Marrying Business Intelligence to GIS Data: A Power Couple for Your Business

As Hurricane Katrina swept into the Gulf of Mexico late last summer, there were a lot of unanswered questions: Would the levees in New Orleans hold? Would the roofs of houses along the fragile Mississippi coastline withstand the storm? What would happen to the oil rigs in the Gulf and to the refineries in the region?

For companies selling roofing supplies, government officials planning evacuations, or oil industry execs estimating the impact on production, data from previous storms and company records provided clues, but they were often cumbersome to sift through. And geospatial data is often left out of the equation. The ï¿1⁄2whereï¿1⁄2 question ï¿1⁄2 rarely answered through traditional means ï¿1⁄2 is becoming increasingly important.

Geospatial data and analysis add value beyond simple visualization of relationships. Marrying GIS and business intelligence (BI) software provides a visual means of forecasting and analyzing data to assist in making better-informed decisions. Companies need to know not merely what happened, but what is likely to happen. Being able to extract, manipulate, store, retrieve, report and forecast complex data, including geospatial data, helps us understand the rest of the story.

Storms aren't the only situations in which forecasting and analytics, coupled with mapping software, can provide governments and companies with critical information. For example, real estate executives searching for the right location for a new outlet can make better decisions if they can combine demographic data with mapping. Which is easier to understand - a list of zip codes with the right demographics, or geospatial software that highlights busy intersections in those zip codes? Maps can convey a significant amount of information in a digestible form. You can identify a region, select and interact with the data for that region, and then narrow and pinpoint what data you want analyzed using BI software.

A question of 'where'
As Hurricane Katrina began to turn toward the Gulf Coast, the National Hurricane Center had reams of data that showed the paths of every hurricane since the mid-1850s, and updates ï¿1⁄2 several times a day ï¿1⁄2 on the path of Katrina itself. How the hurricane would impact oil production was critical from an economic standpoint, which is why the Mineral Management Services website exists. This is a U.S. government-maintained site that contains a wealth of GIS data. The site has data specific to oil and gas production from the Gulf of Mexico, as well as historical oil and gas production data reported monthly over the last 10 years for every productive well in the Gulf of Mexico.

But all this information is useless unless the user can understand relationships between a location and its accumulated data. To get to this level of understanding, data must be turned into intelligence that informs decisions. Raw data alone does not suffice; it is the ability to analyze it that makes the difference.

Returning to the Hurricane Katrina example, oil markets got shaky as the hurricane gathered strength in the Gulf of Mexico. The Gulf is the country's leading oil-producing region, with production valued at $282 billion over the past 10 years. Some areas are richer in hydrocarbons than others ï¿1⁄2 specifically, the Mississippi Canyon protraction area, which is located off Louisiana's southern tip. This protraction area accounts for 17 percent of the value of oil pumped from the Gulf of Mexico during the past 10 years. MC807, also known as ï¿1⁄2Mars,ï¿1⁄2 is the most dominant production platform in that protraction area, accounting for 7.5 percent of overall Gulf hydrocarbon value produced over the past 10 years. Mars was hit hard by Hurricane Katrina. As of April 2006, it is still not back online.

There's no question that Mars' owners were aware that Hurricane Katrina posed a threat, but could they have handled the damage control afterward if they'd had the right tools and data available to them? Could they ï¿1⁄2 in a matter of minutes ï¿1⁄2 have pointed and clicked their way through the projected path of Hurricane Katrina to understand exactly how, to the last dollar, this was going to impact production? Could the company have examined information from previous hurricanes ï¿1⁄2 and company data on how long it took to get rigs back online ï¿1⁄2 and figured out how long production would be curtailed?

These ï¿1⁄2what ifï¿1⁄2 scenarios help companies make decisions before a catastrophic event ï¿1⁄2 from getting people out of harm's way, to positioning supplies or preparing shareholders for the impact. What if the hurricane moves through a more populated sector of platforms but ones that aren't as productive? What if it makes a beeline for a critical refinery? Forecasting is ultimately about being proactive, being able to anticipate what needs to be done next rather than waiting helplessly to see what might happen.

It's important to note that ï¿1⁄2what ifï¿1⁄2 scenarios don't need to be created by a programmer in the IT department. Geographic data coupled with analytics and a forecasting tool can be available on the desktop to the financial analyst, the production specialist, the sector executive, the foreman assigning staff to the rig, even to the people responsible for repairing a hurricane-damaged rig. These people don't need to learn a new system or be statistics experts. This type of ï¿1⁄2intelligentï¿1⁄2 information can be deployed via Web interfaces to reach a much broader community through tools with which the user is already familiar.

Combining geographic data and maps with powerful BI software helps uncover previously hidden connections and relationships. Users can easily and quickly pan and zoom, drill up and down, expand the fields they're looking at or collapse them, filter and rank, create calculated measures, export the data and analyze it. An oil company, for example, could use these data to figure out quickly which rigs need to be fixed first (based on productivity) and which might wait until later. Forecasting can help a company quickly set priorities.

BI software helps separate the important from the unimportant, turning data into strategic information. Programs and applications can be stored in a way that allows numerous users to access them, view metadata and create customized applications that export accurate, critical data to a company's BI system.

The power of 'where'
Businesses in almost every industry can benefit from coupling geographic data with powerful BI software. Retailers, for instance, are always looking for the perfect location. They buy reams of demographic data. Hundreds of developers pitch their shopping malls and strip centers to retailers. What if a coffee shop's real estate department could sort for highest-income zip codes with the most coffee drinkers and literally call up a map that shows major intersections under development or existing properties in the areas they most desire?

Integrating geospatial information with BI software can also help save lives. Epidemiologists can track outbreaks of deadly illnesses like SARS or bird flu. In the public sector, local governments could work on ï¿1⁄2what ifï¿1⁄2 scenarios before changing zoning, helping them to better understand how new development would affect tax revenues or commuter drive times.

Analyzing data that has been merged with geospatial software will be a critical step in helping organizations succeed and grow in today's competitive markets ï¿1⁄2 from oil refineries to retail stores to medical research labs. The combination of BI and geospatial information will give decision makers an accurate picture of the future and the ability to reliably measure the impact of economic and marketplace factors, so that they can operate more efficiently. Today, a primary use of spatial data in BI is with location information, which is becoming readily available: GPS-enabled cell phones, RFID-tagged packages, etc. BI analytic processes allow decision makers to answer not only the ï¿1⁄2whereï¿1⁄2 question, but also to understand why ï¿1⁄2whereï¿1⁄2 matters.

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