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인사이트 엔진 시장 : 성장, 동향, COVID-19의 영향, 예측(2021-2026년)

Insight Engines Market - Growth, Trends, COVID-19 Impact, and Forecasts (2022 - 2027)

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인사이트 엔진 시장 : 성장, 동향, COVID-19의 영향, 예측(2021-2026년) Insight Engines Market - Growth, Trends, COVID-19 Impact, and Forecasts (2022 - 2027)
발행일 : 2022년 01월 페이지 정보 : 영문

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인사이트 엔진은 내부 및 외부 데이터 소스와 구조화 및 비구조화 컨텐츠를 크롤, 인덱싱, 마이닝하여 새로운 인덱스를 작성하고, 폭넓은 일련의 정보는 간단하게 찾아낼 수 있습니다. 이러한 인덱스는 온톨로지나 그래프 등의 언어 및 컨텍스트 모델에 의해 보완되는 경우가 많으며, 데이터와 지식 사이의 상관관계를 모델화하기 위해 다양한 형식으로 자연스럽게 보유되거나 다양한 스킴으로 표현되고, 관련성이 향상되고, 역할이나 그 때 비지니스 내용에 따라 검색 및 검출에 대한 개인화를 지원합니다. 사용자와 관리자 양방이 관련성 규칙과 알고리즘을 지속적으로 교육 및 진화시킬 수 있어 특정 업계 또는 이용 사례의 가속요인이 됩니다. 예를 들어 IBM Watson Discovery를 사용하면 응답 검색에 소비하는 시간이 75% 단축되는 등 대폭적인 변혁의 결과를 얻을 수 있었습니다.

결과의 유연한 제시는 인사이트 엔진의 중요한 기능입니다. 문서나 비디오 등의 소스 자료로의 링크를 제공하는 검색 엔진과는 대조적으로 인사이트 엔진은 사실 또는 엔티티에 관한 문맥 정보도 제공할 수 있습니다. 챗봇 Q&A 시스템은 편협하고 종종 맞춤 개발인 것과는 대조적으로, 인사이트 엔진은 보통 기업 전체에 걸쳐있습니다. 그것들은 CRM, 외부 소셜 데이터, 마케팅, IT 서비스 관리, 인사, 판매, 기타 점포 등 다양한 분야로부터 입력된 자연 언어의 사실과 지식을 통해 표면화할 수 있습니다. 조직이 디지털화를 계속해 보다 비구조화 및 구조화된 컨텐츠를 생성함에 따라 컨텐츠, 관련 사실 및 지식을 이해관계자에게 제시하기 위한 인사이트 엔진 테크놀러지의 요건은 매우 중요합니다.

적용된 인공지능(AI)과 효과적인 상황 인식 데이터 표시에 의해 기존 검색 테크놀러지와 인사이트 엔진의 차이가 발생합니다. 인사이트 엔진은 머신러닝과 딥러닝을 사용해 데이터를 추출하고, 기업의 지식을 묶고, 이것을 자기 학습 프로세스로 합니다. 이 테크놀러지는 사용자의 행동 분석과 이전 이벤트에 기반해 정보를 분류하고, 각 사용자에게 개인화된 종합적인 모습을 제공하는 것을 학습합니다. 자연언어처리(NLP)와 자연언어질문응답(NLQA)을 사용하면 검색 쿼리를 자연언어로 전달하고 직접 처리할 수 있습니다. 이러한 인텔리전트 테크놀러지는 구조화된 메타데이터와 텍스트 컨텐츠를 분석 및 이해하고, 이것을 사용해 사용자가 정확하게 필요한 것을 판단할 수 있습니다.

Findwise에 의한 2019 Search & Findability Survey에 의하면 관련 정보를 찾아내는 것은 대부분의 조직에 있어서 여전히 중요한 과제입니다. 내부 정보에 관해서는 55% 가까이가 찾고 있는 것을 발견하기 어렵거나 매우 어렵다고 느끼고 있습니다. 정보의 질이 낮은 점이 검색성이 빈약한 주요 이유의 하나입니다. 정보의 질이 불충분하면 검색성이 저하하지만, 일반적으로 디지털 전환에도 악영향을 미칩니다. 데이터 인사이트로부터 가치를 얻기 위해 머신러닝을 이용해 사용자 의도를 예측하고, 인사이트를 제공하는 엔진을 배치합니다. 고객과 직원은 중요한 인사이트를 찾아내고, 차선의 행동으로 이동하고, 적절한 타이밍에 적절한 응답을 취득하는데 도움이 됩니다.

2019년 12월 Sinequa SAS는 인텔리전트 검색 플랫폼을 개시하고, 250만 명의 디지털 워커가 1,000억 건의 레코드와 50억 건의 문서를 이용하고, 실용적인 정보와 인사이트를 추출하고, 업무 개선과 보다 스마트한 의사결정을 지원하고 있습니다. COVID-19의 발생에 대응하기 위해 Sinequa는 COVID 인텔리전트 인사이트로 불리는 과학적 조사 툴을 작성했습니다. 이 툴은 과학 및 의료 전문가가 빠르게 진화하는 과학 조사 논문이나 출판물의 많은 소스로부터 인사이트를 얻어 정보를 분석하고, 모든 컨텐츠를 면밀히 조사해 필요한 정보를 빠르게 입수할 수 있도록 하는 것을 목적으로 하고 있습니다. 여기에는 7만 건 이상의 논문, 기사, 출판물의 리포지토리가 포함되어 있습니다.

인사이트 엔진(Insight Engines) 시장에 대해 조사했으며, 시장 개요와 함께 컴포넌트별, 전개 방식별, 기업 규모별, 최종사용자 업계별, 지역별 동향 및 시장에 참여하는 기업 개요 등을 제공합니다.

목차

제1장 서론

제2장 조사 방법

제3장 주요 요약

제4장 시장 인사이트

  • 시장 개요
  • 업계의 밸류체인 분석
  • Porter's Five Forces 분석
  • COVID-19의 업계에 대한 영향

제5장 시장 역학

  • 시장 성장 촉진요인
    • 데이터량 증가와 구조화 데이터의 요건
    • 검색과 자연언어처리를 통한 분석 쿼리 생성 증가
  • 시장 성장 억제요인
    • 데이터 품질과 데이터 소스 검증에 관한 우려

제6장 시장 세분화

  • 컴포넌트별
    • 소프트웨어
    • 서비스
  • 전개 방식별
    • 온프레미스
    • 클라우드
  • 기업 규모별
    • 중소기업
    • 대기업
  • 최종사용자 업계별
    • BFSI
    • 소매
    • IT와 통신
    • 헬스케어
    • 제조
    • 기타
  • 지역별
    • 북미
    • 유럽
    • 아시아태평양
    • 라틴아메리카
    • 중동 및 아프리카

제7장 경쟁 상황

  • 기업 개요
    • IBM Corporation
    • Mindbreeze GmbH
    • Coveo Solutions Inc.
    • Sinequa SAS
    • LucidWorks, Inc.
    • ServiceNow, Inc.(Attivio Cognitive Search Platform)
    • Micro Focus International plc
    • Google LLC
    • Microsoft Corporation
    • Funnelback Pty Ltd
    • IntraFind Inc.
    • Dassault Systemes SA
    • EPAM Systems, Inc.(Infongen)
    • Expert System SpA
    • IHS Markit Ltd
    • Insight Engines, Inc.

제8장 투자 분석

제9장 시장 전망

KSM 20.11.09

The Insight Engines Market is expected to register a CAGR of 23.18% over the forecast period from 2021 - 2026. Insight engines create a new index by crawling, indexing, and mining internal and external data sources and structured and unstructured content to ensure that a broad set of information is easily discoverable. These indexes are often complemented by language and context models such as ontologies and graphs to model correlations between data and knowledge that may be held natively in different formats or represented by different schemas, improve relevance and support personalization of the search and discovery experience by role or business moment context, where both users and administrators can continually train and evolve relevance rules and algorithms and provide accelerators for particular industries or use cases. For instance, usage of IBM Watson Discovery has experienced significant transformative results, including a 75% reduction of time spent searching for answers.

Key Highlights

  • Flexible presentation of results is a crucial capability of insight engines. In contrast to search engines that provide links to source materials such as documents and videos, insight engines can also provide contextual information about the fact or entity. In contrast to the narrow and often custom-made development of chatbot Q&A systems, insight engines typically span the enterprise. They can surface via typed natural language facts and knowledge from various areas such as CRM, external social data, marketing, IT service management, HR, sales, and other stores. As organizations continue to become digital and generate more unstructured and structured content, the requirement for insight engine technology to surface content, relevant facts, and knowledge to stakeholders is significantly critical.
  • The applied artificial intelligence (AI) and the effective context-aware presentation of data make the difference between traditional search technologies and insight engines. Insight Engines use the machine and deep learning to extract data, bundle enterprise knowledge, and make this a self-learning process. Based on user behavior analysis and previous events, the technology learns to categorize information to provide a personalized, comprehensive picture to each user. Using natural language processing (NLP) and natural language question-answering (NLQA), search queries can be delivered in natural language and processed directly. These intelligent technologies can analyze and understand structured metadata and text content and use this to determine what the user needs correctly.
  • According to the 2019 Search & Findability Survey by Findwise, finding relevant information is still a significant challenge to most organizations. When it comes to internal information, almost 55% find it difficult or very difficult to find what they are looking for. Bad information quality is one of the main reasons for poor findability. Insufficient information quality leads to poor findability, but it also harms digital transformation in general. To extract value from data insight, engines cold be deployed where machine learning is utilized to predict user intention and provide insights. Customers and employees can locate critical insights to help them move to their next best action and retrieve the right answer at the right time.
  • In December 2019, Sinequa SAS launched an intelligent Search platform is helping 2.5 million digital workers utilize 100 billion records and 5 billion documents to extract actionable information and insights for improved business operations and smarter decision-making. In response to the COVID-19 outbreak, Sinequa created a scientific research tool called COVID Intelligent Insight. This tool aims to help scientific and medial professionals get insights and analyze information across the many sources of rapidly evolving scientific research papers and publications so that one can sift through all the content and get the information required quickly. It contains a repository of over 70,000 papers, articles, and publications.

Key Market Trends

BFSI is Expected Hold Significant Share

  • Banks deal with a unique set of challenges as they navigate an ever-changing consumer landscape and business expectations. Search technology is at the forefront of making sense of this new world of finance. The variety of data sources for usage has evolved beyond the traditional mix. Enterprise workers at financial institutions need access to data stored in the cloud, behind SaaS services, and other silos. Insight Engines scales to billions of documents in various formats and connects to all of the data for real-time access. Insurers increasingly face a regulatory landscape while trying to mitigate game-changing trends like cyber-risk and disruptive innovation. Search can help these organizations stay nimble and maintain growth.
  • Insight engines leverage ML & AI to retrieve relevant results from disparate data repositories. It gives bankers a complete view of their clients by giving them access to annual reports, risk analytics, social media, industry blogs, and many other data points. It also enables informed investment-decision-making, opportunity sourcing, and deal origination. Banks have several transactional data and digital interaction points around customer profiles, claims, customer payment history, etc. Insight engines could exploit these massive data repositories to access authentic and reliable credit reports. Banks can proactively leverage these reports to anticipate fraud while uncovering payment irregularities and other unusual activities.
  • Banks and other financial organizations are also utilizing insight engines to find and parse client sentiment by checking social media and analyzing discussions about their services and strategies with the usage of Natural Language Processing. Financial services analysts can compose increasingly accurate reports and give better advice to customers and internal decision-makers with the capacity to get to essential and separated data. Using data to personalize banking improves customer engagement and increases revenue. According to Accenture, a major global bank used personalized insights delivered to customers to increase savings balances by EUR 60 million in just 18 months.
  • For instance, 3rd largest bank in the United States with 38 million searches and 293 thousand unique users deployed search apps built with Lucidworks Fusion, and now only 0.14% of queries have zero results, and employees rate their search as the most valuable feature of their intranet. A top five global investment bank built an app with Lucidworks Fusion that searched across 250 million rows, each with 60-70 fields per document and 50 million rows with 1000 fields per document, an entire two billion row collection. Credit Agricole, one of the largest banks in the world, has launched a project to deliver a new digital workplace, where more than 60,000 internal users can know the exact situation of the customer in front of them, which could be utilized to find the most relevant offerings for the customer.

North America is Expected to Hold Significant Share

  • The North American region houses the presence of significant players such as IBM, Microsoft, and Conveo Solutions, etc. to name a few. Several organizations in the region have been looking at how to utilize decades of information and reports and to extract valuable insights from those data stores. In the past, knowledge managers and corporate librarians helped with that process, but now insight engines are providing these insights using machine learning, state of the art natural language processing, and knowledge mining. With the emergence of rapid processing, models enabled the same instance of data to support data analytics and file-based models in different types of organizations in the United States. Insight engines are used to derive the data from indexed content for analysis and reporting.
  • In November 2019, Science Applications International Corp. (SAIC) and Sinequa worked together to give an intelligent search experience with Sinequa's machine learning and advanced natural language processing technologies for NASA's global information access capability situated at the Marshall Space Flight Center in Huntsville, Alabama. SAIC achieved a contract to deploy and sustain a comprehensive knowledge management capability for NASA Marshall Space Flight Center, utilized Sinequa's insight engine platform for the search and analysis of NASA's structured and unstructured content for improving the search experience, which significantly supports missions and operations.
  • In October 2019, ReFED, a national nonprofit working to advance solutions to reduce the amount of food going to waste in the U.S., announced to launch the ReFED Insights Engine in 2020, a digital-first, continuously updated platform to house the next generation of data, insights, and guidance on food waste and solutions. The company is developing the Insights Engine to leverage the best data available to answer to identify the most effective and practical solutions that the food sector should focus the efforts on implementing. This innovative platform will combine proprietary and public data and subject matter expertise from ReFED's 30+ member Expert Network to deliver the guidance and insights needed to focus action and to reduce food waste in half by 2030.
  • In November 2019, ServiceNow, California-based provider of a cloud-based platform, announced to acquire the cognitive search capabilities of Attivio, an AI-powered answers and insights platform company based in Boston. With the addition of Attivio's search engine, ServiceNow can change from a keyword-based search to deliver conversational AI and search experiences at scale to customers. Attivio's search capabilities will make ServiceNow significantly understand the technique involved in natural language searches on the Now Platform to deliver personalized and relevant results that users can act from the search results window. By combining Attivio into the Now Platform, the company plans to improve the search natively across its IT, Employee, and Customer workflows through the ServiceNow Now Mobile app, Service Portal, and Virtual Agent chatbot solution.

Competitive Landscape

The Insight Engines Market is moderately fragmented due to the significant presence of players such as IBM Corporation, Mindbreeze GmbH, LucidWorks, Inc., Sinequa SAS, etc. Vendors in the market are also extending the reach of their content indexing capabilities to rich-media either natively or via partnership by using machine learning capabilities such as computer vision, speech-to-text functions, etc.

  • June 2020 - IBM Corporation announced significant changes and additions to IBM Watson Discovery. The company introduced the Watson Discovery Premium plan, where users can experience a new user interface, a guided experience to help users quickly start using Watson Discovery for their specific use case, and many latest features, including content mining.
  • March 2020 - LucidWorks, Inc. launched a new series of enhancements to Lucidworks Fusion. Fusion 5.1 extended the platform's cloud-native, microservices architecture with tools and features that streamline development, simplify operations, and supercharge data science. This release enriches the company's ability to help customers maximize the value of data discovery and provide personalized experiences to their customers.

Additional Benefits:

  • The market estimate (ME) sheet in Excel format
  • 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

  • 1.1 Study Assumptions and Market Definition
  • 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET INSIGHTS

  • 4.1 Market Overview
  • 4.2 Industry Value Chain Analysis
  • 4.3 Industry Attractiveness - Porter's Five Forces Analysis
    • 4.3.1 Bargaining Power of Suppliers
    • 4.3.2 Bargaining Power of Consumers
    • 4.3.3 Threat of New Entrants
    • 4.3.4 Intensity of Competitive Rivalry
    • 4.3.5 Threat of Substitutes
  • 4.4 Impact Of COVID-19 on the Industry

5 MARKET DYNAMICS

  • 5.1 Market Drivers
    • 5.1.1 Increasing Volumes of Data and the Requirement of Structured Data
    • 5.1.2 Rising Generation of Analytical Queries Via Search and Natural Language Processing
  • 5.2 Market Restraints
    • 5.2.1 Concerns Regarding the Data Quality and Data Sources Validation

6 MARKET SEGMENTATION

  • 6.1 By Component
    • 6.1.1 Software
    • 6.1.2 Services
  • 6.2 By Deployment Type
    • 6.2.1 On-premise
    • 6.2.2 Cloud
  • 6.3 By Size of the Enterprise
    • 6.3.1 Small and Medium-Sized Enterprises
    • 6.3.2 Large Enterprises
  • 6.4 By End-User Industry
    • 6.4.1 BFSI
    • 6.4.2 Retail
    • 6.4.3 IT and Telecom
    • 6.4.4 Healthcare
    • 6.4.5 Manufacturing
    • 6.4.6 Other End-User Industries
  • 6.5 Geography
    • 6.5.1 North America
    • 6.5.2 Europe
    • 6.5.3 Asia-Pacific
    • 6.5.4 Latin America
    • 6.5.5 Middle East and Africa

7 COMPETITIVE LANDSCAPE

  • 7.1 Company Profiles
    • 7.1.1 IBM Corporation
    • 7.1.2 Mindbreeze GmbH
    • 7.1.3 Coveo Solutions Inc.
    • 7.1.4 Sinequa SAS
    • 7.1.5 LucidWorks, Inc.
    • 7.1.6 ServiceNow, Inc. (Attivio Cognitive Search Platform)
    • 7.1.7 Micro Focus International plc
    • 7.1.8 Google LLC
    • 7.1.9 Microsoft Corporation
    • 7.1.10 Funnelback Pty Ltd
    • 7.1.11 IntraFind Inc.
    • 7.1.12 Dassault Systemes S.A.
    • 7.1.13 EPAM Systems, Inc. (Infongen)
    • 7.1.14 Expert System S.p.A.
    • 7.1.15 IHS Markit Ltd
    • 7.1.16 Insight Engines, Inc.

8 INVESTMENT ANALYSIS

9 FUTURE OF THE MARKET

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