Opening Mainframe Applications to Geospace

Opening Mainframe Apps to Geospace

Big data is good. Geospatial insights make it even better. By correlating other types of data with geospatial information, organizations can gain a new level of visibility into the information that powers their applications and business.

A new generation of products and devices are making it easier than ever to collect geospatial information. What does this mean for mainframe applications, and how can organizations seamlessly leverage geospatial data on mainframes? This article answers both of those questions.

What is Geospatial Data, and Where Does It Come From?

Geospatial data’s meaning is just what its name implies. The term refers to entries within a database that describe the location, size or other physical attributes of an object.

While it has long been possible to collect and include geospatial data within a larger dataset, newer types of devices are creating novel opportunities for working with geospatial information. For example, mobile devices with built-in geolocation technology allow applications to map users to physical locations automatically. Even data as basic as an IP address can be used to approximate the geographical location of a user or endpoint.

The difference between these methods of geospatial data collection and older approaches is that modern geospatial data can be collected in large volumes and automatically. There is no need for users to report it manually. Plus, modern geospatial data in many cases can be very exact. Applications can tell when a mobile user has moved just a few feet down the street, for example.

The Value of Geospatial Data

Geospatial data is important because it helps to reveal trends within large sets of data that would otherwise not be obvious. By collecting geospatial information about users, networks, and devices, and analyzing how that data relates to other information within a dataset, organizations can gain new insights into big data.

As a basic example, a company could use geospatial information on a mainframe application to help understand how user purchasing behavior varies across a geographic area. In more sophisticated use cases, geospatial information can be leveraged for tasks like analyzing the relationship between ATM location and fraudulent activity on a bank’s network. Similarly, a retailer could use Internet-connected sensors to help track the location and size of items in its inventory, then store that information on a mainframe.

Even in the realm of gaming, geospatial data is changing the way people compute. The most obvious example is Pokémon Go, a game that relies on geospatial information to control the way players interact.

Pokémon Go is a prime example of geospatial data

The Geospatial Challenge

A number of new products have made it easier to collect and work with geospatial data over the past several years, including on mainframes. IBM offers the DB2 Spatial Extender for adding geospatial information to mainframe databases. SAP now also provides a Geographical Framework to help integrate geospatial information into data analytics workflows on mainframes.

Products like these simplify the process of storing and analyzing geospatial information on mainframes. They’re important because they make mainframe applications geospatially aware. They allow geospatial data to become a natural part of mainframe applications, rather than something that is tacked onto a mainframe database in an awkward way.

The catch, however, is that solutions like these are designed to work only on mainframe systems. They do not provide an easy facility for accessing and integrating mainframe geospatial data with other environments.

New products have made it easier to collect and work with geospatial data over the past several years, including on mainframes

Solving the Challenge

That’s where products like Syncsort Ironstream come in. If you want to take geospatial data that is collected or stored on mainframes, then analyze that data on an external data analytics platform, Ironstream is your answer. By streaming mainframe data to platforms like Splunk, Ironstream enables more opportunity for working with geospatial data, without requiring special configuration on the mainframe side.

This integration is important for companies seeking to leverage geospatial data to build the next generation of mainframe applications. If you want your mainframe applications to be able to derive value from geospatial data, rather than simply collect it, you’ll want to be able to feed the data to advanced analytics tools.

Consider, for example, a mainframe system that uses geospatial information to help detect fraudulent transaction activity on a bank’s network. In this scenario, being able to correlate transaction data with geospatial information about similar transactions will likely provide a lot of insight into whether a transaction is legitimate. There is a good chance that a type of purchase that is highly unusual for a given geographic location is fraudulent.

In order to detect such a transaction before it is complete, a mainframe banking application will need to leverage real-time analytics. That requires data to be evaluated on a distributed system using a solution like Ironstream.

Conclusion

Geospatial data is changing the way organizations work with information. It opens up a whole new set of opportunities for deriving value from data and creating business intelligence.

In order to make the most of geospatial data for mainframe applications, however, organizations need to be able to deploy the resources running on both their mainframe and distributed systems in order to make applications geospatially aware.

Products like Syncsort’s Ironstream are the key to making mainframes and distributed systems work together for building the next generation of geospatially aware applications.

Christopher Tozzi

Authored by Christopher Tozzi

Christopher Tozzi has written about emerging technologies for a decade. His latest book, For Fun and Profit: A History of the Free and Open Source Software Revolution, is forthcoming with MIT Press in July 2017.
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