Our products are used by private and public organizations
to improve not only efficiency but also the creation of
value for its stakeholders.

Integrated Big Data Management

Organizations never had access to such amount of structured and unstructured content in different formats (documents, reports, e-mail, surveys, blogs, web sites, social networks, enterprise applications, ...) as today, and this expansion is accelerating rapidly.

Obtain data, facts and relevant concepts from this content to understand and to improve operations, profitability and business growth continually presents opportunities but also challenges.

In this context, the integrated Big Data management services of AISR enable to process, analyze (including extract data, facts and concepts), classify, organize and provide this vast amount of content to any person or system related to the organization, also enables the integration and the improvement of processes.

Data Management

The AISR's methodology for data management increases the organization's ability to obtain, evaluate, preserve, manage, access and deliver the right data, where and when needed.

In addition to answering the following questions:

• Which data should be obtained?

• Which data can be obtained?

• What is the data quality?

• When the data are needed?

• What is the data lifecycle?

• How will data be obtained?

• How will data be processed?

• How will data be analyzed?


• Enabling more accurate and reliable insights.

• Anticipate and shape business outcomes.

• Enable a better service to customers and suppliers.

• Identify unknown visions and realities that are impacting or will impact the business.

• Minimize the amount lost opportunities.

• Identify the remaining or undiscovered issues.

• Uncover operational inefficiencies.

• Enable more accurate decisions.

• Enable easy and aggregate access to multiple sources of information (internal and external) and
  to content types.

Application Examples

• Extraction of Data, Facts and Concepts

• Semantic Search

• Sentiment Analysis

• Response to Questions

• Content Classification / Categorization

• Identification Trends and Patterns

• Identification and Extraction of Entities (Locations, People, organizations, brands, ...)

• Summarization

• Response to Events

• Identification of Similarities between Contents

• Content Conversion from Non-structured to Structured

• Content Filter by Relevance

• Content Accuracy Rating