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Data science is a multidisciplinary approach to extracting actionable insights from the large and ever-increasing volumes of data collected and created by today's organizations. Data science encompasses preparing data for analysis and processing, performing advanced data analysis, and presenting the results to reveal patterns and enable stakeholders to draw informed conclusions. Here are the top 10 trends related to data science jobs one should know in 2022.
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. Data science models and artificial intelligence come to the rescue at this point. 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 2022 is the use of public and private cloud services for big data and data analytics.
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. 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 up 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 2022. It is referred to as DaaS in short.
Augmented analytics 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.
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 2022 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.
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 2022 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.
For advanced analytics in 2022, having data is not sufficient. 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 2022. Natural Language Processing will have access to quality information that will result in quality insights.
