Project Analytics - Revision history

S213235 at 18:08, 4 May 2023

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	5. Cost and time: Collecting and analysing data can be a time-consuming and expensive process. In some cases, the cost and time required to gather and analyse data may not be worth the insights gained, particularly in smaller projects or projects with limited budgets.		5. Cost and time: Collecting and analysing data can be a time-consuming and expensive process. In some cases, the cost and time required to gather and analyse data may not be worth the insights gained, particularly in smaller projects or projects with limited budgets.
	It's important to be aware of these limitations when using analytics in projects to ensure that the insights gained are accurate, comprehensive, and useful.		It's important to be aware of these limitations when using analytics in projects to ensure that the insights gained are accurate, comprehensive, and useful.<ref name="Limitations" />


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	<ref name="EVM">https://project-management.info/earned-value-analysis-management-eva-evm/</ref>		<ref name="EVM">https://project-management.info/earned-value-analysis-management-eva-evm/</ref>
	<ref name="predictive graph">https://www.blastanalytics.com/predictive-analytics-consulting</ref>		<ref name="predictive graph">https://www.blastanalytics.com/predictive-analytics-consulting</ref>

	<ref name="Agile graph"> https://pm-training.net/agile-development-methodology-wiki/100agiletechniques/</ref>		<ref name="Agile graph"> https://pm-training.net/agile-development-methodology-wiki/100agiletechniques/</ref>
			<ref name="Limitations">Michael Brenner, Forbes, July 13 2018. ''Limitations of Analytics: What you need to know''</ref>
	~~13.~~ Michael Brenner, Forbes, July 13 2018. ''Limitations of Analytics: What you need to know''

S213235 at 18:07, 4 May 2023

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	[[File:Th.jpg\|left\|thumb\|400px\| Figure 4: Agile Metrics illustration methods.]]		[[File:Th.jpg\|left\|thumb\|400px\| Figure 4: Agile Metrics illustration methods.<ref name="Agile graph" />]]


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	<ref name="predictive graph">https://www.blastanalytics.com/predictive-analytics-consulting</ref>		<ref name="predictive graph">https://www.blastanalytics.com/predictive-analytics-consulting</ref>

	~~12.~~ https://pm-training.net/agile-development-methodology-wiki/100agiletechniques/		<ref name="Agile graph"> https://pm-training.net/agile-development-methodology-wiki/100agiletechniques/</ref>

	13. Michael Brenner, Forbes, July 13 2018. ''Limitations of Analytics: What you need to know''		13. Michael Brenner, Forbes, July 13 2018. ''Limitations of Analytics: What you need to know''

S213235 at 18:04, 4 May 2023

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	[[File:Predicitive analytics.jpg \|left\|thumb\|400px\|Figure 3: Predictive analytics brainstorm. ]]<ref name="predictive graph" />		[[File:Predicitive analytics.jpg \|left\|thumb\|400px\|Figure 3: Predictive analytics brainstorm.<ref name="predictive graph" /> ]]

S213235 at 18:03, 4 May 2023

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	[[File:Predicitive analytics.jpg \|left\|thumb\|400px\|Figure 3: Predictive analytics brainstorm. ]]		[[File:Predicitive analytics.jpg \|left\|thumb\|400px\|Figure 3: Predictive analytics brainstorm. ]]<ref name="predictive graph" />


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	<ref name="text">https://azure.microsoft.com/en-us/products/cognitive-services/text-analytics/</ref>		<ref name="text">https://azure.microsoft.com/en-us/products/cognitive-services/text-analytics/</ref>
	<ref name="EVM">https://project-management.info/earned-value-analysis-management-eva-evm/</ref>		<ref name="EVM">https://project-management.info/earned-value-analysis-management-eva-evm/</ref>
			<ref name="predictive graph">https://www.blastanalytics.com/predictive-analytics-consulting</ref>
	~~11.~~ https://www.blastanalytics.com/predictive-analytics-consulting

	12. https://pm-training.net/agile-development-methodology-wiki/100agiletechniques/		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''		13. Michael Brenner, Forbes, July 13 2018. ''Limitations of Analytics: What you need to know''

S213235 at 18:00, 4 May 2023

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	4. Prescriptive Analytics: Prescriptive analytics involves using data and models to make recommendations for specific actions or decisions. This category of analytics is used to optimize and improve business processes, such as supply chain management or marketing strategies.<ref name="Data analysis" />		4. Prescriptive Analytics: Prescriptive analytics involves using data and models to make recommendations for specific actions or decisions. This category of analytics is used to optimize and improve business processes, such as supply chain management or marketing strategies.<ref name="Data analysis" />

	5. Cognitive Analytics: Cognitive analytics involves the use of artificial intelligence and machine learning to analyse data and identify patterns and trends that are not immediately obvious. This category of analytics is used to identify new opportunities and insights that can drive innovation and competitive advantage.		5. Cognitive Analytics: Cognitive analytics involves the use of artificial intelligence and machine learning to analyse data and identify patterns and trends that are not immediately obvious. This category of analytics is used to identify new opportunities and insights that can drive innovation and competitive advantage.<ref name="cognitive" />

	6. Spatial Analytics: Spatial analytics involves the analysis of data that has a geographic or spatial component. This category of analytics is used to understand patterns and relationships within geographic areas and to make decisions based on spatial data.		6. Spatial Analytics: Spatial analytics involves the analysis of data that has a geographic or spatial component. This category of analytics is used to understand patterns and relationships within geographic areas and to make decisions based on spatial data.<ref name="spatial" />

	7. Text Analytics: Text analytics involves the analysis of unstructured data, such as text documents or social media posts. This category of analytics is used to extract insights from unstructured data and to understand the sentiment and opinions of customers or stakeholders.		7. Text Analytics: Text analytics involves the analysis of unstructured data, such as text documents or social media posts. This category of analytics is used to extract insights from unstructured data and to understand the sentiment and opinions of customers or stakeholders.<ref name="text" />

	8. Web Analytics: Web analytics involves the analysis of data related to website traffic and user behaviour. This category of analytics is used to understand how users interact with websites and to optimize website design and user experience.		8. Web Analytics: Web analytics involves the analysis of data related to website traffic and user behaviour. This category of analytics is used to understand how users interact with websites and to optimize website design and user experience.
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	== Analytics graphic tools ==		== Analytics graphic tools ==
	1. Earned Value Management (EVM) Analysis: EVM is a project management technique used to measure project performance by integrating scope, time, and cost. By analysing EVM data, project managers can identify the progress of the project in real-time, as well as potential delays and cost overruns. The following graph shows an example of an EVM chart, which compares the planned value (PV), earned value (EV), and actual cost (AC) of a project over time.		1. Earned Value Management (EVM) Analysis: EVM is a project management technique used to measure project performance by integrating scope, time, and cost. By analysing EVM data, project managers can identify the progress of the project in real-time, as well as potential delays and cost overruns. The following graph shows an example of an EVM chart, which compares the planned value (PV), earned value (EV), and actual cost (AC) of a project over time.<ref name="EVM" />


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	<ref name="Life cycle"> Grolemund G, Wickham H. R for data science [Internet]. Available from: https://r4ds.had.co.nz/explore-intro.html</ref>		<ref name="Life cycle"> Grolemund G, Wickham H. R for data science [Internet]. Available from: https://r4ds.had.co.nz/explore-intro.html</ref>
	<ref name="Data analysis"> https://online.hbs.edu/blog/post/types-of-data-analysis</ref>		<ref name="Data analysis"> https://online.hbs.edu/blog/post/types-of-data-analysis</ref>
			<ref name="cognitive">https://www.dataversity.net/fundamentals-of-cognitiveanalytics</ref>
	7. https://www.dataversity.net/fundamentals-of-cognitiveanalytics		<ref name="spatial">https://www.pwc.com/</ref>
			<ref name="text">https://azure.microsoft.com/en-us/products/cognitive-services/text-analytics/</ref>
	8. https://www.pwc.com/		<ref name="EVM">https://project-management.info/earned-value-analysis-management-eva-evm/</ref>

	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		11. https://www.blastanalytics.com/predictive-analytics-consulting

S213235 at 17:51, 4 May 2023

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	3. Predictive Analytics: Predictive analytics involves using historical data and statistical models to forecast future events or trends. This category of analytics is used to make predictions about future outcomes, such as customer behaviour or market trends.		3. Predictive Analytics: Predictive analytics involves using historical data and statistical models to forecast future events or trends. This category of analytics is used to make predictions about future outcomes, such as customer behaviour or market trends.

	4. Prescriptive Analytics: Prescriptive analytics involves using data and models to make recommendations for specific actions or decisions. This category of analytics is used to optimize and improve business processes, such as supply chain management or marketing strategies.		4. Prescriptive Analytics: Prescriptive analytics involves using data and models to make recommendations for specific actions or decisions. This category of analytics is used to optimize and improve business processes, such as supply chain management or marketing strategies.<ref name="Data analysis" />

	5. Cognitive Analytics: Cognitive analytics involves the use of artificial intelligence and machine learning to analyse data and identify patterns and trends that are not immediately obvious. This category of analytics is used to identify new opportunities and insights that can drive innovation and competitive advantage.		5. Cognitive Analytics: Cognitive analytics involves the use of artificial intelligence and machine learning to analyse data and identify patterns and trends that are not immediately obvious. This category of analytics is used to identify new opportunities and insights that can drive innovation and competitive advantage.
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	<ref name="Podcast">Cornelius Fichtner, The Project Management Podcast</ref>		<ref name="Podcast">Cornelius Fichtner, The Project Management Podcast</ref>
	<ref name="Life cycle"> Grolemund G, Wickham H. R for data science [Internet]. Available from: https://r4ds.had.co.nz/explore-intro.html</ref>		<ref name="Life cycle"> Grolemund G, Wickham H. R for data science [Internet]. Available from: https://r4ds.had.co.nz/explore-intro.html</ref>
			<ref name="Data analysis"> https://online.hbs.edu/blog/post/types-of-data-analysis</ref>
	~~5. https://www.futurelearn.com/info/courses/data-analytics-for-business-basic-~~analysis~~-and-statistics/0/steps/177288~~

	6. https://online.hbs.edu/blog/post/types-of-data-analysis

	7. https://www.dataversity.net/fundamentals-of-cognitiveanalytics		7. https://www.dataversity.net/fundamentals-of-cognitiveanalytics

S213235 at 17:47, 4 May 2023

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	*potential improvements		*potential improvements

	Likewise, it is required to conclude on the best approach and method to present useful insights in the future.		Likewise, it is required to conclude on the best approach and method to present useful insights in the future.<ref name="Life cycle" />

	== Analytics categorization ==		== Analytics categorization ==
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	<ref name="PM Analytics">Harjit Singh, 2015. Project Management Analytics: A Data-Driven Approach to Making Rational and Effective Project Decisions</ref>		<ref name="PM Analytics">Harjit Singh, 2015. Project Management Analytics: A Data-Driven Approach to Making Rational and Effective Project Decisions</ref>

	<ref name="Podcast">Cornelius Fichtner, The Project Management Podcast</ref>		<ref name="Podcast">Cornelius Fichtner, The Project Management Podcast</ref>
			<ref name="Life cycle"> Grolemund G, Wickham H. R for data science [Internet]. Available from: https://r4ds.had.co.nz/explore-intro.html</ref>
	4. https://~~www~~.~~futurelearn~~.~~com~~/~~info/courses/data~~-~~analytics-for-business-basic-analysis-and-statistics/0/steps~~/~~177288~~

	5. https://www.futurelearn.com/info/courses/data-analytics-for-business-basic-analysis-and-statistics/0/steps/177288		5. https://www.futurelearn.com/info/courses/data-analytics-for-business-basic-analysis-and-statistics/0/steps/177288

S213235 at 17:40, 4 May 2023

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	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.		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.		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.<ref name="Podcast">		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.<ref name="Podcast" />

S213235 at 17:39, 4 May 2023

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	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.		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.		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.<ref name="~~PM Analytics~~">		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.<ref name="Podcast">


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	<ref name="PM Analytics">Harjit Singh, 2015. Project Management Analytics: A Data-Driven Approach to Making Rational and Effective Project Decisions</ref>		<ref name="PM Analytics">Harjit Singh, 2015. Project Management Analytics: A Data-Driven Approach to Making Rational and Effective Project Decisions</ref>

	<ref name="~~PM Analytics~~">Cornelius Fichtner, The Project Management Podcast</ref>		<ref name="Podcast">Cornelius Fichtner, The Project Management Podcast</ref>

	4. https://www.futurelearn.com/info/courses/data-analytics-for-business-basic-analysis-and-statistics/0/steps/177288		4. https://www.futurelearn.com/info/courses/data-analytics-for-business-basic-analysis-and-statistics/0/steps/177288

S213235 at 17:38, 4 May 2023

2023-05-04T17:38:43Z

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	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.		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.		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.		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.<ref name="PM Analytics">


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	<ref name="PM Analytics">Harjit Singh, 2015. Project Management Analytics: A Data-Driven Approach to Making Rational and Effective Project Decisions</ref>		<ref name="PM Analytics">Harjit Singh, 2015. Project Management Analytics: A Data-Driven Approach to Making Rational and Effective Project Decisions</ref>

	3. Cornelius Fichtner, The Project Management Podcast		<ref name="PM Analytics">Cornelius Fichtner, The Project Management Podcast</ref>

	4. https://www.futurelearn.com/info/courses/data-analytics-for-business-basic-analysis-and-statistics/0/steps/177288		4. https://www.futurelearn.com/info/courses/data-analytics-for-business-basic-analysis-and-statistics/0/steps/177288