In a data-agnostic world, EO industry moves towards analytics as a service

In a data-agnostic world, EO industry moves towards analytics as a service

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DigitalGlobe's GBDX platform provides access to our vast geospatial library along with the tools and algorithms to extract useful information from that data at scale.
DigitalGlobe‘s GBDX platform provides access to our vast geospatial library along with the tools and algorithms to extract useful information from that data at scale. Image courtesy DigitalGlobe

There is a silent revolution happening with space technology. The ability of satellites to transform businesses and quality of life today is more relevant than ever, and associated technologies have expanded at an exponential space in the recent times.

On one hand, small satellite constellations, improved communication systems, and cost-efficient launchers have all brought a new level of disruption in the earth observation game. On the other hand, democratization of Cloud computing and hosting have made access to imagery and software easy. Progress in machine learning and automation are allowing the industry and its customers retrieve more and more value from geospatial information on large scales, with less human intervention, opening up new markets. At the same time, business trends, such as volume-based imagery subscriptions, the rise of analytics, and the demand for a higher refresh of global monitoring are shaking up the traditional paradigm, lowering imagery prices, and expanding opportunities in the value chain.

Until now, most satellite operators have been content with supplying imagery. But with the world awash with data, and customers becoming largely data-agnostic, emerging downstream companies have begun unlocking the value behind the pixel, through Big Data analytics, major satellite operators are beginning to shift focus.

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Betting on data analytics

While DigitalGobe (now a subsidiary of Maxar Technologies) have for some time been focusing on analytics via GBDX, its geospatial big data analytics platform, last month Planet completed its Mission 1 to image the entire globe every day and has since focused on Big Data analytics. Earlier this year, DigitalGlobe, Harris and Esri formed a partnership to offer ArcGIS customers access to a library of satellite imagery along with analytical and deep learning tools designed to help users gain insights from imagery at scale. A similar collaboration with Esri by Urthecast shows a desire to move to a more application-focused, Software-as-a-Service (SaaS) model.

Recent funding for companies such as Orbital InsightUrsa Space, and Descartes Labs support this move, with each company stating the need to expand partnerships and international sales operations, as well as in improving the technology behind their pipelines specifically to ensure a more frequent, ongoing delivery of insights to their customers.

The growth line

Big Data analytics for EO is growing five times faster than the EO industry itself.
Big Data analytics for EO is growing five times faster than the EO industry itself. Courtesy NSR

A recently released industry report by Norther Sky Research, Satellite-based Earth Observation, 9th Edition,  forecasts annual revenues from satellite-based Big Data analytics for EO to reach $1.3 billion by 2026. At 28.1% CAGR, this is five times faster than the EO industry. As expected, North America will lead the way, reaching $480 million in revenues by 2026, with Europe and Asia close behind. Services are expected to be the largest vertical, and Energy continues to be the fastest-growing, at 34.1% CAGR over the next decade.

‘Vertical’ is an important focus for Big Data analytics companies.
‘Vertical’ is an important focus for Big Data analytics companies. Courtesy NSR

Beyond Volume, Variety, Velocity, and Veracity, NSR contends that . From counting cars to analyzing agricultural financial risk, most ideas in this space have a single vertical focus, with specific challenges to overcome. However, while the entire market is expected to grow, competition and consolidation are looming threats, and many downstream players will find a vertically-focused approach difficult to scale up, especially as satellite operators shift focus toward analytics, the research finds.

The main hurdle for Big Data analytics is commercial sustainability — Project-based models are only as sustainable as the buying power and interest of anchor customers. Therefore, moving to a service-based model will ensure that revenue opportunities are inherently long-lasting.

Pipelines supporting the long-term analysis and delivery of market insights, through a subscription and expanding beyond algorithms to easy-to-use platforms, are just some ways to develop a service-based model.

Big Data growing and how

Big Data analytics has received tremendous boost in recent years.  A March 2017 update to the Worldwide Semiannual Big Data and Analytics Spending Guide from International Data Corporation (IDC) forecasts worldwide revenues for big data and business analytics (BDA) will reach $150.8 billion in 2017, an increase of 12.4% over 2016. The geospatial imagery analytics market is projected to grow from $3.41 Billion in 2017 to $13.21 Billion By 2022, at a CAGR of 31.1%, according to a Research and Markets’ study.

This growth can be attributed to the significant advancements in geospatial imagery analytics with the introduction of artificial intelligence and Big Data and the need for enterprises to ensure market competitiveness.

Clearly in keeping with the trend, the US National Geospatial-Intelligence Agency (NGA) — the largest buyer of satellite imagery in the world — has stated shift in its strategy from plain data towards data analytics. “I envision the future where we will move from analyzing Big Data towards realizing a potential of Fast Data. We’ll buy basic imagery analysis as a commodity – much like we buy foundation data today,” NGA Director Robert Cardillo had said.

Until now, most of the effort has been in creating and improving the technology responsible for extracting information from imagery. Artificial intelligence, machine learning, and vertical-specific algorithms are sure to increase the information/pixel ratio, increasing the value of the process and final product. Yet, there is a difference between technological capability and commercial sustainability.

Service-based Big Data analytics applications may be the most difficult to develop, necessitating not only timely and precise information, but accessible and relevant insights. However, these challenges, once overcome, expand the market outside of the norm, beyond traditional markets, and will represent the greatest revenue opportunity for players in this space.

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