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Business Intelligence has become a very important in business arena irrespective of domain due to fact that managers need to analyze comprehensively in order to face challenges. To make business intelligence effective, having a Data Warehouse is an essential thing, because without power of a data warehouse, it is practically impossible to provide all information, reports and views required by management. There are many technical concerns when it comes to a technical implementation, but business stakeholders need to show right direction if they are to achieve success. This paper discusses business concerns related to a Business Intelligence initiative together with a Data Warehouse.

1 Introduction

Business Intelligence (BI) is an effort of increasing competitive advantage of a business by intelligent use of available data in decision making. Irrespective of domain that business is engaged in, competition in today’s business world has increased as never before. Thus all sorts of facilities are required to face challenge. Information Technology (IT) plays a major role in this regard and one of main concerns within overall IT strategy is Business Intelligence.

2 Business Intelligence & Data Warehousing in a Business Perspective - Body

2.1 Business Intelligence

Data sourcing, data analysing, extracting correct information for a given criteria, assessing risks and finally supporting decision making process are main components of BI.

In a business perspective, core stakeholders need to be well aware of all above stages and be crystal clear on expectations. The person, who is being assigned with role of Business Analyst (BA) for BI initiative either from BI solution providers’ side or company itself, needs to take full responsibility on assuring that all above steps are correctly being carried out, in a way that it would ultimately give business expected leverage. The management, who will be users of BI solution, and business stakeholders, need to communicate with BA correctly and elaborately on their expectations and help him throughout process.

Data sourcing is an initial yet crucial step that would have a direct impact on system where extracting information from multiple sources of data has to be carried out. The data may be on text documents such as memos, reports, email messages, and it may be on formats such as photographs, images, sounds, and they can be on more computer oriented sources like databases, formatted tables, web pages and URL lists. The key to data sourcing is to obtain information in electronic form. Therefore, typically scanners, digital cameras, database queries, web searches, computer file access etc, would play significant roles. In a business perspective, emphasis should be placed on identification of correct relevant data sources, granularity of data to be extracted, possibility of data being extracted from identified sources and confirmation that only correct and accurate data is extracted and passed on to data analysis stage of BI process. Business oriented stake holders guided by BA need to put in lot of thought during analyzing stage as well, which is second phase. Synthesizing useful knowledge from collections of data should be done in an analytical way using in-depth business knowledge whilst estimating current trends, integrating and summarizing disparate information, validating models of understanding, and predicting missing information or future trends. This process of data analysis is also called data mining or knowledge discovery. Probability theory, statistical analysis methods, operational research and artificial intelligence are tools to be used within this stage. It is not expected that business oriented stake holders (including BA) are experts of all above theoretical concepts and application methodologies, but they need to be able to guide relevant resources in order to achieve ultimate expectations of BI, which they know best.

Identifying relevant criteria, conditions and parameters of report generation is solely based on business requirements, which need to be well communicated by users and correctly captured by BA. Ultimately, correct decision support will be facilitated through BI initiative and it aims to provide warnings on important events, such as takeovers, market changes, and poor staff performance, so that preventative steps could be taken. It seeks to help analyze and make better business decisions, to improve sales or customer satisfaction or staff morale. It presents information that manager’s need, as and when they need it.

In a business sense, BI should go several steps forward bypassing mere conventional reporting, which should explain “what has happened?” through baseline metrics. The value addition will be higher if it can produce descriptive metrics, which will explain “why has it happened?” and value added to business will be much higher if predictive metrics could be provided to explain “what will happen?” Therefore, when providing a BI solution, it is important to think in these additional value adding lines.

2.2 Data warehousing

In context of BI, data warehousing (DW) is also a critical resource to be implemented to maximize effectiveness of BI process. BI and DW are two terminologies that go in line. It has come to a level where a true BI system is ineffective without a powerful DW, in order to understand reality behind this statement, it’s important to have an insight in to what DW really is.

A data warehouse is one large data store for business in concern which has integrated, time variant, non volatile collection of data in support of management's decision making process. It will mainly have transactional data which would facilitate effective querying, analyzing and report generation, which in turn would give management required level of information for decision making.

2.3 The reasons to have BI together with DW