Have you ever hopped upon a roller coaster and regretted it instantly? 2020 has been that mean roller coaster ride. It opened with the Australian wildfire and the headlining act – the Covid-pandemic has claimed 1.84 million human lives and counting.
But 2020 has given technological evolution a strong push and moved it ahead with speed. The year has also reminded us how easy it is to lose track of the critical changes in the world of technology in the midst of a great humanitarian crisis. We have seen every business move online, and every person has had to work from home at some point this year. Consequently, there have been some pretty unexpected turns in the way data is dealt with. Data science as a business tool and applied AI were already pretty big in 2019 itself. 2020 has given the existing framework a run for its money. Now, as 2021 rolls out, it is essential for data science and AI enthusiasts to keep a keen eye on the trends.
The world will see a boost in Natural Language Processing; Computer vision will grow in potency and importance in sectors like defense and security; Healthcare analytics will have a year of unprecedented rise, just to name a few. As data science enthusiasts and students, you must align your minds with the shifting paradigms of the industry.
Fortunately enough, AnalytixLabs and Analytics India Magazine have been collaborating for the last few years to dish out the most important Analytics and Data Science trends that may go on to characterize the year. The 2021 edition of the report gives you access to the thoughts of some of the finest minds in the sector. You will get a glimpse of the report here, but it is highly recommended that you go through the whole thing.
The Data Science and AI trends that will Characterize 2021
1. Increased Cloud Dependency
Cloud will be the backbone of the remote workforces
Revamping data centers with new IT infrastructure to provide the agility and scalability required to maintain a robust remote workforce will be too inconvenient. Cloud dependency will increase manifold. Cloud and edge computing will be of paramount importance, with data security becoming the core concern.
Use of cloud for analytics
Cloud-based data governance will grow in currency, and analytical efforts will revolve around the cloud. Cloud technology will transcend from a mode of transaction to a platform for research and analysis.
The reign of Hybrid clouds
The reliance on hybrid clouds for business management and data analytics will be a key trend this year. Businesses need a blend of agility and security, and that exactly is the promise of a hybrid cloud.
The Democratization of the cloud with the rapid adoption of AI and ML
Sectors like healthcare and manufacturing are constantly trying to inculcate applied AI and machine learning in their processes. This increases the demand as well as the production of public and private clouds. As a result, the cloud becomes more affordable for startups.
2. RPA and IPA become the Industrial Mainstay
Robotic process automation for data mining
RPA will be used more vigorously to automate low-value efforts. This will add great speed to data analytics.
Intelligent process automation for decision making
AI will augment knowledge workers’ decision-making capabilities by helping them factor in a lot of data points while making a call.
Abundance of chatbots
The current year will see a strong dependence on AI-based chatbots among businesses.
3. NLP Gains More Currency than Ever
Natural language processing will grow everywhere
NLP has been one of the primary concerns of AI researchers right from the very beginning. NLP technology is now matured enough to be used by every sector for human to machine conversation.
Cognitive bots as de-facto personal assistants
Bots powered by NLP will augment the capabilities of knowledge workers.
Enhanced access to unstructured data
Businesses will be able to tap into more dark data with the help of NLP.
NLP for security and fraud detection
Multimodal technology incorporating NLP and computer vision will enable video KYC and forgery detection.
4. Bridging the Gaps with AI in Non-traditional Domains
Holistic solutions for unforeseen problems
Artificial intelligence has so far been used to perform specific tasks in certain niches. 2021 will see a more holistic approach towards applied AI.
Data science as a core business function
Instead of being a support function, data science has become a core business function. This will become the mainstay in 2021.
All-pervading application of AI in healthcare
Augmenting healthcare processes with AI will be one of the key highlights.
5. Addressing Cybersecurity Loopholes with AI
Catching up with the threats
2020 ended with the uncovering of a massive data theft popularly known as the Solarwinds scandal. This opens our eyes once again to the increasing data security hazards. It will be a challenge for companies in 2021 to counter these threats.
More investments in cybersecurity
AI-based cybersecurity will see massive investments for obvious reasons.
Cybersecurity as a part of digital transformation
As businesses compete on the digital frontiers, cybersecurity will be a critical component.
6. Real-time Data Consumption
We have been encountering the phrase real-time analysis for quite some time now, but most business data still lie idle until it is too late to act upon them. We will see a tendency towards real-time consumption in 2021.
7. Technology to Serve Customers Better
The digital experience created for the customers plays a massive role in the survival of an enterprise. Creating an E-commerce storefront with specific customers in mind is the key. Prioritizing the customers will be a big deal in 2021.
8. Federated Learning and Counterfactuals are the Buzz
Personalizing services with federated learning
Federated learning is a kind of machine learning where the algorithms are applied for decentralized data and then aggregated. This helps fintech companies to personalize their services for specific customers.
Eliminating biases with counterfactuals
Counterfactuals allow the inclusion of new features and variables in the analysis of data to neutralize preconceived biases.
9. AIOps and MLOps to Productize AI and Machine Learning
Combining machine learning and IT operations will help turn machine learning into a scalable and reusable product. This will ease up the process of AI integration for businesses.
10. Policy and Regulations to Control Production of AI and ML
As the utility of artificial intelligence increases, so does the concerns regarding data security, artificial bias, ethics, and safety. Since we have moved a long way towards machine-led data policies, there have to be bold initiatives governing those policies. We will surely see some crucial changes in 2021.
2021 is going to be a hyperactive year for data science and AI in all probability. We have covered only a fraction of what you get in the report created by AnalytixLabs and Analytics India Magazine. Make sure you read the full report and get ready for the year ahead.
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