Today, organizations are collecting data at every level of their business and in volumes that in the past were unimaginable. Data sets are stored in different database systems or in files with distinctive formats, all reflecting business process, application, program software, or information type dependencies. Adding to this complexity is the distribution of these data sets across the enterprise in silos requiring a varied set of tools and/or specialized business rules for data transformation, classification, matching, and integration. Because of the massive amounts of data stored in a variety of representation formats, decision makers strain to derive insights and create business solutions that adequately span and integrate information from these disparate technology islands. Learn more today!
WHITE PAPER
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T able of Contents Introduction ................................................................................................................... 3 What is Geographic Business Intelligence? .................................................................. 3 Deriving Insight from Spatial Analysis ........................................................................... 4 Overcoming Data Challenges ....................................................................................... 5 Organization and Operational Scalability .................................................................. 5 Information Integration .............................................................................................. 6 Information Quality .................................................................................................... 6 Addressing the Challenges ....................................................................................... 6 Geographic Business Intelligence Case Studies .......................................................... 7 Direct to Retail: VF Corporation's Retail Floor Space Management ......................... 7 Business-to-Business Services: Experian's Micromarketer Generation3 ................. 8 Summary - Return on Investment ................................................................................ 8
2 Introduction Today, organizations are collecting data at every level of their business and in volumes that in the past were unimaginable. Data sets are stored in different database systems or in files with distinctive formats, all reflecting business process, application, program software, or information type dependencies. Adding to this complexity is the distribution of these data sets across the enterprise in silos requiring a varied set of tools and/or specialized business rules for data transformation, classification, matching, and integration. Because of the massive amounts of data stored in a variety of representation formats, decision makers strain to derive insights and create business solutions that adequately span and integrate information from these disparate technology islands. To be most effective in meeting their business requirements, these decision makers must process information on demand from everywhere, along with the means to visually render, critically evaluate, and carefully develop accurate understandings of all their business transactions and activities. The importance of this outcome highlights the criticality of the decision makers' need to establish valuable information flow throughout the enterprise. Currently, due to the way that most enterprises collect and store data, the critical eye of the decision maker is frequently blinded by vast amounts of data that remain inaccessible to open and timely analysis. Spatial data, in particular, has long been considered ancillary to solving business process problems or in deriving actionable consumer and market insight, because of the challenges of integrating spatial and nonspatial data within traditional information management solutions. Yet spatial data plays a more centralized role than most organizations realize in the predictive and analytical underpinnings of complex business problems. Spatial data is essential to obtaining accurate and actionable insight, because there is a significant geographic dimension to every business transaction. Business questions focusing on where raw materials are obtained; where products are manufactured; where products are shipped; where product inventories are aging on the shelves of which stores; where products are advertised; or where products are consumed all highlight obvious geographic components. Ignoring the geographic dimension of inquiries such as these within the overall analytical capabilities of an organization's information management infrastructure can obscure insights t... [download for more]
