Application of Data Warehouse Technology in Indian Enterprises

With China's accession to the WTO and the wide improvement in the degree of global informatization, the printing industry has brought great development opportunities and great challenges. In such an era of fierce competition, how can a printing company win in many enterprises? Today, in the information age, companies can more effectively tap into core information resources from massive information, thus providing more for business managers. The decision reference for value is worth paying attention to.

A variety of application subsystems have also been established in many of our printing companies, such as customer service subsystems, production subsystems, financial subsystems, sales subsystems, etc. Satisfying the requirement means here that the analysis process only involves less data information. When the amount of data rapidly increases and query requirements continue to be complicated, database systems that are frequently manipulated are often overwhelmed. Therefore, a new technology must be adopted to enable it to perform complex analysis and data warehouse technology emerged.

First, data warehouse technology

Data warehouse technology emerged in the 1980s. In the 1990s, Dr. W.H. Inmor, a well-known American engineering engineer, gave the definition of data warehouse: The data warehouse is topic-oriented, integrated, non-renewable (stability ) Data collections that change over time (including historical data at different times) to support the decision-making process in business management. It is an enterprise information management solution that is a system architecture, not a software product or application. The data warehouse architecture can integrate business data distributed in various structural databases at different sites in the enterprise network and is the core of the decision support system environment based on large-scale databases. It is a relatively effective solution to the current problem of over-consumption of data and lack of useful information in the business management and decision-making activities.

1. Data Warehouse Features

Unlike data warehouses and dedicated databases, it has the following characteristics:

1 The data warehouse is subject-oriented. It takes the content of business work topics as the main line, obtains effective information data from a large number of decentralized dedicated databases, performs physical partitions according to the subject areas, converts and organizes them into new storage systems, and aggregates the data. The new special structure, and establish the link relationship between the various topic areas. It should be said that the topic is the standard for data acquisition and classification in data warehouses.

2 Integration is an important feature of data warehouses. The data warehouse data comes from different application systems, uses different data structures and types, and has different encoding methods. Therefore, it is not possible to simply copy every detail data, but to process the data and unify the different types of data to the data warehouse model. Data integration is a crucial link in the construction of data warehouses.

3 Changes over time are another feature of data warehouses. The general private database only stores the currently running application data. The data at other times is only stored as a backup. There is no contact before and after. Data warehouse data is often used as a trend analysis. It needs sufficient historical data and the time span can be very long. Timeliness is an inevitable requirement for data warehouses to be used as internal rules for analyzing data.

4 Another characteristic of data warehousing is the non-volatile nature of the data (ie stability). The data warehouse data is the analysis data for a comprehensive processing of a certain topic. Once the data is formed and loaded into the data warehouse, the manager is not allowed to change or delete at will, and can only be periodically refreshed.

2. The composition of the data warehouse system

Data warehouse technology is actually an information integration technology. The data warehouse is to obtain raw data from a variety of information sources, after finishing processing and then stored in the internal database of the data warehouse. Then it provides integrated information to users through access tools to help the company's business managers conduct in-depth and comprehensive analysis to support the company's overall decision-making. Based on these needs, a data warehouse generally includes the following parts:

1 Data Source: Provides source data for the data warehouse, such as business database, production database, etc.

2 Data extraction, transformation and loading tools: Extract data from data sources, reorganize processing, and load it into the target database.

3 Data modeling tools: Establish information models for source databases and target databases.

4 Core Warehousing: Store data models and metadata.

5 Target database: Stores data that has been verified, organized, processed, and reorganized.

6 Front-end data access and analysis tools: Enterprise decision-makers and business analysts use these tools to further analyze the data in the target database.

7 Data warehouse management tools: Provide management tools for data warehouse operations, such as security management, storage management, and so on.

Second, the data warehouse module in the printing industry

At present, the establishment of data warehouses in most enterprises is mainly based on business topics and data integration. The printing companies are also the same. According to the characteristics of the printing industry, the current printing enterprise data warehouse mainly includes the following modules:

1 Customer analysis module: It mainly analyzes the types and composition of customers, finds out core customers and valuable customers based on past business distribution, and finally analyzes the key factors affecting business volume. Through this module, companies can clearly analyze the market prospects and establish a good mutual trust mechanism with customers.

2 production management analysis module: systematically analyzes the entire production process, mainly from the production efficiency analysis, the degree of reasonable arrangement of various departments processes, analysis of efficiency trends and other aspects to help managers to grasp the production and operation situation and affect the production The key factors, and then take corresponding solutions to the problems, in order to improve the company's production efficiency and management level.

3 Order Business Analysis Module: As a printing industry in a special industry, it does not provide sales work on its own. It is based on orders for production. Therefore, the analysis of orders is very important, mainly from the order type, business volume, regional distribution, customers, with the single situation and printing methods, etc. to analyze, and then the company's business volume in recent years, mining, analysis The changes in business volume and major influencing factors will accurately reflect market trends and make correct decisions for the further development of the company.

4 Logistics Management Analysis Module: This section mainly includes analysis of purchase, inventory, shipment, material utilization, transportation costs, distribution costs, etc., from which the main aspects of impact costs are obtained, and the relevant improvement systems are established in time to improve the efficiency of the company.

In addition, there are financial analysis, personnel analysis and other modules, depending on the specific circumstances of the company. In addition to these analysis modules in the data warehouse, there must also be corresponding fact tables, such as customer data fact tables, quote management fact tables, and so on.

Third, the establishment of printing enterprise data warehouse

Establishing a data warehouse in a printing company, like other industries, must follow the following principles:

1 Gradual Progressive Principle: The construction of a data warehouse is a large-scale, high-risk and long-term investment and cannot be accomplished overnight. Do not expect to build a large-scale, comprehensive data warehouse from the beginning. However, it is necessary to start from the smaller, clear-cut, and more data-consistent themes, from simple to complex, from local to global, in phases.

2 Scalability Principle: The scale of data warehouses expands with the enlargement of the subject area. As for a certain topic, it also dynamically changes with the increase of data. Therefore, building a data warehouse must exhibit scalability in terms of data architecture, data storage, and data processing.

3Principles of practicality: The structure of the data warehouse is driven by business requirements, and data is integrated according to business topics.

To build a data warehouse in current printing companies cannot use the traditional life cycle method. Instead, a rapid development method is used, similar to the rapid prototyping method. After determining the topics such as order business, customer, and production, the company conducts investigation and analysis, and then builds a mathematical model (usually a star model) based on the data of various existing subsystems, and directly establishes a data warehouse. And to achieve a system prototype for users to try out, timely feedback on the use of information; then companies based on these feedback information, and gradually adjust the system prototype, so that it gradually improved, to provide managers with more satisfactory decision-making services. This data warehouse system is built under a new architecture. It has comprehensive development tools that can meet the needs of various users in a timely manner.

Source: "Guangdong printing" On one of: Meng Jie