful early warning that helps mission managers act earlier than points escalate.
Conclusion
In conclusion, this text confirmed how information science will help make tasks smoother by offering a clearer understanding of the causes of delays.
Information doesn’t substitute the instinct of a Mission Supervisor, however it strengthens it, very like giving a pilot higher devices to navigate with precision and with a greater view of what’s occurring.
By predicting dangers and figuring out at-risk tickets, we are able to scale back delays, stop conflicts, and finally ship extra worth.
Mission Managers ought to embrace information science. Right now, there are two forms of PMs: conventional ones and data-driven ones. They don’t compete in the identical league.
Lastly, these expertise aren’t restricted to mission administration. They prolong to product administration and enterprise evaluation. Studying SQL or Python enhances your skill to collaborate with builders, perceive product efficiency, and talk successfully throughout all ranges of the enterprise.
Lesson for Mission Managers
What number of of our mission selections are primarily based on so-called “greatest practices” which might be, in actual fact, unverified assumptions? Whether or not it’s about assembly schedules, staff construction, or communication strategies, information will help us problem our biases and uncover what really works.
Relying on the group, the evaluation can even go deeper: grouping tickets by mission section, discipline, or stakeholder might reveal hidden bottlenecks and systemic points.
For instance, velocity usually drops through the QA section. Is it as a result of QA engineers underperform? By no means. They do their job very nicely. The true difficulty is the fixed back-and-forth with builders: clarifying tickets, determining how testing must be carried out, or asking for lacking info.
To unravel this, we launched a easy course of: builders now add clearer testing particulars within the ticket and spend 5 minutes on a fast handover name with QA. That small funding of time boosted staff productiveness and velocity by greater than 15%.
Who am I?
I’m Yassin, an IT Mission Supervisor who determined to be taught Information Science, Python, and SQL to bridge the hole between enterprise wants and technical options. This journey has taught me that essentially the most helpful mission insights come from combining area experience with data-driven approaches. Let’s join on LinkedIn
