Inteligencia de negocios basada en Bases de Datos In-Memory

La tecnologí­a In-Memory ha sido propiciada por la necesidad de procesamiento de grandes volúmenes de datos de manera muy rápida y fundamentalmente por el desarrollo de los procesadores y el incremento en la capacidad de memoria basada en la arquitectura de 64-bits. Esto ha hecho posible el procesamiento paralelo masivo de las operaciones de base de datos, albergando todos los datos relevantes en memoria. Una base de datos In-Memory (IMDB) usa la memoria como el principal soporte de almacenamiento, y para su procesamiento en memoria no requieren su paso desde el disco duro hacia ella, lo que reduce el tiempo de respuesta de la base de datos dramáticamente. Las bases de datos tradicionales almacenan la data en disco y las operaciones de I/O son muy lentas comparadas con aquellas hechas en memoria RAM.Las IMDBs poseen una técnica de almacenamiento columnar lo que posibilita el acceso a la data a grandes velocidades y capacidades de analí­tica en tiempo-real. Al organizar los valores en la forma de un vector de atributos (columnar) permite una fácil compresión de datos y también permite una alta velocidad de escaneo y filtraje. La velocidad es en efecto tan alta que se puede dejar de lado la idea de pre-agregación de la data transaccional, la base de los sistemas de información en las décadas pasadas

In-Memory Technology and the Agility of Business Intelligence – A Case Study at a German Sportswear Company

The retail industry has changed significantly due to altered shopping behavior of customers and technological advancements in recent years. This enforces organizations to quickly adapt to these dynamically evolving circumstances. Most of the major organizations utilize Business intelligence (BI) to support their corporate strategies. Therefore, the adaptability of BI gained increasing importance in theory and industry practice over the last years. Agility is particularly challenging in the domain of BI since the underlying architecture of enterprise-wide decision support with data warehouse (DWH)- based BI is not built upon agility, but on reliability and robustness. Although the usage of agile project approaches like Scrum has been explored, there is still a lack of research investigating further effects on BI agility. Hence, we analyzed whether the characteristics of DWH and BI impact the agility of BI in an in-depth case study at a globally operating German sportswear designer and manufacturer. In particular, we want to identify if a technology like in-memory can help to achieve more BI agility. The findings indicate that IM technology acts as a technology enabler for agile BI. The impact of some DWH characteristics on BI agility is significantly positively influenced if IM technology is used

Ketterän liiketoimintatiedon hallinnan mahdollistajat - Case SAP

One of the key requirements for achieving competitive advantage is to utilize gathered information more effectively than before with the help of emergent technology innovations and enhanced information management. In order to remain competitive and compete with the help of data, organizations and researchers have paid attention to a new wave of business intelligence, referred to as agile business intelligence. Agile business intelligence enables faster decision-making in faster pace than traditional business intelligence due to the emergence of new technology directions. Hence, the technology has evolved in a way that agile business intelligence can bring more value to the organizations simplifying the business intelligence architecture and enhancing data processing by utilizing operational data more effectively. The primary objective of the thesis was to identify the key factors that enable agile business intelligence. The secondary objective was related to the benefits that agile business intelligence provides to the organizations compared with the traditional business intelligence solutions and platforms. The thesis consisted of two different parts: the first part was related to investigate agile business intelligence from the academic point of view using a systematic literature review as a research method. In this part, the definition of agile business intelligence was formalized and the different enablers and benefits were discovered based on the literature. The second part was related to investigate agile BI enablers, which were founded in internal training materials regarding the SAP landscape. Findings from the latter part were reflected on the findings from the first part drawing a synthesis between the enablers and benefit from the different parts. The key findings of agile BI were divided into two main categories: agile methodologies and agile technologies. The first ones were related to the different agile development methods of business intelligence such as Scrum in order to organizations are able to react faster pace to the changing requirements in the business environment. The key enablers of the latter category were in-memory BI, mobile BI, cloud BI, operational BI and self-service BI. The main benefits of these enablers were related to the reduced query processing providing real-time data on decision-making, the increased flexibility of the systems and easier access to the data which facilitate more accurate and punctual decision-making. These benefits reflected on SAP BI landscape which provided the same benefits but also simplification was in a central role in SAP BI landscape which reduces the need for extract and load data from the different source systems