Enterprise Search: Solving Real Problem for Real People

Powerful search engines like ‘Google’ have transformed the way we search information, so much so that just the mention of the phrase ‘enterprise search’ may elicit yawns. In the era of machine learning, cognitive computing, artificial intelligence, big data & analytics; finding the right information at the right time has become more crucial than ever. There are diverse requirements for search capabilities that emerge within an enterprise on a day-to-day basis. In this blog, Jaswinder Singh highlights the challenges that organizations are facing today related to their enterprise search solutions.

In the world of Big-data where we are generating data at mind boggling rate, search isn’t just about finding information, it has to be relevant and of value.

Search has to come with an ability to find something useful and profitable to the organization and irrelevant searches can cost a great many dollars. Leading organizations like Amazon, Google and others have aced customer behavior and gained competitive edge using big-data analytics over the available enterprise data.

Most enterprises are in the early stages of using a combination of internal “systems of record” data, external “syndicated” data and social media data to create cutting-edge analytics to understand their customers better.

For an organization to keep growing, it needs to provide information workers with an access to widespread unstructured sources as well as structured and line-of-business (LOB) system data while respecting an organization’s varied security needs.

Current Enterprise Search Challenges

Variety of Data: An organization holds tremendous amount of information in all different formats like share point, xml, word, pdf, text, image, amongst others. The data is scattered across different repositories. With social media proliferating in every sphere of our lives, much data today is not natively in structured format; for example, tweets and blogs are weakly structured pieces of text. With the traditional tools and technologies, it’s difficult to provide an efficient and relevant search solution over huge amount of multi-structured data.

Infrastructure limitation: Powered by the internet-of-things (IoT), unprecedented computing power, addictive gadgets, inexpensive storage and (to-date) highly elastic network capacity, we are leading into the era of cognitive systems where humans and machines collaborate to gain insights and knowledge from data by uncovering patterns and anomalies. The current infrastructure and the information management systems are finding it difficult to keep pace with the rate at which information is generated.

Complexity and Speed of Information Access: Ease in the way information is available over the internet has raised the expectations on the day-to-day enterprise searches. Providing relevant information in the required format is not just crucial but also complex.

For e.g. If an insurance agent is searching for a customer, he is not just expecting the basic information, an efficient search solution should respond with all the relevant information about the customer like his profile, policy details, phone directory, photo, linked referrals and anything that exists in the organization about that customer within a blink of an eye.

Security: For an enterprise search solution, it is expected that most of the content is secured against an unauthorized access. In this manner, it is very different from internet based searches. Given the typical high variance of permissions needed across combination of users, repositories, and content, a search system needs to integrate well with the security model and provide results based on permissions of the person conducting the search.

Relevant enterprise search is important from the perspective of deriving valuable results for making right decisions. With enterprises depending heavily on their data pool, the relevancy of accurate enterprise search will just grow multi-fold in the near future.

What is your experience dealing with these challenges? Leave your comments in the box below.

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About Jaswinder Singh

mmJaswinder is a data and science enthusiast who loves exploring problems with data to make out meaningful insights. Over last 10 years he has been contributing heavily as a data expert through data analysis, management and analytic. Over the last two years he has been leading teams across multiple analytical solutions like Fraud Analytics, Usage Based Insurance, Voice Analytics, Employee Content Search, Enterprise Search


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