The Artificial Intelligence Guide for Communications Teams
Data science is playing a growing role within marketing and corporate communications departments. From mouse clicks, to social media and customer information, data is now central to the functions of both departments. Yet a distinct disconnect currently exists between the terminology used by data analysts and marketing/communications professionals.
These different lexicons can hinder collaboration and communication between the two teams and result in suboptimal outcomes. To help solve this problem, Zignal curated a list of the most common phrases associated with Artificial Intelligence and simplified in a language understood by today’s marketing and communications professionals. By breaking down this linguistic barrier, communications and marketing teams can better align their respective workflows with data and predictive-based analytics (aka Artificial Intelligence).
Big Data — extremely large data sets that can be analyzed by computers to reveal patterns, trends and associations. This dataset could take the form of CRM data or media data.
Machine Learning — a type of statistics that provides computers with the ability to learn without being explicitly programmed. This type of algorithm could be used to identify trends in datasets.
Artificial Intelligence — the attempt to understand high level human cognition in terms of things like Natural Language Processing, puzzle solving, cognitive tasks like game playing.
Neural Networks — a computer algorithm modelled on the human brain. Neural nets are arranged in layers, where each layer can communicate with the ones directly in front and behind. The algorithm has an input layer and an output layer, while the layers in between (where the calculations take place) are called the hidden layers. These types of algorithms are used to identify faces in an image.
Deep Learning — neural networks with many hidden layers. Convolutional neural networks are the most commonly used type
Intelligence Augmentation — A tool that allows humans to perform a task better than they could on their own. In terms of Artificial Intelligence, AI allows humans to understand massive datasets in seconds.
Natural Language Processing — a field of Artificial Intelligence concerned with the interactions between computers and human languages, placing an emphasis on interpreting language. These types of algorithms are used so computers can interpret any article or post on social media.
Media and Social Intelligence — detailed insights derived from the massive quantities of information that exist across all forms of earned media, such as the key topics around a brand and the sentiment associated with those topics.
Descriptive analytics — details what has already happened/is happening through analyzing very large data sets.
Diagnostic Analytics — context supplied to descriptive data. This could be things like the sentiment in a post, the topic being discussed or the catalyst for the peak in mentions.
Predictive Analytics — analyzes trends found in past data to predict future outcomes. These types of analytics could be used to predict how big a story will become.
Prescriptive Analytics — through analyzing data to see what happened in similar situations in the past, Machine Learning can recommend a course of action.
Recommend — actions that are recommended by analyzing previous outcomes.
Peak Detection — identifying the timeframes when the volume of data is abnormally high. This could be identifying when a story has reached its’ peak.
LDA (Latent Dirichlet Allocation) Cluster — the key cluster of words in a set of posts, stories or social conversations that are concentrated about single topic.
Structured Data — information that has a high level of organization. When information is highly structured and predictable, traditional software can easily (and cheaply) organize and display data. A good example of structured data would be a CRM system.
Unstructured Data — information that is not organized in a predefined manner. Unstructured data has traditionally been text heavy, but now includes images and videos. A good example of unstructured data is videos on Youtube.
If you would like to learn more about how marketing and communications professionals can leverage Artificial Intelligence, download our White Paper.