that occurs to each venture supervisor: the stand-up assembly begins and all of the sudden a crucial ticket is blocked, a developer calls in sick, a dependency slips or a key characteristic is delayed. Instantly, 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 venture delays earlier than they hit the timeline, chopping prices and decreasing last-minute firefighting.
The issue: 62% of IT tasks miss their deadlines in 2025
As a Venture Supervisor working with Agile groups, I’ve usually handled delays and blockers, they shortly grew to become a part of on a regular basis life. However once I got here throughout the 2025 Wellington State of Venture Administration research 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 Occupation research the place it was 51% in 2017. Venture delays are reaching a crucial degree.
I do know delays are frequent, however I hadn’t imagined it could be that prime. However right this moment, we now have instruments to anticipate and higher perceive these dangers. Utilizing Python and Knowledge Science, I constructed a mannequin to foretell venture delays earlier than they occurred.
This statistic highlights two crucial factors: delays usually stem from recurring causes, and so they carry an enormous enterprise influence. On this article, we’ll discover how data-driven approaches can uncover these causes and assist venture managers anticipate them.
With this information, we will select one of the best plan of action.
That is the place we will use knowledge science. Surprisingly, the 2020 Wellington State of Venture Administration report proclaims that solely 23% of firms use venture administration software program to handle their tasks, although these instruments generate a wealth of worthwhile knowledge.
By analyzing info from venture tickets, we will 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 venture but additionally from previous initiatives.
Venture administration software program, it seems, is an unbelievable supply of knowledge ready to be leveraged.
The Knowledge Hole in Venture Administration
In conventional venture administration, reporting performs a central function, but few stories provide a complete, detailed retrospective of the venture 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 offer us the entire image. Knowledge science can.
As Venture Managers, we might know from expertise the place the crucial factors lie, however validating these assumptions with knowledge makes our selections much more dependable.
Constructing the Dataset
To discover this concept, I analyzed 5,000 Jira tickets—one of many richest sources of venture knowledge obtainable.
Since actual venture knowledge can’t all the time be shared, I generated an artificial dataset in Python that mirrors actuality, together with key variables reminiscent of precedence, story factors, staff measurement, dependencies, and delay.
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Having constructed a sensible dataset, we will now discover the totally different ticket profiles it incorporates. This units the stage for our exploratory knowledge evaluation.
Most tickets are of low or medium precedence, which is in line with how venture 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 significant precedence tickets characterize a smaller share of the full, they’re disproportionately extra prone to be delayed.
This bar plot confirms the phenomenon: high-priority tickets are strongly related to delays. Nevertheless, this could stem from two totally different dynamics:
- Excessive-priority tickets are inherently extra advanced and subsequently at larger danger of delay.
- Some tickets solely grow to be excessive precedence as a result of they had 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 venture managers face.
The following step is to maneuver past easy counts and uncover the patterns hidden within the knowledge. By means of Exploratory Knowledge Evaluation (EDA), we will check our assumptions: do increased priorities and extra dependencies actually improve the chance of delay? Let’s discover out.
Exploratory Knowledge Evaluation (EDA)
Earlier than shifting to modeling, it’s vital to step again and visualize how our variables work together. Exploratory Knowledge Evaluation (EDA) permits us to uncover patterns in:
- How delays fluctuate with precedence.
- The influence 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 larger the chance of delay.
Dependencies amplify this impact, the extra there are, the upper the possibilities of one thing slipping via.
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 probably the most consideration from venture managers: whereas not crucial at first, their danger can shortly escalate and set off delays.
Excessive-risk tickets, although fewer, carry a disproportionate influence 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 implies that agile practices are typically being adopted.
Now, we’ll go additional and have 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, Venture 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 not observe a transparent development right here. The danger rating doesn’t appear to strongly rely upon both ticket complexity or precedence, suggesting that different hidden elements may drive delays.
Technical Deep Dive: Predictive Mannequin
The uncooked knowledge offers a strong basis, however area data is crucial to constructing a very sturdy mannequin. To higher seize the dynamics of real-world tasks, we engineered new options that replicate venture 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 degree multiplied by story factors.
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We selected a Random Forest mannequin as a result of it will possibly deal with non-linear relationships and offers insights into characteristic significance.
Our important focus is on Recall for the optimistic class (1 = delayed). As an illustration, a recall of 0.6 would imply the mannequin appropriately identifies 60% of all really delayed tickets.
The target shouldn’t be excellent precision however early detection. In venture administration, it’s higher to flag potential delays, even with some false positives, than to overlook crucial points that would derail your entire venture.
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The mannequin achieved a recall of 0.41, that means it efficiently detected 41% of the delayed tickets.
This may increasingly appear modest. Nevertheless, in a venture administration context, even this degree of early warning is effective. It provides Venture Managers actionable alerts to anticipate dangers and put together mitigations.
With additional refinement, the mannequin will 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, but it surely additionally generates 373 false alarms, duties flagged as delayed that really completed on time. For a Venture Supervisor, this trade-off is suitable as a result of it’s higher to analyze a number of false positives than to overlook a crucial delay. This is part of danger administration.
Nevertheless, the mannequin nonetheless misses 245 delayed tickets, that means its predictions are removed from excellent.
General, this mannequin works finest as an early warning system. It offers worthwhile alerts however nonetheless wants additional coaching and refinement. Most significantly, it ought to be complemented with human experience, the judgment and expertise of Venture Managers, to make sure a whole and dependable venture overview.
Mannequin Interpretability, Scoring, Enterprise influence, Dashboard & mannequin validation
To actually perceive why the mannequin makes these predictions, we have to look beneath the hood. Which options drive the danger of delay probably 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 Venture Managers? As a result of we will 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 affirm the development: solely excessive and significant precedence duties carry the best danger.
This perception issues not just for managing the venture timeline but additionally for its monetary influence on the enterprise. Delays don’t simply decelerate supply, they improve prices, scale back consumer satisfaction, and eat worthwhile staff assets.
To quantify this, we will estimate the enterprise worth of the predictions by simulating how a lot value will be averted 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 Venture Managers might focus solely on the riskiest 20%? We are going to now simulate this focused intervention and see how a lot influence 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 whole venture delays.
To make this extra concrete, we translate the influence into enterprise phrases with a medium-size venture valued at $100,000. By making use of preventive actions on these high-risk tickets, we will estimate the potential value financial savings.
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By taking early actions, we might save $9,270, almost 10% of the full venture value. That’s not simply danger mitigation; it’s a direct enterprise benefit.
PM Dashboard
To make these insights actionable, we will additionally construct a Venture 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 a whole venture 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 venture administration it’s extra vital to catch potential delays than to maximise general 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, but it surely persistently flags round 40% of delays, a worthwhile early warning that helps venture managers act earlier than points escalate.
Conclusion
In conclusion, this text confirmed how knowledge science may help make tasks smoother by offering a clearer understanding of the causes of delays.
Knowledge doesn’t change the instinct of a Venture Supervisor, but it surely 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 will scale back delays, stop conflicts, and finally ship extra worth.
Venture Managers ought to embrace knowledge science. At the moment, there are two kinds of PMs: conventional ones and data-driven ones. They don’t compete in the identical league.
Lastly, these expertise are usually not restricted to venture administration. They lengthen to product administration and enterprise evaluation. Studying SQL or Python enhances your capability to collaborate with builders, perceive product efficiency, and talk successfully throughout all ranges of the enterprise.
Lesson for Venture Managers
What number of of our venture selections are primarily based on so-called “finest practices” which are, in actual fact, unverified assumptions? Whether or not it’s about assembly schedules, staff construction, or communication strategies, knowledge may help us problem our biases and uncover what really works.
Relying on the group, the evaluation also can go deeper: grouping tickets by venture part, area, or stakeholder might reveal hidden bottlenecks and systemic points.
For instance, velocity usually drops through the QA part. Is it as a result of QA engineers underperform? In no way. They do their job very effectively. The true situation is the fixed back-and-forth with builders: clarifying tickets, determining how testing ought to be carried out, or asking for lacking info.
To resolve 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 Venture Supervisor who determined to be taught Knowledge Science, Python, and SQL to bridge the hole between enterprise wants and technical options. This journey has taught me that probably the most worthwhile venture insights come from combining area experience with data-driven approaches. Let’s join on LinkedIn
