There’s nothing constant in our lives but change. Over the years, we’ve seen how businesses have become more modern, adopting the latest technology to boost productivity and increase the return on investment.
Data analytics, big data, artificial intelligence, and data science are the trending keywords in the current scenario. Enterprises want to adopt data-driven models to streamline their business processes and make better decisions based on data analytical insights.
With the pandemic disrupting industries around the world, SMEs and large enterprises had no option but to adapt to the changes in less time. This led to increasing investments in data analytics and data science. Data has become the center point for almost every organization.
As businesses rely on data analytics to avoid and overcome several challenges, we see new trends emerging in the industries. AI trends 2023 by Gartner are an example of development. The trends have been divided into three major heads- accelerating change, operationalizing business value, and distribution of everything (data and insights).
In this blog, we’ll look at the most important data science trends in 2023 and understand how big data and data analytics are becoming an inherent part of every enterprise, irrespective of the industry.
Data is already being generated in abundance. The problem lies with collecting, tagging, cleaning, structuring, formatting, and analyzing this huge volume of data in one place. How to collect data? Where to store and process it? How should we share the insights with others?
Data science models and artificial intelligence come to the rescue. However, storage of data is still a concern. It has been found that around 45% of enterprises have moved their big data to cloud platforms. Businesses are increasingly turning towards cloud services for data storage, processing, and distribution. One of the major data management trends in 2023 is the use of public and private cloud services for big data and data analytics.
What use is data in its raw, unstructured, and complex format if you don’t know what to do with it? The emphasis is on actionable data that brings together big data and business processes to help you make the right decisions.
Investing in expensive data software will not give any results unless the data is analyzed to derive actionable insights. It is these insights that help you in understanding the current position of your business, the trends in the market, the challenges and opportunities, etc. Actionable data empowers you to become a better decision-maker and do what’s right for the business. From arranging activities/ jobs in the enterprise, streamlining the workflows, and distributing projects between teams, insights from actionable data help you increase the overall efficiency of the business.
Data is now being offered as a service as well. How is that possible?
You must have seen websites embedding Covid-19 data to show the number of cases in a region or the number of deaths, etc. This data is provided by other companies that offer data as a service. This data can be used by enterprises as a part of their business processes.
Since it might lead to data privacy issues and complications, companies are coming with procedures that minimize the data risk of a data breach or attract a lawsuit. Data can be moved from the vendor’s platform to the buyer’s platforms with little or no disturbance and data breach of any kind. Data exchange in marketplaces for analytics and insights is one of the prominent data analytics trends in 2023. It is referred to as DaaS in short.
What is augmented analytics? AA is a concept of data analytics that uses AI, machine learning, and natural language processing to automate the analysis of massive data. What is normally handled by a data scientist is now being automated in delivering insights in real-time.
It takes less time for enterprises to process the data and derives insights from it. The result is also more accurate, thus leading to better decisions. From assisting with data preparation to data processing, analytics, and visualization, AI, ML, and NLP help experts explore data and generate in-depth reports and predictions. Data from within the enterprise and outside the enterprise can be combined through augmented analytics.
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The automation of cloud computing services for public and private clouds is achieved using artificial intelligence and machine learning. AIOps is artificial intelligence for IT operations. This is bringing a change in the way enterprises look at big data and cloud services by offering more data security, scalability, centralized database and governance system, and ownership of data at low cost.
One of the big data predictions for 2023 is the increase in the use of hybrid cloud services. A hybrid cloud is an amalgamation of a public cloud and a private cloud platform.
Public clouds are cost-effective but do not provide high data security. A private cloud is more secure but expensive and not a feasible option for all SMEs. The feasible solution is a combination of both where cost and security are balanced to offer more agility. A hybrid cloud helps optimize the resources and performance of the enterprise.
Gartner and Forrester have predicted that edge computing will become a mainstream process in 2023. Edge computing or edge intelligence is where data analysis and data aggregation are done close to the network. Industries wish to take advantage of the internet of things (IoT) and data transformation services to incorporate edge computing into business systems.
This results in greater flexibility, scalability, and reliability, leading to a better performance of the enterprise. It also reduces latency and increases the processing speed. When combined with cloud computing services, edge intelligence allows employees to work remotely while improving the quality and speed of productivity.
Another dominant trend in data science in 2023 is hyper-automation, which began in 2020. Brian Burke, Research Vice President of Gartner, has once said that hyper-automation is inevitable and irreversible, and anything and everything that can be automated should be automated to improve efficiency.
By combining automation with artificial intelligence, machine learning, and smart business processes, you can unlock a higher level of digital transformation in your enterprise. Advanced analytics, business process management, and robotic process automation are considered the core concepts of hyper-automation. The trend is all set to grow in the next few years, with more emphasis on robotic process automation (RPA).
Internet of Things (IoT) is a network of physical things embedded with software, sensors, and the latest technology. This allows different devices across the network to connect with each other and exchange information over the internet. By integrating the Internet of Things with machine learning and data analytics, you can increase the flexibility of the system and improve the accuracy of the responses provided by the machine learning algorithm.
While many large-scale enterprises are already using IoT in their business, SMEs are starting to follow the trend and become better equipped to handle data. When this occurs in full swing, it is bound to disrupt the traditional business systems and result in tremendous changes in how business systems and processes are developed and used.
For advanced analytics in 2023, having data is not sufficient. We already mentioned in the previous points how big data is of no use if it’s not clean enough for analytics. It also refers to incorrect data, data redundancy, and duplicate data with no structure or format.
This causes the data retrieval process to slow down. That directly leads to the loss of time and money for enterprises. On a large scale, this loss could be counted in millions. Many researchers and enterprises are looking for ways to automate data cleaning or scrubbing to speed up data analytics and gain accurate insights from big data. Artificial intelligence and machine learning will play a major role in data cleaning automation.
Famously known as NLP, it started as a subset of artificial intelligence. It is now considered a part of the business processes used to study data to find patterns and trends. It is said that NLP will be used for the immediate retrieval of information from data repositories in 2023. Natural Language Processing will have access to quality information that will result in quality insights.
Not just that, NLP also provides access to sentiment analysis. This way, you will have a clear picture of what your customers think and feel about your business and your competitors. When you know what your customers and target audience expect, it becomes easier to provide them with the required products/ services and enhance customer satisfaction.
One of the trending research topics in data science is Quantum computing. Google is already working on this, where decisions are not taken by the binary digits 0 and 1. The decisions are made using quantum bits of a processor called Sycamore. This processor is said to solve a problem in just 200 seconds.
However, Quantum computing is very much in its early stages and needs a lot of fine-tuning before it can be adopted by a range of enterprises in different industries. Nevertheless, it has started to make its presence felt and will soon become an integral part of business processes. The aim of using Quantum computing is to integrate data by comparing data sets for faster analysis. It also helps in understanding the relationship between two or more models.
We have already seen how DaaS is becoming famous. The same is now being applied to machine learning models as well. Thanks to the increase in demand for cloud services, AI and ML models are easier to be offered as a part of cloud computing services and tools.
You can contact a data science company in India to use MLaaS (Machine Learning as a Service) for data visualization, NLP, and deep learning. MLaaS would be a perfect tool for predictive analytics. When you invest in DaaS and MLaaS, you don’t need to build an exclusive data science team in your enterprise. The services are provided by offshore companies.
Automated machine learning can automate various data science processes such as cleaning data, training models, predicting results and insights, interpreting the results, and much more. These tasks are usually performed by data science teams. We’ve mentioned how data cleaning will be automated for faster analytics. The other manual processes will also follow suit when enterprises adopt AutoML in their business. This is yet in the early stages of development.
Forrester has predicted that more than 1/3rd of the enterprises will depend on artificial intelligence to reduce workplace disruptions. The advent of the covid-19 pandemic has forced organizations to make some drastic changes to their business processes. The remote working facility has become necessary for most businesses. Similarly, automation is being considered a better option than relying on workers and the human touch.
Using computer vision for high-dimensional data analytics is one of the data science trends in 2023 that helps enterprises detect inconsistencies, perform quality checks, assure safe practices, speed up the processes, and perform more such actions. Especially seen in the manufacturing industry, CV is making it possible to automate production monitoring and quality assurance.
Remember the Tiktok videos that were supposedly by Tom Cruise? The videos were created using generative AI, where new content is created using existing data. This trend is set to enter other industries and help train the ML algorithms using synthetic data.
Synthetic data is artificially manufactured instead of being taken from real-life events. There is a surge in privacy concerns for using the images of real people to train facial recognition apps. The challenge can be overcome by using synthetic images of people who don’t exist. Generative AI and synthetic data will become a part of more industries and impact how the AI software works.
While blockchain has become a part of FinTech and healthcare industries, it’s now entering the IT industry. So how does blockchain help with data science?
Data scientists have to structure the information in a centralized manner to make it ready for data analytics. This process is still time-consuming and requires effort from data scientists. Blockchain can solve the issue effectively.
Many data scientists feel that Python is an integral part of data science and will continue to be. It shouldn’t be surprising that Python will continue to rule the data science and ML world even in 2023. It’s agile, allows collaborations, and simplifies integrations for other programming languages and libraries. Wannabe data scientists will find that mastering the Python programming language will give them better opportunities in the field.
Data science will continue to be in the limelight in the coming years. We will see more such developments and innovations. The demand for data scientists, data analysts, and AI engineers is set to increase. The easiest way to adopt the latest changes in the business is by hiring a data analytics company.
Stay relevant in this competitive market by adopting the data-driven model in your enterprise. Be prepared to tackle the changing trends and make the right decisions to increase returns.