Avoid Project Failure by Using Predictive Analytics

Avoid Project Failure by Using Predictive Analytics

Project management is indispensable to the functioning and competitive advantage of an enterprise. Yet, project management is far from perfect. Research by The Standish Group shows that 60% of companies experience project failure, and a whopping 71% of projects are late, over budget, or fail to deliver to the level of their requirements.

Deloitte sheds more light on this issue stating that the more complex the project the more likelihood of failure. Their report shows that projects worth between $1-3M have a 34% chance of success, and projects worth $10M+ have only a 7% chance of success.

In other research, PMAlliance notes that 43% of organizations have experienced project failures recently, and 17% of large IT projects fail so badly that they threaten the very existence of the company. These stats may sound alarming, but they aren’t an exaggeration. If you’ve worked in project management for a couple of years, you can look back on the number of projects that failed to live up to expectations for various reasons. You’ll see that these stats are close to reality.

How do enterprises and project management consultants beat these odds of failure? It can only be done with the help of data science, and the new capabilities it brings.

Project management meets Predictive analytics


Analytics plays a key role in project management.

According to ProjectTimes.com “Most project management software solutions are excellent at capturing project detail. Simply capturing data points, however, has limited strategic value. Strategic value comes from turning data into reports that inform strategic decisions.”

Simple project management metrics are not enough to ensure your project is a success. Today’s projects that are becoming more complex need more than simple analytics, they need predictive project analytics.

Overlap between Business Intelligence and predictive analysis on diagrams

Source: IronSideGroup.com

The big data movement has brought to the forefront the ability of computers to crunch large volumes of complex unstructured data, and derive meaningful insights from this data. In fact, these insights are so accurate that they are now being used not just to look back at what happened, but to look ahead into what is about to happen.

Predictive analytics is being used in various scenarios like lead scoring, e-commerce recommendation engines, cyber security, and financial analysis. But why limit something so powerful to just those practices? Today, predictive analytics can and should be used to change the way project management works. This is what Predictive project analytics is all about.

What is Predictive Project Analytics?


If project analytics gives you the vital metrics to keep track of projects, predictive project analytics includes this, but goes further to forecast what could be the future outcome of the project under various scenarios. This is done by using machines to process large volumes of performance and contextual data and predict outcomes that were previously impossible to foresee.

Elements of predictive analysis​:

Chart showcasing the elements of predictive analysis

Source: tdwi.org

  • Inherent risk and complexity assessment: Predictive project analytics looks at inherent risks that a project may be prone to, and surfaces them. These risks are most often outside of the regular metrics that project managers are used to. They are outliers, and external factors that influence the outcome of a project.
  • Interviews and structured content: A predictive project analytics process involves interviews of key stakeholders in the project and all project related documents to understand the state of a project. 
  • Analysis and synthesis: It involves analysis during the course of the project considering all factors that influence the project.
  • Reporting: Finally, it includes timely reporting on the health of the project, and extensive end of project evaluation reports.

It starts with putting in place the right process for doing predictive analytics, and is possible only with the right set of tools to make this kind of analytics possible.

What areas benefit from leveraging Predictive Project Analytics


Several aspects of project management are impacted by predictive project analytics. Its impact is felt across the project lifecycle. Here are a few stages that are improved by predictive project analytics:

1

Project Portfolio Management

Being able to look at all projects and their cumulative impact on the organization is possible with predictive project analytics. You can understand how multiple projects will impact the future of the organization. This is when your project portfolio management becomes strategic.

2

Enhanced M&A

When mergers and acquisitions happen, it can be a time of stress because of all the change. All the more, you need predictive project analytics to bring stability and clarity to the otherwise stressful phase.

3

Knowledge Management

As a project progresses you need a way to store and extract insight from the knowledge gathered by teams. Predictive project analytics can ensure the right information reaches the right place and the right time.

4

PMO Methodology

The project management office which strives to create consistent systems across all projects will benefit greatly by predictive project analytics. The PMO can see correlations and connections between projects that they were previously blind to. This enables them to bring more consistency across all projects in an organization.

5

Resource Management

Allocating resources to projects, and making changes midway is essential to the success of projects. Predictive project analytics is able to tell you how resource allocation can impact the outcome of a project.

In short, predictive project analytics can mean the difference between the success and failure of your projects.

Key factors of successful predictive analytics usage


There are certain important characteristics that are essential to successful predictive project analytics. Here are a few of them:

Clear Goals & Objectives

The first task is to agree on the outcomes of the project. This can be a challenge considering the various stakeholders and priorities that may clash. But in order to predict success or failure at every step of the project, you need to define what success means, and consequently what failure means.

Based on this, your predictive models can estimate success or failure.

Measuring Performance Differently

Unlike traditional analytics, which merely reports on happenings, successful predictive project analytics uses models to predict future outcomes based on available metrics. This way of measuring performance is more powerful than traditional analytics.

Behaviour of Interest

When training predictive models, you supply data to algorithms that learn to identify which events are expected, and which are outliers. Additionally, algorithms can spot which events are beneficial and which aren’t.

These beneficial events are behaviors of interest that you would want to focus on to ensure your project is successful.

Sufficient Data

Predictive models require a certain amount of data to make predictions about user behavior in advance. The amount of data required for a predictive model to draw conclusions with confidence is the sufficient data.

Smarter Resource Allocation

During the course of a project, some tasks may be overstaffed, while other are understaffed. Some tasks may require the expertise of people involved in another task. In these situations, you need to allocate resources by considering all possible outcomes. This requires predictive analytics that factors in lots of data.

All successful predictive project analytics processes share these common traits.

Predictive Analytics Software Example


Take the following example into consideration when trying to understand how predictive analytics software, like Allocable, can help your organization.

Let’s say Company A has an employee resource who is entered into their software for 100 hours of billable time. Because Company A utilizes Allocable’s predictive analytics software, they will be able to take a snapshot - in real time - on the Allocable dashboard to see how quickly the resource is burning through their allotted hours.

If they are burning more hours than originally anticipated, Company A will be able to see this before it happens. They can then take action in real-time to either: adjust resource allocations by leveraging different, less expensive resources, or, take measures to ensure their employee does not work more than their allocated hours, or, contact the client in advance (before its too late), to let them know hours or scope needs to be adjusted to meet the budget.

The cost you pay for a software like Allocable is a minor expense compared to the ROI received, when you have real-time insight into how your projects are progressing and how to navigate the best actions to gain the biggest return.

Conclusion


In conclusion, predictive project analytics is made possible by the big data revolution. Projects can no longer be managed with the standard metrics that are backward looking. Instead, project management requires smarter analytics that is based on training models with large quantities of data.

Predictive project analytics brings with it many benefits; enhanced M&A, a holistic project portfolio view, and better resource allocation management. It uses algorithms to crunch performance data and churn out unique insights that were not possible before. It equips modern project managers to ensure their complex projects stand a better chance of succeeding.

We hope this article gives you an overview of predictive project analytics and how you can implement it in your projects. If you’re looking for a software platform that is capable of delivering predictive project analytics, you should consider Allocable.

Allocable provides powerful, past, current and forward-looking visualizations. It’s able to give you the top-level view of what to expect from your entire project portfolio, and drill down to individual sections that are not performing as expected.

​It enables you to dynamically assign resources based on predictive data. This ensures your projects have a high chance of being successful.

View a demo of Allocable to get started today.

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Allocable is a cloud-based automated time tracking and business intelligence (BI) software platform that provides  a complete visualization of your workforce and project productivity data empowering you to turn information into actionable insight to optimize and forecast performance with more certainty.

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