Data Science is one of the most lucrative industries in the world right now. The importance of Data Science in almost every sector is ever-growing as both government and private corporations are benefitting by adopting Data Science processes. Single-handedly, just the analytics industry amounted for 23.4% of the Indian IT/ITES market in 2021, and it is projected to grow even more (41.5% of the market value of IT/ITES domains by 2026).
The analytics market in India grew at 26.5% from $35.9 billion in the fiscal year of 2020 to $45.4 billion in the fiscal year of 2021. The value of the Indian analytics market is projected to grow to $98 billion by 2025.
MNCs and conglomerates have always been using Data Science for forecasting and gaining crucial business insights. And, Data Science was always an integral part of software development, automation, and various IT processes; however, in recent times, non-IT businesses have started adopting analytics and Data Science technologies that allow them to utilize data more effectively.
A simple example would be the rapid adoption of database technologies and ERP (Enterprise Resource Planning) systems by medium and small businesses as compared to manually recording transactions, figures, and resources such as journaling.
With the help of digitizing data, factory owners and managers can use analytics to monitor their recent performance and then be able to identify the factors that are influencing a drop in performance or revenue. Data visualization is also becoming extremely popular among small and medium sized enterprises, enabling them to build interactive reports and better understand their data.
Data scientists, data engineers, and data architects have always been in demand. There are thousands of vacancies for Data Science related job roles every year, and there is an enormous skill gap in the industry right now. Companies across the globe are in dire need of skilled data professionals, and this demand is only increasing with every passing year as more businesses are moving towards automation and digitization.
Top 11 Data Science Trends in 2022
Let us learn more about some important Data Science trends that are the prime focus in 2022.
Automated Data Cleaning
Without cleaning datasets, companies cannot use large amounts of data for analytics or development. Duplicate data, unstructured data, unnecessary facts or figures, etc. are harmful to any kind of data-driven task. Removing noise from data effectively and in an autonomous fashion is one of the top priorities for data-driven companies.
By manually cleaning data, businesses lose both money and time, thus making the case for automated data cleaning solutions. A large bunch of companies will be shifting to automated data cleaning with the help of Machine Learning and AI-based solutions. Automated cleaning comes handy when dealing with big data.
With the growing demand for real-time analytics, companies also want automated solutions for data discovery, data analytics, and gaining business insights. With the help of Machine Learning and Deep Learning approaches such as Natural Language Processing, companies can automate their analytical processes.
Tasks such as data collection, data preparation, and even data sharing can all be automated for businesses. Companies in modern times are adopting augmented analytics in their Business Intelligence solutions as well. Augmented analytics is especially useful when used with modern data solutions such as distributed file systems or NoSQL databases such as MongoDB.
Using quantum computing for analytics is also the next big thing for real-time analytics on massive amounts of data. Once quantum computing is ready for widespread applications, it will be used for extremely fast analysis and statistical procedures.
Hybrid and AI-driven Cloud Systems
Hybrid cloud solutions are one of the new Data Science trends that you should look out for. Hybrid cloud systems are not as expensive as private cloud services but provide the security and reliability of the same.
Compared to hybrid and private clouds, public clouds are not as safe.
Hybrid cloud systems also offer data scalability, and companies can choose to upgrade or downgrade based on their needs. With the help of AI, hybrid clouds are being developed to be more stable, access-friendly, and monitorable while still being centralized. Many AI-driven systems are also being offered with cloud services, and many firms have started building AI implementations on the cloud as well.
Blockchain in Data Science
The amount of data we are generating is increasing every passing year. This is why there is a need for a system that goes beyond the limitations of centralized databases. Decentralized ledger technology such as the blockchain allows large amounts of data to be managed easily and in a secure fashion.
These ledger networks do not depend on any primary node or system in the network and they cannot be affected by hackers or malicious behaviour. The use of decentralized ledgers simplifies the management of large amounts of data as well. With the help of other nodes (or devices) in the network, data transactions are verified in real time, removing the chance of glitches, bugs, and other factors that compromise the provenance or security of data.
Analytics and data storage in the blockchain also makes it easy for data scientists to collaborate. Many problems that occur in Data Science pipelines can be solved by adopting blockchain technology.
Big Data in the Internet of Things
The IoT or Internet of Things is one of the prime focuses of the IT and Data Science industries. Data is collected by various sensors, devices, and machines around us and on us, and data scientists are figuring out innovative new ways of using this data. At one point, all of this data will become too large for current IoT data management technologies.
Thus, big data technologies must be adopted for IoT and for promoting modern solutions such as edge cloud computing. By incorporating big data technologies and Artificial Intelligence with IoT devices, companies can use the data collected by thousands of sensors and devices and use that data for analytics or other data science processes such as data visualizations.
Dynamic Dashboards and Business Intelligence
In earlier times, we only had static dashboards that were predefined by business intelligence tool developers. Some older dashboards need the data to also be manually engineered before being connected to the business intelligence system. Even now, many companies still use older tools and technologies that only have a selected range of visualizations.
However, this is slowly changing as all kinds of businesses are adopting smart business intelligence tools that feature dynamic dashboards and great visualizations. A great example would be Microsoft Power BI, which easily connects to a variety of data sources. Dynamic dashboards that are easily customizable to only show the crucial insights that are currently relevant are essential for modern businesses.
Time is everything in today’s world and new BI solutions help businesses gain an edge over the competition. New BI solutions also offer automated dashboards and report building.
Edge cloud computing allows IoT devices to process data remotely and autonomously without the help of the central data processing system or database. Especially with the world having adopted big data technologies as the standard solution for data management and processing, there was never a better time for edge computing to be introduced for widespread use.
By 2022, edge computing and edge intelligence will become the standard practice. All kinds of industries and their factories, sites, and offices will be adopting edge solutions for generating various kinds of data that can be used across other organizational devices and systems. With the help of AI, this data can also be pre-processed and prepared so that it can be effectively used for tasks such as performance analytics or operations management.
Edge computing also gets rid of various intermediary devices that pre-process and relay the data to other systems.
Data visualization has started replacing traditional reports and manually built charts in meetings and end-of-the-day emails to shareholders. With better BI tools and office suites, smaller firms can also visualize data in an automated manner. Data visualization is an important step after data discovery as it allows one to find out the main context of the data.
Visualizing helps companies easily figure out majorities and minorities from figures and information. Identifying patterns and trends also becomes easier when data is visualized properly. More companies are shifting to modern visualization methods and smart visualizations that allow one to gain important insights from just a single look at the visualization.
BI software, such as Power BI and analytics tools such as Tableau, has also been offering even better and more dynamic visualizations so that data science professionals can support their employers more effectively.
Natural Language Processing for Data Retrieval or Data Extraction
NLP or Natural Language Processing helps machines understand the context or relevance of textual and auditory data. When it comes to automated data retrieval processes such as scraping website data, NLP comes especially helpful.
Why? It is because NLP allows the process to become AI-driven. An NLP model can be built with deep learning or machine learning methodologies that only selectively extract certain types of information and figures. NLP also allows companies to conduct social media analytics and important analysis processes in Data Science such as sentiment analysis and behaviour analysis.
With the availability of all kinds of data and with the growing capabilities of AI systems, humans can now be assisted by data-driven systems when making decisions. Whether it is political decisions, government decisions, or business decisions, AI can provide substantial support for all of these.
Data-driven models can be taught to selectively fetch and utilize certain types of data when given specific scenarios or situations. Decision intelligence is being adopted across the world right now and is one of the foremost trends in Data Science. More than anything, an additional set of intelligent recommendations that are based on intelligent forecasting and predictive analytics would definitely help any business owner make good business decisions.
No-Code Data Science and Low-Code Technologies
With the help of AI-based development environments, data scientists can now pre-process and clean data without the use of code or with minimal code. There are also many low-code technologies that come with pre-built models and templates that budding Data Science professionals can use to complete their tasks faster. Also, with the increase in the availability of no-code systems, one can even completely avoid using code.
Even databases require queries and code that involve writing syntax and parameters in order to fetch data or carry out any operation with the data. For instance, one must use SQL to carry out CRUD (Create, Read, Update, Delete) functions on SQL databases such as MariaDB or MySQL. Companies are now interested in solutions that do not require database administrators and developers to use code when working with databases.
Almost all computing systems, portable devices, and software are developed with the help of Data Science pipelines. Any device that deals with data also requires its architecture to be modelled to do so.
Similarly, any application or program that is intelligent (AI-driven) also requires training with prepared datasets. Netflix, Spotify, Youtube, and all the platforms that we use are built and continuously run with the help of dozens (if not hundreds) of data science professionals.
Upskilling in Data Science opens up many new opportunities in your career as you can get involved with a variety of processes and industries. The prospects are limitless once you graduate from a Data Science certification program.
Check out Hero Vired’s Integrated Program in Data Science, Machine Learning, and Artificial Intelligence if you wish to learn essential skills for Artificial Intelligence and Data Science. This is a holistic Data Science course for working professionals and freshers that will help you learn all you need for becoming a full-fledged Data Science professional.