Maintaining a comprehensive method of search within an enterprise network ensures swift responses to queries throughout the company. Retrieving varying data files, information, and documentation provides a boost in productivity within the organization and drives increased customer experience from visitors to a website. When combined with cloud storage, powerful search tactics allow users with granted access the ability to locate files and information from the entire network rather than just one system or database. This is why cognitive search is so valuable and why it is important for searching within an enterprise. While varying search methods for large business networks have existed almost since the creation of internal office networks (which pre-date the Internet), these methods continue to evolve. Many companies have turned to enhancing former search applications, including enterprise search, with cognitive search. Before an expanding corporation implements a new search technology though, fully understanding what it is and how it can improve search is necessary.
The idea of cognitive search remains in its infancy. Due to this, a universally accepted definition does not exist as of yet. As the cognitive market comes together, more specifics will develop, but for now, each developer may bring in different variables. The Cognitive Computing Consortium is an organization designed to bring the industry together, but as has been the case in the emergency and development of any new technology, varying developers will always have their own specific ideas as to what should be included. (Search Technologies, 2017).
To fully understand what cognitive search is, breaking down the term itself is necessary. The word cognitive comes from the word cognition, which means the "the act or process of knowing; perception" (Dictionary.com, 2017). So cognitive search is in reference to not only processing knowledge, but using perception to make a search. The term perception itself means to become aware of something using senses. As a computer system does not have senses in the literal context (taste, touch, smell, etc.), a system utilizing cognitive search takes advantage of implemented, or artificial senses. Essentially, a cognitive form of search takes advantage of artificial intelligence, or A.I. (IBM Research, 2017).
When it comes to artificial intelligence, most conger up ideas of robots mimicking the movements and thought process of humans in futuristic, science fiction movies. While the implementation is different, the idea is the similar in search. The drive of AI is to mimic that of the human brain and to allow it to interact with the environment or process data based on local variables. Google Search has become a prime example of this. Google search now has the ability to process inquiries based on not only local variables but previous search patterns by a user. It allows for more accurate search results based on geography, and how someone has searched for items in the past. Essentially, it learns and adapts.
Cognitive search uses this form of A.I. to provide more in-depth search results based on local information, previous search history and other variables. These results are not only more in-depth, but more specific to an end user as the system learns how a person or system perform these searches. It is what makes the cognitive search method such a variable implementation into an enterprise's network search capability.
As is the case with any other form of technology, cognitive search did not simply happen overnight. It took a long road of small improvements and the evolution of previous forms of search to reach this point. Cognitive search is directly connected to the idea of machine learning, in which a computer system is able to process new information and change the way it reacts based on the newly obtained data. Cognitive methods of search do the same (this is where the difference between machine learning, AI and cognitive search begin to blur into one basic idea).
Machine learning is the combination of three important steps. Representation, evaluation and optimization. Representation is the language a computer system understands while evaluation allows a computer to decipher the language, which often comes in multiple parts and come up with an answer (similar to a complex equation). Optimization is what allows a system to identify the highest possible result. This is because when performing a search calculation, there isn't just one possible answer. In a closed network, there may be hundreds, if not tens of thousands of results. This requires both representation and evaluation of each of these results. Optimization allows the machine to identify the calculation with the highest outcome based on the information it processes, which in turn generates the recommended search result (Wired, 2017). Taking this a step further, the results may actually be specific or personalized to the user or person that is performing the search when taking all of the factors into place. As mentioned previously, geography and previous searches can have a dramatic effect on what is relevant for that specific user as the system gets more intelligent over time to attempt to get that specific person the best search results.
Nearly all search methods in use today are, in some shape or form, based off of Google. Google did not create search engines. The Google search engine came out in 1996, while others had existed for several years prior to this. However, Google functions differently than all other engines in the early years. Initial search engines looked for exact keyword matches. Google, on the other hand, created an algorithm that provided value to certain keywords. The earliest Google search algorithm (which the Google creators naked "BackRub") crawled websites to see how frequently a keyword appeared, so the more frequently the word appeared the higher in the search rankings it appeared. Of course, this led to extreme keyword stuffing (if you remember the early days of the Internet you could scroll down to the bottom of a page and see individual keywords used dozens of times. Sadly, some ill-informed Web designers attempt to replicate this practice today).
Early internal network searches within organizations worked very much in the same way. A user would type in a keyword and all files matching a keyword would appear.The problem with this approach is that most network files and database records can share similar information and often share similar keywords. Due to this, a continual evolutionary step in network search needed to occur. Google understood the importance of improving its own search at the same time, which is why Google re-wrote the book on Internet search in 2000 (Leverage Marketing, 2017).
In 2000, Google started to move further into its search algorithm, making improvements to ensure the highest quality results. As Google search directs traffic to websites, which in turn brings revenue to the receiving site, there are many variables a network search does not possess. This includes the false linking, keyword stuffing, social media signaling, spamming and ad-heavy websites. While these aspects of search are not found in a corporate level network, the development of identifying this information is important to current inner-network search as it helped Google develop its cognitive learning search method in 2015. The ability for Google to allow its search algorithm to not only read keywords and offer rankings but to read into the context of a search based on past search results, selected entries, browser history, location and other variables marked a major step towards complete artificial intelligence within its search engine. It also gave a point for office network developers to aim for in regards to their own search development. Google created a new gold standard for how internal search solutions should behave. It is how cognitive search has come about and why it has become so effective. Network developers are able to weight search results differently and tweak algorithms to better fit the needs of the company, but the general concept of machine learning and cognitive search based off of Google's search development is present (Leverage Marketing, 2017).
Designing specific algorithms for any large corporation is difficult. Locating information that often shares similarities in keywords and file type make for a complex design challenge for any network infrastructure designer. Corporate files may also utilize extremely data sets, which in turn makes for a difficult algorithm design as well. Customizing a search method capable of understanding and functioning within this network can cumbersome and often not provide desirable results. Due to this, bringing in additional variables within the search field and the ability for the system to learn on the fly based off of previous searches and files/data selected can prove especially helpful. Because of this, machine learning and cognitive search is not only helpful within a business network but vital to productivity.
Machine learning does not simply improve the ability to search for content within a closed network though. it also makes it possible to implement new software applications and in-depth analytical systems capable of deciphering this information. Cognitive search makes all of this possible. It is why bringing in cognitive search into a network not only boosts the speed of locating files and information, but it helps the entire network function better with the specially designed applications. The need to run and analyze large amounts of data grows with every new customer, so when a company brings in thousands of new customers every single day, the amount of data growth is exponential. Machine learning and cognitive search allows for an easier time to decipher this information for use within different departments of a company. For example, a sales staff in Tokyo can analyze sales made during a specific time frame based off of marketing made within New York. Extensive data analysis is essential to the growth of any business and machine learning is the pinnacle of analyzing, learning from and extracting information from these large amounts of data (Wired, 2017)
One concept or idea within the corporate community is in order to implement cognitive search within a network, the current enterprise search must be completely stripped down, disassembled and tossed to the curb in order to make way for the shiny new machine learning cognitive search. This, however, is not the case. Cognitive search can be implemented into the current search method within a closed network, improving upon the strong search system already in place. Using cognitive search, machine learning, and artificial intelligence to find the right data, analyze it, and put it into the right context, and then the traditional search solution index it properly for fast retrieval and search results is important to make sure that the response time is maintained.
Enterprise search allows for in-depth tagging, indexing and keyword implementation. However, this is not always enough when making decisions based on data within a network. This is where cognitive search adds on to what enterprise search simply is not able to provide. Enterprise search only goes so far. While helpful, there are specific limitations to what enterprise search is capable of doing. Cognitive also brings in new forms of technology and application potential, each of which not only works with cognitive but within enterprise search as well. This makes analyzing and leveraging data easier and faster.
Implementing cognitive search on top of enterprise can help provide context to a search query as well. One of the downsides to enterprise search is while it can help identify information and locate files, it does not help in actually understanding the information. While enterprise locates the data, cognitive applies user analytics to the data in order to improve understanding while also uncovering deeper trends enterprise may simply miss.
Many of the design elements used to construct enterprise search can be used as the foundation of implementing cognitive search, so it is not a complete tear down/rebuild for the IT department. A company doesn't even need to abandon its enterprise search. It can remain in place in many forms, even if just as a supplemental form of search. However, in order to improve business search results, it is necessary to utilize three pillars of cognitive exploration and computing learning. These are to improve upon the current "tagging" system, analyzing all information found and use the cognitive computing system in order to leverage all content (IBM, 2017). All of this will help in the continual develop of search potential within a corporation's own network.
Internet search methods, as is the case with all other forms of technology with a corporation, needs to continually evolve and grow. This ensures a company can access its growing infrastructure of data instantly, without timely delays that may be the difference between landing a major client and missing out. Cognitive search has quickly become the go to search method used by corporations around the world to stay on top of their data location needs. By taking advantage of what cognitive search offers over other search schemes such as enterprise search, corporations future proof search potential for the foreseeable future.
