The Big Data Lingo Every Marketer Should Know
Entering the world of big data can be daunting. With all the complex jargon, making sense of the terminology is a feat in itself, not to mention making sense of the actual data. Before even finding a big data management software, you need to have a strong grasp of related terminologies, so you can fully understand all of the features each product is offering Here are some of the more commonplace big data terms that you need to know before delving into the wide world of big data.
Artificial Intelligence (AI): The development of software applications that are capable of conducting tasks independently of human intervention.
Behavioral analytics: Data pertaining to the behavior and habits of people, used to identify relevant trends and predict their future intentions.
Cloud computing: A method of computing that relies on Internet-based data storage, as opposed to on-premises data storage.
Data aggregation: The process of collecting and compiling data from a variety of sources for analysis.
Data cleansing: The process of ridding a database of incomplete, outdated, duplicate, or incorrect datasets.
Data silos: A phenomenon that occurs when business data becomes isolated within separate departments, thus showing only a portion of the overall picture.
Dirty data: Any data that is incomplete, improperly formatted, outdated, duplicated, or incorrect.
Exploratory analysis: The process of identifying patterns within the data without relying on standard methodologies. It is used to identify the primary characteristics of the data.
Failover: The automatic process of switching to an alternate server when one server fails.
Grid computing: The process of relying on a multitude of different computer systems from a variety of locations, all linked via the cloud.
Internet of Things (IoT): When devices like cellphones and tablets can connect and interact with everyday objects via the Internet.
Marketing automation software: A software application that offers a cloud-based data management platform, built-in automation features, and a variety of analytic tools to improve data analysis.
Metadata: A brief summary of the data, providing its most basic attributes, so it’s easier to organize. For example, the metadata of an article might include the author’s name, date of publication, and the file size.
Pattern recognition: When algorithms identify recurring patterns within large sets of data. It’s sometimes referred to as data mining.
Predictive analytics: Using data to identify trends and predict future trends.
Software-as-a-Service (SaaS): When software providers offer a cloud-based software application, as opposed to software that’s managed on-premises.
Structured data: Structured data refers to all data that can be easily organized within tables and graphs to see how it pertains to other data.
Unstructured data: Unstructured data refers to data that can’t easily be formatted into tables or graphs, such as emails and social media posts.
The world of big data can seem overwhelming at first. Newbies often feel bombarded with complex jargon, which can make it challenging to find the best software applications. However, by understanding some of the more common terms, you’ll be able to compare and contrast big data software applications easily, enabling you to make an informed decision about the software you invest in.
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