Verdict lists the top five terms tweeted on big data in November 2020 based on data from GlobalData’s Influencer Platform. The top tweeted terms are the trending industry discussions happening on Twitter by key individuals (influencers) as tracked by the platform.

1. Artificial Intelligence – 3,745 mentions

The massive adoption of artificial intelligence (AI) for driving innovations, top applications of AI, and risks associated with AI were popularly discussed in November. According to an article shared by Dr Omkar Rai, director general of Software Technology Parks of India (STPI), the massive adoption of AI is driving innovations in areas such as health research, data analytics, and robotic assistants, to name a few.

Research from UnivDatos Market Insights, a market research firm, finds that AI’s contribution to the healthcare sector is expected to grow at a compounded annual growth rate (CAGR) of 41% between 2018 and 2025 and will be worth $26.6bn by 2025.

AI also trended in discussions with respect to major breakthroughs in the technology, according to an article shared by Kirk Borne, a principal data scientist and astrophysicist. One of the major AI breakthroughs has been in the automotive sector, with the introduction of driverless cars that are able to process large amounts of driving data at high speeds, the article noted.

In other discussions, Linda Grasso, an industrial engineer, shared an article on the risks associated with AI that have raised concerns among business leaders and the structures and innovations being adopted by organisations to mitigate these risks. Managing ethical risk is an area of opportunity, but not many firms have been able to establish policies and processes for risk management and technology governance such as understanding how algorithms deliver the results, the article detailed.

2. Machine Learning – 3,355 mentions

Best practices to build automated machine learning (ML) models, tools needed to master ML and advanced analytics, and the application of ML in healthcare were popularly discussed topics during the month. According to an article shared by Andreas Staub, a digital transformation leader, ML design and maintenance can be broken down to ten steps including pre-processing of data, feature engineering, dealing with diverse algorithms, and algorithm selection, among others.

Experts believe that data-based decision making, and automation will improve customer experience. Therefore, it is important to understand how raw data can be transformed into understandable data through best practices that can save time, reduce risks, and increase accuracy.

ML also trended with regards to the tools needed for ML and advanced analytics, according to an article shared by Samuel Wong, founder and director of DATAFYiNG, a data management company. For instance, some excellent sources of information that can assist ML projects are websites, podcasts, and videos, the article noted.

In other discussions, Marcus Borba, a global thought leader and influencer, believes that predicting heart disease using ML is an example of how the technology should not be applied to all problems, especially those areas that need more experience and expertise.

3. IoT – 1,780 mentions

Factors driving the progress in internet of things (IoT) automation and achieving remote workforce efficiency with IoT security were popularly discussed topics during the month. According to an article shared by Kirk Borne, progress in IoT automation has been the result of new methods that have employed technologies such as ML, big data, edge computing, and asset intelligence.

IoT automation has gained greater significance during the pandemic, with an increasing need to use IoT sensors, robots and software to aid remote monitoring, the article noted. Additionally, cybersecurity is expected to be the driving force for IoT automation, as experts opine that it is not possible for humans to manually track all the machine information.

Among other discussions, Tamara McCleary, the CEO of Thulium, a social media marketing agency, explains how remote workforce efficiency will be driven with IoT security. The current Covid-19 crisis has allowed technology companies to embrace remote working trends to cut costs and sail through the economic crisis. Organisations have adopted IoT devices to promote social distancing, continue operations and reduce manual tasks. Remote working, however, opens the IT environment to numerous cybersecurity hacks, which can be curtailed only with best practices, the article detailed.

4. Analytics – 1,293 mentions

Agile data and analytics capabilities being leveraged by organisations and how effective they have been to power their business models were popularly discussed in November. According to an article shared by Linda Grasso, an industrial engineer and digital transformation influencer, the most successful companies create cross-functional team to leverage data and analytics that ultimately drive digital and overall growth.

Numerous organisations are revisiting their analytics capabilities to combat the current health crisis and prepare ahead. A number of leaders have even embraced AI and advanced analytics that are expected to add trillions to the annual economic value.

Analytics was also discussed with respect to organisations being able to understand how effective they have been at tapping data and analytics to power their business models, according to an article shared by Bill Schmarzo, chief technology officer of the Big Data Practice of EMC Global Services. Schmarzo provides a Big Data Business Model Maturity Index to help organisations measure their steps through different phases such as monitoring insights, insights optimisation, optimisation monetisation, and monetisation metamorphosis.

5. Deep Learning – 1,245 mentions

The future of deep learning being able to resemble the human brain and deep learning techniques for developing smarter IoT systems were popularly discussed during the month. According to an article shared by Terrence Mills, the CEO of, a data science and engineering company delivering AI solutions, deep learning has been a huge contributor to the advancements being made in the field of AI.

In fact, deep learning can learn from unstructured data that is available through social media, internet history, and others. Despite these sources of data being vast and unrelenting, deep learning models can recognise speech, objects, take decisions, and even translate languages at remarkable speeds, the article noted. Experts believe that AI advancements can be pursued by reimagining deep learning from its core. For instance, deep learning can be developed to resemble the human brain from the early years of development.

In other discussions, Chuck Moeller, an enterprise architect, tweeted about a book on machine and deep learning techniques that can help develop smarter IoT systems. For instance, one could forecast time series data by using varied deep learning procedures.