Analytics can be both traditional and advanced. There is a fundamental difference between traditional and advanced analytics. In the healthcare industry, this difference is prominent in the continued relevance of Advanced Healthcare Analytics. The process followed to design and solve analytical problems is more complicated and multi-layered in advanced analytics. For example, in traditional analytics, the analysis is typically built to be repeatable and templates are built and made available for use. Thus, the types of information analyzed and the format in which the information is presented is predefined.

Advanced Healthcare Analytics has become more mainstream in recent times and it uses an approach of asking questions and expressing doubt before diving into a set of data for analysis. This way, the data can be interpreted in different ways and the end or receiving part of the information, collect what they want from it. In advanced healthcare analytics, software providers provide a friendly interface that enables people of various backgrounds that are not necessarily in IT to be able to access and utilize data to answer the questions they have. The user-friendly software provides suggestions and techniques that guide the user in selecting and processing relevant information from multiple resources

Advanced Analytics in Other Industries

Besides advanced healthcare analytics, advanced analytics is also present in other industries. It is used quite a lot and you’ve probably used it at some point even if you didn’t know you were using advanced analytics at that time. Let’s discuss some industries that use consumer-facing advanced analytics.

Advanced Analytics in the Online Industry

You can find advanced analytics in almost every corner of the internet. One major place you’re probably familiar with is Google Search. Google search uses advanced analytics both at the user interface and the backend with business and website owners to sort through data and provide you information whenever you search for something or post something on the web. A huge part of this process is called semantic search. Semantic search is the study of words and their meaning or logic. In the internet, Google and other search engines, use semantic search to improve search result accuracy by trying to understand what a person is searching through contextual meaning. Semantic search uses, synonyms, copy matching, natural language processing, and algorithms. With this tools, semantic search can provide an advanced analytics meaning of a user or searcher’s intent and with this, it can deliver more personalized answers and results to a search query.

Semantic search provides a plethora of advantages. Providing more relevant data means less spam and maximizes users experience. With Big data growing ever so rapidly, and world data doubling every two years, being able to source for relevant data has become a necessity. The process of organizing, structuring, and semantically connecting data has become more important than ever.

Google has advanced engines and systems like latent semantic indexing (LSI), latent Dirichlet allocation (LDA), and term frequency-inverse document frequency (TF-IDF) that use weighing schemes and predetermined weighted relationships to determine quality. Put simply, these robots, through their use and understanding of natural language processing, know what words usually go together and so they can filter out spam because spam is usually a stuffing of words that have no meaning and are not natural to read. Semantic search and advanced analytics are used as a weapon in the war against spam.

Advanced Analytics in the Auto-Industry

Advanced analytics is used in the creation of driverless cars. There have been attempts to create driverless cars since the 70s but it wasn’t very successful. The current development of small supercomputers, AI, super GPS navigation systems and powerful computer ships, self-driving cars are becoming more of a reality than it is science fiction.

The recognized leader in driverless cars used to be Google but now it’s hard to say as Tesla and Apple are also struggling to lead that space. These companies have invested millions of dollars in research and development and in inventing new technology that would help them make autonomous self-driving cars. Both Tesla and Google have tested their driverless cars on roads and are nearing the point where they can start to commercialize these self-driving cars. In October 2016, Tesla announced that all new Tesla vehicles will come equipped with all the hardware needed for fully-automated driving. Tesla which is known to lead in innovation is quite comfortable that their technology is ready to be shared with the public. Before this is done, they would have to go through some regulatory tests and pass all the approval processes needed. Another company that’s also entering this space is  Uber. Uber has announced plans to have a fleet of fully automated Volvo XC90 SUVs on the road by 2021. Already deep into the R&D stage, Uber is convinced that the Uber driver will become extinct in Pittsburg, where it plans to begin testing sooner rather than later. Talk about replacing humans with machine huh…

Now the big companies, the so-called Unicorn companies might be jumping head first into the driverless car technology but other companies prefer to ease into it. These manufacturers are entering the space by introducing features that can assist you when driving without necessarily taking the wheel. Some of these features are Automatic braking, collision avoidance systems, pedestrian and cyclists alerts, cross-traffic alerts, and intelligent cruise controls etc. These driver-assist features are powered by AI.