Business Intelligence and Data Warehouses

Business Intelligence and Data Warehouses

Business intelligence (BI) is an expertise-motivated technique that assesses facts and avails actionable data to support business managers, corporate administrators and other end-users in decision-making (Loshin, 2012). Business intelligence incorporates data warehousing, data mining, database managing schemes, etc. At the same time, data warehouse setting encompasses mining, transmission, transfiguration, solution stuffing, and online analytical processing (OLAP). A database is a systematic assemblage of interrelated information. Databases are important in informed decision-making and are helpful in online transaction processing (OLTP) (Loshin, 2012). A database management system (DBMS) refers to an assortment of interconnected data and a set of programs to access such data. A DBMS is responsible for creation and management of databases, i.e. data updating, deletion, retrieval, and creation (Loshin, 2012). The purpose of the current paper is to evaluate business intelligence components and data warehouses, i.e. databases, and DBMS.

Dissimilarities between a Relational Database Structure for Online Transactions and a Data Warehouse for Processing and Summarizing Large Amounts of Data

Schematic Strategy

Data warehouses are typically well-organized and use de-stabilized or moderately de-stabilized structures to advance review depiction. Nonetheless, relational databanks use homogenous plans to modify, describe, supplement, and obliterate presentation (Grundspenkis, Morzy, & Vossen, 2009).

Information Modifications

Data warehouse is rationalized on a methodical base with the help of bulk data alteration approaches. However, in OLTP structures, end-users recurrently distribute disconnected information modification avowals to the database (Grundspenkis, Morzy, & Vossen, 2009). Nevertheless, the OLTP collection is continuously upated and duplicates the contemporary position of all procedures.

Workload Capacity

Data storeroom is calculated to support special explorations, hence, facilitating a business to recognize its data warehouse volume in advance. Consequently, it helps countenance the augmentation of data storage to implement a wide-ranging multiplicity of probable request processes (Grundspenkis, Morzy, & Vossen, 2009). A relational database is regulated to only tolerate demarcated responsibilities.

Dissimilarities between Database Necessities for Operational Data and Decision Support Data

Time Duration

Operational data displays the actual position of corporate dealings. Nevertheless, decision support information is a historic periodic sequence of operating data, i.e. company’s description information at a specified period (Laudon & Laudon, 2014).

Information Configurations

Operational information is kept in table-organized interpersonal files to evade data inconsistencies, whereas decision support data is combined of numerous occasionally amassed and condensed operating files in the databank to support pre-defined decision support enquiries (Laudon & Laudon, 2014).

Data Precariousness

Operational facts are unstable as variations occur each time fresh dealings transpire. On the contrary, decision support data is stable as it allows the addition of data bunches online without apprising data alteration (Laudon & Laudon, 2014).

Ways in which Databases Can Support Decision-Making

Tracking dispatch notes are necessary to study consumer purchase patterns to enable firms to produce the mostly purchased products in bulk to ensure their sufficient amount.

Manufacturing firms, construction companies, etc. keep invoice records of their raw materials suppliers, which enables them to compare differentt providers, hence, choose the supplier with affordable prices. In its turn, it helps reduce operating costs and increase profits (Laudon & Laudon, 2014).

Observing databank portfolio of the last customer who purchased a significant number of products from the organization helps study the customer better. Moreover, the company can make such customers its corporate clients and always inform them about new products (Loshin, 2012).

Ways in which Data Warehouses and Data Mining Can Support Data Processing and Trend Analysis

Order record structures is one of the ways to support data processing and trend analysis. Different firms use order file systems to assist them in tracking all company undertakings to establish daily operations that will aid in monthly financial reports preparation (Laudon & Laudon, 2014).

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Scanner-based point of sale catalogues in corporations aid in recording the organization’s transactions and retain proof, if receipts are misplaced (Laudon & Laudon, 2014). Record maintenance helps compare revenues in different periods.

ATMs (automatic teller machines) minimize the banks’ work as they help reduce lines in the banking rooms and also retain chronicles that help in trend investigation concerning the number of customers who were served in a particular ATM at a given moment (Laudon & Laudon, 2014).


Information alterations, workload capacity, and schematic policy are the key ways that differentiate OLTP and OLAP. On the other hand, operational data and decision support data contrast in information arrangements, data capriciousness, and time duration. At the same time, databases systems are helpful in identifying corporate clients, invoice tracking, etc., while order record structures, ATMs, and scanner-based point of sale catalogues help in trend analysis and data back-up.

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