Enterprise search within the membership association industry has dramatically improved over last 5 years. With the most recent upgrades, searches allow for more accurate results, which in turn boosts productivity, cuts downtime and helps avoid the frustration of tracking down seemingly missing information in the organization. The continued progress in search allows for professionals throughout the company to locate the information that they are looking for much faster than they ever have been able to in the past. While it is difficult to always know what the future holds, based on the current technology and the features most requested, this blog post explores some of the most likely predictions for the direction of enterprise search in membership associations.
The idea of treating everyone the same is no longer a viable strategy. Users are asking for more relevant data and content and research shows that they are willing to give up personal information in order to get it. Technology is at the point where search personalization is not only possible, but effective. Providing real-time personalization that is customized to the person searching, where they are located, previous searches and clicks/pages visited, demographics (particularly if they are logged in), and many more are all possible with machine learning and artificial intelligence. All of this allows cognitive search to adjust search results over time based on learned behavior. Enterprise search for membership associations will continue to move in this direction in order to try to improve customer satisfaction and allow their members to find information that they are specifically looking for faster.
Search has always been dependent on the collection of data. The ability to provide quality results relies on the where the data is coming from and how it is structured within the data source. Despite this, most data is boxed into very specific categories. While this made classifying information and categorizing it easier, it also limited the power of search. No matter how small the category or sub-category, structured data provides structured results, which sometimes is not the most accurate or organic. As enterprise search in membership associations continues to develop, so to does the need for less structured data (Search Business Analytics, 2017).
Also referred to as unstructured data, this is information that does not have a pre-defined category or inherent structure. In the past, the information might be specifically in folders that tried to create or mimic their structure. However, as more and more information created through membership associations is unique in nature, the subsequent file and file system itself would then share unstructured data qualities. It was also difficult to enforce where the the data would live with some employees not following the prescribed structures. Other times, a file may fall in-between the gray area of containing some structure yet also is not completely within the defined specifications of the structured data. Custom databases, new systems as well as mobile applications make up only part of the unstructured data that causes these issues. With the evolution of enterprise search in membership associations, unstructured and less structured data will become more prominent, which should provide unique, organic search results (BrightPlanet, 2012).
As enterprise search grows, so to does the number of integrated systems within it. The additional systems help provide additional data analysis to different departments, as well as a growing number of file types and . Some of these newly integrated systems include the CMS (Content Management Systems), KMS (Knowledge Management Systems), AMS (Association Management Systems) and LMS (Learning Management Systems), just to name a few of the SaaS software that has been created and used by growing associations. Larger membership associations will require the implementation of many, or all, of these systems. From providing a unique user experience on their own website through the use of a content management system to tracking member's learning and CME credits through a Learning Management System, this is not something that will slow down. It is very likely that many more cloud-based solutions that are designed specifically to assist associations to run their business will be added. This will ultimately increasing the kinds of content that will be available and place more demand on the enterprise search solutions in membership associations.
Voice search works in a very similar way to keyword search. However, instead of typing out the information manually, the information is spoken to a microphone and translated using natural language processing (NLP). Personal, digital assistants such as those from Amazon and Google both listen for voice directed commands. Even Apple Siri works similar to this (although not on the same level as Google or Amazon's Alexa (Microsoft Cortana works in a similar method to Siri, although again it does not have the same kind of integration options as Amazon and Google). The ability to use voice commands frees up an individual's hands, allowing them to continue on with their work or another activity rather than typing and looking at a screen. With voice search integration into enterprise search, a user would have the ability to perform in-depth searches for information within an entire business network, all without typing on a computer. With proper voice search integration, a user not only could ask through a computer workstation, but any microphone interface that was connected to the network.
Artificial intelligence and machine learning are often one in the same. AI occurs when a man made device is able to perform intelligent tasks, ranging from speech recognition, the ability to identify one language from another or communicate back with a human. Machine learning on the other hand refers to a computer's ability to learn off of previous actions. Both of these are extensively used in varying forms today and are considered very hot technologies that will shape the future. Voice search services such as that of Amazon and Google can be considered artificial intelligence as the devices can understand language and respond appropriately. Search engines offer one example of machine learning. Google has the ability to adjust and edit its search results based off of how an individual has previously used the search engine or where they are located. With the ability to evolve off of what it learns, a computer system becomes much more capable of handling intelligent tasks (IBM, 2017). While elements of both AI and machine learning are covered in other segments, it is highly likely both will become growing areas for development within the enterprise search industry in the near future.
Visual search has grown in popularity over the last several years. While the technology continues to evolve, this is an entirely new search area beyond the simple keyword. Visual search can record hundreds of data points for a specific image. The saved data is is then compared against that of other images saved within the network in order to identify potential matches. This is different from attempting to search for an image based off of keywords. While keyword image searches still have a place within enterprise search, the idea of uploading an image into the system and then have the enterprise search algorithm locate where other, similar (if not identical) images are stored can prove especially beneficial (Search Engine Land, 2017). One example of this is within a medical association where surgeons could upload images of a particular injury and have the system look for similar cases/patients.
More searches are now performed over mobile devices than a traditional desktop. This trend will only continue as more and more work can be completed over a smartphone or tablet and the line between desktop and mobile blur with devices like the Microsoft Surface Pro getting more powerful. In addition, mobile devices generate additional information that a laptop or desktop simply doesn't. Location/geography, operating system type, phone type and screen size can all be useful information to personalize the experience. The need to use this additional information will develop into a major requirement for enterprise search (Smart Insights, 2017).
Changes within enterprise search in membership associations is likely to happen drastically over the next decade. What search looks like in 2027 may very well be completely different from what it looks like today. However, as more and more of these predicted changes come about, it puts additional data in the hands of those within an enterprise who need it. This additional data and power can be used to boost the bottom line by improving efficiency and productivity in the association. While an exact timeline for these potential changes is not possible to predict, many of these changes are already taking place, so it would be wise to expect some, if not all, of these changes to begin appearing within enterprise search solutions in membership associations in the near future.
