7 trends that will emerge in the 2021 big data industry

“The best-laid plans of mice and men often go amiss”– a saying by poet Robert Burns.

In January 2020, most businesses laid out ambitious plans, covering a complete roadmap to steer organizations through the months to follow. But to our dismay, COVID-19 impacted the world in ways we could never imagine, proclaiming pointless many of these best-laid plans.

And to avert the crisis, organizations had to become more adaptable seemingly overnight.

As the pandemic continues to disrupt lives, markets, and societies at large, organizations are seeking mindful ways to pivot and weather all types of disruptions.

Big data trends in 2021

Big data has been and will continue to be a crucial resource for both private and public enterprises.

A report by Statista estimated the global big data market to reach USD 103 billion by 2027.

Despite the benefits big data promised over these past years, it is only now that those promises are coming to fruition. Here are seven top big data trends organizations will need to watch to better reinforce and secure disrupted businesses. Have a look at the summary of those trends:

1. Cloud automation

Capturing big data is easy. What’s difficult is to corral, tag, govern, and utilize it.

NetApp, a hybrid cloud provider, sees cloud automation as a practice that enables IT, developers, and teams to develop, modify, and disassemble resources automatically on the cloud.

Cloud computing provides services whenever it is required. Yet, you need support to utilize these resources to further test, identify, and take them down when the requirement is no longer needed. Completing the process requires a lot of manual effort and is time-consuming. This is when cloud automation intervenes.

Cloud automation mitigates the burden of cloud systems – public and private.

Artificial intelligence (AI), machine learning, and artificial intelligence for IT operations (AIOps) also help cloud automation to review swaths of data, spot trends, and analyze results.

Cloud automation, along with AI, is revolutionizing the future of work by offering:

• Security
• Centralized governance
• Lower total cost of ownership (TCO)
• Scalability
• Continued innovation with the latest version of cloud platform

2. Hybrid cloud

Hybrid cloud is paramount to improve business continuity.

Most organizations are skeptical about sharing data on the cloud for multiple reasons: poor latency, security, privacy, and much alike. But with the hybrid cloud, components and applications from multiple cloud services can easily interoperate across boundaries and architectures. For instance, cloud vs on-premises and traditional integration vs modern digital integration. The present big data industry is converging around hybrid clouds. Therefore, making it an intermediate point for enterprise data to have a structured deployment in public clouds.

One of the major benefits hybrid cloud offers is agility. The ability to adapt quickly is the key to success for current businesses. Your organization might need to facilitate both private and public clouds with on-premise resources to become agile.

Hybrid clouds can:

• Build efficient infrastructure
• Optimize performance
• Improve security
• Strengthen regulatory compliance system

3. Hyperautomation

Listed as one of Gartner’s Top 10 Strategic Technology Trends for 2020, the term ‘hyperautomation’ will continue to be significant in 2021.

“Hyperautomation is irreversible and inevitable. Everything that can and should be automated will be automated,” says Brian Burke, Research Vice President, Gartner.

Automation, when combined with technologies such as AI, machine learning, and intelligent business processes, achieves a new level of digital transformation. Moreover, it helps businesses automate countless IT and decision-making processes.

The core components of hyperautomation are:

• Robotic process automation (RPA)
• Business process management (BPM)
• Advanced analytics
• AI and machine learning

RPA is also referred to as the foundation stone of hyperautomation, and the technology is anticipated to grow to USD 25.56 billion by 2027, according to Grand View Research.

With remote work on the rise, organizations have been pushed to the brink to adopt a digital-first approach. This instilled fear among employees since it started impacting the way they work, leading to a spike in security concerns:

Further use of hyperautomation can easily resolve 80% of threats even before any user can report them, says Security Boulevard.

4. Actionable data

There is no reward for an organization owning large amounts of data that are not useful. You need to transform raw data into actionable insight to help businesses make informed decisions. This can be possible through ‘actionable data.’

“What big data represents is an opportunity; an opportunity for actionable insight, an opportunity to create value, an opportunity to effect relevant and profitable organizational change. The opportunity lies in which information is integrated, how it is visualized and where actionable insight is extracted.” – CIS Wired

The need to glean accurate data and information that further establishes relevant insights for decision-makers is critical for business impact.

Big data will continue its rise in 2021. This might be the first year where we will experience the potential of actionable data.

5. Immersive experience

The immersive web is already undergoing a sudden change we believe will shape 2021.

“Everything that is on a smartphone will soon be possible in XR, and in addition, a range of new applications will be invented that are only possible using VR/AR,” says Ferhan Ozkan, co-founder of VR First and XR Bootcamp.

The future of the immersive web is set to take flight by virtual reality (VR) and augmented reality (AR), also called immersive experience.

In 2020, we experienced a year with a drastic impact on digital entertainment, on apps like Discord, TikTok, and Roblox. Despite being early iterations of immersive web, this trend will be further driven by Gen Z.

Lockdown measures implemented in 2020 have accentuated this drastic shift, more so bringing forth an opportunity for businesses to take charge of the interests of society.

6. Data marketplace and exchanges

By 2022, most of the online marketplace will attract nearly 35 percent of large organizations to stay connected by making them become sellers or buyers of data, predicts Gartner. Top companies like Acxiom, White Pages, and ZoomInfo were already selling data for decades. But with emerging data exchanges, you can easily find platforms to integrate data offerings even from a third-party, e.g. SingularityNET.

This trend will definitely accelerate the rise of technologies like data science, machine learning, deep learning, and the cloud.

7. Edge computing

Edge computing will go mainstream in 2021, predict Gartner and Forrester.

“Edge computing is entering the mainstream as organizations look to extend cloud to on-premises and to take advantage of IoT and transformational digital business applications. I&O leaders must incorporate edge computing into their cloud computing plans as a foundation for new application types over the long term.” – Gartner 2021 Strategic Roadmap for Edge Computing

Many organizations are pushing toward implementing edge computing, to gain benefits like greater reliability, increased scalability, improved performance, and better regulatory compliance options.

The continued rise in utilizing data by technologies like VR, AR, and 5G networks will further drive the growing demand for edge computing.

With organizations switching to remote work globally, many have shifted from traditional servers to cloud computing services to boost security, while some have started turning to edge computing to reduce latency, increase internet speed, and boost network performance.

Stay certified and get ready for the big data change in 2021!

Source: Dasca

Edge computing in a Nutshell

Edge computing (EC) allows data generated by the Internet of Things (IoT) to be processed near its source, rather than sending the data great distances, to data centers or a cloud. More specifically, edge computing uses a network of micro-data stations to process or store the data locally, within a range of 100 square feet. Prior to edge computing, it was assumed all data would be sent to the cloud using a large and stable pipeline between the edge/IoT device and the cloud.

Typically, IoT devices transfer data, sometimes massive amounts, sending it all to a data center, or cloud, for processing. With edge computing, processing starts near the source. Once the initial processing has occurred, only the data needing further analysis is sent. EC screens the data locally, reducing the volume of data traffic sent to the central repository.

This tactic allows organizations to process data in “almost” real time. It also reduces the network’s data stream volume and eliminates the potential for bottlenecks. Additionally, nearby edge devices can “potentially” record the same information, providing backup data for the system.

A variety of factors are promoting the expansion of edge computing. The cost of sensors has been decreasing, while simultaneously, the pace of business continues to increase, with real-time responses providing a competitive advantage to its users. Businesses using edge computing can analyze and store portions of data quickly and inexpensively. Some are theorizing edge computing means an end to the cloud. Others believe it will complement and support cloud computing.

The Uses of Edge Computing

Edge computing can be used to help resolve a variety of situations. When IoT devices have a poor connectivity, or when the connection is intermittent, edge computing provides a convenient solution because it doesn’t need a connection to process the data, or make a decision.

It also has the effect of reducing time loss, because the data doesn’t have to travel across a network to reach a data center or cloud. In situations where a loss of milliseconds is unacceptable, such as in manufacturing or financial services, edge computing can be quite useful.

Smart cities, smart buildings, and building management systems are ideal for the use of edge computing. Sensors can make decisions on the spot, without waiting for a decision from another location. Edge computing can be used for energy and power management, controlling lighting, HVAC, and energy efficiency.

A few years ago, PointGrab announced an investment in CogniPointTM, and its Edge Analytics sensor solution for smart buildings, by Philips Lighting and  Mitsubishi UFJ Capital. PointGrab is a company which provides smart sensor solutions to automated buildings.

The company uses a deep learning technology in developing its sensors, which detects the occupant’s locations, maintains a head count, monitors their movements, and adjusts its internal environment using real-time analytics. PointGrab’s Chief Business Officer, Itamar Rothat stated:

“CogniPoint’s ultra-intelligent edge-analytics sensor technology will be a key facilitator for capturing critical data for building operations optimization, energy savings improvement, and business intelligence.”

Another example of edge computing is the telecommunication companies’ expansion of 5G cellular networks. Kelly Quinn, an IDC research manager, predicts telecom providers will add micro-data stations that are integrated into 5G towers, or located near the towers. Business customers can own or rent the micro-data stations for edge computing. (If rented, negotiate direct access to the provider’s broader network, which can then connect to an in-house data center, or cloud.)

Edge Computing vs. Fog Computing

Edge computing and fog computing both deal with processing and screening data prior to its arrival at a data center or cloud. Technically, edge computing is a subdivision of fog computing. The primary difference is where the processing takes place.

With fog computing, the processing typically happens near the local area network (but technically, can happen anywhere between the edge and a data center/cloud), using a fog node or an IoT gateway to screen and process data. Edge computing processes data within the same device, or a nearby one, and uses the communication capabilities of edge gateways or appliances to send the data. (A gateway is a device/node that opens and closes to send and receive data. A gateway node can be part of a network’s “edge.”)

Edge Computing Security

There are two arguments regarding the security of edge computing. Some suggest security is better with edge computing because the data stays closer to its source and does not move through a network. They argue the less data stored in a corporate data center, or cloud, the less data that is vulnerable to hackers.

Others suggest edge computing is significantly less secure because “edge devices” can be extremely vulnerable, and the more entrances to a system, the more points of attack available to a hacker. This makes security an important aspect in the design of any “edge” deployment. Access control, data encryption, and the use of virtual private network tunneling are important parts of defending an edge computing system.

The Need for Edge Computing

There is an ever-increasing number of sensors providing a base of information for the Internet of Things. It has traditionally been a source of big data. Edge computing, however, attempts to screen the incoming information, processing useful data on the spot, and sending it directly to the user. Consider the sheer volume of data being supplied to the Internet of Things by airports, cities, the oil drilling industry, and the smart phone industry. The huge amounts of data being communicated creates problems with network latency, bandwidth, and the most significant problem, speed. Many IoT applications are mission-critical, and the need for speed is crucial.

EC can lower costs and provide a smooth flow of service. Mission critical data can be analyzed, allowing a business to choose the services running at the edge, and to screen data sent to the cloud, lowering IoT costs and getting the most value from IoT data transfers. Additionally, edge computing provides “Screened” big data.

Transmitting immense amounts of data is expensive and can strain a network’s resources. Edge computing processes data from, or near, the source, and sends only relevant data through network to a data processor or cloud. For instance, a smart refrigerator doesn’t need to continuously send temperature data to a cloud for analysis. Instead, the refrigerator can be designed to send data only when the temperature changes beyond a certain range, minimizing unnecessary data. Similarly, a security camera would only send data after detecting motion.

Depending on how the system is designed, edge computing can direct manufacturing equipment (or other smart devices) to continue operating without interruption, should internet connectivity become intermittent, or drop off, completely, providing an ideal backup system.

It is an excellent solution for businesses needing to analyze data quickly in unusual circumstances, such as airplanes, ships, and some rural areas. For example, edge devices could detect equipment failures, while “not” being connected to a cloud or control system. Examples of edge computing include:

Internet of Things

• Smart streetlights
• Home appliances
• Motor vehicles (Cars and trucks)
• Traffic lights
• Thermostats
• Mobile devices

Industrial Internet of Things (IIoT)

• Smart power grid technology
• Magnetic resonance (MR) scanner
• Automated industrial machines
• Undersea blowout preventers
• Wind turbines

Edge Computing Compliments the Cloud

The majority of businesses using EC continue to use the cloud for data analysis. They use a combination of the systems, depending on the problem. In some situations, the data is processed locally, and in others, data is sent to the cloud for further analysis. The cloud can manage and configure IoT devices, and analyze the “Screened” big data provided by Edge Devices. Combining the power of edge computing and the cloud maximizes the value of Internet of Things. Businesses will have the ability to analyze screened big data, and act on it with greater speed and precision, offering an advantage against competitors.

Data Relationship Management

Device Relationship Management (DRM) is about monitoring and maintaining equipment using the Internet, and includes controlling these “sensors on the edge.” DRM is designed specifically to communicate with the software and microprocessors of IoT devices and lets organizations supervise and schedule the maintenance of its devices, ranging from printers to industrial machines to data storage systems. DRM provides preventative maintenance support by giving organizations detailed diagnostic reports, etc. If an edge device is lacking the necessary hardware or software, these can be installed. Outsourcing maintenance on edge devices can be more cost effective at this time than hiring an in-house maintenance staff, particularly if the maintenance company can access the system by way of the internet.

Author: Keith D. Foote

Source: Dataversity