As contractors plan to begin using data analytics to arrive at meaningful insights and customized intelligence, they usually run across a few unfamiliar terms. We’ve provided this glossary for you to learn from and refer to.

A

Algorithm
Basically a set of instructions for running computations and solving problems. Construction data analytics engines plug contractors’ existing data into algorithms that provide new insight into project and organizational performance; predictive algorithms give contractors a better sense of what to expect when they game-plan future scenarios.

API
Application Programming Interfaces (APIs) allow different applications to talk to each other. Data analytics platforms can use APIs to break down the silos that separate contractors’ different mobile and other apps.

Artificial Intelligence (A.I.)

A machine’s ability to make decisions and perform tasks that simulate human intelligence and behavior. Across engineering and construction, there’s a growing focus on tech solutions that incorporate A.I.-powered algorithms. (Related: McKinsey & Co. report)

B

Big Data
As contractors continue to embrace technology, they are generating huge amounts of data that can be stored and analyzed to give them a competitive edge. The construction industry’s use of big data will only accelerate into the future (Related: How Big Data & Analytics are Transforming the Construction Industry)

C

Cloud Computing
Construction contractors increasingly rely on the Internet (aka “the cloud”) to connect their teams in the field and the office to servers, storage, databases, networking, software, analytics and business intelligence. That may sound abstract, but it’s the difference between getting an answer now on your tablet versus criss-crossing job sites over and over to get the same type of information. Proven benefits include lower operating costs and greater efficiency. (Related: “The Untold Benefits of Cloud Computing in the Construction Industry”)

Cognitive Process Automation
Cognitive automation leverages different algorithms and technology approaches such as natural language processing, text analytics and data mining, semantic technology and machine learning.

D

Data Analytics
The science of analyzing raw data in order to draw conclusions about that information. Contractors are now leveraging many of the techniques and processes of data analytics to uncover trends and clarify metrics in ways that yield a competitive edge.

Database
An organized collection of data, generally stored and accessed electronically from a computer system.

Data Cleansing
A process of revising data to remove incorrect spellings, eliminate duplicate entries and add missing data. It’s an important step for contractors: incorrect data can lead to bad analysis and wrong conclusions. (Related: Keeping It Clean: Analytics Can Benefit Contractors–So Long As They Maintain Good Data Hygiene).

Data Collection
In the construction context, the systematic gathering and measuring of information from sources in the office and the field. The goal is to get a complete and accurate picture of labor, costs, equipment, safety and much more. Everytime a controller updates an invoice for a project, or a crew member fills out a safety checklist on an app, that’s data-collection.

Data Hygiene
Processes conducted to ensure that contractors’ data is as error-free as possible. That means eliminating duplicate records, incomplete or outdated data, and the improper parsing of record fields from disparate systems. (Related: Keeping It Clean: Analytics Can Benefit Contractors–So Long As They Maintain Good Data Hygiene).

Data Lake Collections
A storage warehouse that holds a significant amount of raw data in its original format until it is required.  Contracting firms can use data lakes to run queries, and tools like ProNovos can examine smaller data sets to answer a vast array of business questions.

Data Modeling
Modeling is all about turning data into predictive and actionable information. Building models that can predict and explain outcomes is a key part of construction data analytics.

Data Science
At its essence, data science is a field that works with and analyzes large amounts of data to provide meaningful information that can be used to make decisions and solve problems. The work, which is increasingly important for contractors, includes computation, statistics, analytics, data mining and programming.

Data Set
Quite simply, a collection of data, particularly one that is specifically structured. In the case of construction contractors, a data set could be from a particular project or the entire accounts receivable for the year. Data sets can be small and simple to work with or large and complex.

Data Visualization
To make data more easily digestible by rendering it in a visual context, construction data analytics platforms automatically generate all kinds of visualizations, from basic charts and graphs to complex “heat maps.” (Related: “Nine Data Visualization Tools”).

Data Warehouse
Essentially, the central place where contractors flow their data from the gamut of project and organizational sources.

Data Wrangling
The process of formatting or restructuring raw data to suit specific needs or increase its decision-making power (sometimes referred to as data munging).

Decision Tree
A tool that data scientists use to visually lay out decisions and decision making. Widely used in data mining and machine learning.

Descriptive Analytics
What happened? When contractors have a better grasp of historical data, they can see important changes with greater clarity. Descriptive analytics is essentially the process of using a platform like ProNovos to draw such comparisons. See ProNovos’ Key Performance Indicators page for a sense of this. [Link]

Diagnostic Analytics
Why did it happen? Referred to as root-cause analysis, this includes using processes such as data discovery, data mining and drill down and drill through.

E

Extraction, Transformation and Loading (ETL)
Three database functions that are combined into one tool to pull data out of one database and place it into another database.

I

Internet of Things (IoT)
A system of interconnected devices provided with unique identifiers  and the ability to transfer data over a network without requiring people to manually do the work. (Related: “How IoT Sensors are Building Smart Jobsite Ecosystems”)

M

Machine Learning
The computational process wherein a machine “learns” and adjusts its behaviors based on feedback from data. Usually manifesting as an adaptable algorithm, machine learning helps computers predict outcomes without explicit human input.

N

Natural Language Analytics (NLP)
A field of Artificial Intelligence that gives machines the ability to read, understand and derive meaning from human languages.

P

Predictive Analytics
What will happen? Aimed at making predictions about future outcomes based on historical data and analytics techniques such as statistical modeling and machine learning. With the help of sophisticated predictive analytics tools and models, construction contractors can use past and current data to reliably forecast trends.

Prescriptive Analytics
What should we do? Factors information about possible situations or scenarios, available resources, past performance, and current performance, and suggests a course of action or strategy. ProNovos contractors use prescriptive analytics to make decisions on any time horizon, from immediate to long term.

S

Structure Query Language
SQL is a standard programming language that is used to retrieve and manage data in a relational database. This language is very useful to create and query relational databases. It’s how ProNovos connects so seamlessly with Foundation and other construction-specific software tools.

U

Unstructured Data
Any data that does not fit a predefined data model. Often this data does not fit into the typical row-column structure of a database. Data generated from conversations, declarations or even tweets are examples of unstructured data.

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