Data analytics - Revision history

Designing Buildings at 13:53, 15 August 2024

2024-08-15T13:53:11Z

← Older revision		Revision as of 13:53, 15 August 2024
Line 43:		Line 43:
	* Data manager.		* Data manager.
	* How data can stop waste.		* How data can stop waste.
		+	* Interoperability.
	* Internet of things.		* Internet of things.
	* Making the most of big data.		* Making the most of big data.
	* Open data - how can it aid the development of the construction industry?		* Open data - how can it aid the development of the construction industry?
	* Predictive analytics.		* Predictive analytics.
		+	* Procurement analytics.
	* Top big data tools used to store and analyze data.		* Top big data tools used to store and analyze data.
	* Treating data as part of infrastructure		* Treating data as part of infrastructure

Designing Buildings at 07:52, 30 September 2022

2022-09-30T07:52:57Z

← Older revision		Revision as of 07:52, 30 September 2022
Line 26:		Line 26:
	[[File:The_construction_knowledge_gap.gif\|link=File:The_construction_knowledge_gap.gif]]		[[File:The_construction_knowledge_gap.gif\|link=File:The_construction_knowledge_gap.gif]]

−	= Related articles on Designing Buildings ~~Wiki~~ =	+	NB [https://www.pwc.com/gx/en/services/consulting/digital-operations/digital-procurement-survey.html PwC Global, Digital procurement survey 2022], defines Data analytics as: ‘…the science of analyzing raw data to make conclusions about that information.’
		+
		+	= Related articles on Designing Buildings =

	* Asset information model.		* Asset information model.

Editor at 19:04, 8 October 2020

2020-10-08T19:04:07Z

← Older revision		Revision as of 19:04, 8 October 2020
Line 44:		Line 44:
	* Making the most of big data.		* Making the most of big data.
	* Open data - how can it aid the development of the construction industry?		* Open data - how can it aid the development of the construction industry?
		+	* Predictive analytics.
	* Top big data tools used to store and analyze data.		* Top big data tools used to store and analyze data.
	* Treating data as part of infrastructure		* Treating data as part of infrastructure
	* Virtual reality and big data disrupting digital construction.		* Virtual reality and big data disrupting digital construction.

−	[[Category:Client_procedures]] [[Category:Construction_management]] [[Category:Construction_techniques]] [[Category:Cost_/_business_planning]]	+	[[Category:DCN_Definition]] [[Category:DCN_Research,_Development_and_Innovation]] [[Category:Client_procedures]] [[Category:Construction_management]] [[Category:Construction_techniques]] [[Category:Cost_/_business_planning]]

Editor at 18:51, 10 August 2020

2020-08-10T18:51:54Z

← Older revision		Revision as of 18:51, 10 August 2020
Line 24:		Line 24:
	Analyics were used by Designing Buildings Wiki to assess the relationships between subjects on the website, and the difference between what authors write about and what users read about. For more information see: Fit for purpose - Big data reveals the construction knowledge gap		Analyics were used by Designing Buildings Wiki to assess the relationships between subjects on the website, and the difference between what authors write about and what users read about. For more information see: Fit for purpose - Big data reveals the construction knowledge gap

−	[[File:~~The construction knowledge gap~~.gif]]	+	[[File:The_construction_knowledge_gap.gif\|link=File:The_construction_knowledge_gap.gif]]

	= Related articles on Designing Buildings Wiki =		= Related articles on Designing Buildings Wiki =
Line 31:		Line 31:
	* Big data.		* Big data.
	* Building data exchange.		* Building data exchange.
		+	* Clustering.
	* Common data environment.		* Common data environment.
	* Data-centric business model.		* Data-centric business model.

Designing Buildings at 08:32, 31 December 2019

2019-12-31T08:32:37Z

← Older revision		Revision as of 08:32, 31 December 2019
Line 1:		Line 1:
	{\|		{\|
−	\| [[File:~~Digital tech~~.jpg]]	+	\| [[File:Digital_tech.jpg\|link=File:Digital_tech.jpg]]
	\|}		\|}

−	Analytics is a method which uses logical analysis to interpret large ~~streams~~ of data ~~so as~~ to ~~assist in better~~ prediction and decision making. The analysis of data may include discerning ~~any~~ trends and patterns, their interpretation and communication. Analytics is therefore the link between the data and making informed decisions. Organisations can use ~~data~~ analytics to gain a predictive intelligence that can help shape their future plans.	+	Analytics is a method which uses logical analysis to interpret large quantities of data to help with prediction and decision making. The analysis of data may include discerning trends and patterns, their interpretation and communication. Analytics is therefore the link between the data and making informed decisions. Organisations can use analytics to gain a predictive intelligence that can help shape their future plans.

−	Data used for analytics can be structured ~~and~~ unstructured. Structured data ~~is data that can~~ be formatted into a table; unstructured data ~~is anything that~~ cannot be tabulated e.g ~~JSON*, videos, pictures,~~ documents, hearsay ~~etc~~.	+	Data used for analytics can be structured or unstructured. Structured data, for example, might be formatted into a table; whereas unstructured data cannot be tabulated e.g some documents or hearsay.

−	~~(*JSON - A~~ data ~~interchange format comprising text that is readable by humans).~~	+	There are two main types of data:

−	~~=== There are two main types of data: ===~~	+	* Descriptive data – this is data which describes things in the past e.g customer records, past performance, purchase history etc.
−		+
−	* Descriptive data – this is data which describes things in the past e.g customer records, past performance, purchase history etc	+
	* Predictive data – this is closely linked to machine learning and looks at the future, and how to make predictions based on past events.		* Predictive data – this is closely linked to machine learning and looks at the future, and how to make predictions based on past events.

−	Once ~~the~~ descriptive data has been gathered, it is processed by algorithms (mathematical formulas or models) to create a ~~machine-learning~~ model that identifies relationships in variables existing in the data and allows predictions to be made. Uncertain data for which the answers are not known e.g the type of goods that certain people in a geographical area might be interested in, is then fed into the model which subsequently outputs what their preferences might be. This can then be reported or communicated in various formats ~~e.g data visualisation,~~ tables, bar and line charts, etc.	+	Once descriptive data has been gathered, it can be processed by algorithms (mathematical formulas or models) to create a model that identifies relationships in variables existing in the data and allows predictions to be made. Uncertain data for which the answers are not known e.g the type of goods that certain people in a geographical area might be interested in, can then be fed into the model which subsequently outputs what their preferences might be. This can then be reported or communicated in various formats such as tables, bar and line charts, etc.

−	The important result of the process is the creation of the model. Once established, data can be inputted to produce a prediction. This is the basis of machine learning which ~~leads~~ to the attainment of artificial intelligence (AI)~~. AI can be thought of as a collection of models produced by machine learning.~~	+	The important result of the process is the creation of the model. Once established, data can be inputted to produce a prediction. This is the basis of machine learning which can lead to the attainment of artificial intelligence (AI).
−		+
−	Data analytics can be used for digital management to enable construction firms to win projects and deliver them more efficiently. This is particularly apposite for large capital projects to allow owners to challenge trends in low-performance. Given there may be hundreds of RFIs and change orders on a large project, the use of data analytics can enable teams to get a better understanding of project performance, root causes and to prioritise daily activities.	+

	Data analytics is useful in all walks of life but particularly in marketing where possible future consumer preferences can be predicted and therefore accommodated in campaigns. It can blend into performance analytics which may help a company measure its progress toward specific goals and to determine which actions will help achieve them.		Data analytics is useful in all walks of life but particularly in marketing where possible future consumer preferences can be predicted and therefore accommodated in campaigns. It can blend into performance analytics which may help a company measure its progress toward specific goals and to determine which actions will help achieve them.
		+
		+	Data analytics can be used for digital management to help construction firms to win projects and deliver them more efficiently. This is particularly apposite for large capital projects, for example, providing analysis to challenge trends in low-performance, getting a better understanding of project performance, root causes and prioritising daily activities.
		+
		+	Analytics will become increasingly important with the growth of big data, the internet of things and the use of sensors in the built environment.
		+
		+	Analyics were used by Designing Buildings Wiki to assess the relationships between subjects on the website, and the difference between what authors write about and what users read about. For more information see: Fit for purpose - Big data reveals the construction knowledge gap
		+
		+	[[File:The construction knowledge gap.gif]]

	= Related articles on Designing Buildings Wiki =		= Related articles on Designing Buildings Wiki =

Editor at 18:50, 18 December 2019

2019-12-18T18:50:37Z

← Older revision		Revision as of 18:50, 18 December 2019
Line 1:		Line 1:
		+	{\|
		+	\| [[File:Digital tech.jpg]]
		+	\|}
		+
	Analytics is a method which uses logical analysis to interpret large streams of data so as to assist in better prediction and decision making. The analysis of data may include discerning any trends and patterns, their interpretation and communication. Analytics is therefore the link between the data and making informed decisions. Organisations can use data analytics to gain a predictive intelligence that can help shape their future plans.		Analytics is a method which uses logical analysis to interpret large streams of data so as to assist in better prediction and decision making. The analysis of data may include discerning any trends and patterns, their interpretation and communication. Analytics is therefore the link between the data and making informed decisions. Organisations can use data analytics to gain a predictive intelligence that can help shape their future plans.

Editor: Created page with "Analytics is a method which uses logical analysis to interpret large streams of data so as to assist in better prediction and decision making. The analysis of data may include di..."

2019-12-18T18:45:44Z

Created page with "Analytics is a method which uses logical analysis to interpret large streams of data so as to assist in better prediction and decision making. The analysis of data may include di..."

New page

Analytics is a method which uses logical analysis to interpret large streams of data so as to assist in better prediction and decision making. The analysis of data may include discerning any trends and patterns, their interpretation and communication. Analytics is therefore the link between the data and making informed decisions. Organisations can use data analytics to gain a predictive intelligence that can help shape their future plans.

Data used for analytics can be structured and unstructured. Structured data is data that can be formatted into a table; unstructured data is anything that cannot be tabulated e.g JSON*, videos, pictures, documents, hearsay etc.

(*JSON - A data interchange format comprising text that is readable by humans).

=== There are two main types of data: ===

* Descriptive data – this is data which describes things in the past e.g customer records, past performance, purchase history etc
* Predictive data – this is closely linked to machine learning and looks at the future, and how to make predictions based on past events.

Once the descriptive data has been gathered, it is processed by algorithms (mathematical formulas or models) to create a machine-learning model that identifies relationships in variables existing in the data and allows predictions to be made. Uncertain data for which the answers are not known e.g the type of goods that certain people in a geographical area might be interested in, is then fed into the model which subsequently outputs what their preferences might be. This can then be reported or communicated in various formats e.g data visualisation, tables, bar and line charts, etc.

The important result of the process is the creation of the model. Once established, data can be inputted to produce a prediction. This is the basis of machine learning which leads to the attainment of artificial intelligence (AI). AI can be thought of as a collection of models produced by machine learning.

Data analytics can be used for digital management to enable construction firms to win projects and deliver them more efficiently. This is particularly apposite for large capital projects to allow owners to challenge trends in low-performance. Given there may be hundreds of RFIs and change orders on a large project, the use of data analytics can enable teams to get a better understanding of project performance, root causes and to prioritise daily activities.

Data analytics is useful in all walks of life but particularly in marketing where possible future consumer preferences can be predicted and therefore accommodated in campaigns. It can blend into performance analytics which may help a company measure its progress toward specific goals and to determine which actions will help achieve them.

= Related articles on Designing Buildings Wiki =

* Asset information model.
* Big data.
* Building data exchange.
* Common data environment.
* Data-centric business model.
* Data-driven mobility.
* Data and behaviours in construction.
* Data and infrastructure productivity.
* Data collection strategies.
* Data drop.
* Data manager.
* How data can stop waste.
* Internet of things.
* Making the most of big data.
* Open data - how can it aid the development of the construction industry?
* Top big data tools used to store and analyze data.
* Treating data as part of infrastructure
* Virtual reality and big data disrupting digital construction.

[[Category:Client_procedures]] [[Category:Construction_management]] [[Category:Construction_techniques]] [[Category:Cost_/_business_planning]]