 | • レポートコード：D0MOR-NV144 • 出版社/出版日：Mordor Intelligence / 2020年8月 • レポート形態：英文、PDF、120ページ • 納品方法：Eメール（受注後2-3営業日） • 産業分類：電子 |
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レポート概要
| 本調査レポートでは、インサイトエンジンの世界市場について調査し、イントロダクション、調査手法、エグゼクティブサマリー、市場インサイト、市場動向、コンポーネント別（ソフトウェア、サービス）分析、展開方式別（オンプレミス、クラウド）分析、組織規模別（中小企業、大企業）分析、エンドユース産業別（金融、小売り、IT・通信、医療、製造）分析、地域別分析、競争状況、投資分析、将来市場などを整理しました。 ・イントロダクション ・調査手法 ・エグゼクティブサマリー ・市場インサイト ・市場動向 ・インサイトエンジンの世界市場規模：コンポーネント別（ソフトウェア、サービス） ・インサイトエンジンの世界市場規模：展開方式別（オンプレミス、クラウド） ・インサイトエンジンの世界市場規模：組織規模別（中小企業、大企業） ・インサイトエンジンの世界市場規模：エンドユース産業別（金融、小売り、IT・通信、医療、製造） ・インサイトエンジンの世界市場規模：地域別 ・競争状況 ・投資分析 ・将来市場 |
The Insight Engines Market is expected to register a CAGR of 23.18% over the forecast period from 2020 to 2025. 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.
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 custommade 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 contextaware 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 selflearning 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 questionanswering (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 decisionmaking. In response to the COVID19 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 everchanging 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 realtime access. Insurers increasingly face a regulatory landscape while trying to mitigate gamechanging trends like cyberrisk 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 investmentdecisionmaking, 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 decisionmakers 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 6070 fields per document and 50 million rows with 1000 fields per document, an entire two billion row collection. Crédit 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 filebased 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 digitalfirst, 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, Californiabased provider of a cloud based platform, announced to acquire the cognitive search capabilities of Attivio, an AIpowered answers and insights platform company based in Boston. With the addition of Attivio’s search engine, ServiceNow can change from a keywordbased 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 richmedia either natively or via partnership by using machine learning capabilities such as computer vision, speechtotext 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 cloudnative, 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.
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レポート目次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 Systèmes 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