Understanding the paradigm shift in information retrieval, insights and data analytics
In today's competitive, data-intensive world, only a company that is able to use information faster and better can survive. The methods that were used to collect customer insights, the formats of the information, the multitude of platforms and applications and the nature of the content have all undergone a massive transformation. Needless to say, the information discovery tools have also changed. This article will cover the key challenges of Enterprise Search and Discovery and provide a comprehensive solution to tackle these challenges.
Exponential growth of unstructured data
Unstructured data is growing faster than ever. Given the complexity of the data and its sheer volume, expenses with regards to its analysis, storage, accessibility and security are also on the rise. Enterprises are finding it difficult to keep these expenses under control. Matching the search speeds with the speed of increase in data is also a challenge.
More often than not, the results of an Enterprise Search do not match the expectations of users. This is what differentiates it from say a Google search. While the latter shows relevant results using backlinks and linking across webpages, the former needs a platform that understands the industry and context sensitivity and also has inbuilt controlled vocabularies, among other factors.
Variety of data
Data can be in the form of a webpage, an image, an audio/video file or a document, to name just a few formats. Another aspect of information is the language it's in. It could be a local language or an international one. It is essential that the Search Platform has inbuilt Natural Language Processing (NLP) and other applications for Advanced Chunking and Sentiment Analysis.
Shorter time cycle
Unlike a generic Google search, a lot rides on Enterprise Search results. Critical decisions with regards to meeting customer expectations, medical diagnosis, legal cases and STP publishing, to name a few, depend on this search. In today's dynamic environment, changes happen quickly and hence, the response to these changes also need to speedup.
Irrelevant information, distracting advertisements and absence of good filters, all act as hindrances to the Search and Discovery process. Not only do these factors make the process time consuming, they also result in a loss of effort and money. Furthermore, once the information is discovered, determining its trustworthiness is also a lengthy process.
An Ideal Solution
So, taking into account the factors above, we can conclude that the ideal Enterprise Search platform is one that has all of these factors and is built to derive key insights from organizational data, a major chunk of which is in the unstructured form. To uncover the hidden insights from chunks of unstructured data, the new generation of platforms have an array of built-in text mining functionalities. Coveo, Commvault, 3RDi Search, and Algolia are all examples of such modern enterprise search platforms. These platforms are equipped with advanced security features, a distributed cloud environment, collaborative features, a set of vocabularies for different domains, and many more features.