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Deep Learning Project Life Cycle: Bridging the Project Management Gap

Apr 3, 2019 | By Steven Burns, FAIA | 0 Comments

Topics: Project Management

The machine learning industry is set to exceed $35 billion by 2025 as artificial intelligence continues to advance. This has profound implications for project managers.

A recent study identified a number of primary reasons for a project's failure. Among them were inaccurate requirements gathering and inadequate resource forecasting. Traditionally, project managers use their intuition, data, and knowledge of the company to help lead teams to their goals. Using resources and time well is an important part of a successful project. A few missteps can cause poor results if overlooked.

This is where machine learning is offering a powerful, data-driven solution.

When used correctly, deep learning brings new insights into a project life cycle. With new data, project managers can change their strategies to have even better outcomes.

Starting the Deep Learning Project Life Cycle

For a machine learning system to produce effective results it needs a goal to work towards.

For example, a warehouse could want to improve the way goods are brought into and out of the building. Or a global company could seek to improve how staff members talk to each other.

Having a well-chosen goal will help create real value so you can then move on to the second stage of the process.

Gather and Isolate the Important Data

Going back to the global business example, knowing how many accounting staff you hire a year would be useless. But knowing how long it takes for a team to communicate and send files to each other would be helpful.

Whatever your goals, define the relevant data points and think about how you would track it.

From there, flat files like .csv are good formats for deep learning analysis to collect data in preparation for this.

Collect Model Data and Refine

With the data collected, you then begin creating a model based on them. It is in this first stage the deep learning system begins to develop and verify its results against the existing data.

You qualify the results and refine the input data until you can test the ability to predict outcomes. It's important to keep testing new data and analyze the new points that emerge.

This helps create a stronger data set and better predictions.

Begin Understanding the Outcomes

With a successful launch of the model and predictions being made, it is then important to think about what paths to follow.

No model is 100% accurate and some predictions may be efficient but have consequences. It's at this point you want to ensure you're following the best outcomes for you and the business.

Implementing the Decisions

With the direction defined, project managers can then start to improve their process. Whereas before they were working off intuition, they now can combine that with the new information provided by the new models.

With 80% of business leaders promoting AI's ability to improve productivity, there's a good chance you'll see results.

Approach with Caution

It's worth bearing in mind that deep machine learning will not solve every problem.
It is simply a tool to help you make better-informed judgments along the project life cycle that will lead to improved outcomes.

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The Author

Steven Burns, FAIA

Steven Burns, FAIA, spent 14 years managing his firm Burns + Beyerl Architects. After creating ArchiOffice®, the smart office and project management solution for architectural firms, Steve brought his management expertise to BQE Software, where he is perfecting the business strategy and product development.

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