Abstract
Disaster recovery management requires agile decision making and action that can be supported through business intelligence (BI) and analytics. Yet, fundamental data issues such as challenges in data quality have continued to plague disaster recovery efforts leading to delays and high costs in disaster support. This chapter presents an example of these issues from the 2005 Atlantic hurricane season, where Hurricane Katrina wreaked havoc upon the city of New Orleans forcing the Federal Emergency Management Agency (FEMA) to begin an unprecedented cleanup effort. The chapter brings to light the failings in record keeping during this disaster and highlight how a simple BI application can improve the accuracy and quality of data and save costs. It also highlights the ongoing data driven issues in disaster recovery management that FEMA continues to confront and the need for integrated centralized BI and analytics solutions extending to the supply chain that FEMA needs to become more nimble and effective when dealing with disasters.
TopBusiness Intelligence
Much like past trends in information systems, BI has a rich background that is several decades old. BI’s background can be traced back to decision support systems (DSS) in the mid 1960’s (Power, 2003). The purpose of the first decision support systems was to help managers make key decisions. Since then, its functionality has been repackaged with new technology additions. Starting with executive information systems (EIS), DSS has evolved to real time dashboards to specifically address the needs of senior executives (Watson and Frolick, 1993) An EIS provides electronic dashboards, a graphical user interface, that offers an intuitive arrangement of key measures customized for senior executive needs. Furthermore, it grants senior management the ability to drill down to the level of detailed data required.
Key Terms in this Chapter
Agile Development: A group of software development methodologies based on iterative development, where requirements and solutions evolve through collaboration between self-organizing cross-functional teams.
BI Cloud: An external service provider hosts the technical infrastructure for BI (e.g., servers, BI software) and company data is transmitted, stored, analyzed, and the analyses returned over the internet.
Machine Learning: Composed of many technologies (such as deep learning, neural networks and natural language processing), used in unsupervised and supervised learning, that operate guided by existing data.
Augmented Analytics: The use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation and insight explanation to augment how people explore and analyze data in analytics and BI platforms.
Analytics: An umbrella term for data analysis applications. It is the use of “rocket science” algorithms (e.g., machine learning, neural networks) to analyze data.
Business Intelligence: A broad category of applications, technologies, and processes for gathering, storing, accessing, and analyzing data to help business users make better decisions.
Artificial Intelligence (AI): Applies advanced analysis and logic-based techniques to interpret events, support and automate decisions, and take actions.
Data Warehouse: Provides a single version (or source) of the truth for decision support data. The data is extracted from source systems (e.g., operational systems, ERPs), transformed (e.g., consistent formats), integrated (e.g., around a common key, such as a customer ID), and loaded into the data warehouse.