Internet of Things is the mighty next leap the world is witnessing and it is bringing a lot of other requirements in its wake. Smart innovation in the field of Internet of Things has propelled the need of other features like context based security and smart analytics. Since, these systems rely heavily on the embedded systems procuring inputs from a multitude of sensors, the repository gets glutted with data in a very short time period. What is required is to extract only the relevant information and then present it to the user. Here is where advanced, pervasive and invisible analytics step in.

The Requirement

When the topic of Internet of Things is brought to discussion, it is put to heed that every app within the system must be designed with an analytical point of view to prevent the glut of data. The traditional convention of data analysis has undergone a drastic change that requires data to be collected, analyzed and then the action to be taken. Data analysis is no more a single streamed process as it once was, rather now it needs to be categorized and evaluated separately to garner only the required clues needed for processing an action. Analytic intelligence is boasted to an extent with Big Data analytics but it adheres itself to data rather than the processing while the current need is to   shift away from data sets and filter massive amounts of data before processing.

Factors Driving the Invisible Analytics

Real time system applications have posed a serious challenge for data analysts to make sense out of enormous input clues. This has given birth to the necessity to facilitate each system to invisibly embed the analytical algorithms to provide minimal data for external processing, moreover, the data needs to be insightful to maintain the accuracy and precision level.  With the concept made clear that Big Data is no more the point of concern more emphasis is being paid on analytics like text analytics, predictive analytics, machine learning and natural language processing. Invisible analytics and pervasive techniques empower the rise of context aware systems that make smart decision on basis of physical clues and providing minimal data to burden analytics.

What to Expect from Pervasive Techniques?

Though the conventional business tools are apt enough in converting crude raw data into information that can further be utilized, they will no longer serve when it comes to real time systems and context aware machines. For this purpose we need pervasive techniques to fish out the useful contents and reduce the processing complexities steeply. Intelligent analytics invisibly embeds the analysis phase to data coverage streamlining the entire process and making it possible to material huge concepts such as Internet of Things and Context Aware Machines. Furthermore it adds additional charm to customer satisfaction by garnering data more quickly without compromising the quality, accuracy and precision which is the ultimate goal.

The Bottom Line

Advanced Analytics will provide the businesses a new level of growth opportunity by diminishing the processing load and generating space of novel customer activities, the focus still remaining on efficiency.