Read the text: Information Analytics (Part II)

Information analytics is a term used to describe the collection and analysis of data. The use of powerful computers to identify patterns and trends in a data set is not new, but has been greatly enhanced in the past few years. As a result, the quality of the output is high enough to be used as a tool in the decision making process.

The most common use of information analytics is to study business data with a combination of statistical analysis and data mining techniques. The purpose of this type of analysis is to identify trends that can result in action. It is very common for statisticians to identify trends or patterns in behavior that are interesting. However, in a business setting, the organization needs to be aware of patterns that can be used to generate revenue or decrease losses.

Every company interested in information analytics needs to invest in both staff and technology. Information analytics is typically organized as part of a business intelligence within theinformation technology department. Staff members working with analytics have typically completed post-secondary education in statistics, advanced mathematics, informationtechnology, or programming. It is not uncommon for all the staff to have master's and doctoral degrees in any of these fields. The level of skill and knowledge required to effectively use these tools, design queries, and analyze the results are quite high.

From a technological perspective, a business intelligence or statistical data management tool is required to perform the types of complex analysis required. In addition to specialized software, there is a substantial investment in hardware or connectivity necessary. In an ideal scenario, business data from the enterprise resource planning (ERP) system is accessible with the analysis tool. A common optional method is to duplicate the relevant data into a separate data warehouse used strictly for reporting and data mining or analysis activity.

Setting up this structure is quite complicated and requires a project team with staff who have expertise in both software implementation, data management, infrastructure, and related tools. If a separate environment is created for data analysis, additional hardware will need to be purchased, supported, and maintained.

The primary purpose of information analytics is to use existing data to gain valuable insights into customer behavior and motivation. From an organizational perspective, it is essential to use this information to help in the decision making process. The correct use of this type of data can help firms decide what types of products to launch, when, and in which markets.

Information Analytics was formed in January, 1995. The owners, Kenneth Livingston and Mark Dahmke have been in the information services, database and computer graphics business for over twenty years. Our design team is fluent in Java, C, C++, Visual Basic, PHP, XML, SOAP, ASP, JSP, databases, client-server programming, 3D computer graphics, Windows and Unix.

Information Analytics

Intervise Information Analytics utilizes Semantic Web technology to analyze and visualize undiscovered relationships between multiple data sources regardless of the platform, language and/or thetechnology used to create it. Intervise provides this unprecedented offering by combining advanced technologies with Feast™, a unique Semantic-based information management tool for data analysis which provides support for visualization, transformation, data source integration and asynchronous operations and is ontology agnostic.     

Developed internally, the intervise Semantic Web Implementation Methodology (SWIM) has been endorsed by the Massachusetts Institute of Technology Research Establishment (MITRE) and the National Institute of Standards and Technology (NIST). 

Specifically, Intervise Information Analytics provides:

Relationship Visualization

Intervise provides the capability to mine data and allow it to be visualized in multiple and customizable ways in order to highlight previously undiscovered relationships between multiple data sets.

Interoperability

Intervise delivers accelerated information retrieval capable of pulling data from multiple and varied systems, platforms, databases, web applications and documents regardless of the language and method by which it was originally created.

Information Analytics Suite

Intervise combines the Relationship Visualization and Interoperability tools to provide unprecedented capabilities in data search, extraction, visualization, relationship visualization, documentation and interoperability.

The exceptional benefits of Semantic Web technology include:

•      Communication between systems is simpler.

•      Data can be extracted and integrated across organizational boundaries and varied data sets.

•      Systems are significantly less likely to need expensive software changes.

•      Changing data requirements are easily managed through pluggable ontologies or vocabularies.

•      Previously undiscovered relationships between data can be visualized.

I.     Answer the questions:

1)    What is the most common use of information analytics?

2)    What is every company interested in?

3)    Database is universal, isn`t it? And why?

4)    What is Semantic Web Implementation Methodology?

5)    What are the advantages of Semantic Web technology?      

II. Name the key-words that help you to catch the main idea of the text.

Lesson 5