Level:
Basic
TCM Section(s):
10.4. Project Historical Database Management
11.3. Information Management
Venue: 2022 AACE International Conference & Expo
Abstract: Many owners have access to an abundance of project data. That includes schedules, risk logs, and cost reports to name a few. Owners may also have data on other project attributes. Historical project data always existed and have an important reference in managing projects on hand. However, the popularity of information management systems in the recent years has led to an unprecedented growth in the quality and scale of available historical data. Such a trend has made historical data increasingly more valuable in managing projects.
Data analytics is the extraction of meaningful business insights from data. Recent advancements in artificial intelligence and computing power have accelerated the adoption of data analytics. This adoption promotes a data driven culture in managing projects and managing them proactively and more effectively.Owners can now leverage empirical data in developing more robust schedules and cost forecast.A robust plan is one that is not just reliable at the onset but can adapt well to a changed environment. A data driven approach to planning complements the knowledge and experience of experienced professionals in managing projects and gets its own seat on the table.
To implement a data analytics framework, planning is necessary. A good plan covers the approach to data acquisition, data enhancement, data predictions and delivery of insights. Explained with a real-world example, the paper describes a practical approach to the first two steps and how it results in a best-in-class historical database and data model.
TCM Section(s):
10.4. Project Historical Database Management
11.3. Information Management
Venue: 2022 AACE International Conference & Expo
Abstract: Many owners have access to an abundance of project data. That includes schedules, risk logs, and cost reports to name a few. Owners may also have data on other project attributes. Historical project data always existed and have an important reference in managing projects on hand. However, the popularity of information management systems in the recent years has led to an unprecedented growth in the quality and scale of available historical data. Such a trend has made historical data increasingly more valuable in managing projects.
Data analytics is the extraction of meaningful business insights from data. Recent advancements in artificial intelligence and computing power have accelerated the adoption of data analytics. This adoption promotes a data driven culture in managing projects and managing them proactively and more effectively.Owners can now leverage empirical data in developing more robust schedules and cost forecast.A robust plan is one that is not just reliable at the onset but can adapt well to a changed environment. A data driven approach to planning complements the knowledge and experience of experienced professionals in managing projects and gets its own seat on the table.
To implement a data analytics framework, planning is necessary. A good plan covers the approach to data acquisition, data enhancement, data predictions and delivery of insights. Explained with a real-world example, the paper describes a practical approach to the first two steps and how it results in a best-in-class historical database and data model.