What is Big data?
Big Data is a term used to define vast volumes of data that is still growing exponentially with time and is too large or too complicated for ordinary computing devices to process. It is estimated that Big data analytics via satellite imagery will generate nearly $18 billion in cumulative revenues and 3.1 billion in revenue opportunities by 2028. The African data centre market size is expected to cross $3 billion by 2025, growing at a CAGR of over 12% during the forecast period (2020-2025).
How is Big data used?
Big data has found applications in precision medicine, fraud detection & handling, advertising, entertainment & media, but to mention a few. It has been estimated that 80% of big data is associated with spatial information, which could be geographical metadata, spatial coordinates, associated street addresses, or where the content of data events refers to a place in physical space.
Satellite data is a subset of big data, and its services have become increasingly affordable. In farming, satellite data can be used to monitor factors which influence crop yield. Some elements can be detected from space, such as weather patterns, exposure to sunlight, air quality or pest activity, so optimum conditions can be determined. In real estate, areas prone to flooding or sinkholes can be accurately identified, impacting property developments and prices. In retail, foot traffic around shopping centres can be monitored in real-time, giving an increased overview of how customers behave. Space data can also be used to monitor conflicts and track refugee movement and is integral to developing driverless cars and low carbon technologies.
Data analytics software and machine learning tools are designed to sift through imagery, infrared data and other information to create bespoke solutions to global problems and return commercially useful insights, which conventional technologies cannot perform. Vast amounts of data are being churned out every day, from online shopping to phone apps, watching on-demand TV to buying insurance. While much of this data is left unstructured or not analysed, harvesting just a small section of relevant data can prove extremely valuable. In essence, Big Data technologies can reveal trends and metrics that would otherwise be lost in the chunk of information.
Big data and satellite technology
Space data has the potential to revolutionise how we understand a wide array of industries and environmental phenomena. African countries are increasingly employing space technologies in addressing several challenges. Satellites communicate by using radio waves to send signals to the antennas on the Earth. These antennas, in turn, capture those signals and process the information coming from those signals. The data can include:
- Scientific data (pictures the satellite took)
- The health of the satellite
- The satellite’s current location in space
Data is accumulated on the satellite, sent via telemetry and downlinks to receiving systems(which could be satellite relays or ground antenna) in batches and stored in respective data centres, awaiting further handling. While getting the satellites’ data is imperative, analysing them properly and effectively knowing how to handle this large volume of data is equally important. Emerging technologies like Big Data, Machine Learning and Computer Vision are steadily providing solutions to the rising challenges that face our world today.
Big data and the African space industry
Collecting and analysing big data can have broad commercial application. In-depth personalisation and accurate customer segmentation can be achieved by analysing troves of customer data. South Africa’s Vodacom gathers customer data by asking users which digital lifestyle they identify with before offering personalised bundles based on their usage and unique profile. From improving the accuracy of flood predictions in Malawi to helping the South African government better map the locations of informal settlements and allowing insurance firms to offer tailored services by anticipating the churn rate, big data can change how people live and work. Nairobi-based Twiga Foods worked with Liquid Telecom to implement precision agriculture using the internet of things to enhance it “with satellite weather data using technologies like AI and big data to improve yields and cut the cost of inputs in farming,” says Roberts.
South Africa and China have held two workshops on the Big Data challenge in Astronomy under Chinese-South African collaborations. The workshops aimed to extend the collaboration and discussion between the two countries on significant data challenges in SKA and other astronomical surveys in the big data era technologies and methods such as; virtual observatory and astroinformatics astronomy for societal development. South Africa also held a science, innovation and opportunity conference in 2018 to address SKA-driven big data challenges in Africa. Nigeria-based agritech startup is currently working on launching a satellite-enabled big data analytics dashboard called “Village Chief” which will help institutions have near real-time information on what the farmer is planting, harvest timeline, the required amount of farm inputs, among other vital insights. Outer space and disaster response experts also gathered at a United Nations forum in Germany in 2018 to discuss big space data benefits to disaster response in Africa.
The future of Big data in the African space industry
Global data started to grow exponentially about a decade ago and has shown no signs of slowing down. It is aggregated mainly via the internet, including social networks, web search requests, text messages, and media files. IoT devices and sensors create another enormous share of data. With this ever-increasing data availability, the challenge of storing the data is that many organisations will have to migrate to cloud services. This would invariably mean that Cloud computing would be a major hotspot in Africa in this decade. AWS, Microsoft Azure, and Google Cloud Platform have transformed how big data is stored and processed. Before, when companies intended to run data-intensive apps, they needed to grow their own data centres physically. Now, with its pay-as-you-go services, the cloud infrastructure provides agility, scalability, and ease of use.
Beyond self-driving cars, fraud detection devices or retail trend analyses, machine learning will drastically impact Africa’s future. Its applications are becoming more sophisticated with each passing year in augmenting everyday operations and optimising business processes. Computers’ ability to learn from data will improve considerably due to more advanced unsupervised algorithms, deeper personalisation, and cognitive services. As a result, more intelligent and capable machines will read emotions, drive cars, explore space, and treat patients.
There will also be a surge in the demand of skilled data scientists in the coming years to handle all of the technicalities that come with big data and its applications. Data scientists are among the top fastest-growing jobs today, along with machine learning engineers and big data engineers. Big data is useless without analysis, and data scientists are those professionals who collect and analyse data with the help of analytics and reporting tools, turning it into actionable insights.
While Big Data brings promises impressive things such as understanding a wide array of phenomena and numerous applications in our everyday lives, there is still the challenge of insufficient understanding and acceptance of big data, the confusing variety of big data technologies, the complexity of managing data quality, but to mention a few. Regardless, Big Data is already transforming our world; it’s only a matter of time before our technologies catch up. The future of Big Data in Africa is frightening and fascinating at the same time. Still, it promises to change the way businesses operate in finance, healthcare, manufacturing and other industries, including the African space industry. Big data will give rise to new job categories, new regulatory structures and standards of conduct. Companies will shift from “data-generating” to “data-powered”.