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.

The only way to deal with this amount of collected information is to manage it with data-scanning and analytics using artificial intelligence software algorithms.

What Are the Key Offerings for Big Data Processing?

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 Can Big Data Be Managed Effectively?

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.

What Are the Best Practices for 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.

How Is Artificial Intelligence Transforming Data Analysis?

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.

How Are AI and Big Data Interconnected in Today’s Technology?

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:

• 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).

How Are IoT and AI Together Enhancing Big Data’s Future and Security?

The Internet of Things is a key driver of Big Data and AI’s evolution. With estimates predicting over 29 billion IoT devices by 2030, the large amounts of data generated are set to exponentially expand Big Data. This surge both increases the data for AI to analyze and also shows how crucial it is to use advanced analytics to get useful insights from the worldwide IoT network.

AI is useful for identifying people using biometric data such as facial recognition, fingerprints, and retinal scans of eyes. AI is key in improving security and trust in technology by using biometric data like facial recognition, fingerprints, and retinal scans to identify people accurately. This role of AI in managing and protecting data is essential for dealing with the challenges of a world full of data and connections, highlighting how IoT and AI can change the future of technology.

What Infrastructure Challenges Do AI and Big Data Face?

The growth of Generative AI (GenAI) and Big Data is driving technology forward, greatly affecting data analytics and machine learning. However, this advancement faces a crucial challenge: the scarcity of AI GPUs and the urgent need for advanced data center infrastructure.

Data center operators, and the tech industry as a whole, are encountering delays in securing essential components for infrastructure expansion, leading to increased costs and potential innovation bottlenecks. This issue potentially affects smaller operators who could find it challenging to find the necessary space and power for high-performance computing services.

The increasing need for data centers, driven by a predicted doubling of data by 2027, requires building energy-efficient facilities to meet the high-power needs of GenAI. Shifting to liquid cooling and other new cooling methods over old ones highlights the need for updated data center designs to support the growing demands of AI and Big Data processing.

How Can We Overcome Infrastructure Challenges for a Sustainable AI and Big Data Future?

Facing the quick changes in AI and Big Data, the lack of GPUs, and the urgent need for better data centers show how critical the situation is for technology today. These challenges also create chances to innovate and make our digital infrastructure strong and lasting.

Tech companies, infrastructure providers, and regulators will need to come together to embrace green technologies, renewable energy, and better cooling methods. This will help us keep up with the digital age. By doing this, we can fully use AI and Big Data to improve our future responsibly and sustainably.

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Clouds are a necessity for modern technology as they offer speed and agility unparalleled by all but the fastest networks and servers. That being said, IT cloud solutions have their own security challenges that require companies to invest in a security with their cloud service providers. It’s these security challenges that often cause hesitation in companies to switch to cloud services.

Dependency on Cloud Service Providers for Control and Transparency

One of the benefits of using cloud managed services is not needing to manage the resources such as servers and networks associated with the cloud. The downside is that you don’t get the control nor the visibility of what is going on there. You’re putting your trust in cloud services companies to ensure that your data remains safe and not compromised. This can be a severe challenge of cloud security, as you may not correctly see what’s going on. This is where you need to have a plan to ensure that your data remains safe, and you can avoid possible data breaches. You can do so by:

• Choosing cloud service companies with a proven security track record.
• Verify the cloud computing services enacts up-to-date security measures to avoid possible data breaches.
• Develop your own action plan should a data breach occur and institute safeguards to ensure your data isn’t compromised.

Protecting Your Data from Malicious Hacker Attacks and Unscrupulous Employees

Credit: SecureDynamics

Using cloud managed services has its advantages, but like any IT solution, it is subject to hacker attacks. Whether it’s a distributed denial of service (DDoS) attack or a simple data breach, your company can lose valuable data should your cloud services come under attack. Unsecured APIs can often leave open back doors to hackers who discover ways of finding weaknesses in your architecture.

As you plan your applications and your overall architecture, you need to develop a strong disaster recovery plan that ensures you have backups and a plan for locking down your systems if a problem occurs. Usually, DDoS attacks are used to cover up data breaches and malicious tampering, so to avoid such issues, be sure you have frequent backups.

Another problem is when companies give their employees access to all critical data. Employees can be just as dangerous as hackers if they have access to data that they otherwise don’t need to access to do their jobs. Whether intentional or by accident, if there aren’t enough safeguards in place, employees can access sensitive data that can harm the company due to its proprietary nature. By using an application and cloud service that ensures that employees will only get the data they’re allowed, the company can be confident that sensitive data stays out of the wrong hands.

Locking down Insecure Access Points

One of the great features of using cloud services is being able to access your data from anywhere using applications that use APIs. Applications that often use cloud services use API to communicate between the app and the cloud. This makes the cloud flexible for many different applications.

The downside is that this leaves your data open to the world if you have unsecured APIs or a firewall that doesn’t screen out potential spoofed addresses. By installing a behavioral web-application firewall, you will have greater security. This type of firewall examines the HTTP requests and analyzes them to ensure that the traffic is legitimate. Encrypting the data as it travels between the app and the cloud will also reduce the chances of data being hijacked.

The Cloud Doesn’t Comply with Government Required Industry Regulations

Credit: Rackspace Blog

Some industries, such as finance and healthcare, are regulated through the government and require compliance due to mandates. Companies and organizations that fail to comply face severe fines, lawsuits, criminal penalties, and even prosecution. Some regulations include HIPAA, PCI DSS, FISMA, GLBA, and GDPR. If your industry is required to abide by specific laws, your cloud service providers need to comply with the regulations as set forth by the Government.

Your company or organization can ensure compliance by choosing and verifying that the cloud services companies you choose are compliant with the regulations. The best way is to check with the regulatory agency and obtain a list of those cloud service providers who are certified to be compliant with the mandated regulations.

Knowing When a Security Breach Has Occurred

Despite the best efforts of your company and the cloud host, you may still have a security breach, and you may have your data compromised. This is why it is imperative to have alerts and notifications, as well as security logs, should there be a security breach. Knowing what files and data were affected will go a long way toward rectifying the situation.

If your systems can log when a breach occurs, not only will you know what data was affected, but also have a way of identifying which customers or clients were affected. Rather than sending a blanket notification to all your clients or customers, you can simply contact the ones who were affected by the data breach and take appropriate measures.

Like any technology, clouds have their own challenges when it comes to security. Even though security is an issue for those companies who use cloud services, these issues can be addressed with planning foresight to ensure that your company’s data is safe.

Main Photo Credit: The Devolutions Blog

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Understanding the Basics

With more interconnected devices, businesses can receive helpful insights and data analytics. However, there is a challenge on how to analyze it. AI helps in the processing of the big data collected by IoT. Both technologies are evident in such devices or applications as Siri, Alexa, and fitness trackers.

Through the application of AI’s analytic capabilities, companies can identify patterns and make informed decisions. This has a variety of benefits to the organizations as well as their consumers which include:

• Provision of highly personalized services
• Proactive interventions
• Smart automation

AI and IoT in the Business Context

Across the globe, the merging of intelligent machines and innovative technologies is reshaping the future of businesses both large and small. Futuristic businesses are making use of data from multiple interconnected devices to reach new levels of financial and operational efficiency. Leading-edge companies are excelling at analyzing competitors and collecting client data to make their products and services more appealing.

Readily available data makes it easy to anticipate client demands, evaluate their needs, and analyze competitors’ reactions to client behaviors. AI benefits businesses in the following ways:

• It supports knowledge-sharing and collaboration among employees. This helps build teamwork
• It promotes operational efficiency while ensuring little minimum financial and human risk
• It makes decision making more comfortable and more accurate

Businesses in the manufacturing industry can make use of IoT sensors to monitor their manufacturing equipment. This can help identify the causes of defects which in turn improves production and value.

AI and IoT in the Supply Chain

In a world where e-commerce and online trading has become the norm, companies are digitizing big data and robotizing supply chains to enhance the customer experience. The availability of data such as traffic or weather conditions makes it easy to make decisions on such areas as supply chain sourcing, planning, and logistics.

E-commerce businesses also make use of AI and IoT to manage their inventory and optimize their operations.

AI and IoT in Healthcare

The rising costs of providing healthcare to an ever-increasing population is driving many healthcare providers to source for IoT services. Doctors are making use of smart wearables to obtain pertinent patient data.

This enables them to diagnose patients even before they go to the hospital. IoT also makes service delivery in hospitals easier as robots are used to carry out mundane tasks. This frees up the physicians’ time. The population is also empowered to make healthy and fit decisions.

AI and IoT in the Transport Industry

Analysts predict that 75% of manufactured cars will have IoT connectivity by 2020. Public transport routes and vehicles have a network of cameras, sensors, and software, which helps gather data from the environments they are in and respond appropriately.

This has led to effective smart fleet management. It is also expected to garner interest from other sectors, such as logistics and shipping.

AI and IoT in Smart Cities

A smart city is one that utilizes communication and information technologies to enhance the quality of the services they provide to city dwellers. This reduces the overall cost of running the city, resource consumption, and wastage.

IoT and AI help smart cities manage traffic and parking through intelligent devices, deter crime through smart policing, and smart lighting.

AI and IoT in Agriculture

The agricultural sector is making use of smart agriculture to cater to a growing population while leaving a minimal environmental footprint. Farmers are making use of hybrid reality or mixed reality. This refers to the mixing of what’s real and virtual to bring about hybrid reality.

This helps agriculture industry players to make use of effective farming practices through the use of the right agro-chemicals, mechanization, and irrigation.

AI and IoT in the Telecom Sector

AI and IoT have enabled telecom companies to set up data centers through which they can handle the masses of client information they collect. Through self-optimizing networks, telecom service providers can improve network quality based on the time zone as well as traffic information.

Operators can now easily monitor the state of their equipment based on the data they collect. This enables them to anticipate future failures in hardware such as data center servers, cell towers, and power lines, and proactively fix these problems.

AI and IoT in the Energy Sector

Efficient energy management is critical in a world primarily affected by high energy consumption and pollution. Smart buildings are using IoT sensors in such systems as elevators, HVAC, room equipment, air ventilation, and so forth. These sensors help control and monitor the power consumed in such.

This helps municipalities make actionable decisions that will improve energy efficiency.

Challenges Faced AI in IoT

Some of the challenges facing AI in IoT include:

• Compatibility – IoT is a network of systems that are fundamentally different in terms of space and time, but which are managed through the IoT cloud.
• Legal and ethical issues – continuous innovation brings about unprecedented changes that have never been tested.
• Privacy – this, along with data security and safety, is always an issue for each concept or technology.
• Complexity – IoT is a complex system with a constant stream of data. This results in a complicated ecosystem.

Although we are continually amazed by what IoT can achieve, it cannot exists without AI. Both technologies have to be on a similar level to function seamlessly. While IoT solution providers complain that AI seems to be lagging, there’s a huge possibility that they will be at the same level in the future.

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