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The combination of a pandemic and a record-setting year of extreme weather events has reminded leaders in every industry that the health of our people, our global economy and the environment are inextricably linked.
Sustainability is now a strategic business imperative, critical to creating new levels of resiliency and responsible practices that preserve our planet for future generations. Consider that 59% of investors choose companies that are environmentally sustainable. Leaders in enterprise sustainability are applying digital technologies like AI, IoT, blockchain, geospatial analytics and hybrid cloud to help operationalize sustainability at scale, uncover new opportunities to lower costs, win customers, and embrace new business models. While all of these technologies can help, geospatial data plays a key role in protecting wildlife, creating a healthier planet and a more resilient economy, too.
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What is geospatial data and analytics?
If you’ve ever typed into your phone, “Coffeeshop near me,” you’ve used geospatial data. Put simply, geospatial data represents objects, events, or other features on or near the surface of the earth. Geospatial analytics takes this data and creates geographic models and visualizations – “The closest coffeeshop is on 4th street. Do you want me to map a route?” – and can show historical changes and current trends. This additional context allows for a more complete picture of events. Geospatial data is also used in nearly every industry, from enterprise asset management to managing occupancy in facilities, a key focus for the future workplace.
Helping governments understand critical habitats and threatened species
The geospatial data and analytics embedded in the IBM Environmental Intelligence Suite isn’t just for helping businesses address sustainability and climate risk. In Australia, with more than 1,700 species and ecological communities threatened or at risk of extinction, the IBM Service Corps is helping upgrade Taronga Conservation Society Australia’s conservation efforts with the same geospatial data technology available to businesses.
“The team will draw on IBM software and IBM Cloud solutions to create a concept that will improve conservation research based on past weather patterns and future predictions,” said Katrina Troughton, IBM Managing Director, Australia and New Zealand.
Big, big data needs the right software to yield big results
Dealing with large geospatial data sets presents many challenges. For this reason, many organizations struggle to take full advantage of it. First, there is the sheer volume of geospatial data. For example, it is estimated that 100 terabytes of weather-related data is generated daily. So, while 96% of executives surveyed said they incorporate weather data into their organization’s operational plans, without analytics to put that data in context, organizations can’t ensure weather events and climate risk won’t affect their continuity.
Geospatial data and geospatial analysis tools – like those found in the IBM Environmental Intelligence Suite – provide many business advantages including mapping wildfires or tracking tornadoes and super storms that could disrupt a company’s logistics, affect supply chains, or even inform what a retailer might offer.
There are many ways industries can take advantage of geospatial data analysis within the Environmental Intelligence Suite:
- A government can derive actionable insights on health, disease and weather, in order to better advise the public during a natural disaster or pandemic.
- An electric utility can better predict disruption risks to optimize maintenance and crew schedules.
- An insurer can better project risk and alert policy holders before issues occur, increasing efficiency.
- A lender can improve its credit risk scoring methodology and reduce bad loan placements.
- A food company can better respond to consumer demand for greater quality and ensure sustainability initiatives are being met.
Visualizing geospatial data to help manage climate risk
When data is made visual, it makes it easier to derive insights from trends. Geospatial analysis lets data scientists effectively convey the shape and the energy of a changing situation. As increasing amounts of data about a scenario are gathered, it becomes easier to spot even more subtle nuances within it.
Geospatial analysis affects matters as critically important as natural resource management and national intelligence. Experts expect geospatial technology to become increasingly sophisticated, especially as it integrates with machine learning and AI to generate advanced modeling that will aid in critical decision making.
Experts also forecast the evolution of GIS mapping as it relates to geospatial data, for remarkably high-resolution mapping in nearly any scenario.
You can’t control the weather, but you can be prepared for it
Fair or foul, when weather strikes, the Environmental Intelligence Suite provides actionable insights so organizations can plan for and respond to changing conditions to ensure business continuity. The suite of applications frees up data scientists and developers from the cumbersome process of conventional data preparation by providing search-friendly access to diverse and continually updated geospatial-temporal data for even deeper insights around decisions that matter.
The IBM Environmental Intelligence Suite of applications brings together a company’s proprietary data, third-party geospatial data and weather data, with geospatial analytics, custom weather alerts and dashboard visualizations. Industry add-ons, including renewable energy forecasting, agribusiness, airports, vegetation management and outage predictions, provide even deeper insights so you can plan and adjust to changing conditions to ensure business continuity.
The IBM Environmental Intelligence Suite is engineered for geospatial analytics so that businesses can go from blaming the weather to capitalizing on it, moving beyond weather predictions to business projections. Because today, weather decisions are business decisions.
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Read the guide to environmental intelligence.
Read the original article, published July 8, 2021 on the IBM Business Operations blog.
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