The trouble with Big Data is that there is too much of it. An example is the data collected from the video surveillance cameras for a small community. The community is safer because of the 100 or so video cameras covering the streets, parking lots, and intersections.
These cameras operate 24 hours per day, 365 days per year. They collect a total of 2,400 hours of video footage every day, which equals 876,000 hours of video footage each year. If human beings had to review this data for suspicious activity, at real-time speed, this would require a staff of 60 people. That is not economically feasible. https://www.youtube.com/embed/n47Ns20v7OYThe only way to deal with this amount of collected information is to manage it with data-scanning and analytics using artificial intelligence (AI) software algorithms.
For companies that want to process Big Data, they need robust IT computer systems, AI programming, consultants for analytics, and support services. Here are some considerations:
Big Data is managed well by using large data centers that are strategically placed. The main benefit of using redundant processing systems that are physically located in separate geographic areas is that any localized failure, such as one caused by a natural disaster, does not take the entire system down.
Business-critical hardware needs to have at least triple redundancy to achieve 99.9% uptime performance. Foundational IT structures consider the risk of calamities and plan for those that can impact an IT network. Load-balancing, in real-time, manages any partial systemic failure of some network servers to re-route the processing to the servers that remain operating in the network.
Data storage requirements for Big Data are substantial . One approach is to capture and process the localized data and then forward the storage to a more extensive storage system that is maintained in the cloud.
Another approach is to use a “virtualized” data system that creates a virtual layer of the data. This virtual layer knows where the data is stored on the network. When calculations are being made using the AI algorithm in a virtual system, only the data needed for that specific calculation is accessed. The original data storage remains intact and in place, without the need for copying data files
This approach utilizes a network-wide data management protocol. It reduces the need for data storage memory as well as improves the computational processing speeds.
Firstmark produced an infographic chart that shows the 2019 Data & AI Landscape . The trend of moving Big Data computational processing to the cloud is undisputable. Forbes reports that distributed data storage is being rapidly replaced by storing Big Data on the cloud and then data mining using SaaS AI programs.
A major digital transformation allows companies to improve management decisions and discover insights that lead to innovation. The change comes from deep learning. Deep learning is a technique of using AI programming to enhance its functions through machine learning.
No human intervention or detailed programming by human beings, for each conceivable instance, is necessary. Instead, a set of algorithms are designed that the programming uses to learn by the application of the algorithms to a large data set.
AI and Big Data are being used increasingly by companies of modest size. They access the IT hardware resources available from data centers. Then, they apply the AI tools available as cloud services to the Big Data that they collect.
DZone notes that some ways that AI is applied to Big Data Analytics include:
Another key driver of this trend is that Big Data is increasing through the explosion of connected devices being deployed with the expansion of the Internet of Things (IoT).
Estimates by Techjury are that by 2025, there will be over 64 billion devices worldwide that are connected on the Internet of Things (IoT). There already are about 24 billion IoT devices.
Each device collects data. This trend is responsible for an exponential increase in Big Data. Collecting such massive data from numerous “smart” devices is only useful if it can be processed, and data mined in meaningful ways.
AI is useful for identification of people using biometric data such as facial recognition, fingerprints, and retinal scans of eyes.
AI and Big Data are now permanently connected. Their combined usage will expand significantly over the years. This megatrend is driven by the strong value proposition of the AI analytics applied to Big Data and the rapid expansion of the IoT, increasing the amount of Big Data.