News and Updates
It is time to modernize Enterprise Search
Rafael Oliveira
Sep 29, 2020
Search is something we all take for granted: Every day our searches of the internet are easy, comprehensive and instantaneous.
Not so in companies.
It’s hard to believe, but finding information in their company is still a hard problem for employees as part of their everyday work. For many people, hitting “search” is synonymous with getting a cup of coffee while waiting for results and being transported back to the times where Internet Explorer was still a modern browser.
Enterprise search has been stuck since the 90s and it’s not uncommon to see employees in large companies using search systems that look like this:
IDC and McKinsey estimate that anything between 15% to 30% of knowledge workers’ time is wasted looking for information — that’s an entire day of productivity lost every week! 🤯
Hayley Sutherland, senior research analyst for AI Software Platforms and author of the IDC study, says:
Enterprise search has traditionally lagged behind consumer search when it comes to understanding natural language queries and delivering accurate, relevant insights in context, but the need for intelligent tools that can do this effectively is stronger than ever. The timely emergence of a new generation of AI-enabled search tools, leveraging techniques and technologies like machine learning, knowledge graphs, and NLP, is helping close the gap between consumer and enterprise search capabilities, thus enabling knowledge workers to discover new contextualized insights and be more efficient, effective, and satisfied in their jobs. [emphasis added]
Search is especially important in companies because information is often spread across legacy systems and hidden in unstructured texts (emails, PDFs, documents, images, etc.). Often the data sources are silos so employees need to know exactly where to look or have excellent detective skills.
With more and more unstructured data being produced every day, enterprise search systems have become a bottleneck for many companies to perform at speed, and agility is becoming an almost impossible goal for large organizations in our hyper-accelerated information age.
Why enterprise search is still hard
When we founded Curiosity, we started on a mission of helping companies make use of their knowledge. We noticed how hard it is to access, process, and find information, and how this is caused by the sheer complexity of existing search solutions. They’re hard to deploy, integrate, manage configure, and update.
It’s not uncommon to see even the simplest of the systems need multiple systems to handle tasks such as data extraction, queueing, data transformation, search indexing, query planning, user management, search interfaces. Improving a search system or adding more data takes months or even years, making it impossible to update it to the constant stream of new software and information.
As a consequence, most search solutions within companies aim to become the “One Search to Rule them All”, with a large centralized team trying to solve the entire company search in within a system. That exacerbates the time required to build the systems with a multi-year lead time for improvements or new data-sources, large and complex teams with a broad knowledge of different systems for implementing and managing each of the moving parts being required, and significant time spent building ontologies that are not really guaranteed to help.
The result is a search system that are not relevant or focused, and is soon outdated, leaving each team on their own searching for the information that is actually relevant for their daily jobs.
Time for a new start
At Curiosity, we believe that finding information should never be a bottleneck. That means search has to be easy for the people looking for the information, but also for the people deploying and managing it.
Curiosity’s mission is to make the most powerful and uncomplicated enterprise search solution, one that can be deployed over and over for each team and their data. And being simple doesn’t mean lacking features: it also integrates at its core the latest advances in natural language processing, a powerful knowledge graph, multiple data-connectors and a fully customizable search experience. It is ready to help your users find the information they need in a friendly and modern search experience that adapts to your data and your use-case.
Check out one of our demos based on the NASA STI public dataset to see what a modern search system can look like! We’ll follow up with a post on how we built this demo in an afternoon, and how you can do the same on your own!
Search is something we all take for granted: Every day our searches of the internet are easy, comprehensive and instantaneous.
Not so in companies.
It’s hard to believe, but finding information in their company is still a hard problem for employees as part of their everyday work. For many people, hitting “search” is synonymous with getting a cup of coffee while waiting for results and being transported back to the times where Internet Explorer was still a modern browser.
Enterprise search has been stuck since the 90s and it’s not uncommon to see employees in large companies using search systems that look like this:
IDC and McKinsey estimate that anything between 15% to 30% of knowledge workers’ time is wasted looking for information — that’s an entire day of productivity lost every week! 🤯
Hayley Sutherland, senior research analyst for AI Software Platforms and author of the IDC study, says:
Enterprise search has traditionally lagged behind consumer search when it comes to understanding natural language queries and delivering accurate, relevant insights in context, but the need for intelligent tools that can do this effectively is stronger than ever. The timely emergence of a new generation of AI-enabled search tools, leveraging techniques and technologies like machine learning, knowledge graphs, and NLP, is helping close the gap between consumer and enterprise search capabilities, thus enabling knowledge workers to discover new contextualized insights and be more efficient, effective, and satisfied in their jobs. [emphasis added]
Search is especially important in companies because information is often spread across legacy systems and hidden in unstructured texts (emails, PDFs, documents, images, etc.). Often the data sources are silos so employees need to know exactly where to look or have excellent detective skills.
With more and more unstructured data being produced every day, enterprise search systems have become a bottleneck for many companies to perform at speed, and agility is becoming an almost impossible goal for large organizations in our hyper-accelerated information age.
Why enterprise search is still hard
When we founded Curiosity, we started on a mission of helping companies make use of their knowledge. We noticed how hard it is to access, process, and find information, and how this is caused by the sheer complexity of existing search solutions. They’re hard to deploy, integrate, manage configure, and update.
It’s not uncommon to see even the simplest of the systems need multiple systems to handle tasks such as data extraction, queueing, data transformation, search indexing, query planning, user management, search interfaces. Improving a search system or adding more data takes months or even years, making it impossible to update it to the constant stream of new software and information.
As a consequence, most search solutions within companies aim to become the “One Search to Rule them All”, with a large centralized team trying to solve the entire company search in within a system. That exacerbates the time required to build the systems with a multi-year lead time for improvements or new data-sources, large and complex teams with a broad knowledge of different systems for implementing and managing each of the moving parts being required, and significant time spent building ontologies that are not really guaranteed to help.
The result is a search system that are not relevant or focused, and is soon outdated, leaving each team on their own searching for the information that is actually relevant for their daily jobs.
Time for a new start
At Curiosity, we believe that finding information should never be a bottleneck. That means search has to be easy for the people looking for the information, but also for the people deploying and managing it.
Curiosity’s mission is to make the most powerful and uncomplicated enterprise search solution, one that can be deployed over and over for each team and their data. And being simple doesn’t mean lacking features: it also integrates at its core the latest advances in natural language processing, a powerful knowledge graph, multiple data-connectors and a fully customizable search experience. It is ready to help your users find the information they need in a friendly and modern search experience that adapts to your data and your use-case.
Check out one of our demos based on the NASA STI public dataset to see what a modern search system can look like! We’ll follow up with a post on how we built this demo in an afternoon, and how you can do the same on your own!
Search is something we all take for granted: Every day our searches of the internet are easy, comprehensive and instantaneous.
Not so in companies.
It’s hard to believe, but finding information in their company is still a hard problem for employees as part of their everyday work. For many people, hitting “search” is synonymous with getting a cup of coffee while waiting for results and being transported back to the times where Internet Explorer was still a modern browser.
Enterprise search has been stuck since the 90s and it’s not uncommon to see employees in large companies using search systems that look like this:
IDC and McKinsey estimate that anything between 15% to 30% of knowledge workers’ time is wasted looking for information — that’s an entire day of productivity lost every week! 🤯
Hayley Sutherland, senior research analyst for AI Software Platforms and author of the IDC study, says:
Enterprise search has traditionally lagged behind consumer search when it comes to understanding natural language queries and delivering accurate, relevant insights in context, but the need for intelligent tools that can do this effectively is stronger than ever. The timely emergence of a new generation of AI-enabled search tools, leveraging techniques and technologies like machine learning, knowledge graphs, and NLP, is helping close the gap between consumer and enterprise search capabilities, thus enabling knowledge workers to discover new contextualized insights and be more efficient, effective, and satisfied in their jobs. [emphasis added]
Search is especially important in companies because information is often spread across legacy systems and hidden in unstructured texts (emails, PDFs, documents, images, etc.). Often the data sources are silos so employees need to know exactly where to look or have excellent detective skills.
With more and more unstructured data being produced every day, enterprise search systems have become a bottleneck for many companies to perform at speed, and agility is becoming an almost impossible goal for large organizations in our hyper-accelerated information age.
Why enterprise search is still hard
When we founded Curiosity, we started on a mission of helping companies make use of their knowledge. We noticed how hard it is to access, process, and find information, and how this is caused by the sheer complexity of existing search solutions. They’re hard to deploy, integrate, manage configure, and update.
It’s not uncommon to see even the simplest of the systems need multiple systems to handle tasks such as data extraction, queueing, data transformation, search indexing, query planning, user management, search interfaces. Improving a search system or adding more data takes months or even years, making it impossible to update it to the constant stream of new software and information.
As a consequence, most search solutions within companies aim to become the “One Search to Rule them All”, with a large centralized team trying to solve the entire company search in within a system. That exacerbates the time required to build the systems with a multi-year lead time for improvements or new data-sources, large and complex teams with a broad knowledge of different systems for implementing and managing each of the moving parts being required, and significant time spent building ontologies that are not really guaranteed to help.
The result is a search system that are not relevant or focused, and is soon outdated, leaving each team on their own searching for the information that is actually relevant for their daily jobs.
Time for a new start
At Curiosity, we believe that finding information should never be a bottleneck. That means search has to be easy for the people looking for the information, but also for the people deploying and managing it.
Curiosity’s mission is to make the most powerful and uncomplicated enterprise search solution, one that can be deployed over and over for each team and their data. And being simple doesn’t mean lacking features: it also integrates at its core the latest advances in natural language processing, a powerful knowledge graph, multiple data-connectors and a fully customizable search experience. It is ready to help your users find the information they need in a friendly and modern search experience that adapts to your data and your use-case.
Check out one of our demos based on the NASA STI public dataset to see what a modern search system can look like! We’ll follow up with a post on how we built this demo in an afternoon, and how you can do the same on your own!
Search is something we all take for granted: Every day our searches of the internet are easy, comprehensive and instantaneous.
Not so in companies.
It’s hard to believe, but finding information in their company is still a hard problem for employees as part of their everyday work. For many people, hitting “search” is synonymous with getting a cup of coffee while waiting for results and being transported back to the times where Internet Explorer was still a modern browser.
Enterprise search has been stuck since the 90s and it’s not uncommon to see employees in large companies using search systems that look like this:
IDC and McKinsey estimate that anything between 15% to 30% of knowledge workers’ time is wasted looking for information — that’s an entire day of productivity lost every week! 🤯
Hayley Sutherland, senior research analyst for AI Software Platforms and author of the IDC study, says:
Enterprise search has traditionally lagged behind consumer search when it comes to understanding natural language queries and delivering accurate, relevant insights in context, but the need for intelligent tools that can do this effectively is stronger than ever. The timely emergence of a new generation of AI-enabled search tools, leveraging techniques and technologies like machine learning, knowledge graphs, and NLP, is helping close the gap between consumer and enterprise search capabilities, thus enabling knowledge workers to discover new contextualized insights and be more efficient, effective, and satisfied in their jobs. [emphasis added]
Search is especially important in companies because information is often spread across legacy systems and hidden in unstructured texts (emails, PDFs, documents, images, etc.). Often the data sources are silos so employees need to know exactly where to look or have excellent detective skills.
With more and more unstructured data being produced every day, enterprise search systems have become a bottleneck for many companies to perform at speed, and agility is becoming an almost impossible goal for large organizations in our hyper-accelerated information age.
Why enterprise search is still hard
When we founded Curiosity, we started on a mission of helping companies make use of their knowledge. We noticed how hard it is to access, process, and find information, and how this is caused by the sheer complexity of existing search solutions. They’re hard to deploy, integrate, manage configure, and update.
It’s not uncommon to see even the simplest of the systems need multiple systems to handle tasks such as data extraction, queueing, data transformation, search indexing, query planning, user management, search interfaces. Improving a search system or adding more data takes months or even years, making it impossible to update it to the constant stream of new software and information.
As a consequence, most search solutions within companies aim to become the “One Search to Rule them All”, with a large centralized team trying to solve the entire company search in within a system. That exacerbates the time required to build the systems with a multi-year lead time for improvements or new data-sources, large and complex teams with a broad knowledge of different systems for implementing and managing each of the moving parts being required, and significant time spent building ontologies that are not really guaranteed to help.
The result is a search system that are not relevant or focused, and is soon outdated, leaving each team on their own searching for the information that is actually relevant for their daily jobs.
Time for a new start
At Curiosity, we believe that finding information should never be a bottleneck. That means search has to be easy for the people looking for the information, but also for the people deploying and managing it.
Curiosity’s mission is to make the most powerful and uncomplicated enterprise search solution, one that can be deployed over and over for each team and their data. And being simple doesn’t mean lacking features: it also integrates at its core the latest advances in natural language processing, a powerful knowledge graph, multiple data-connectors and a fully customizable search experience. It is ready to help your users find the information they need in a friendly and modern search experience that adapts to your data and your use-case.
Check out one of our demos based on the NASA STI public dataset to see what a modern search system can look like! We’ll follow up with a post on how we built this demo in an afternoon, and how you can do the same on your own!
