Predictive analysis of project schedule and costs

Certainly, predictive analysis is not an easy thing to perform.

However, making the right predictions about project schedules and costs will save the organization money and offer greater results.

However, how is it possible to make an accurate and realistic predictive analysis of project schedules and costs?

The answer is quite straightforward: get the right data!

With the right data, you can predict future business results more accurately.

However, successfully implementing predictive analysis remains a big challenge, especially for small businesses with limited data management resources.

Collecting, organizing, and archiving the correct data are key prerequisites for the adoption of predictive analysis. Otherwise, wrong business decisions will almost certainly be taken.

What is a predictive analysis?

The predictive analysis is defined as a form of advanced analysis that examines data or content to answer questions such as “What’s likely to happen?”.

Predictive analysis uses historical data, artificial intelligence and automated learning to predict future outcomes.

There are solutions that employ statistical tools such as regression analysis, data modelling, forecasting and statistics to answer questions about what is likely to happen in the future.

What is important is that predictive analysis can help you find solutions to different business challenges and help you achieve your business goals.

5 steps to begin a predictive analysis of project schedules and costs

To make sure you are generating the kind of data you need to get the right predictive analysis, you need to create a data-based knowledge within your organization.

Here, therefore, are the 5 steps that will guide the project manager in the preparation of a predictive analysis of project schedules and costs:

1. Define the business result you want to achieve

Predictive analysis, as we have said, allows you to visualize future results. Clearly defined objectives help to customize the solutions to be implemented to achieve better results.

However, there is the possibility of realising that the existing data is not sufficient to answer the questions that concern us. In these cases, you will have to work to collect relevant data for a given period of time or edit the questions to address the same issue from a different perspective.

2. Collect relevant data from all available sources

By now, you know it well, the models of predictive analysis are data-driven.

It is important, therefore, to also identify the sources through which to find the right data to answer the questions that relate to the business challenge.

Storing the data in a spreadsheet and then inserting it into predictive models, for example, can be a tedious, risky and in many cases impossible process. Instead, using special applications, sometimes also included in project management software (link to the Homepage), can be the ideal solution for archiving and processing relevant data.

These tools also provide the ability to store large amounts of data – often in cloud, helping to save IT infrastructure costs – in an orderly manner. This means one can use data mining tools to get relevant data from multiple sources.

3. Improve data quality by using data cleaning techniques

“Garbage in, garbage out” is a terminology of the industry that refers to the fact that low quality inputs in turn generate poor output values.

Predictive analysis will be inaccurate if input data is bad. It is therefore necessary to ensure that team members, stakeholders, or whoever is responsible for data entry, log the correct data values in the specified and agreed format. This will help to reduce the time needed to clean and format the data.

Duplicate records should also be prevented and corrected, and data normalised to ensure consistency in the records.

Most project management software solutions offer data cleaning capabilities such as: data deletion, data standardization, data harmonization and data profiling.

4. Create predictive analysis templates to test data or choose one correctly

Building one’s own predictive analysis model requires experience in the field of project management and in science and data management.

A project manager will probably need the help of a data scientist or someone who possesses advanced analytical skills to create predictive models from scratch.

One way, if one does not possess adequate internal resources, is to outsource this work to a consulting firm that provides analysis services. However, if cost problems prevent a small business from employing experts, there are many software tools available with integrated features of predictive modeling tools.

Although these tools may not offer the advanced knowledge that an expert data scientist can provide, they still deliver integrated predictive models, are easy to use and certainly come at a lower cost.

Software with predictive analysis of project schedules and costs can be a good starting point for small businesses looking to make predictions. You can try TwProject for free for 15 days and if you don’t know how to use it, receive help from our support team.

5. Evaluate and validate the predictive model to ensure soundness

To verify the chosen model, the evaluation and validation of the predictive model with alternative datasets allows the identification of weak points in the model, as well as ensuring that the model works well in different scenarios.

But this is not the only technique available. There are several techniques for validating predictive models, such as cross-validation, regression validation and many more.

Even if you are unfamiliar with these techniques, nowadays most predictive analysis tools offer model validation capabilities within the software.

Incorporating predictive models into business processes and using the results, will be helpful and to make better business decisions.

Predictive Analysis: Conclusions

The implementation of predictive modeling tools is not free of obstacles. Here are some of the challenges that a project manager may face:

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Predictive analysis foresees the probability of an event, not its certainty
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Although you may want the data to help you make certain and accurate predictions, what you can actually predict is the probability of an event. All predictions, including those based on the correct and relevant data, always leave some room for error or uncertainty.

Therefore, the final call to any business decision should be based on a set of elements and should not be limited to one aspect.

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Creating predictive analyses can take quite some time
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Predictive analysis cannot be implemented overnight. Building and implementing sound and effective predictive models can take months, depending on the level of expertise and knowledge of the individuals involved.

What’s important to note is that robust and reusable predictive models provide long-term gains and cost savings.

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The adoption of predictive analysis implies some costs
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In addition to the cost of any project management software that includes a predictive analysis tool, the cost of training team members who will have a direct role in performing predictive analysis should be taken into account.

It is possible to begin by identifying business cases where predictive analysis has already been successfully used and adapting it to new situations.

The tip we can suggest especially to non-experienced project managers is to start experimenting with predictive analysis on a small scale and expand further as experience is gained and favourable results are achieved.