This website uses first-party cookies and cookies _ga / _utm owned by Google Analytics, persistent for 2 years, to enable the function of control visits, in order to facilitate your browsing the website. If you continue browsing we consider that you agree with its use. You can revoke the consent and obtain more information by consulting our Cookie Policy. Accept

4 Keys to an Intelligent Information Management System

Feb. 22, 2017

In the knowledge age, information management poses a major challenge. Companies have more information than ever, but they cannot take full advantage of it with traditional tools. The solution lies in intelligent systems that are able to fit each user's needs.

An intelligent system can combine different solutions, such as Natural Language Processing (NLP), Semantic Networks, N-gram statistics, Support Vector Machine (SVM), or clustering and classification algorithms. Their selection depends on the goals and needs of each organization. However, we have detected four pillars that support the intelligence of an advanced information management system. When correctly applied, these keys maximize the competitive advantages detailed in this previous article .

Intelligent search engine

The ability to retrieve information quickly and accurately is key to managing knowledge. A good information management system needs a good search engine; that is, one able to understand the true intention of a query.

A word can have different meanings because of phenomena such as polysemy and synonymy. The process of identifying which sense of a word is used in a given context is called disambiguation. Semantic networks, which represent meaning relations between concepts, allow this word-sense disambiguation.

An intelligent search engine is also able to offer different answers to the same question. The application of machine learning solutions allows the engine to adapt the results to each user. Likewise, the results will be different depending on the time and place of the search.


A large volume of information is unmanageable without a labeling system that identifies, categorizes and hierarchically organizes data. Information Classification solutions are essential for fast retrieval of information, accuracy of search results, ease of navigation, data protection or precision of recommendations.

It is possible to combine automatic and manual systems:

  • Automatic tagging: The system is able to extract the most relevant information from each content. Based on this information, it forms groups, draws relationships and establishes hierarchies.
  • Manual tagging: Users categorize content by using their own tags. That way, they can adapt classification to their criteria.

Social system

Social features can be very useful in an information management system since they encourage knowledge sharing among employees.

Vertical social networks create productive synergies, reduce information silos, and maximize resource utilization. This tool can be used to solve doubts, to work in team, to recommend content, to learn collectively, to share good practices, to warn of risks or to exchange ideas.

By using a group system it is possible to connect users with common needs or goals. A group system also provides valuable information for user segmentation. Features such as likes, ratings or favorites improve the content selection process. In addition, such tools help users to take advantage of existing resources.

These advantages are maximized with the application of machine learning solutions . An intelligent system is able to learn from users’ social activity and, consequently, to adapt better and better to their needs.

Recommendation system

With an advanced information management system, users cannot only find what they are searching, but also discover interesting content that they did not even know existed. A recommendation system helps to make better use of existing resources and to avoid redundant efforts.

An intelligent system must be able to take into account all available information about the user (demographic variables, search history, social activity ...), as well as the preferences of users who share similar characteristics. By using Machine Learning algorithms, the system is able to learn from experience and constantly adjust its recommendations.