As a person who previously worked in a project management software company and monitored technology trends in the domain, I learned there are many project forecasting tools powered by predictive analytics that can cure the most nagging pains of project experts and improve project performance overall.
At the same time, I noticed that project managers wouldn’t believe that a piece of forecasting software can improve project performance. Many felt uncomfortable with the idea to share their project data. Many were skeptical. Skepticism, clearly, was the first reason to keep them away from enjoying a major benefit – the possibility to travel in time.
What is predictive analytics in project management?
If you ask me, predictive analytics in project management is software functionality that helps managers see beyond their capacity. IBM experts define predictive analytics as a way to “discover patterns in data and go beyond knowing what has happened to anticipating what is likely to happen next.”
While I understand that there are projects that last less than a year, lots of construction, aerospace, finance, or software initiatives take years to reach the finish line. People assigned to manage such complex projects lack the ability to see the project landscape until they find a proper tool. Contractors, in turn, risk delivering a pig in a poke.
That’s precisely when project forecasting tools come into play.
To understand how predictive analytics evolved in project management, let’s travel a few years back to when, where, and, most importantly, why it became a trend.
Since the beginning of the 21st century, the increased innovation-led competition has called on businesses to revamp their old approaches in favor of the new ones. Companies started to look for efficient ways to secure their competitive stronghold and advance business capacity. Sitting on huge volumes of data, many realized that the trove of available project information was a valuable asset.
According to MathWorks, “Data-driven predictive models can help companies solve long-standing problems in new ways.” Project management is no exception and a variety of machine learning algorithms are applied specifically to anticipate cost overruns, forecast resource needs, and make time tracking effortless.
That’s why predictive project analytics is pretty much equivalent to the lever in the time machine that could allow project managers to travel in time.
Deloitte was the first in a class of its own to introduce the notion of predictive project analytics (PPA) for the sake of reducing project risks, especially in complex projects. Deloitte consultants are of opinion that knowing key factors that cause project failure, it’s possible to build tools that benchmark project success. I agree with them on the following:
Research shows that project success hinges on a range of factors, including the project’s inherent complexity, the project team’s capability level and the maturity of existing controls and governance processes. So it stands to reason that your organization could mitigate project risk, reduce the incidence of failure and close gaps if you could accurately benchmark your capabilities in each of these areas against similar projects. – Deloitte
The term first coined by Deloitte, predictive project analytics blazed a trail for many companies that further developed techniques of forecasting in management. Forecast project management has become a widespread practice among PM software providers.
Why do project managers need predictive analytics?
Project managers work in swamps of data – tasks, time estimates, priorities, milestones, capacity, workload, costs, and other digitized variables. According to Clive Humby, a UK Mathematician and architect of Tesco’s Clubcard who coined the phrase “data is the new oil,” data is valuable only if it’s broken down, analyzed, and refined. So far, in project management, a possible way to unlock the value of project data is to apply a predictive model to it. This will help project managers analyze their projects under various scenarios and, literally, create a simulation out of their programs and portfolios. If you’re thinking of developing your own project forecasting tool, it’s always good to engage with developers and IT pros who have experience in data science.
What predictive analytics certainly does is helping project managers answer a vast range of “what-if” questions that predict the possible outcome of the project if something has to be changed. Project forecasting software powered by predictive analytics will instantly move project managers in time to help them investigate the chain reaction and make decisions backed up by data when project cost management is under concern.
Ideally, a project manager armed with a project forecasting tool is able to detect floundering projects, find hot spots in project plans, and identify areas for improvement. Having access to this information (and the possibility for project managers to travel in time) grows the chance of setting and meeting quality goals.
Overall, a data-driven approach to project management helps businesses take confident momentous decisions and save millions of dollars of investments.
How forecasting applies to agile project management
Today, only lazy project managers haven’t tried to blend Agile into their context. It’s no secret that once applied, this methodology has a tendency to bring spectacular results to companies, willing to embrace change. Guy Maslen, the Head of Geohazards Monitoring Department at GNS Science, sees agility delivering a reduction in risk, “specifically the risk that we fail to create value.” According to Maslen, “The frameworks, practices, and culture place barriers in the way of human error occurring and minimize the consequences of an error should those barriers fail. We might still get things wrong, however, it will be at the smallest cost of time and effort, with the least possible sunk costs.”
Many are moving towards agile to better deal with uncertainty and predictability, but there are two camps of Agilists arguing over the relevance of project forecasting software in project management. While the representatives of the first camp believe that tools are ineffective and hinder Agile, the second camp of representatives, as Perry Watkins consider predictive models to be an asset:
I would disagree with the notion that attempting to predict future outcomes is not agile. Being agile has to be relative to something. You are agile today because of some perceived threat to some future state. If you have not defined what you think that future state should be, why would you need to act agilely? – Perry Watkins, the President of The TAPFT Company
The truth is that equipped with a predictive analytics tool, agile project managers have more power. They are able to experiment with different variables and see how changing one variable can influence the outcome.
The days of solidly intuitive predictions are long GONE.
What are the best examples of forecasting and predictive analytics in project management?
There are analytical project management tools designed to show your progress on each step of the way – be it in the past, in the present, or in the future. They provide project managers with vital metrics and information to keep them posted on the project’s progress. But true predictive analytics tools should do more than that. They are able to forecast what could be the future outcome of the project under various scenarios based on the data you have. According to Robert Wells, the CEO of Allocable who has nearly 20 years of experience in Robotic Process Automation and Intelligent Automation, “This is done by using machines to process large volumes of performance and contextual data and predict outcomes that were previously impossible to foresee.”
Creating the list of these applications for project managers, I basically took into account two important things – available predictive modeling features and seamless integration with other tools, where you keep your data. These two things are critical to successful traveling in time. Apart from these preferences, I made sure that the tools can offer an exceptional user experience and help project managers at every stage of project management.
- Forecast – http://forecast.app
- Celoxis – https://www.celoxis.com
- Epicflow – https://www.epicflow.com
- Hive – https://hive.com
- ScopeMaster – https://www.scopemaster.com
- Lili.ai – http://www.lili.ai
- TeamAmp – https://certus3.com/ai-assurance-suite/teamamp
- nTask – https://www.ntaskmanager.com
1. Forecast – http://forecast.app
What makes Forecast stand out as a project management tool is that it learns from your project history and creates a regression model to provide future estimates of budget and task duration. It’s the first tool on the market that uses machine learning to automate project planning and thus provides you with valuable insights. The beauty of Forecast is in algorithms that can learn and adjust for the future, but its main plus is uniting many project segments like planning, budgeting, time tracking, resource scheduling etc. in one platform. Additionally, Forecast has a myriad of integration capabilities that ease the transition and make connection simpler. For those tempted to try it out, the service includes a free 14-days trial.
2. Epicflow – https://www.epicflow.com
Epicflow is an all-in-one tool for project and portfolio management powered by predictive analytics. Collecting all your data under one Epicflow roof, you can figure out the most efficient way to perform well within set budgets and deadlines. Based on the project schedules, resource calendars, and resource load, Epicflow can run multiple what-if simulations in parallel that show you the progress of your projects in the future and help figure out what’s optimal for you in terms of costs, time, and value. What-if scenarios allow project managers to check the impact analysis on the demand plan. Thus it’s possible to define common areas of potential risk and incorporate appropriate checks and balances into the project plan to mitigate those risks.
The software proved itself exceptionally effective for companies from different industries, like manufacturing, construction, shipbuilding, automotive, tech, and healthcare. A free trial is available after the live demonstration.
3. Celoxis – https://www.celoxis.com
Celoxis is a complete powerhouse of algorithms that promise to get you and your projects covered from risk and uncertainty. The tool has been the platform of choice for brands like HBO, Bombardier, KPMG, Tesla, Adobe, and others. It excels at matching demand with capacity. Celoxis also provides powerful data-driven predictive analytics around slippages, costs, and revenues, so that you don’t have to depend on guesswork and rather act on your findings.
4. Hive – https://hive.com
Hive is the productivity platform for project managers. By leveraging AI and machine learning, Hive Analytics provides interactive and customizable dashboards to gain actionable insights on team productivity and proactively spot risks. It forecasts how long it will take to finish projects based on how long it has taken your team in the past. Hive uses algorithms to track and predict the estimates.
5. ScopeMaster – https://www.scopemaster.com
ScopeMaster tends to be a real game-changer for large software projects. The software is a go-to for project managers to reduce rework and scope churn. It can be used as a standalone SaaS or Jira plugin. Both provide some rule of thumb forecasts for effort, cost, time, defects based on regression analysis of thousands of previous projects. But the instrumental part of ScopeMaster is the dedication to the analysis of user stories. The company examined over 10,000 user stories from different sources to build up some insight into what represents a good user story. According to the experts behind ScopeMaster, problems with requirements in large software projects tend to be the most costly if they are undetected until later stages of the project. The tool objective is to reduce risks, improve estimation, negotiation, and project control by making requirements and user stories clearer.
6. Lili.ai – http://www.lili.ai
Lili.ai is a recent new-comer in advanced project management that immediately became popular in professional circles due to its powerful AI algorithms. What its CEO
7. TeamAmp – https://certus3.com/ai-assurance-suite/teamamp/
TeamAmp is the first cognitive example of forecasting in management. The machine learning technology inside the tool analyzes how people are performing together as a team and optimizes the best route for them, counting the probability of project success in. To do this, TeamAmp considers six peak performance attributes – clarity of purpose, balance, alliance, drive, certainty, and effectiveness – and suggests areas for improvements. It is a good addition to such project management methodologies as Agile, DevOps, and Prince2.
8. nTask – https://www.ntaskmanager.com/
nTask is a versatile project management software available freely in the market. It comes packed with intelligent features providing a smart over-all coverage to many project needs. Through native modules, nTask mitigates the need to constantly switch applications for effective project management. The tool also ensures an intuitive and smooth experience on the application via a smartly developed user interface.
Whether you’re a freelancer, part of a small team, member of a big corporation, or simply a blogger with lots of tasks at hand, the tool caters a diverse range of professionals. nTask helps you manage several teams, workspaces, projects, tasks, issues and has risk management modules.
What’s next for predictive analytics in project management?
Even though there are many efficient tools, the experts committed to driving change in the project management space don’t stop, looking for better ways to improve project performance. A new market report reveals that the global online project management software market is expected to reach a value of 6.68 billion dollars by 2026. GlobeNewswire suggests these numbers are due to the increasing adoption of cloud-based project management solutions. According to the report, the market is projected to expand at a CAGR of 9.4% during the forecast period from 2018 to 2026. I’ve collected insights from experts who develop tools expecting them to change project managers’ experience.
Perry Watkins, the President of The TAPFT Company is in the middle of developing the project management tool powered by predictive analytics. It’s called 2nd Brain. He created it precisely because of the inflexibility of existing tools. “People invest enormous amounts of energy in creating project plans but from there on, they basically become static, adjusting only to completion or slippage of tasks,” remarks Watkins.
2nd Brain shouldn’t work that way. It would propose a new roadmap if, for instance, one of your best customers calls and says they need the product they have been delaying for 3 months but they still want you to stick to the estimated delivery date you committed to 3 months ago. It would also make adjustments when you find out that your lead programmer just broke his leg in 3 places and will be out for 6 weeks.
2nd Brain works like GPS software. You tell it where you want to go and, based on your current location, it tells you the best route for getting there and approximately when you are supposed to arrive. If that ETA is acceptable, great. You just follow the plan provided. If not, adjust accordingly. You might speed up. You might remove stops you had planned to make along the way. The options are limitless. The point is that you adjust the data until the ETA is within an acceptable range. – Perry Watkins
Project Outcome Prediction Software
Paul Boudreau, the President of Stonemeadow Consulting, Canada is currently involved in research about how AI technology can provide value to the project management methodology used by organizations. Boudreau is now developing the logic for a predictive analytics tool that will forecast project success before the project starts and in real time as the project progresses. Currently, his team of four people is building the model which will then be coded in Python programming language so they can run the data through a classifier such as a neural network. The project is being done as part of work at the college where Boudreau teaches.
“The most difficult part at this time is getting sample data. Companies are reluctant to provide historical data where their projects have failed” admits the expert. Boudreau is working more towards AI tools that predict success and can simulate the entire project based on key success factors. The downfall of predictive analytics, in his opinion, is that it is based on historical data so he plans to include the current environment and future factors as well.
To sum up, there are particularly effective tools to do project forecasts and refine your data and I can tell that these will certainly pave our way to better project management decisions in the future. I also hope you’ll find a project forecasting tool that applies to your context and method. Leave your comments below and don’t forget to subscribe to the next flow of project management inspiration.
Illustration: Copyright © Margarita Winkler