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Is IoT data taking geospatial analytics to the next level?

IoT data geospatial analyticsOn its own, sensor data is one dimensional. IoT data when combined with geospatial analytics, business data, and operational data, the data reveals hidden patterns and relationships for more effective outcomes.

IoT connected devices surround us both at home and work. We encounter IoT connected devices every day but their reach extends even further into businesses. By 2020, 200 billion devices are expected to be connected to the IoT.

Interestingly, the biggest portion, (70.5%) of IoT connected devices will be in business/manufacturing and healthcare. Within these and other market sectors, connected devices with fixed and moving sensors will be the source of vast amounts of real-time and near real-time data. On its own, the sensor data tells an interesting story, but it’s one dimensional. When combined with geospatial analytics, business data, and operational data, the data can reveal hidden patterns and relationships that deliver better business outcomes.

Companies within the insurance, travel, agriculture, and construction industries are enriching data from IoT sensors with business applications and geographic data from GIS. They are overlaying business data, device location information, customer locations and more with detailed geographic information, like topography and satellite imagery. Rich graphics, such as heat maps and charts help users visualize business activities, relationships, and patterns.

IoT in the field

In commercial agriculture, farmers are overlaying maps of their fields with historical data and sensor data to produce crops more efficiently. Through an app, they can take a virtual walk-though of any sector and understand exactly what is happening with their crop.
Sensors in the soil measure moisture, pH, and more to show farmers the state of their soil. A crop history shows past planting schedules and harvests. From a dropdown list, scheduled and unscheduled tasks display irrigation, fertilization, planting, spraying, and harvesting timelines. Farmers can schedule these tasks and assign a contractor to complete them.

When a crop needs irrigating, for example, the farmer first tests the land’s moisture. The sensors can tell him the exact percentage of moisture in the land at 6 am, 1pm and 5pm. He can look at records from last year at this same time, and he can run a quick analysis to see what the moisture should be three days, two days and one day before planting a crop of wheat. If rain is in the forecast, the farmer can cancel the contractor and avoid an unnecessary expense.

IoT in the sky

Likewise, in the travel industry, geolocation apps and IoT data can be the ticket for cost savings and improved operations. With the right geospatial analytics solution an airline could track global flight operations more closely and accurately with spatial capabilities by monitoring airport, meteorological, and fleet data in real time to improve its flight planning solution and optimize fuel and staff costs. Thousands of flights can be tracked each day on a 3D mapping interface that displays spatial and temporal coordinates. Flight delays based on hurricanes, volcano eruptions, or other major travel inhibitors can be rerouted based on the data’s projections.

IoT assesses risk

In another interesting example one of the world’s largest reinsurance companies uses spatial data processing, predictive analytics, and simulation capabilities to assess risk based on hurricanes, wildfires, tornadoes, and other weather-related events. The company is tapping into a cloud-based earth observation analysis service to analyze natural disaster data with its customer data to make more informed decisions about insurance risks.

The company looks at topography, occupancy, climate rainfall, vegetation, and more to better calculate risks and costs. Its customers benefit as the company pushes down costs based on accurate, timely, historical and real-time information.

Also Read: Geospatial data fuel for the digital economy

IoT and remote asset monitoring

Within construction, a global industrial machinery company has equipped its drills and heavy equipment with sensors connected to the Iot. The company is embedding SIMs into its popular line of drill products, as well as other heavy equipment that customers use on their construction sites. When customers purchase or lease the equipment, the machinery company begins capturing a range of data about the equipment, such as usage time, vibration, speed, and temperature.

When the equipment moves from a site in Belgium to a site in Austria, the company continues to track usage and collect data without interruption. The company is easily able to not only track the location of its equipment, but also monitor and maintain it remotely. It can provide maintenance recommendations and upgrades before the equipment becomes faulty.

IoT data geospatial analytics
Analyze data from a central platform that handles structured and unstructured data in a variety of formats

One platform for all data types

Geospatial analysis of IoT and other data types has arrived, but we have only just begun to realize its potential. With both volume and variety of data growing, companies choosing to harness the power of various forms of spatial data can outpace competitors by tapping into new methodologies to analyze existing data. Geospatial analysis can lead to improved decision making, better results, new revenue opportunities, and overall a better view of the company’s data, thus opening up new areas for growth.

Based on these examples, companies are pushing ahead in their digital strategies and leaving many of their peers behind. What they all have in common and gives them a clear advantage is that they standardized on a single platform that supports their needs of managing their data regardless of its format, source, or location. Other companies that are still supporting multiple platforms are experiencing increased costs to support multiple technologies, increased effort from management staff, as well as insights latency and data availability concerns.

Also Read: Security a prime concern in IoT

These findings were recently reported in a Forrester Consulting Opportunity Snapshot1, commissioned by SAP. Additional findings revealed that the most compelling use cases and initiatives rely on multiple data formats. In fact, organizations regularly process several types of structured and unstructured data for high priority use cases. On average firms deal with four different data types, and they work with these different data types on four to eight of their initiatives.

“This means that not only are firms losing productivity and investing more time and money than they’d budgeted, but they’re still not getting the results they want from the data,” according to the study.

Other challenges are increasing time to value and inefficient data management. The bottom line is that when organizations can’t combine business, operational, IoT and geospatial information, they can’t fully unlock the value of their data.

To overcome these challenges, successful early adopters are recommending these best practices:

• Analyze data from a central platform that handles structured and unstructured data in a variety of formats
• Be prepared to collect and analyze data from multiple sources
• Enable real-time and near real-time analytics
• Support massive amounts of data with in-memory processing
• Leverage reports that visualize business insights for both data scientists and anyone who works regularly with data

We are not so far away from 2020 or the 200 billion IoT connected devices. Preparing to manage those devices and their data is possible for those ready to undertake the challenge now and begin defining a strategy for combining and realizing the power of analyzing their business, operational, IoT and geospatial data.

Do More With Data Through Multimodel Capabilities, Forrester Consulting Opportunity Snapshot: A Custom Study Commissioned By SAP HANA, August 2018

Also Read: Digital Transformation : Powering new breakthroughs in the era of disruptions