Tune into nearly any Hollywood thriller, and you’re bound to see it: an agent of some sort banging on a keyboard to access instantaneously the most complex set of cross-referenced data, resulting in a high-resolution digital map that pinpoints exactly where the nefarious activity is all going down. Problem solved. Nothing left to do but take down the bad guys.
The reality, of course, is a bit different. The Big Data behind geospatial analysis and map-rendering is big indeed – and many organizations are not yet equipped to manage it all. But here’s the thing: the vision of on-demand, in-an-instant data presented in popular movies is in many ways already here. The technology to make it all happen is reality. But something stands in the way.
It’s hardly a secret that disparate systems and data silos stand in the way of moving forward with many digital transformation initiatives. This issue is particularly pronounced when it comes to geospatial data and mapping tools.
The challenge is that insight from geospatial analysis often depends on disparate pieces of data, which are often cross-referenced. Sometimes the insight comes from sentiment analysis in social media, sometimes the analysis of surveillance video in time-series sets, sometimes from back-end systems containing customer information.
For many organizations, the time and energy involved in bringing this data together for targeted purposes on an ad hoc basis imposes a cost that is difficult to justify. This is why at SAP, we emphasize data aggregation and governance to enable a single view of data across the organization. If geospatial is your goal, then better data management is the way forward.
An in-memory approach
The geospatial analysis tool you choose, in other words, needs to put data first. SAP advocates an in-memory database approach that takes calls to disk out of the equation. Instead, you hold all data in memory – which dramatically speeds performance.
It also improves accuracy. Instead of aggregates and representative samples, you work with the raw data – all of it. You can also manage real-time transactions and analytics simultaneously in one system for faster insights and accelerated response times.
Based on a solid data foundation as described, organizations can get a lot out of geospatial.
Four solid use cases
Geospatial data and technology is used not only in cases of espionage and national defense, but for more everyday business purposes, as well. Generally speaking, organizations use geospatial tools to:
- Pinpoint events: An oil company may need to know where to drill, a healthcare organization may need to visualize the epicenter of disease outbreak, or a defense agency may need to highlight disturbances in contested areas.
- Resolve boundaries: A government may need to identify borders in a dispute, a gas company may need to identify the territory its pipeline traverses, or a company seeking to expand may need to understand areas of commercial vs. residential zoning and habitation.
- Locate people: A consumer products manufacturer may need a heat map to visualize its customer base, an epidemiologist may want to show the geographical distribution of a disease, or a retailer may want to identify areas where sales are underperforming.
- Visualize routing: A shipyard operator may want real-time visualizations of all moving vehicles within the facilities, a logistics provider may want to optimize routing patterns, or a military support team may want to track supply routes in real time to facilitate the mission.
Driving the intelligent enterprise with geospatial
At SAP, our focus is on supporting the intelligent enterprise – the kind of organization that can use data to generate and act on insights to improve outcomes for customers. In the context of geospatial tools, this can mean using data to prevent disasters, engage consumers, detect patterns, correlate events, and act in the moment with real-time spatial intelligence. These use cases require connecting to new data sources (sensors, satellites, and drones), delivering insights in real time and incorporating powerful engines for predictive analytics, machine learning, and graph processing.
Predictive analytics, in particular, is gaining increased traction. Companies want to perform advanced analytics on geospatial data to see in advance what customers want, identify and prevent fraud, eliminate machine downtime with predictive maintenance, and much more.
But to move forward with these technologies, you not only need data access and faster processing; you need algorithms, too. Look for solutions that give you algorithms out of the box – with tools for fine-tuning them to your business needs and the nature of your data.
For a deeper dive, please register for “Let’s Talk Data” today. You can join live sessions August 8 and 22 or listen to replays of past events.