According to a report from IBM, about 90% of all the data available in the world was created in the last two years. Data is abundantly available, but unless it is put to good use, storing it in drives or in the cloud will only cost time and money with zero return. The logical question for anyone who creates, collects, or stores data should be: How can I monetize this?

Monetizing Location InformationEnabling Data-Driven Government

While raw data has some inherent value, actionable information is usually in higher demand and therefore worth more.Based on that idea, the answer to the question of how to monetize data can be found by addressing three core challenges:1. Efficient data management and distribution2. Productive extraction of information3. Providing effective applications using the information

DATA INFORMATION APPLICATIONS

1st Challenge: Efficient Data Management and Distribution

Governmental structures for data acquisition

Data management is already being done on a large scale by governments around the world. Every government has a surveying authority, cadaster, or mapping office that manages the data it needs for its governmental tasks.

These authorities or departments typically manage and process only the data they need for their own tasks. But much more could be done with the rest of the raw data available. Often the procedures in place simply do not allow all the data to be processed because of a lack of time and resources. One satellite mission can provide data in many forms for a wide geographic area, but a local authority will not be interested in managing all the data collected. It’s too much work for their use case. Instead, they extract the data that is relevant for them and the rest is left aside, unstructured and unmanaged. The unused data might be of great value to other organizations, but it is useless unless they are able to obtain, manage, and process it.

The inability to manage incoming data is a pressing issue today and will only become more significant in the future.

The first issue is finding the right data. While in the recent past you could work with specific data searches (e.g. imagery of scale X for a certain region), soon this will be impossible. There will be hundreds or thousands of different imagery sources for one region, and a user won’t be able to make a well-informed decision about which to use or at what scale. We will need smart search engines that enable users to interactively browse through the wilderness of data, guided by smart and intuitive tools.

Such tools require the ability to extract metadata and filter through it. Our server products, LuciadFusion and ERDAS APOLLO, have just such capabilities.

The second issue is providing the data in a convenient way. Downloading a dataset is often not an option, since these datasets are usually terabytes in size. The future will rely on online data services that provide users with exactly the data they need in a standardized form.

Users should be free from the hassle of using a specific tool to access data. Using multiple tools to acquire data from multiple sources is not sustainable.

Data for those who want it: The need for data sharing

It is in the best interest of governments that adopt automation technologies to share their data. Data has become fuel for modern economies, and traditional methods of monetizing it will soon no longer suffice. By sharing data and making it publicly available, governments can open the door to creative ways of using data.

One way of doing this is through Hexagon’s cloud-based sharing solution, Terra Map Server. Implemented in Germany, it allows users to manage and share geospatial data across the whole country in an efficient and timely manner. It consists of a cloud infrastructure that offers various German agencies data access and

processing in a variety of ways and formats. Terra Map Server also organizes all the data across the different jurisdictions. The outcome is that different agencies with different data packs can work together and make smarter decisions.

Hexagon products offer flexible server solutions that can easily be adapted to the workflows andrequirements of any project: data crawling; metadata gathering and search; data centralization; on-the-fly OGC service creation; distribution of 2D and 3D data including image, vector, elevation, multi-spectral, and multidimensional data; 3D meshes; LiDAR and timed data; etc. The distributed data can be raw or pre-treated via advanced analysis workflows or machine learning processes. Hexagon also offers the necessary solutions to access all those services via easy-to-create web portals, so nothing has to be installed on client machines.

Visualization and analysis of multi-dimensional weather data, served by LuciadFusion as multidimensional WMTS.

2nd Challenge: Effective Information Management

From raw data to actionable information

Today’s world is highly interconnected. Every gadget has a sensor and generates information, from phones to cars to homes to infrastructure. The sheer amount of data produced is such that we are no longer able to process it with raw manpower. Automation is the key to turning vast quantities of data into useful information.

Data is growing fast: The need for automation

One conclusion to be drawn from our ever-growing collection of data is that data is necessary. City mayors need data to monitor their green areas, governments need data to implement better policies, and companies use massive databases of information to make financial decisions. We crave data; therefore, we generate data. But if you generate data faster than you can process it, a bottleneck arises.

This problem is familiar to most data consumers, but it can be solved with automation that prepares data to be used by different technologies, such as:

• Object recognition• Machine learning/AI• Spatial recipes

After processing data to be usable, automated analysis yields actionable information.

The need for information management technologies

It’s impossible for decision makers to plow through petabytes of data. What they need, both in public government and in private industry, is technology that allows them to abstract data — to “zoom out” to see trends and patterns — and to combine and overlay data to find correlations.

There are a huge number of possible analyses using all kinds of data, and most actionable information comes from linking up these analyses. A city mayor monitoring green space, for instance, could use a combination of “bands” in satellite imagery that detects vegetation, then apply a color code so that the vegetation is highlighted in green. A contour analysis for green would extract and calculate the areas covered by plants. Repeating the process annually for four years would provide visualization of how the green space evolved.

Though these analyses seem simple, they are often the result of complex calculations and require a certain level of expertise and specialized tools.Hexagon’s Geospatial division offers an intuitive solution for this called the Spatial Modeler. The Spatial Modeler provides users with hundreds of functions, algorithms, and analytical routines that can easily be chained together into models that solve geospatial problems. The interface is an intuitive graphical one that works by dragging and dropping operations and linking them up, or by combining the hundreds of available functions with your own developed ones.

3rd Challenge: Enabling a Thriving Applications MarketData is the fuel, information is the engine, applications are the vehicle

Once automation makes vast amounts of data available to the public and various analyses have extracted information from it, almost everything is in place to facilitate informed decision making. The last piece of the puzzle is enabling officials to understand the information they are given.

Organizations can develop their own applications or application frameworks for this purpose, but not many are able to do so effectively. Very often they don’t have the resources, and when they do, they do not always excel in the important areas of user experience and openness.

User Experience:

The unfortunate truth is that if users don’t like an application within five minutes of exposure, they won’t continue to use it. Getting people to adopt applications and programs means hooking them in those first moments of contact.

Openness:

One of the proven keys to long-term success is empowering end users with the liberty of choice. Compare, for instance, the early days of the Google App Store and the Apple App Store. From the beginning, Google was open to applications from any developer, which made their store thrive and gain huge market share. The Android store had far more applications and grew at a greater rate than its Apple counterpart. Seeing this, Apple had to change its original strategy of offering only its own applications and open the app store to third-party developers to compete with Android.

M.App Enterprise is the Hexagon product that enables your organization to be just as flexible. Easy to set up and to use, it allows you to create your own apps to conduct simple and complex tasks. It can be deployed locally or in the cloud, and its high-performance engine can be open to third parties, enlarging the ecosystem and enhancing its adaptability.

Understanding the user experience and open source software standards is critical to delivering meaningful data and information to citizens and consumers.

Crash analysis data from the city of Chattanooga.

Business models

Data automation and actionable information for all

Once you’ve overcome the three challenges, you can act. Monetizing geospatial data and information can be done in various business models. The model depends on the data, its use, and its users.

• Data as a Service (DaaS): raw or curated data is made available to users and applications.We see this used for imagery and derived data products such as roads or cadaster. Raw data is a competitive market, leading to small margins on the data. Some governments are even obligated to provide raw data, as acquired, for free.

• Information as a Service (IaaS): involves derived information, possibly from combined data sources. A query might request complex information for a specific area or time window. The value of this information varies widely depending on use case. Margins on this type of service can be higher than DaaS.

• Software as a Service (SaaS): this business model is based on ready-to-use applications that provide data and information targeted to specific use cases.

• For citizens: the value of SaaS does not need to be monetary – the value can be improved safety, or easier access to government services.

• For government agencies: many agencies struggle with getting the information they need but do not have the budget for large IT projects to solve the problem. Centralized services and applications with a cost cross charge allow them to distribute the cost across several agencies.

• For industry: reducing administrative overhead for companies by providing dedicated applications can have high value.

• For startups: governments making data, information, and services available can spark new ideas and lead to the creation of startups that add value and monetize it.

Conclusion

The world is awash with data, and that won’t change any time soon. More and more information is being generated each day. To navigate this sea of data, you not only need to know what you want — for example, to monetize it — but you must also have specific tools that were created for the current and future digital environments.

In this paper, we identified three major challenges to which Hexagon provides solutions.

The first is data ingestion. Data must be stored and then made usable. A process must exist to clean up mistakes and get rid of inaccuracies. These tasks can be done via an immense amount of manpower, or, preferably, with software that runs them automatically. In our Hexagon portfolio you can find solutions.

The second challenge we identified is turning data into information. Data itself has little value compared to the intelligence created when it is linked with different analyses and combined with other datasets. Converting data into information is key to being able to use it. Hexagon’s Spatial Modeler offers a solution for this challenge.

Lastly, we determined the need to put data in context. The best way to leverage the information we obtain is to use specializedapplications that show the data in an easy-to-understand and easy-to-read manner. Doing so provides perspective, changing the way you see your data. Applications configured with Hexagon’s smart dashboarding and analytics capabilities in M.App Enterprise are designed to meet these challenges.

In the end, our goal is to build holistic platforms that allow us to create ecosystems of applications, users, and even communal spaces. Hexagon portfolios are not only able to communicate to one another, but are designed from the ground up with the intention of doing so.

Hexagon is a global leader in sensor, software and autonomous solutions. We are putting data to work to boost efficiency, productivity, and quality across industrial, manufacturing, infrastructure, safety, and mobility applications. Our technologies are shaping urban and production ecosystems to become increasingly connected and autonomous - ensuring a scalable, sustainable future. Our division creates solutions that visualize location intelligence. From the desktop to the browser to the edge, we bridge the divide between the geospatial and the operational worlds. Hexagon (Nasdaq OMX Stockholm: HEXA B) has approximately 21,000 employees in 50 countries and net sales of approximately 4.4bn USD. Learn more at hexagon.com. © 2020 Hexagon AB and/or its subsidiaries and affiliates. All rights reserved. Hexagon and the Hexagon logo are registered trademarks of Hexagon AB or its subsidiaries. All other trademarks or service marks used herein are property of their respective owners.