How to Leverage the Analytical Process of Predictive Modeling to Plan Better

How to Leverage the Analytical Process of Predictive Modeling to Plan Better

Predictive modeling is being used across a wide range of industries. It powers self-driving cars, predicts the quality of leads, detects fraudulent behavior, and guesses the outcome of presidential elections. With such a variety of applications, predictive modeling is changing the way we use data and making it more valuable for critical decision making in business and daily life.

Thus, it’s no surprise that predictive modeling also has an influence on project management. Being one of the key activities of enterprises, project management has high stakes riding on it. Despite its importance, applying predictive modeling to the field of project management has its own challenges.

The data problem of predictive project planning


Predictive modeling in project management uses various relevant data points to predict the outcome of a project. It identifies key factors that would influence a project, and lets you take action to influence the outcome.

For example, predictive modeling could tell you if your project is going to be over or under budget weeks or months ahead with data that shows how your resources are allocating their time.

Digital Elevator, a digital marketing company based in West Palm Beach, uses predictive project planning to determine if they are leveraging their resources properly for web design and development projects. “Understanding that, for example, our developers are working 35 hours a week on a project when they are scheduled for 30, is something we can adjust early with predictive project planning software,” says Digital Elevator CEO Daniel Lofaso.

However, as Lofaso has learned, you have to provide your predictive models the right set of data in order for them to help you forecast trends in your business that are useful.

Because predictive modeling often depends on data from start the start to the end of a cycle, it takes a lot of data to bring out the best and most accurate predictions. However, data volume is usually not a problem with enterprises. In fact, every organization has too much data at its disposal. Key decision makers are drowning in a sea of data, unable to separate the signal from the noise.

There are various tools that are used within an enterprise, each with its own rich set of data. Here are some of the tools:

  • Enterprise resource planning (ERP)
  • Customer relationship management (CRM)
  • IT service management (ITSM)
  • HR management systems (HRMS)
  • Financial systems
  • Business intelligence systems (BI)
  • Governance, risk management, and compliance systems (GRC)

These systems are good at the narrow tasks they perform. They are great at utilizing data in their own silos, but are unable to perform cross-system analysis. Project management is a function that cuts across all these systems in an enterprise. Yet, because these systems are so closed, they’re often hardly useful to project planning.

The analysis problem of predictive project planning


Even if you were to integrate these systems, you’d still have an additional set of problems related to analysis. The data quality is questionable because of duplicate data, incomplete data, and inaccurate and out-of-date data that each system may have. The data formats are different, and syncing data across systems would be a big challenge.

The biggest problem, however, is that all this data will only tell you what happened, and why it happened. But project management needs to know more than that. Traditional methods of data-wrangling will not produce the kind of holistic analytics that today’s project planning requires. We need a different approach, and predictive modeling is gaining prominence in this fight against data overload.

How to make better decisions using predictive project planning


Modern project management doesn’t need historical analysis. It needs to know what is going to happen, and how to influence it. It’s a flaw to use past trends to predict future ones, this regression analysis will not yield much insight. It’s like the stock market: influenced by many factors and can’t be predicted based on past behavior.

Predictive project planning needs a new strategy. It takes wrangling all your data and fixing issues with data quality. This should be followed by data analysis using a single platform that can derive predictive insights from that data.

Forward-looking insight is what makes predictive project planning valuable. But this forward-looking insight is not just good to know, it helps make decisions today that will influence tomorrow. If you know why your projects could fail, you can attempt to fix those issues right away and possibly save your project. This is the power of predictive project planning.

Predictive project planning can answer the following questions:

  • How do we expect resource utilization to change?
  • How can we predict demand and minimize our use of contractors?
  • Where is the next bottleneck going to come from?
  • Where are we over/under-staffed?
  • Which parts of our talent pool are overachieving their goals?

Predictive project planning is made possible by the use of modern technologies like cloud computing, in-memory analytics, and real-time data analysis frameworks. A tool that does predictive project planning should employ these technologies. It should be able to process large quantities of data at cloud scale, and in real-time.

Additionally, you don’t want the burden of managing and updating this platform yourself. It needs to be a hosted solution that’s always up-to-date and evolving, without you having to lift a finger to maintain it. You focus on using the tool for predictive modeling, while your vendor focuses on building and maintaining it.

Predictive Project Planning - Integrating predictive analytics with business planning


Integrated business planning uses the predictive modelling process to plan and support business decisions. Because it is integrated, it can leverage data that resides in disparate systems across the enterprise to produce meaningful insight.

The predictive planning model should evolve along with the kind of project. No two projects are the same, and every project has a unique set of deliverables, and challenges. A predictive model should be able to adapt to these variations and still provide meaningful insight.

Augmenting, not replacing humans


With the big promises that predictive project analytics makes, some wonder if machines will take over human jobs soon. This, however, is far from the truth.

For example, Google used predictive algorithms to help their HR team decide promotions for employees. It was part of their push to become more data driven as an organization. The algorithms achieved 90% accuracy in predicting promotions.

However, Google didn’t let the algorithms make the final decision because they wanted to have people make people-related decisions. That said, Google says that this is an experiment in predictive modeling, where data is used to augment HR decisions. This doesn’t mean the HR function will go away.

The journey to predictive project planning


As organizations strive to adopt predictive project planning, they typically go on a journey from operational analytics, to strategic analytics, to predictive analytics. This is a normal progression. As a key decision maker in your enterprise, you need to understand where you stand in this progression.

It’s a lot easier to adopt predictive project planning if you are already strategic in your use of analytics. On the other hand, it may take longer if your analytics is still operational and need-based.

In time, the top 10% of organizations will adopt predictive modeling and move far ahead of the competition. The other 90% will lag behind and struggle with the same cyclic problems. This is the great ‘digital divide’ that will play out. As an enterprise, you want to be on the right side of this divide.

Conclusion


Project management doesn’t live in a silo. It reacts and responds to changes in the world around. Today, predictive analytics is sweeping across every function of the enterprise, and project management is no exception.

There are hurdles to cross such as the integration, and cleansing of siloed data across the enterprise. Then, you need to run forward-looking analysis on this data, which requires an analysis platform based on modern technologies like cloud computing, in-memory analytics, and real-time data analysis.

Finally, predictive project planning is a journey where you build on traditional operational and strategic analytics.

We hope this article gives you a working plan to start your journey to predictive project planning. As you look for the right predictive analytics platform for your project management, consider Allocable. Its intelligent people-planning features can transform how you manage projects. For billable projects, how you manage your people assets is of critical importance.

Allocable brings you visibility across every layer of the team, and even across departments in your enterprise. You’ll never be left wondering if your project is over or under budget. And you’ll always be a step ahead in resource allocation.

A cloud-based platform, Allocable can scale to meet the demands of enterprises of any size. Try Allocable, and start enjoying the power of predictive project planning for the first time.

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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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