IBM 5 in 5 | Five innovations that will help change our lives within five years

Macroscopes will help us understand Earth's complexity in infinite detail

The people that shaped the future

In five years, we will use machine-learning algorithms and software to help us organize the information about the physical world to help bring the vast and complex data gathered by billions of devices within the range of our vision and understanding. We call this a "macroscope" – but unlike the microscope to see the very small, or the telescope that can see far away, it is a system of software and algorithms to bring all of Earth's complex data together to analyze it by space and time for meaning.




Today, the physical world only gives us a glimpse into our interconnected and complex ecosystem. We collect exabytes of data – but most of it is unorganized. In fact, an estimated 80 percent of a data scientist’s time is spent scrubbing data instead of analyzing and understanding what that data is trying to tell us.

Thanks to the Internet of Things, new sources of data are pouring in from millions of connected objects -- from refrigerators, light bulbs and your heart rate monitor to remote sensors such as drones, cameras, satellites and telescope arrays. There are already more than six billion connected devices generating tens of exabytes of data per month, with a growth rate of more than 30 percent per year. After successfully digitizing information, business transactions and social interactions, we are now in the process of digitizing the physical world.


In five years


Macroscope technology will transform many industries while revealing new insights about some of the most fundamental problems we face, such as the availability of food, water and energy. By aggregating, organizing and analyzing data on climate, soil conditions, water resources and their relationship to irrigation practices, for example, a new generation of farmers will have insights that help them determine the right crop choices, where to plant them and how to produce optimal yields while conserving precious water supplies.

Beyond our own planet, macroscope technologies could handle, for example, the complicated indexing and correlation of various layers and volumes of data collected by telescopes to predict asteroid collisions with one another and learn more about their composition.


How this could change the world

iot icon

Organize the IoT

New tools like macroscopes will organize all the world's data -- whether gathered by microscopes, telescopes or everything in between.

industry icon

Transform industries

Macroscopes will reveal new insights about some of the most fundamental problems we face, such as the availability of food, water and energy.

geo data icon

Search data by time and space

Macroscope technology will be built on platforms that collect and curate geospatial data so it can be easily searched.


Hendrik Hamman, Research Manager for Physical Analytics at IBM Research


In 2012, IBM Research began investigating this concept at Gallo Winery, integrating irrigation, soil and weather data with satellite images and other sensor data to predict the specific irrigation needed to produce an optimal grape yield and quality. In the future, macroscope technologies will help us scale this concept to anywhere in the world.

Using new indexing schemes for data from the physical world, smart cognitive data curation, parallel processing and physics-inspired machine learning, we will illuminate complex, invisible systems that our planet depends on.

hyper imager platform

Hendrik Hamman, Research Manager for Physical Analytics at IBM Research, running a demonstration of his team's big data platform for aggregating, organizing and analyzing geospatial information from billions of physical objects to improve our understanding of the world.

dwave array sensor

Group photo of IBM Research team building the world’s first platform for collecting, curating and searching global data by space and time. From left to right: Rong Chang, Hendrik Hamman, Xiaoyan Shao, Marcus Freitag, Ildar Khabibrakhmanov, Siyuan Lu.

What is IBM Research disrupting today?