Business Intelligence Should be Centralized

The implementation of BI into the business strategy and culture is laden with many potential points that could result in failure of the initiative, leaving BI to be underdeveloped and a source of wasted resources for the company. Due to the unique nature of BI in the business space, properly setting up BI within the organizational structure from the onset of integration minimizes the impact of the most common hurdles to BI implementation. Many companies choose to mitigate these problems by using a centralized approach by building a Center of Excellence, but their place in the company’s organizational structure needs to be well-defined and properly empowered to be effective. This paper also reviews how the concept of centralization is defined, how it relates to the implementation of BI, and how it can effectively in overcome the common implementation hurdles.

Mitigating Risk

Business Intelligence (BI) has often been described as the tools and systems that play an essential role in the strategic planning process of a corporation. The application of BI is most commonly associated with the analysis of sales and stock trends, pricing and customer behavior to inform business decision-making. There is a growing trend in utilizing the tools and processes used in the analysis of data and applying them to security event management. Security Information and Event Management (SIEM) has emerged within the last 10 years providing a centralized source to enable both real-time and deep level analysis of historical event data to drive security standards and align IT resources in a more efficient manner.

Strategies for Improving the Efficacy of Fusion Question Answering Systems

Web search engines typically retrieve a large number of web pages and overload business analysts with irrelevant information. One approach that has been proposed for overcoming some of these problems is automated Question Answering (QA). This paper describes a case study that was designed to determine the efficacy of QA systems for generating answers to original, fusion, list questions (questions that have not previously been asked and answered, questions for which the answer cannot be found on a single web site, and questions for which the answer is a list of items). Results indicate that QA algorithms are not very good at producing complete answer lists and that searchers are not very good at constructing answer lists from snippets. These findings indicate a need for QA research to focus on crowd sourcing answer lists and improving output format.

Agile Development in Data Warehousing

Traditional data warehouse projects follow a waterfall development model in which the project goes through distinct phases such as requirements gathering, design, development, testing, deployment, and stabilization. However, both business requirements and technology are complex in nature and the waterfall model can take six to nine months to fully implement a solution; by then business as well as technology has often changed considerably. The result is disappointed stakeholders and frustrated development teams. Agile development implements projects in an iterative fashion. Also known as the sixty percent solution, the agile approach seeks to deliver more than half of the user requirements in the initial release, with refinements coming in a series of subsequent releases which are scheduled at regular intervals. An agile data warehousing approach greatly increases the likelihood of successful implementation on time and within budget. This article discusses agile development methodologies in data warehousing and business intelligence, implications of the agile methodology, managing changes in data warehouses given frequent change in business intelligence (BI) requirements, and demonstrates the impact of agility on the business.

Intelligent Analytics

Organizations use web analytic tools and technologies to measure, collect, analyze, and report web usage data to help optimize websites. Traditionally, most of this data tends to be non-transactional and non-identifiable. In this regard, there has not been much integration with transactional data that is collected, stored, analyzed, and reported through Business Intelligence (BI). Emerging trends in web analytics provide organizations the ability to aggregate and analyze web analytics data with transactional data to provide valuable insights for building better customer relationship strategies. In this paper, the authors give an overview of web analytics tools, key players, new technology trends and capabilities to integrate web analytics with BI so organizations can leverage intelligent analytics for new marketing initiatives. While the benefits are significant, there are some challenges associated with the integration and a few possible solutions to address.

Discovering Business Intelligence from the Subjective Web Data

The online word-of-mouth behavior that exists today in the Web represents new and measurable sources of information. The automated discovery or mining of consumer opinions from these sources is of great importance for marketing intelligence and product benchmarking. Techniques are now being developed to effectively and easily mine the consumer opinions from the Web data and to timely deliver them to companies and individual consumers. This study investigates this emerging field named ‘opinion mining’ in terms of what it is, what it can do, and how it could be used effectively for business intelligence (BI). A rigorous review of the research literature on opinion mining is conducted to explore its current state, issues and challenges for its use in developing business applications for competitive advantage. The study aims to assist business managers to better understand the current opportunities and challenges in using opinion mining for deriving BI. Future research directions for further development of the field are also identified.

Enterprise Intelligence

This paper examines the key issues associated with current and future implementations of business intelligence (BI). The authors review the literature and discover both the growing importance and emerging issues associated with BI. The issues are further examined with an exploratory, but detailed, case study of organizations from a variety of industries, yielding a series of lessons learned. The authors find that organizations are rapidly moving to an enterprise perspective on BI, but in an unsystematic way. The authors present a prescription for the future of BI called “enterprise intelligence” (EI). EI is described in a framework that combines elements of hierarchy theory, organization modeling, and intellectual capital.

Champion for Business Intelligence

Although Gartner’s EXP 2006 CIO Survey ranked Business Intelligence (BI) as the top technology priority, BI projects face tough competition from other projects in IT portfolios promising more tangible financial returns (Wu & Weitzman, 2006) Two major hurdles that prevent BI projects from shining in portfolios are vague requirements and weak benefits calculations. Both can be addressed by examining and learning from a number of case studies that prove tangible ROI on BI solutions when scoped and designed with a focus on specific, measurable, achievable, results-oriented, and time bound SMART business goals. In order for BI projects to compete in IT portfolios based on financial measures, like ROI, BI champions need to approach BI requirements gathering with the goal of addressing a specific business problem as well as employ standard ways of calculating BI benefits post project go live. By examining common failures with BI requirements and case studies which demonstrate how successful BI implementations translate into tangible benefits for the organization, BI champions develop a toolkit of tips, tricks, and lessons learned for successful requirements gathering, design, implementation, and measure of business results on BI initiatives.

Using Business Intelligence in College Admissions

Higher education often lags behind industry in the adoption of new or emerging technologies. As competition increases among colleges and universities for a diminishing supply of prospective students, the need to adopt the principles of business intelligence becomes increasingly more important. Data from first-year enrolling students for the 2006-2008 fall terms at a private, master’s-level institution in the northeastern United States was analyzed for the purpose of developing predictive models. A decision tree analysis, a neural network analysis, and a multiple regression analysis were conducted to predict each student’s grade point average (GPA) at the end of the first year of academic study. Numerous geodemographic variables were analyzed to develop the models to predict the target variable. The overall performance of the models developed in the analysis was evaluated by using the average square error (ASE). The three models had similar ASE values, which indicated that any of the models could be used for the intended purpose. Suggestions for future analysis include expansion of the scope of the study to include more student-centric variables and to evaluate GPA at other student levels.

Test-Driven Development of Data Warehouses

Test-driven development is a software development methodology that has recently gained a great deal of traction in the software development community. It focuses on creating software-based test cases that define the business requirements of an application before beginning the coding of the application itself. This paper proposes that test-driven development could be a useful methodology for data warehouse projects, in that it could help team members avoid some of the major pitfalls of data warehousing, and result in a higher-quality end product.