by Angela Guess
Hannah Augur recently put together a guide in Dataconomy of some of the most basic business data terminology for non-tech execs and others new to the wide world of data. Her guide begins with “Algorithms: Mathematical formulas or statistical processes used to analyze data. These are used in software to process and analyze any input data. Analytics: The process of drawing conclusions based on raw information. Through analysis, otherwise meaningless data and numbers can be transformed into something useful. The focus here is on inference rather than big software systems. Perhaps that’s why data analysts are often well-versed in the art of story-telling.”
She goes on, “There are three main types of analytics in data, and they appear in the following order: Descriptive Analytics: Condensing big numbers into smaller pieces of information. This is similar to summarizing the data story. Rather than listing every single number and detail, there is a general thrust and narrative. Predictive Analytics: Studying recent and historical data, analysts are now able to make predictions about the future. It is hardly 100% accurate, but it provides insight as to what will most likely happen next. This process often involves data mining, machine learning and statistics. Prescriptive Analytics: Finally, having a solid prediction for the future, analysts can prescribe a course of action. This turns data into action and leads to real-world decisions.”
Augur’s list continues, “Cloud: It’s available any and everywhere. Cloud computing simply means storing or accessing data (programs, files, data) over the internet instead of a hard drive. DaaS: Data-as-a-service treats data as a product. DaaS providers use the cloud to give on-demand access of data to customers. This allows companies to get high quality data quickly. DaaS has been a popular word in 2015, and is playing a major role in marketing. Data Mining: Data miners explore large sets of data to find patterns and insight. This is a highly analytical process that emphasizes making use of large datasets. This process could likely involve artificial intelligence, machine learning or statistics.”
Photo credit: Flickr