By N.Gopinath AP/CSE

There are 5 categories of Decision support tools, They are; 1. Reporting 2. Managed Query 3. Executive Information Systems 4. OLAP 5. Data Mining

Reporting tools can be divided into two types. 1. Production reporting tools 2. Desktop report writers. Production Reporting Tools: It will let companies to generate regular operational reports or support high volume batch jobs, such as calculating and printing paychecks. This tool includes third generation languages such as COBOL, specialized fourth generation languages and high end client/server tools.

2. Report writers: Inexpensive desktop tools designed for End users. Generally they have graphical interfaces and built in charting functions. They can pull a group of data from a variety of data sources and integrating them in a single report. Vendors are trying to increase the scalability of report writers by supporting 3-tired architecture in Windows NT and Unix server. At the beginning they are offered for Object oriented interfaces for designing and manipulating reports and modules for performing ad hoc queries and OLAP Analysis.

It shields end users from the complexities of SQL and Data base structures by inserting a meta layer between Users and the Data base. Meta layer is the software that provides subject oriented views of a data base and supports point –and –click creation of SQL. They have embraced three tiered architecture to improve scalability They support asynchronous Query execution and integrate with web servers. These vendors are racing to embed support for OLAP and data mining features.

 An executive information system (EIS) is a type of management information system intended to facilitate and support the information and decision-making needs of senior executives by providing easy access to both internal and external information relevant to meeting the strategic goals of the organization.  It is commonly considered as a specialized form of decision support system (DSS).

 The emphasis of EIS is on graphical displays and easy-to-use user interfaces.  They offer strong reporting and drill- down capabilities.  In general, EIS are enterprise-wide DSS that help top-level executives analyze, compare, and highlight trends in important variables so that they can monitor performance and identify opportunities and problems.

Advantages of EIS  Easy for upper-level executives to use, extensive computer experience is not required in operations  Provides timely delivery of company summary information  Information that is provided is better understood Disadvantages of EIS  System dependent  Limited functionality, by design  Information overload for some managers

 In computing, online analytical processing, or OLAP is an approach to swiftly answer multi-dimensional analytical (MDA) queries.  OLAP is part of the broader category of business intelligence, which also encompasses relational reporting and data mining.  Typical applications of OLAP include business reporting for sales, marketing, management reporting, business process management (BPM),budgeting and forecasting, financial reporting and similar areas, with new applications coming up, such as agriculture.  The term OLAP was created as a slight modification of the traditional database term OLTP (Online Transaction Processing).

 OLAP tools enable users to interactively analyze multidimensional data from multiple perspectives.  OLAP consists of three basic analytical operations: consolidation (roll-up), drill-down, and slicing and dicing.  Consolidation involves the aggregation of data that can be accumulated and computed in one or more dimensions.  Slicing and dicing is a feature whereby users can take out (slicing) a specific set of data of the cube and view (dicing) the slices from different viewpoints.

 Data mining tools are software components and theories that allow users to extract information from data.  The tools provide individuals and companies with the ability to gather large amounts of data and use it to make determinations about a particular user or groups of users.  Data mining tools can be classified into one of three categories: 1. traditional data mining tools 2. dashboards, and 3. text-mining tools.

 Help companies establish data patterns and trends by using a number of complex algorithms and techniques.  Some of these tools are installed on the desktop to monitor the data and highlight trends and others capture information residing outside a database.  The majority are available in both Windows and UNIX versions, although some specialize in one operating system only.  While some may concentrate on one database type, most will be able to handle any data using online analytical processing or a similar technology.

 Installed in computers to monitor information in a database.  Dashboards reflect data changes and updates onscreen — often in the form of a chart or table — enabling the user to see how the business is performing.  Historical data also can be referenced, enabling the user to see where things have changed (e.g., increase in sales from the same period last year).  This functionality makes dashboards easy to use and particularly appealing to managers who wish to have an overview of the company's performance.

 Its ability to mine data from different kinds of text — for example from Microsoft Word and Acrobat PDF documents to simple text files.  These tools scan content and convert the selected data into a format that is compatible with the tool's database, thus providing users with an easy and convenient way of accessing data without the need to open different applications.  Scanned content can be unstructured (i.e., information is scattered almost randomly across the document, including s, Internet pages, audio and video data) or structured (i.e., the data's form and purpose is known, such as content found in a database).

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