Datawarehouse, OLAP, Datamining and Balanced Scorecard

Datawarehouse, OLAP, Datamining and Balanced Scorecard

1. 2.1 BUSINESS INTELLIGENCE Business intelligence is defined as the corporate ability to make decisions. This is achieved through the use of methodologies, applications and technologies that allow gathering, cleaning, transforming data, and applying analytical knowledge extraction techniques to them (Parr 2000), the data can be structured to indicate the characteristics of an area of interest (Stackowiak et al. 2007), generating knowledge about business problems and opportunities so that they can be corrected and taken advantage of respectively. (Ballard et al. 2006) Implementing BI tools within the organization allows supporting the decisions that are made; at the internal level it helps in personnel management (Sharma et al. 2009) and on the external side it produces advantages over its competitors (Maureen 2009). There are times when all the benefits of BI cannot be achieved; due to the process involved in implementing a project of these characteristics, mistakes can be made in defini

   • 2.4 COMPREHENSIVE CONTROL PANEL The Balanced Scorecard is a tool that allows the objectives of the different areas or units to be aligned with the company's strategy and to monitor its evolution6 . The use that can be given to a Balanced Scorecard is so diverse that staff self-assessments can be contemplated (Martínez 2008), up to the definition of purely organizational concepts such as; the mission, the quality policy; communication plan, corporate image, training actions, catalog of services; the preparation of a client portfolio and the carrying out of actions to better understand their opinions and preferences, as well as to personalize the presentation

2. 2.2 DATA WAREHOUSEThis is the process of extracting data from different applications (internal and external), so that once they have been refined and specially structured, they are stored in a consolidated data warehouse for business analysis. It requires a combination of methodologies, techniques, hardware, and software components that together provide the infrastructure to support the information process (Stackowiak et al. 2007). The structure that is defined must reflect the needs and characteristics of the business, its departments, work teams and managers1, this will allow answering questions generated when trying to make decisions (Witten 2000) and over time it becomes the corporate memory (Wang 2009); describing the past and present of the company. Data Warehouse breaks down, summarizes, orders and compares, but it does not discover or predict. (Flores 2004) Three stages are established for the construction of a Data Warehouse; The first is dedicated to examining the Entity Rela

   • 2.5 DATA MINING It is the process of Selecting, Exploring, Modifying, Modeling7 and evaluating large amounts of data with the aim of discovering knowledge (Pérez 2006). The process must be automatic or semi-automatic. The models found must be significant demonstrating a certain pattern or rule of behavior8. The most used applications are those that need some kind of prediction. For example, when a person applies for a credit card, the issuing company wants to predict if the person classifies with the identified profile of delinquent users (Silberschatz et al. 2006). Data mining allows effective real-time management, it is a tool applicable to any type of company. A wide range of companies can have successful applications with it (Angeles et al. 2010). Benefits associated with data mining (López 2004): Increased results as a result of increased market share; Customer loyalty given a better response to their requirements; Performance improvement; Risk factor reduction; Optimization of st

3. 2.3.1 TYPES OF OLAP SYSTEMS Traditionally, this system is classified according to the following categories: • ROLAP. Implementation that stores the data in a relational engine. Typically, the data is detailed, avoiding aggregations and the tables are normalized. •MOLAP. This implementation stores the data in a multidimensional database. To optimize response times, the information summary is usually calculated in advance. •HOLAP (Hybrid OLAP). It stores some data in a relational engine and some in a multidimensional database5. Like Data Warehouse, OLAP is also applicable to a wide range of different topics, one of them is Spatial Databases, providing necessary characteristics for geographic type systems; such as facts, dimensions, members, levels, hierarchies, navigation operations, consolidation operations, and weather behavior (April 2007) (Bernier et al. 2009). MOLAP and ROLAP storage is also used to generate indexes that improve query access times so that information delivery times

   • 3. CONCLUSIONS •BI provides a fast and effective way to collect, abstract, present, format and distribute information from your corporate data sources, allowing business professionals, both inside and outside the organization, to visualize and analyze accurate data about core business activities and use it to improve decision-making and strategic planning. (Zúmel 2008) •A new way of implementing BI within organizations is the use of BI Governance, which combines BI techniques with the Agile manifesto, IT Governance and Data Governance, and this set of theories results in the administration process and monitoring of the implementation of a BI project. •In this research, new and different ways of complementing the work with Data Warehouse were discovered, optimizing data transfer times from the transactional system to the data warehouse, accompanying the assembly process of a Data Warehouse and a study of the return of the investment. •The main teaching that is established with this work