Project Analytics
Created by Nikolaos Kavros
Introduction
Analytics and project management are two related fields that can be used together to drive business success. Analytics refers to the systematic examination of data or statistics to gain insights and make informed decisions. It can involve a variety of techniques and tools, such as data mining, predictive modelling, and data visualization. In the context of project management, analytics can be used to monitor project performance, identify trends and patterns, and make data-driven decisions about project direction and priorities. Project management, on the other hand, is the process of planning, executing, and closing the work of a team to achieve specific goals and meet specific success criteria. It involves defining project scope, setting project goals and objectives, developing a project plan, assembling a project team, and monitoring progress throughout the project lifecycle. By combining these two fields, organizations can leverage the insights gained from analytics to inform project decisions and prioritize initiatives that are most likely to drive business success. Additionally, project managers can use analytics to monitor progress and adjust project plans as needed to ensure that projects remain on track and achieve their desired outcomes.
Definition
Project analytics is the process of analysing data and metrics related to a project in order to gain insights and make informed decisions. It involves collecting, organizing, and analysing data from various sources, such as project management software, financial systems, and team collaboration tools. The goal of project analytics is to provide project managers and stakeholders with valuable information about the performance of the project, identify potential risks and opportunities, and make data-driven decisions that can help improve project outcomes.[1] Examples of project analytics include analysing project schedules, budgets, resource allocation, team productivity, and project risks. By using project analytics, project managers can gain a deeper understanding of the project and its progress, and make adjustments and course corrections as needed to ensure project success.[2]
Use and implementation
One of the most significant advantages of using analytics in projects is the ability to track key performance indicators (KPIs). KPIs are metrics that measure the success of a project against specific goals. By monitoring KPIs, project managers can quickly identify potential issues and take corrective action to keep the project on track. In addition to tracking KPIs, analytics can also help project managers identify patterns in project data. By analysing past project data, project managers can identify trends that can be used to optimize project performance in the future. This data can also help project managers to identify potential areas of risk, allowing them to take steps to mitigate these risks before they become a problem. Analytics can also be used to improve communication and collaboration within project teams. By analysing project data, project managers can identify areas where team members may need additional support or training. This information can then be used to develop targeted training programs or to allocate resources where they are needed most. Another advantage of using analytics in projects is the ability to optimize resource allocation. By analysing data on resource utilization, project managers can identify areas where resources are being overused or underused. This information can then be used to reallocate resources to maximize their effectiveness. Finally, analytics can help project managers to improve project outcomes by providing insights into customer behaviour and preferences. By analysing customer data, project managers can gain insights into customer needs, preferences, and behaviour. This information can be used to develop products and services that better meet customer needs, increasing customer satisfaction and driving revenue growth.Cite error: Closing </ref> missing for <ref> tag
6. https://online.hbs.edu/blog/post/types-of-data-analysis
7. https://www.dataversity.net/fundamentals-of-cognitiveanalytics
9. https://azure.microsoft.com/en-us/products/cognitive-services/text-analytics/
10. https://project-management.info/earned-value-analysis-management-eva-evm/
11. https://www.blastanalytics.com/predictive-analytics-consulting
12. https://pm-training.net/agile-development-methodology-wiki/100agiletechniques/
13. Michael Brenner, Forbes, July 13 2018. Limitations of Analytics: What you need to know
Cite error:
<ref> tags exist, but no <references/> tag was found
