Predictive Analytics: Key to Streamlining Project Management

Predictive analytics ensure project management headaches are a thing of the past with streamlined automation and real-time updates. Charles Seybold, Co-Founder and CTO of dynamic project management solution provider LiquidPlanner explains how.

Businesses have long struggled to complete projects on budget, on time, and within given resource guidelines. Despite that effort, most organizations still fail to manage this process successfully. In fact, a 2019 project management survey found only 19% of organizations deliver successful projects most of the time. Yet accurately predicting the “when” of project completion has a critical impact on business success, in large part because there are multiple organizational dependencies that hinge on project completion. Inaccurate projections and subsequent delays ripple throughout the company, costing money, damaging morale, and ultimately impacting the bottom line. 

Part of the problem, it turns out, is that project management tools and technology haven’t fundamentally advanced much over the past decade or two. Most improvements have been to the UI (making the tools easier to use) or to the delivery model (adding the flexibility of SaaS) instead of directly helping organizations grow better at project management. Fortunately, that’s changing thanks to a greater emphasis on intelligence and predictive analytics.

To assess the potential impact, let’s first understand the issue — why companies fail to accurately predict timelines and see projects across the finish line?

The painful fact is that projects experience a number of setbacks on the road to completion — they face unexpected roadblocks, get sidelined for higher priorities, lose resources, switch project owners or team members, or change course entirely. Given the numerous and complex assumptions, dependencies, estimates and resource allocations that make up the typical project, bumps in the road will be expected. No project plans will ever get all of the details exactly right from the start. 

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That’s where the “management” part of project management is supposed to come in — making the minute corrections and adjustments necessary to keep a project on track. However, that process is breaking down for most organizations. 

It turns out that there are two issues, one human and one technical, that conspire to hinder the ability to accurately predict project completion.

The human factor is relatively straightforward: people are generally terrible at accurately predicting exactly how long something will take. We face this all the time in our personal lives, no one says “this errand will take me exactly 2.5 hours” or similar phrases. Uncertainty and imprecision is part of the human condition.  But apply that lack of precision to a project setting with numerous collaborators, a wealth of dependencies, and conflicting priorities stretching across weeks or months, and it’s no wonder things go spinning off the rails.

From a technical perspective, current tools can actually contribute to the problem rather than help. First, most are rigid in how they handle scheduling – effectively forcing users to select specific start dates, work schedules, and delivery dates – which, as we’ve already discussed, people struggle with. Second, the tools often offer rudimentary automation — simply adding up timelines to get to a delivery estimate that needs frequent manual “tweaking” to accommodate schedule requirements. Finally, they lack any sort of artificial intelligence or predictive analytics to help teams understand and accommodate – with a greater level of clarity and confidence – the dependencies, priorities, and likely outcomes of project planning.

Thankfully, all of that is changing with the latest slate of planning software solutions. For example, newer tools now use ranged estimates to account for project and resource fluctuations. People are much better at achieving accuracy when estimating a range as opposed to identifying a specific delivery date, and the software algorithms can use these ranges to assign probabilities to project completion. This also allows the software to automatically recalculate and rebalance as changes are input – for instance if a resource is made temporarily unavailable – adjusting schedules and probabilities accordingly. In fact, with the appropriate level of project automation, the software can not only accommodate these ranges but intelligently use this data to identify conflicts, balance priorities, and better manage resource workload.

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The ultimate goal is to embed intelligence in the project management software itself, allowing it to evolve from a mere reflection of schedules and priorities to become an active participant in management and workload optimization. Once teams have this ability to track, understand and adjust (in real-time) for the myriad changes that impact project completion, it becomes dramatically easier to marshal the necessary resources to ensure delivery is on time and within budget.

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