Insight Engine - Illuminating Information

„Enterprise search has dominated the way people find information, but significant changes in what is available and what is desired are gathering pace. Digital content leaders must embrace a new technology that is redefining the market around search, one that Gartner terms the ‘insight engine.’”

Gartner, Inc. 2016

Insight Engine Cycle

In Dialogue with Big Data

What does the expression "Insight Engine" actually mean and how can it affect the way we search? The term is based on the information available in the company (big data). An insight engine looks at how this information can best be searched, as does enterprise search; but an insight engine includes a broader spectrum of technologies that dramatically increase the quality and scale of the search. In essence, the properties of an insight engine can be described using the following three terms:


Search in Natural Language
Natural

Search queries can be formulated in natural language. The insight engine interprets the query and delivers relevant search results.

Comprehensive
Comprehensive

All relevant data sources are connected via connectors.

Proactive
Proactive

An autonomous analysis delivers additional data to the search query - information that is not being explicitly searched, but is nonetheless relevant to the context.


Insight engines immediately and proactively provide information in its correct context. Studies show that a shortened response time to complex questions is increasingly becoming a decisive criterion for success in today's competitive business environment. This is why it is more important than ever to be able to access and use your own company data in order to remain competitive (Forbes Insights, 2016. Vermeer, 2014). In the age of big data, the rapidly accumulating data is predominantly unstructured ̶ texts, voice messages, videos, and the like; this data piles up unutilized and inaccessible, because it isn’t in a form that can be processed (IDGE. 2016). Insight engines use semantic analysis to interpret unstructured data and prepare it to be processed and used. Search results are improved and delivered in a structured format.

The Solution is Mindbreeze InSpire

Semantic analysis and machine learning technology can extract meta data and interpret unstructured data and search queries. In a Natural Language Question Answering (NLQA) dialogue, data can be queried intuitively. Mindbreeze InSpire also recognizes the type of data that the query deals with, and delivers the results in a structured form (for example, business reports in tabular form), which is ideal for direct processing. By the same token, the search experience is enhanced by proactive data analysis and automatically triggered tips, which are displayed in addition to the actual search. Relevant information that has not been explicitly searched for is also delivered, enabling Mindbreeze to provide supplementary alternatives to support the search process.

A Solution for all Business Sectors

Insight engines are applicable across industries and are normally used to speed up workflow and qualitatively improve work processes. Mindbreeze InSpire has already proven its effectiveness in various industries and business settings under real-life, practical conditions.


Healthcare

Healthcare

With the increase of digital data in the healthcare sector, a new but currently virtually untapped resource has emerged. Mindbreeze InSpire takes unstructured data and uses it to identify connections in the field of medicine and healthcare, and presents the information in a chronology.
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Customer Care Center

Customer Care Center

Particularly in the area of customer care, a 360-degree view is crucial for enabling staff to give prompt, high quality answers to queries.
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Competitive Intelligence

Competitive Intelligence

Companies use insight engines to analyze their own data and to improve the efficiency and quality of business processes. The goal is to make the best use of the company's own information in order to stand out from the competition.
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Classification

Classification

As an insight engine, Mindbreeze InSpire also enables document classification. This allows all incoming mail to be processed automatically.
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360-Degree View in all Departments

360-Degree View in all Departments

Data often gets scattered throughout the company in different software packages and projects. It is imperative to synthesize this data to avoid missing crucial correlations, and to provide the user with a 360-degree view.
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Why Mindbreeze InSpire

    Draw from the abundance of your own data

  • Link more than 450 data sources with our connectors
  • Semantic analysis makes data interpretable
  • Achieve a comprehensive 360° view

    Find information faster

  • Structured results for specific search terms (who, when, where, etc.)
  • Semantic interpretation of search queries
  • Intuitive search with Natural Language Question Answering (NLQA)

    The personal assistant for your company data

  • Proactive analysis of search queries
  • Real-time analysis with alarm functions (actionable information)
  • Support for critical time-sensitive decision making

Semantic Analysis and Data Extraction

Deep Content Analytics

Deep Content Analytics with NLP and Machine Learning Technology

Mindbreeze offers complete language independence through corpus-based natural language processing (NLP), and is currently being used in over 39 languages (including Asian languages such as Chinese). In combination with language packages, statistical methods and machine learning technologies analyze unstructured data semantically and thus prepare it for the search. This means that Mindbreeze InSpire can understand semantic correlations that are decisive for good search results and provide an interpretation which is far more comprehensive than simple keyword matching.

NLP

Interpretation of Search Queries

Mindbreeze InSpire processes search queries that are placed in natural language, interprets them, and translates them into a language that is formally understandable for machines. The search is thus carried out in an NLQA dialogue with the user; if the request was not concrete enough, or several selection possibilities are available, the system simply asks for more information about the desired results.

Semantic Data Extraction

Semantic Data Extraction

Structured data is frequently contained in unstructured text. Tedious manual recording and entry of this data into a structured database format is common practice. This process is as labor-intensive as it is error-prone, since data is easily overlooked or fed into the system with typing errors. Mindbreeze InSpire extracts data from free texts and automatically validates it with a database to ensure the quality of the results. With the semantic approach, metadata properties can also be learned and assigned (such as discerning between a company name and a surname).