How to Leverage Big Data in Project Management
Big data is nothing short of a game-changer in the world of business.
Insights extracted from big data via machine learning have enabled business-folk to find performance patterns and predict future outcomes with remarkable accuracy. These abilities have found application in areas including digital marketing, product development, accounting & finance, HR, and project management, our topic for the occasion.
Big data and project management are a natural fit. The success of any given project directly depends on informed decision-making, and big data is an engine for producing actionable information. Project managers have already started to treat big data as a reliable problem-solving tool in a variety of contexts and situations.
In the remainder of this article, we will go over a number of common use-cases for big data in project management, which will hopefully help you adopt it for the needs of your organization.
1. Project Planning & Delivery
Planning and delivery are processes that generate a consistent stream of data, which creates an opportunity for big data analytics to produce meaningful insights. The data volume and variety could help project managers reorganize internal planning processes, as well as develop creative solutions to outstanding problems. A whole range of industries, including construction, manufacturing, logistics, transport, and agriculture are already using big data during the project planning phase, and other sectors such as IT and HR are following suite. So far the usage of big data in project management has been limited to larger enterprises, but with the emergence of a variety of affordable big data solutions on the market, even small businesses are starting to explore the advantages of big data for project management.
2. Team Analysis
A large volume of information about various experts working on projects across different industries is being collected on a regular basis. The kind of data being gathered includes past project experience, skills, formal education, training, performance information, and team structure. Data that is less easy to quantify is also being collected, data such as frequency of conflict, team morale, leadership qualities, and work attrition. Once this data gets consolidated into a unified database and undergoes analysis, it will likely provide insights on how to manage teams more effectively, how to create efficient team structures, what skills are needed to form a successful team, and how to pick the most effective leaders.
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3. Knowledge Management
Once the economy entered its digital phase, project management efforts across different industries began to coalesce into a shared pool of information thanks to the efforts of business owners, data experts, and managers. This information includes transcripts from industry events, best practice outlines, troubleshooting data, conversation logs, and more. Most of this information is as of yet unused and is collecting proverbial dust in various company archives. However, attempts are being made to analyze this data to start extracting value from it. Creating a shared knowledge-base from this data could significantly advance project management through the establishment of novel best practices, technology use cases, and solutions to long-standing problems.
4. Risk Management
Project management is a process affected by a whole range of internal and external events. Some of these events can produce recurring issues that can slow down or jeopardize the completion of a project. Such issues are frequently unpredictable, and the only way to manage and solve them is through adequate preparation. Consequently, documenting these issues and their solutions is of prime importance in project management. Once this data-set expands to sufficient size, it will be possible to utilize big data analytics to discover new techniques and procedures for identifying, understanding, prioritizing, and creating response strategies to these issues.
5. Quality Management
Quality management is a resource- and manpower-intensive field of work, and therefore one that is subject to intensive scrutiny and monitoring efforts. These efforts include data gathering through reports, summaries, tests, and experiments. This data is then used to ensure that quality standards are being met, that quality assurance protocols are being respected and followed, and that issues get solved as soon as they arise. Big data analytics has the potential to streamline and accelerate this process beyond its initial scope. It can be used for new quality frameworks and procedures, new tools for monitoring quality, and new paths of development for future products.
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6. Resource Management
Every project is defined by a range of available resources, which include personnel, machinery, infrastructure, knowledge, financial resources, and processes. Once a project is underway, these resources are tightly controlled and monitored to ensure the successful completion of a project. A side-product of these monitoring efforts is data collection on things like resource types, resource amounts, resources unused, and others. Big data analytics offers project managers a way to deduce patterns from long-term resource usage, which can easily be converted into patterns of finance flowing in and out of a given company. This makes it possible to develop more cost-efficient methods of resource extraction, management, and resource use.
The Path Going Forward
Project management is becoming increasingly intertwined with a sophisticated technological apparatus created by the digital revolution. This interplay between technologies such as big data analytics and project management has led to a whole range of new methodologies for solving problems and achieving better results. Big data has already been a huge boon for project management, and going forward we can expect it to become one of its indispensable components.