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Last edited 30 Sep 2022
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.
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 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 can lead to the attainment of artificial intelligence (AI).
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.
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
- 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?
- Predictive analytics.
- Top big data tools used to store and analyze data.
- Treating data as part of infrastructure
- Virtual reality and big data disrupting digital construction.
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