Posted on : October 26th 2022
Author : Jaiganesh Subramanian, Director at Straive
Many companies and teams struggle with project management. Often, quality, cost, cohesion, and delivery goals falter, and supervisory control and resource allocation can go haywire, causing interface interruptions. Project managers have several moving targets to hit—outcomes, cost efficiency, people management, resource optimization, operations scheduling, and so on—that must be managed concurrently. Data analytics offers insights into short- and long-term challenges, which helps prioritize exercises.
Project management, as a discipline, took a focused form in the early 1960s when enterprises began appreciating the benefit of organizing tasks around projects.¹ Now, effective project management tools are critical, providing a competitive advantage and differentiation. Data-led project management helps ensure cohesion and just-in-time resource identification and support aspects like scheduling automation, allowing projects to be managed from one system, irrespective of their complexities.
Resource needs can be planned strategically, and deliverables get tracked in real-time. By accessing accurate and real-time statuses, managers can streamline task execution workflows. A granular view of the entire value chain is a powerful element in a project manager’s toolkit.
Using a framework of basic schedules, milestones, task owners, emergent resource dependencies, and the like helps break down complex projects into manageable modules. Teams need reliable end-to-end visibility into resources for strategic project management planning.
A lifecycle management approach needs data intelligence to integrate teams, processes, technology systems, and resources, among other aspects. Data analytics is at the heart of seamless project execution with high quality and on-time delivery.
Data may change during a project’s lifetime, requiring data sources to be updated. Data-led project management enables better use of historical enterprise data by deploying metadata efficiently. Metadata is defined as “data about data” and highlights data’s fundamental properties, such as author, creation date, and more.
Carefully calibrated and effective project management tools connect human experts and technologies with a digital thread underpinned by data and provide a unified source for combining information and evaluation tools to drive project execution. Data-led project management, with targeted interventions, facilitates process improvements by investigating complex roadblocks to collaborations across functions.
Effective project management needs a centralized and consolidated view of all the moving parts and an interactive and data-led road map that lends itself to visualization, with simplified planning steps that make scheduling easier by using elements like Gantt charts for timeline views.
Articulating and sharing insights becomes seamless as visualization-oriented, dynamic data-based dashboards produce customized project management road maps. Coauthoring tools support collaboration, which is made easier through timely communications, real-time updates, edits to project schedules, milestone updates, and more.
The Project Management Institute, Inc. (PMI) has a famous project management playbook called “A Guide to the Project Management Body of Knowledge (PMBOK® Guide)” that illustrates a set of processes included in an effective project management approach (Exhibit 2).²
Integrating and sharing workflows horizontally across departments and vertically facilitate an agile environment that optimizes decisions through data analytics. Projects generally fail without adequate early projections of likely scenarios. Constant communication across silos is essential for developing nascent plans and projecting influence to realize milestones. Also, the latest visualization tools help establish clear objectives early, allowing a concise approach to anticipate and control risks.
Today, artificial intelligence (AI) is impacting project management in many ways. For instance, the movie division of Warner Bros. leverages an AI-driven project management system’s data and predictive analytics to guide decision-making at the greenlight stage.³ The integrated online platform can assess the value of a star in any territory and how much a film is expected to make in theaters and on other ancillary streams, which allows executives to spend time on core responsibilities, such as packaging, marketing, and distribution decisions, including release dates.
Streamlining project planning, AI-enabled tools use machine learning (ML) algorithms to analyze past enterprise data to detect patterns and insights, which can inform project timeline estimates, costs, resource requirements, and more. Customized workflows become possible, and predictive modeling optimizes planning with higher confidence levels. AI/ML project management platforms support integrating extant tools like Slack or Microsoft Teams for seamless communications and decision-making.
Typically, project managers spend more than half of their time on administrative responsibilities like planning meetings, reporting, and the like. AI/ML project management support can undertake these routine responsibilities by planning meetings, sending automated reminders, preparing basic reports, and so on. Project managers are then free to focus on value-added activities such as mentoring team members and customer engagements and nurturing a relaxed, innovative, and collaborative ecosystem, spawning creative solutions as an effect.
Automated predictive modeling generates data and analytics that feed useful insights and forecasts, taking mundane tasks off the table for project managers while supporting a strategic approach using previously unavailable business intelligence backed by data.⁴
As business-as-usual project management tasks become automated, project managers can focus on critical soft skills essential in the 21st-century workplace. Creative communication, empathic negotiation, and emotional intelligence are necessities for boosting customers’ experiences and inspiring team members. Through seamless and enriched digital capabilities, AI-driven project management strategically supports delivering digitally augmented human-centric customer experience, making embedding customer experience into products and design easier as an end result.
Data-led and AI/ML-enabled project management will transform the role of project managers. As automating mundane project management tasks accelerates, project managers will have increased responsibilities around integrating digital-first practices and creative problem-solving. Analyzing vast historical enterprise data and using AI/ML-based intuitive predictive modeling will drive actionable insights that boost the quality and precision of decision-making across the project management value chain.
¹ Microsoft, “History of Project Management,” n.d.
² Sofia Tokar, “6 Types of Project Management Methodologies to Boost Success,” Southern New Hampshire University, May 18, 2018
³ Viswanathan Chandrasekharan, “AI Lips Syncs for Hollywood & Television,” Straive, July 23, 2021
⁴ PwC, “A Virtual Partnership? How Artificial Intelligence Will Disrupt Project Management and Change the Role of Project Managers,” 2019
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