Top 5 Data Driven Design news this week.

Welcome to the Association of Data-Driven Design roundup. We aim to share a summary of current information and news on Data-Driven Design practices within the construction industry. Please get in touch if you have any ideas, suggestions or have something to share.

This week we are going to focus on Data and Data Science, what sources of data are valuable for construction, an introduction.

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1. Exploratory Data Analysis

This page will show you how to use visualisation and transformation to explore your data in a systematic way, a task that statisticians call exploratory data analysis, or EDA for short. EDA is an iterative cycle. You:

1. Generate questions about your data.
2. Search for answers by visualising, transforming, and modelling your data.
3. Use what you learn to refine your questions and/or generate new questions.

https://r4ds.had.co.nz/exploratory-data-analysis.html

2. 7 Ways Construction Contractors Can Leverage the Power of Big Data

Today’s real-time collaborative technologies are mining large data repositories to get game-changing benefits from keeping all project decision makers on the same page.

https://www.forconstructionpros.com/construction-technology/article/21271680/7-ways-construction-contractors-can-leverage-the-power-of-big-data

3. Structured vs. Unstructured Data: What’s the Difference?

A look into structured and unstructured data, their key differences and which form best meets your needs.

https://www.ibm.com/cloud/blog/structured-vs-unstructured-data

4. What is data quality?

Public sector organisations need the right data in order to run good services, make the right decisions, and create effective policies. Data takes many forms including budget figures, employee data, survey responses, and data gathered in digital services.

https://www.gov.uk/government/news/what-is-data-quality

5. What is Exploratory Data Analysis (EDA)?

Exploratory Data Analysis (EDA) is an approach/philosophy for data analysis that employs a variety of techniques (mostly graphical) to

1. maximize insight into a data set;
2. uncover underlying structure;
3. extract important variables;
4. detect outliers and anomalies;
5. test underlying assumptions;
6. develop parsimonious models; and
7. determine optimal factor settings.

https://www.itl.nist.gov/div898/handbook/eda/section1/eda11.htm

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