It's getting harder and harder to predict IT trends these days since uncertainty permeates every organization. Planning has become more challenging as a result, but firms are discovering new methods to innovate with cutting-edge technology and react swiftly to the rapidly shifting market conditions. Some recurring themes of difficulties and progress are coming to the fore as we anticipate a new year. To begin with, successful technology today is anticipated to deal with reality in real time. This is due to the fact that organizations are expanding quickly to meet consumer demands, and the conventional methods of collecting and storing data are no longer adequate.
Another trend that is sure to make waves in 2023 is the continued growth and development of Artificial Intelligence (AI). With AI-driven applications becoming increasingly dominant, businesses are finding new ways to automate tasks and make data-driven decisions. Platforms like ChatGPT are just the tip of the iceberg when it comes to the potential of AI, and we can expect to see even more advancements in this field in the coming year.
As organizations explore for ways to manage and make sense of the enormous quantity of data they are receiving, data science is becoming a crucial area of growth. Data scientists will be able to examine data more quickly and come to better judgments that will lead to corporate growth when new technologies and approaches emerge.
These trends' rise has shown that companies need to be nimble adapters if they want to stay ahead of the curve. People who can adapt to these new trends and technology will be in a good position to flourish in 2023 and beyond.
1. Data Democratization
One of the most significant developments will be the increasing empowering of whole workforces to use analytics, as opposed to just data engineers and data scientists. In order to help everyone perform their professions more successfully and efficiently, tools, programs, and gadgets are pushing intelligent insights into everyone's hands. As a result, new types of augmented work are emerging.
Businesses will understand in 2023 that data is essential to comprehend their clients, creating better goods and services, and optimizing internal processes to cut costs and waste. It is becoming more and more obvious that this won't completely occur unless frontline, shop floor, and non-technical personnel, as well as departments like marketing and finance, have the ability to act on data-driven insights.
The use of natural language processing (NLP) tools by lawyers to scan pages of legal precedents or by retail sales associates using hand terminals that can access real-time customer purchase information and make cross- and up-selling recommendations are two excellent examples of data democracy in action. According to McKinsey research, businesses that provide all of their employees' access to data are 40 times more likely to claim that analytics has a positive effect on sales.
2. Artificial Intelligence
The technology development that will probably have the most influence on how we live, work, and do business in the future is artificial intelligence (AI). Business analytics will benefit from its use by making predictions that are more accurate, saving time on tedious tasks like data collection and cleaning, and enabling workers to act on data-driven insights regardless of their position or degree of technical ability.
Simply said, AI uses software algorithms that grow better at their jobs as they are fed with more data to allow organizations to analyze data and derive insights far faster than would ever be feasible humanly. The fundamental idea behind machine learning (ML), the type of AI now employed in business, is this. NLP, which enables computers to comprehend and converse with us in human languages, computer vision, which enables computers to comprehend and process visual information using cameras, much like we do with our eyes, and generative AI, which can conceive text, images, sounds, and video from scratch, are some examples of AI and ML technologies.
3. Edge Computing
The expanded usage of edge computing in AI and data science is another trend that is anticipated to gain traction in 2023. Edge computing is the practice of processing and analyzing data locally, as opposed to centrally, in a facility such as a data center or the cloud. As a result, processing times are shortened and responsiveness is increased because no lengthy data transmission is required.
The usage of edge computing in Internet of Things (IoT) devices is one example of this trend. IoT devices produce a lot of data that has to be processed and evaluated in real-time, including smart cameras, sensors, and industrial equipment. These devices may process and analyze the data locally utilizing edge computing as opposed to transferring it to a centralized location, which can speed up reaction times and lower latency.
Autonomous cars are yet another instance. Large volumes of data are produced by autonomous cars' sensors, which include cameras, lidar, and radar. Edge computing enables cars to process and analyze this data in real time, enabling them to react quickly to their surroundings and increase safety and performance.
By keeping sensitive data on the device or local network rather than sending it over the internet to a centralized location, edge computing can increase security in addition to reaction times and latency. Read up on "6 Reasons Why Your Business Needs Cybersecurity Professionals" to understand why security is important for your organization.
4. AI (XAI)
The use of explainable AI (XAI) is a growing trend in data science, as it allows for greater transparency and accountability in AI-driven decision making. Improved knowledge of how the AI came at its findings is possible thanks to XAI, a sort of AI that has the ability to explain its judgments and forecasts. This is crucial in industries like banking and healthcare, where AI choices may have a big influence on people's lives.
The application of AI-powered investment management systems is one instance of XAI in finance. These systems examine market data and make investment judgments using machine learning algorithms. Traditional AI systems, on the other hand, might not be able to explain how they came to a certain conclusion, which can make it challenging to comprehend and trust the system.
Another example is the growing use of AI-driven diagnostic systems in the healthcare industry. Doctors may better understand XAI systems and make better judgments by receiving thorough explanations of how the AI arrived at a particular diagnosis.
Courses in machine learning, data science, and AI that include a focus on interpretability and explainability will be beneficial for those who want a better understanding. GemRain provides this training and all our training is hands-on, experimental-based training where the instructor will teach participants each step carefully and comprehensively.
Automated machine learning is the most rapid advancement in data analytics, and it doesn't seem to be going anywhere anytime soon. The democratization of data science is currently being driven through automated machine learning.
Automated machine learning improves the efficiency of labor-intensive, repetitive tasks that previously required physical labor. Thanks to auto ML, data scientists are no longer concerned with time-consuming tasks like data preparation and purification.
Automated machine learning builds models, algorithms, and neural networks that automate numerous tasks.
Auto ML basically means that the computer keeps working on a job on its own, without guidance from or interference from people. To apply machine learning models to real-world issues, auto ML leverages automation.
Data scientists frequently employ auto ML frameworks for model deployment, model comprehension, and data visualization. One of the key advancements in auto ML is hyperparameter search. Hyperparameters search is useful for selecting a model type, preprocessing elements, and improving their hyperparameters.
6. Natural Language Processing (NPL)
NLP is anticipated to play a bigger role in monitoring and tracking market intelligence as organizations employ data and information to construct future plans.
NLP is one of the many subfields of artificial intelligence, linguistics, and computer science. Due to the available processing performance and the large quantity of data it demands, it has gained popularity in recent years. Millions of dollars are being invested in NLP by digital giants like Facebook, Google, and Amazon to power their chatbots, virtual assistants, recommendation engines, product portals, and other NLP-enabled applications.
NLP mainly focuses on the interaction between human languages and computers, in particular, how to design computers so they can identify, understand, and analyze a sizable amount of data derived from natural languages, hence enhancing their intelligence.
These algorithms use NLP methods like syntactic and semantic analysis to extract the essential information from each text. Unlike semantic analysis, which deals with the interpretation of the data, the syntactic analysis focuses on sentences and grammatical structures.
Python is one of the favorite programming languages for NPL due to transparent semantics and syntax, solid support for integration with other languages and tools to build machine learning models, and because Python provides developers with a flexible set of NLP tools and packages that let them handle a variety of NLP tasks.
7. Data governance and regulation
The importance of data governance and regulation will increase as AI and data science develop in the future. The likelihood of data misuse and abuse will rise as more data is gathered and evaluated. To guarantee that data is utilized properly and ethically, data governance and regulation will be necessary.
The management of personal data is one area where data governance and regulation will probably take on more significance. There will be a growing need to safeguard people's privacy and make sure that their personal data isn't exploited when AI and data science are utilized to analyze more personal data.
Another area where data governance and regulation will become important is the area of bias. As AI and data science are used to make decisions, there is a risk that the algorithms used may perpetuate or even amplify biases that already exist in the data. Therefore, data governance and regulation will be needed to ensure that these biases are identified and addressed.
8. DaaS (Data-as-a-service)
Businesses may access and analyze massive volumes of data using data as a service (DaaS) instead of having to maintain and store it on their own servers. Instead, the information is kept in the cloud and is accessible online by anybody with a working internet connection. A number of tools for data analysis and visualization are often provided by DaaS providers, making it simpler for organizations to get insights and make defensible decisions.
A healthcare organization employing DaaS to evaluate patient data to enhance its treatment plans and results is one example of DaaS in action. Another illustration is a retail business that uses DaaS to examine sales data and consumer behavior to enhance its marketing and stocking plans.
DaaS usage has risen due to the COVID-19 pandemic as more businesses have switched to remote work and require remote data access. Additionally, the accessibility and use of DaaS services for businesses have been facilitated by the expansion of high-speed internet connectivity.
This course covers AI, Data Science and Machine Learning with Python, and can help understand DaaS by providing a strong foundation in ML, AI, DL and discussing their application. It covers the latest algorithms, future learning, and the use of Python, which is essential for data analysis and ML, and helps learners develop skills to work in the growing ML-AI industry and understand the use of DaaS.
9. Blockchain in Data Science
Imagine having the ability to conduct data analytics on the go, directly from your mobile device, while also having the added benefit of easy data validation due to the built-in tracking of data's origin on the blockchain. This is the power of decentralized ledgers in managing vast amounts of data, it makes data science more accessible and efficient, and no more time-consuming data centralization procedures. Blockchain technology is revolutionizing the way data scientists work, making data analysis more exciting and effortless.
In GemRain we have experienced trainers for:
10. Robotic Process Automation
RPA technology is poised to have a significant impact on the way businesses operate this year. RPA will provide workers more time to focus on more crucial and valuable activities by automating repetitive and boring chores. For the organization, this may result in greater efficiency, production, and cost savings.
An example of how RPA is applied in organizations is a bank where personnel manually enter data from client forms into the computer system for hours at a time. This time-consuming and repetitive process can be automated with RPA, freeing up staff members to work on more crucial and valuable projects like delivering exceptional customer care or spotting possible fraud.
Here is an article to understand the significance of RPA, "Power Automate Flows Explained".
Now that you are aware of the importance of Robotic Process Automation, here are the RPA course offered by GemRain.
11. Advancements in Low-Code and No-Code Technology
Companies are beginning to use out-of-the-box foundation models to implement AI in the industry, reducing time-to-value for AI solutions in areas like language, vision, and more. Artificial intelligence (AI) will have a tremendous impact on citizen development. Thanks to AI advancements in low-code technologies, everyone will be able to become a citizen developer. Citizen coders will be able to communicate the problem they're trying to solve in plain English, and conversational AI will generate the necessary code.
12. Cloud Migration
It is the process of moving digital assets, such as data, workloads, IT resources, or applications, to cloud infrastructure that is built on an on-demand, self-service environment. It is intended to achieve efficiency and real-time performance with the least amount of uncertainty. More organizations will race to migrate to the cloud as they become aware of its benefits in order to rethink their services and boost the efficiency, agility, and innovation of their business operations.
This AWS course provides a comprehensive understanding of the migration process to the AWS Cloud platform. It covers different migration methodologies and how to apply each phase of the migration process, including portfolio identification, migration planning, execution, and post-migration optimization
Are you prepared to participate in the current technological revolution? Data has never been more useful and available to enterprises of all kinds thanks to cutting-edge data technology. Imagine being on the cutting edge of these developments and being aware of the new fundamental objectives of the market, such as automation, accessibility, and intuition.
You will be prepared for a highly sought-after and rewarding profession by enrolling in our training program, which will also teach you about the most recent advances in data science and artificial intelligence. The future is today, and that future is data science.
Why are data science and artificial intelligence training important?
Data science and AI training is important because it can help individuals and organizations stay competitive in the job market and industry, unlock insights from data, automate repetitive tasks, improve predictions, lead to cost savings, personal growth, and solve complex problems. It is key to stay up to date with the latest trends and technologies in the field.
How will these advancements affect the job market and job opportunities in AI and Data Science?
As these advancements lead to increased automation and efficiency, there will likely be an increase in demand for professionals with skills in these areas, leading to more job opportunities in AI and Data Science.
How much can a Certified Data Scientist earn in Malaysia?
According to SalaryExpert, the average annual income for data science and AI-certified professionals is a remarkable RM155,469, and this figure is projected to increase to a staggering RM189,881 in the next five years.