Using Database to improve business processes and decision making

Types of Database Design

• Conceptual Design
  • A database's conceptual design, often known as its logical design, is an abstract representation of the database from a business standpoint. It explains how the database's data elements are to be grouped. Following the collection and analysis of all necessary criteria, a conceptual schema for the database is created utilizing a high level conceptual data model. Conceptual design refers to this stage. An ER (Entity-Relationship) diagram is the end outcome of this stage. This high-level data model is intended for the given application domain. It depicts how various entities (items, objects) relate to one another. Additionally, it lists the features (or attributes) that each entity has to provide. All of the ideas (attributes, entities) in the application area are defined within.
• Physical Design
  • The database's real placement on a storage device is depicted in the physical design. The database implementation is the purpose of the physical design. It is important to understand the DBMS being utilized in the physical design. For instance, different DBMSs have different data types and different names for their various data types. The database creation SQL clauses are written. The access rights of the users, the rules (integrity constraints), and the indexes are all clearly stated. The database's test data is finally added.

Distributed Database

Distributed databases are those that store data across multiple locations. Following is a list of various distributed database types:

• Partitioned Database
  • Different database components are kept in separate locations.
• Replicated Database
  • Duplicate copies of a central database spread across several sites.

Data-Warehouse

• Colossal archive of non-volatile data that is recorded according to subject, date, and time (data from data-warehouse can only be accessed not changed). Data warehouse data cannot be updated. Data warehouse is used to enhance decision-making and profitability.

Data Mining

• Obtaining important info from a large data set. It is employed to enhance decision-making, probability growth, etc.

Using Database to Improve Business Processes and Decision Making

• Businesses utilize their databases to record routine business activities including paying suppliers, processing orders, monitoring customers, and paying workers. Databases are essential for organizations because they store information that enables management and staff to operate more effectively and make better decisions. Massive companies with large databases and large systems for distinct functions like manufacturing, sales, and accounting need specialized tools and capabilities for evaluating huge amounts of data and gaining access to data from various systems. Data-warehouse, data mining, and tools for internal database online access are some of these features.
   
  • Data-Warehouse
    • It is a database that contains recent to historical data that decision-makers across the firm may find interesting. Data from online transactions may also originate from many of the major operational transactional systems, such as the sales system, customer account system, and production system.
    • Information from various operational databases is standardized in data warehouses so that it may be used throughout the company for management analysis and decision-making. Anyone can access the data from a data warehouse as needed, but it cannot be changed. A variety of standardized query tools, analytical tools, and graphical representation facilities are also offered by data warehouses.
    • Information from data warehouses is also made freely available throughout the company through intranet. Data in a data warehouse are integrated, subject-specific, and non-volatile.

Data-Mart

It is a subset of a data-warehouse where a condensed or intensely concentrated section of organizational data is placed in a different database for a particular purpose of users. As an illustration, a business might create a marketing and sales data-mart to manage client information.

Tools for Business Intelligence

• Multidimensional Data Analysis –OLAP
  • It might also be referred to as online analytical processing (OLAP). Through the use of multidimensional criteria like product, pricing, cost, area, time period, age, group, etc., OLAP provides multidimensional data analysis and enables users to examine the same data in numerous ways. A production manager could gain information about the status of the product from various views, or dimensions, using a multidimensional data analysis tool. Even if the data are housed in a huge database, OLAP enables users to access online answers in a reasonable amount of time.
• Data Mining
  • Data can be further analyzed using business intelligence tools after being collected and stored in a data warehouse or data mart (BI). Software for database queries, reporting tools for multidimensional data analysis, and data mining tools are among the main BI technologies. driven by discoveries via data mining. It can be simply stated as the process of obtaining hidden knowledge from a sizable data warehouse of data. Data mining uncovers hidden patterns and relationships in huge databases, extrapolating rules from them to forecast future behavior, and thereby gaining insight into company data that cannot be achieved through OLAP. Data mining employs patterns and rules to help in decision-making and to predict the outcomes of those decisions. Data mining yields a variety of information, including:
    • Association
      • Are events connected to a single effect, such that when a client buys a coke, they also typically buy bhujiya eight out of ten times. Pen-refill
    • Sequence
      • Over time, events are connected. For instance, if a house is bought, a new refrigerator is also bought. Iphone-cover
    • Classification
      • It detects patterns that describe the group to which an item belongs by looking at already existing, classed items. It assists in identifying the qualities of a consumer who is most likely to use the product.
    • Clustering
      • It functions similarly to a categorization when no specified groupings have been established. We identify groups of customers for clustering using a demographic or psychographic mechanism.
    • Forecasting
      • It employs prediction in a unique way. It makes predictions about future value based on the perception of current value.

Different Terminologies

• Predictive Analysis
  • Predicts the result of an event using data mining techniques, historical information, and assumptions about future circumstances.
• Text Mining
  • Extracts important components from a huge unstructured data collection. Sample email from a store
• Web Mining
  • Finding and analyzing informative patterns and data from www. Example: Assess the effectiveness of the website to comprehend consumer behavior.
• Web-Content Mining
  • Information gleaned from website content.
• Web-Structure Mining
  • link to and from the website
• Web-Usage Mining
  • Data about user interaction captured by the web server

Reference

Laudon, Laudon, "Management Information Systems Managing the Digital Firm", twelfth edition