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.
Big Data Offerings
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:
- On-Premises Mainframes vs. Off-Site Servers: Major international conglomerates can afford the large mainframe systems to process Big Data on-premises. However, most companies choose dedicated servers or co-location servers, which provide the processing power needed for the analysis of Big Data. This IT solution is managed off-site. The advantages of having servers off-site include outsourcing the maintenance of them, lowering capital investment in facilities, and having the flexibility to increase IT services as needed.
- AI Programming: Data mining of Big Data is achieved by using AI programming that works with algorithms to find patterns in the Big Data that are noteworthy. This provides insights that help management make better-informed decisions.
- Consultants for Analytics: It is cost-effective for most companies to outsource the IT consultant work to develop their analytics of Big Data. These specialists can be engaged on an “as-needed” basis to construct an analytics program that is suitable for a company’s requirements.
- Support Services: Support services include managed hosting, IP transit services, and connections to the cloud.
How to Manage Big Data
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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.
Big Data Storage Management
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.
Artificial Intelligence
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.
Artificial Intelligence and Big Data
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.
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DZone notes that some ways that AI is applied to Big Data Analytics include:
- Detecting Anomalies: AI can analyze Big Data to detect anomalies (unusual occurrences) in the data set. This can be applied to networks of sensors and parameters that have a predefined appropriate range. Any node of the network that is outside of the range is identified as a potential problem that needs attention.
- Probabilities of Future Outcomes: AI can analyze Big Data using Bayes theorem. The likelihood of an event occurring can be determined using known conditions that have a certain probability of influencing the future outcome.
- Recognizing Patterns: AI can analyze Big Data to look for patterns that might otherwise remain undetected by human supervision.
- Data Bars and Graphs: AI can analyze Big Data to look for patterns in bars and graphs that are made from the underlying data set.
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).
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.
Improved Trust
AI is useful for identification of people using biometric data such as facial recognition, fingerprints, and retinal scans of eyes.
Conclusion
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.
