5 Important Trends in Big Data Analytics and AI in 2020


Data analytics and artificial intelligence uses data science and advanced computing algorithms to automate, optimize and find value where the human eye will never see it. It is estimated that artificial intelligence will drive nearly $2 trillion worth of business value worldwide in 2020 alone. With that said, there are numerous emerging trends in big data analytics and AI that you will likely see more of in the rest of 2020.

According to research from International Data Corporation, organisations are seeking to take advantage of the immense business value which data analytics and artificial intelligence brings by investing over $260 billion per year on Big Data and Business Analytics Solutions in 2022.

Those who do not adopt AI will not be able to compete effectively as AI will significantly disrupt every industry globally – by 2030 every industry will be disrupted by AI technology in some capacity. Progressive thinking organizations cannot afford to ignore emerging trends in AI and analytics. This article provides a quick look at a few crucial trends that will be driving forces in 2020. cubes


Data Analytics and business intelligence already require a sophisticated command of information technology, mathematics and statistics. AI and machine learning algorithms have the capability to automate and optimize analytics processes, which in turn creates transformative business insights.

The complexity of merging AI and analytics requires a set plan to guide the transition, most companies do not yet have these roadmaps in place but companies are competing to be the first to create them and embrace AI technology to secure a competitive advantage in their industry.

Most people adopting AI and data analytics use it for customer interaction. Applying AI algorithms to analytics insights from chatbots, ecommerce platforms and other sources can elevate the customer experience. These customer analytics initiatives can lay the groundwork for using AI and analytics across the business.


Important Trends in Big Data Analytics
and Artificial Intelligence


smart applications and decision automation
The Future Is Smart Applications and Decision Automation

Quite a number of enterprises use advanced applications for enterprise resource planning (ERP), customer relationship management (CRM) and other mission-critical functions. Future updates on these platforms will lead to increased AI and machine learning capability in 2020, built around the principle of decision automation. Experts predict rapid growth in decision automation via robotic process operation, or RPA. With RPA, changes can be implemented on business processes as needed without human intervention.

Robotic process operations can produce costs savings of about 2 percent now, but it will be approximately 20 percent in one to two years, according to industry experts. Moreover, a system integrator can combine AI with APIs and other connective technologies to help these platforms collaborate. Therefore, AI, analytics, ERP and CRM can work together to anticipate marketplace demand and untie logistical logjams, driving measurable business value and delivering operational excellence.


digital twins and IOT

Digital Twins Will Help Simplify and Optimise Complex IoT Environments

AI and data analytics are strategic to the development of digital twins — A digital twin is an exact digital replica of a product, process or service. This acts as a mirror of the real world to provides a means to simulate, predict, forecast, service, and self-heal. As such, it presents huge opportunities for businesses and to improve people’s lives.

A digital twin is not really new technology. For several years, weather forecasts have obtained real-time conditions measured by sensors and devices, simulating and visually representing these conditions in digital form to make long-term climate predictions and short-term forecasts.

The digital twin concept brings together the connected world of sensors. These sensors document the activities of hardware and software and network them via Internet of Things (IoT) technologies.

Therefore, an automaker can deploy a fabric of IoT sensors that feed streams of production data to an AI-enhanced analytics platform. That platform generates a digital twin that models the data flowing from all the IoT sensors. Using this approach, digital twins allow humans to interact with IoT sensors to automate asset management.

The increased adoption of digital twins in 2020 means more companies will generate predictive insights that allow them to anticipate problems and fix them before breakdowns send costs spiraling. Predictive maintenance, enabled through digital twins and IoT, will improve safety, reliability and will drive down operating costs.


trends in big data analytics - edge computing

The Future Is No Longer Cloud Only – Edge Computing Will Connect the World and Revolutionize Intelligence on a Global Scale

As the digital twins enable real-time modeling of production environments in 2020, organizations in remote areas will need high-powered processors.

The internet of things (IoT) is solely driven by data. Data collection, sending and processing in massive quantities allows companies to act more intelligently, act quickly and make better-informed decisions. But when such massive amounts of data are being sent to traditional cloud networks, latency is often experienced (delay before a transfer of data).

Edge computing is evolving around the developing need to move more of the data processing nearer to IoT sensors themselves in order to decrease that latency and improve efficiency.

A mine operator, for instance, can use a digital twin to detect a movement in the earths strata and avoid a catastrophic collapse of the cave walls on the miners. But if the mine’s IT operation relies on remote servers hundreds of miles away, a small data delay could result in a major disaster. This problem can be easily solved by deploying edge computing— deploying powerful data center technologies closer to end users.

With edge computing, mine owners can collect data in real time near their location. Applying advanced analytics and artificial intelligence algorithms means they can create predictions that can help to prevent failures – saving money and improving safety. Therefore, greater development in edge computing will likely be one of the biggest trends in big data analytics and AI in years to come.



Our lives will be enriched and aided by AI technology innovation which we will consume through incredibly immersive digital experiences using AR and VR technology.

Virtual reality refers to an interactive computer-generated mostly 3-dimensional experience which takes place within a simulated environment. It makes use of mainly audio and visual feedback, but may also incorporate other types of feedback.

Augmented reality, by contrast, imports critical data and multimedia into mobile apps to enrich user experiences, whether it is a tour around a city or shopping for holiday gifts.

Mixed reality combines elements of virtual- and augmented-reality technologies. It allows an architect, for example, to show his/her client how the new home design will look like — no physical modeling required.

And all of these technologies provide massive reams of data for analytics and AI. The rise of augmented reality, virtual reality and mixed reality is leading to augmented analytics, using natural language processing and machine learning.


blockchain trends in 2020

Blockchain will enable secure and privacy-aware data ecosystems, taking back control of data from data brokers and empowering everyone with ownership and control of their personal data.

Blockchain is a very secure and transparent platform whose data cannot be modified or breached. Thus, it is safe to say that blockchain will be one of the most watched technologies of 2020.

Blockchain works around a principle of a shared digital ledger that’s functionally impossible for hackers to break into, making it attractive for organizations that protect sensitive data. The all-important challenge is that blockchain’s ledger requires complex interactions and processes that are time-consuming and labor-intensive.

These factors create a strong case to deploy AI and analytics to improve blockchain operations. Blockchain adoption is picking up in many industries beyond its early adopters in finance. Healthcare organizations and government agencies will also want to combine AI and analytics to leverage blockchain’s security and transparency advantages.

In an era where personal data is controlled and sold by third party data brokers, blockchain will democratize data ownership – liberating your personal data from the controlling third party data brokers. Blockchain will enable the creation of privacy-aware secure data marketplaces, powered by blockchain, giving back control of your personal data to the people and empowering you to control who has access to your data and what they can do with it.

Above were just some of the most important trends in big data analytics and artificial intelligence. Any organizations looking to embrace AI and analytics should begin with a well-thought-out plan and address the foundations first before looking to deploy data analytics and AI initiatives. There’s no need to over-complicate data analytics and AI initiatives with a big-bang approach, often the simplest solutions created through an iterative experiment based approach are the best.

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