They say a picture is worth a thousand words. That is just as true for a well-rendered map. Even for online or non-retail businesses, visual representation of exactly where your customers (existing an potential), suppliers, field employees, transit corridors, assets, pipelines, cables, and facilities are offers crucial insight into the efficiency and competitive advantage required for ongoing business success. Some of this data is already in-house, but often locked away in data silos invisible to key executives and management. And some of it exists in external sources such as trading partners, government entities and commercial datasets. Whatever the source, with the right tools, methods and expertise, it can all be integrated into an organization’s “canonical view” to enhance business analytics.

This kind of data integration is the traditional domain of Extract-Transform-and-Load (ETL) projects and tools. For decades, Decision Support Systems have provided unified numerical and graphical insight to executives and managers needing to make crucial decisions on which their companies depend for their success, and indeed often for their very survival. However, most ETL tools have little to no support for the acquisition, interpretation, translation, transformation, integration, storage, and presentation of geographic data, which is often in highly specialized formats requiring highly specialized algorithms for manipulation and map projection. Yet we live in an age of mobile devices, digital mapping, deep market geographics, and rapid sourcing and delivery of goods. Even the handful tools that can handle geodata are often limited by missing features and/or rigid assumptions. But the need is there, and continuously growing in all industries, not just those that pioneered Geographic Information Systems (GIS) for historically obvious applications regarding land property and natural resource extraction.

Peter Keenan recognized this back in 2005 when her wrote: “The ability to handle both spatial and non-spatial data appropriately is required for better support for management decision making in a range of applications.”1. Unfortunately, industry is only now catching up to this reality. Use cases including data error detection and cleansing; intelligent routing of people and goods; location-aware business analytics for marketing, sales, operations optimization, etc; Enterprise Resource Planning; and data warehousing have recently come to the forefront in such diverse industries as utilities (traditional as well as renewables), insurance, marketing, sales, mobile communications, and government services to name only a few. And these lists only scratch the surface of what is possible.

So if you are looking to implement a data warehouse/mart or other ETL project, you would be wise to select tools (and a team having the deep specialized experience to use them effectively) that support both regular and geo-data transformations. We use FME because it is the most powerful and flexible of such tools, capable of supporting arbitrarily complex custom business and data processes with both geographic and traditional data. As another old saying goes, “Location, location, location!”

1 Keenan, Peter (2005), “Spatial decision support”, Concepts and Theories of GIS in Business, (p. 16). University College Dublin.