According to IDC, IoT data is forecasted to reach 73 zettabytes by 2025, while a recent study by Deloitte estimates that 40% of IoT devices will be capable of sharing location in 2025, up from 10% in 2020. This means time and space data is the fastest-growing big data category this decade. 

The next few years will see the geospatial technology industry experience rapid growth and change. More location-aware devices and services will expose the world to how technology can utilize data across time and space. Early adopters that take advantage of this will have a vast market opportunity within their respective industries, while slower organizations will risk getting left behind. The key to being an early adopter will be to understand the following: the trends behind this market opportunity, the need for new analytics technology, and the crucial role of the cloud in leveling the playing field.

Time and Space Data: The Rise of Geospatial Insights and Analytics 

The global geographic information systems (GIS) market will be more than double to $13.6 billion by 2027. Three particular industry trends create this.

1. The cost of sensors and devices that collect geospatial data is falling rapidly.
2. The expansion of 5G networks will accelerate IoT deployments. 
3. The cost of launching satellites is falling on a per-kilogram basis, meaning more satellites will be gathering data with a spatial dimension.

A new breed of analytic geospatial capabilities is becoming widely available in the market, allowing more organizations to begin experimenting with geospatial data and analytics. Opportunities abound across industries such as proximity marketing in retail, smart grid operations management in energy, real-time patient tracking in healthcare, fleet optimization in logistics, and autonomous driving in automotive.   

Out With The Old (Traditional Databases) and In With The New (Vectorization) 

As more organizations begin experimenting with geospatial data and analytics, they must understand the need for new analytics technology to successfully process and analyze massive amounts of data in a fast and reasonable amount of time. The current generation of massive parallel processing (MPP) databases for big data analytics simply weren’t designed to handle the speed, unique data integration requirements, and advanced spatial and temporal analytics on data across time and space. The result is slow decision-making, a lack of critical context, and sub-optimized insight. On top of that, using prior generation databases for spatial and temporal data analytics is expensive due to inherent compute inefficiencies, forcing organizations to explore new approaches and technologies. 

Vectorization, which accelerates analytics exponentially by performing the same operation on different data sets at once for maximum performance and efficiency, is one such approach. This method is particularly adept at functions required to perform advanced calculations on time-series and geospatial data, giving organizations full context and results in seconds where traditional analytics took hours. Early adopters that recognize the ability to analyze and track real-time data through many fused sensors enabled by vectorization will have a vast market opportunity within their respective industries. At the same time, slower organizations will risk getting left behind. The idea of using advanced technology such as vectorization and focusing on data with a spatial component may seem daunting and only relevant for big tech companies. However, like other once-flashy technologies such as containers and blockchain, vectorization could soon be the next “must-have” for every organization in the next few years. 

Yet Another Reason to Move to the Cloud

However, organizations should be wary that properly utilizing the onslaught of geospatial data isn’t something that teams can handle in-house. Traditionally, only the most significant organizations (think Fortune 100’s or government agencies) have had the resources to leverage the advanced computing needs (like vectorization) such as high-end computing processors and primitives from NVIDIA and Intel. Furthermore, companies used those initiatives almost exclusively for deep learning and virtual reality simulation projects, using cases that focused on far-sighted research vs. business objectives.

Organizations that invest in new sensor hardware will rightfully be wary of spending even more funds on advanced chips of their own. Instead, they should turn to major cloud service providers like Microsoft Azure. As-a-service databases are readily available and easily capable of leveraging vectorized computing processors for common big data analytics workloads such as time series analysis, location intelligence, visual scenario planning, and other forms of complex mathematics at a scale that incoming geospatial data will fuel.

The Future of Time and Space Data 

As data across time and space continues to rise, organizations must also ensure they are set up with a database that is designed to process and analyze massive amounts of data in a fast and reasonable amount of time. These two elements will be vital to unlocking opportunities, innovations, and instrumental in organization-wide transformation. 

The power of geospatial data lies in answering “where” questions: Where do organizations have exposure to supply chain or regulatory risk? Where should organizations improve product selections to increase sales? Beyond telling us where things are, analyzing data through the lens of location provides organizations new information to make better-informed decisions and enhance performance. The future for organizations across all industries entails taking advantage of geospatial data capabilities.

The Value of Data Goes Beyond Any Number

A quick look around the 21st century marketplace reveals a simple truth: the value of data has changed. Industries that once stood alone and operated in silos, have become interconnected by sheer necessity – collecting, analyzing, sharing and even selling data. Thus, calculating the value of data has become a rather complex task. Companies supporting critical infrastructures have transitioned from tracking basic data, to gathering data from all facets of operations and information technologies, analyzing that data, and turning it into actionable intelligence. The value of data today truly exceeds its numerical quantity.

State of the Geospatial Data

Geospatial data is used by nearly every human throughout the world, either directly or indirectly. Navigation functions on a cell phone? Geospatial data. Restaurant or entertainment recommendations in a specific area? Geospatial data. City bus route coordination? Geospatial data. Most often, we see the end result of geospatial data usage, but what we don’t see is just how many people touch that data to even get it to the point of deployable information.

The implicit value of geospatial data belongs to the 21st century workforce: from the boots on the ground mapping geographic terrain and gathering data in urban and rural settings, to engineers and project managers turning that data into knowledge by developing creative solutions to difficult infrastructure dilemmas; and even to the middle and upper management personas where decision makers are tasked with solving the problems of today with an eye on the obstacles of tomorrow.

The industries affected by geospatial data use that information in innovative ways – from the collection points on the ground to the backend analytics in the corporate office – to drive decision-making, project management, derive creative solutions, and thus increase productivity and a streamlining of workflows. These industries are directly responsible for building and maintaining the critical infrastructure upon which cities – and countries – are built and maintained. With each iteration of geospatial data along the chain, the value of that data increases.

The various touch points of a series of data (for example, each of the aforementioned applications and use cases) creates a chain of inextricably linked decisions that culminate into a set of outcomes. This “value chain” alters the way people and groups of people interact in our daily lives, as a whole, both internally and externally.

So, What Is the Data Value Chain Really?

The Data Value Chain is a framework through which people can view the flow of geospatial data from the instant it is collected throughout its entire lifecycle. Each vertical industry has its own flow (and needs) of data, but eventually, that data intersects with analytics that can turn individual points of information into all different kinds of actionable intelligence. The Data Value Chain depends on a blended technology ecosystem that acts as disruptive force throughout the global marketplace to root out traditional, static practices and supplant them with innovative, purpose-built solutions based on data analytics.

Technology Is Simply A Means to An End

The focus should not be on the newest tools. Rather, it is more important to know what users need to accomplish their tasks. To do this, we first must understand how people work. Who are they, and what is their role in a project or enterprise? What information do they need? Where and how do they use it? And what is the end result?

The answers to these questions often illustrate how people use multiple types of data that come from different sources at different times. End users often need to combine and analyze the data to extract the needed bits. Only when we understand these processes can we ask the next question: How can we use technology to make their work easier?

The solution often lies in a technological ecosystem—a synergistic combination of core technologies to gather and manage data, combined with software and tools for processing, analysis and delivery. Technological ecosystems built around geospatial information support the needs of, and actions for, large portions of an organization.

The use of integrated or blended technologies is one of the most important trends in the geospatial arena. By combining multiple technologies, integrated solutions provide new ways to work and reduce costs, accelerate schedules and supply high-value deliverables along the value chain. And even though many geospatial practitioners are deeply interested in integrated technology, their clients may not share that passion. As long as information is complete, accurate and usable, the people using it may have little interest in how it got to them. That’s a key point to keep in mind. And it raises the next question: How can integrated technologies make work easier for their clients?

Technological ecosystems can be described at two levels. One level, technology fusion, combines sometimes-dissimilar technologies in a way that produces faster operation and more powerful deliverables. A second approach—largely driven by the Internet and information-savvy consumers—is the blending and sharing of information to support workflows and decision processes.

It seems that every day we see new combinations of technologies that are producing ever-larger volumes of data. That trend will continue. But these systems can only deliver data. The value of the data is not realized until it is converted to information and put to work, which brings us to the need for a holistic, purpose-built data value chain across an enterprise.

Reducing Operational Costs Starts in The Forest

Geospatial data is changing how energy, natural resources and utility operations enhance productivity, safety and compliance to manage their internal resources. For instance, in forestry settings, managers can shave 10-25 percent off operational costs based on the reduction of trucks used, scheduling efficiency improvements and driver habit monitoring. Forestry, for instance, requires all manners of logistical consideration to create an effective workflow. Workers collect the actual data, leveraging blended technologies to quickly collect and aggregate data such as: area covered, conditions, locations, etc. Workers share data with software designers to facilitate analytics without needing to return to an office to manually input data. If it takes fewer workers to gather the same amount of accurate data, project managers can allocate fewer vehicles to a project, allowing C-level decision makers to earmark those resources being saved into other areas of business. The strides made across even one organization in the forestry setting can have a disruptive effect on the entire industry and change how work is done.

Why Do We Need a Data Value Chain?

The Data Value Chain has the power to disrupt industries with new ways of thinking and doing, but it also has the ability to unify disparate business practices by putting data – knowledge – into the hands of decision makers across each workgroup or department, informing daily workflow from the simple deployment of resources, to the strategic placement of those resources, to the ultimate value those resources provide in return.

Ultimately, it is of great benefit to embrace and cultivate a Data Value Chain mindset because the benefits are too large to ignore. This conceptual framework ignites a greater capacity to disseminate valued information across an organization (both vertically and horizontally) and helps industries derive actionable intelligence from all points of operation. As the sheer amount of data available continues to grow, so does the importance of understanding the role of the Data Value Chain in the ecosystem of the global marketplace

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The German software giant SAP announced yesterday that it has expanded its integration with Esri, a mapping specialist that provides geospatial services and content. The partnership brings high-performance spatial analytics, self-service mapping and collaboration to geographic information system (GIS) and business users to allow them to leverage real-time location intelligence in both their Esri and SAP environments.

The announcement will see Esri’s mapping technology integrated across the SAP Hana in-memory database, core SAP enterprise applications, the BusinessObjects analytics portfolio, and the SAP Mobile platform. Taking advantage of Hana’s rapid processing times, customers can now avoid moving data out of Hana and into Esri’s ArcGIS server – instead ArcGIS querying can take place natively inside SAP Hana, where the data is stored.

Moreover, as one article reports, the extended integration will also support ArcGIS self-service mapping and collaboration features with SAP Hana as a high-performance engine of the business data that is geospatially enabled.

Steve Lucas, president of platform solutions for SAP, said in a statement,

“By integrating ESRI’s industry leading GIS with SAP HANA, the SAP BusinessObjects BI platform and SAP Mobile Platform as well as geospatial analytics within SAP Lumira, we are enriching business data with geographical context and presenting it in real time — bringing a whole new level of insight to customers.”

Read more here

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(Image Credit: Abhishek Ghate)