It's a known fact that everything generates some form of data. It's also common knowledge that the usefulness of the data depends on the purpose behind it's collection. Whether it's finding out information about someone or something, such details can carry a multitude of connotations. Thus, the need for analytics. Such services can confirm or negate suppositions. And in the case of actionable analytics, they show companies possible steps to take.
When a company utilizes analytics they usually do so with the best intentions in mind. Regardless, endeavors can be beneficial, detrimental or both depending on the direction the company chooses to take. Actionable analytics assists with this decision by employing meaningful patterns which are generated by the data. These patterns will give some clue as to what's working, what's not, target customers/audience and so on.
How does one get to the point of action? It starts with the data itself and how it's collected. Obviously the type of data makes a difference. Equally important to the process, if not more so, are semantic technologies. These services are responsible for the interpretation of the data and act to bring patterns to the surface. Some of the most important pieces of semantic technology revolve around auto recognition of topics, concepts and information/meaning extraction and categorization. In addition, they aid in clarifying more relevant and actionable responses.
Then there are web technologies. These technologies are more specific to providing definitions and relating different kinds of data on the web and internally. It's a combination of standards like RDF (Resource Description Framework, OWL (Object Windows Library), SPARQL (SPARQL Protocol and RDF Query language), RIF (Rule Interchange Format), and RDFa (Resource Description Framework-attribute). These standards refer to things like data models and languages that aid in uncovering concepts and any relationships between the data. There are also various languages that constitute the queries, rules and even markup while on a web page.
Additional puzzle pieces include the elements of transparency and invisibility. Invisible but still transparent? Such terms may seem to juxtapose each other, but the combination actually makes quite a bit of sense and is ideal for most users. Picture having semantics and analytics running in the background. That's the invisible piece. As for transparency, that comes from having a clear understanding of how solutions are reached. Think step-by-step process. After all, having clear insight is a key part of analytics.
While that's all well and good, what does it mean? As stated earlier, a lot has to do with how one responds to the data. Whether one is looking for a rise or fall in the data, negative and positive reactions do make a difference. The meaning constitutes the response.