Data engineering & data science are not exactly the unexplored areas for the world. However, the exponential rise in the usage of data engineering & data science technologies in the recent years can be attributed to the intensified competitive environment across most industrial landscapes. Enterprises have begun to heavily rely on data-driven and information-based insights in order to make more accurate decisions that may decide the future for their businesses.
Combining data science with natural language processing and leveraging AI-powered IoT are turning out to be significant in making better decisions by devising intelligent strategies and predicting potential failures. The incorporation of machine learning to automate data-based tasks is helping businesses to produce intelligent business insights that are assisting businesses in making well-informed decisions. Advanced data science tools and cloud-data warehouses are taking a huge burden off of businesses’ not only by ensuring proper privacy and security of data but also directing them towards profitable paths.
Attributing to the uncertain circumstances triggered by the worldwide outbreak of the novel coronavirus, data engineering and data science technologies are turning out to be a ray of hope for businesses as they are helping them revamp their strategies in order to stay a step ahead of their competition. Companies are thus, treating data as an asset to win competitive advantages in industries. As a result, the data engineering and data science sector is expected to witness the rise of technological trends in the coming years.
Take a look at what will be the top five hottest trends in data engineering & data science and how these sectors will expand in the coming years.
1. Integration of Natural Language Processing with Data Science Tools
Since the outburst of machine learning and deep learning technologies, the implementation of natural language processing (NLP) is growing immensely in data science. Advancements in NLP are triggering the popularity of voice-based applications and analytics tools in the data science landscape.
The increased reliance on data engineering & data science technologies is helping in identifying and classifying writing styles as well as in understanding the meaning of the text. Use of Natural Language Processing is also assisting businesses in monitoring and analysing market intelligence reports in order to devise better and more efficient strategies.
Data science tools with advanced natural language interpretation, recognition, and intonation mechanisms are expected to ride on the coattails of this trend in the coming years.
2. AI-powered IoT Enhancing Productivity with Data Analytics
Artificial Intelligence (AI) and Internet of Things (IoT) are emerging as technologies that are widely used across small and medium-sized enterprises to enhance productivity in most business operations. Similarly, these technologies are also being incorporated by service providers in the data engineering & data science industry to improve the operational efficiency and enhance asset management of businesses.
AI-powered IoT can be a significant factor in mitigating the damages associated with unplanned downtimes by implementing data analytics to predict potential equipment failures and perform necessary maintenance operations. This is helping businesses to enhance their risk management abilities and thereby, giving them a competitive edge over their competitors.
Though AI and IoT are already ubiquitous in data analytics tools, the rapidly growing IoT industry and the improved accessibility of AI will ensure that the trend will continually grow in popularity in data engineering & data science in the coming years.
3.Machine Learning Automating Data-Based Operations
With a majority of the population forced inside their homes as a result of the COVID-19 outbreak, the importance of automating business operations has grown more than ever. Businesses today are generating unique, meaningful insights from big data with the help of machine learning and automation tools. Automation and tooling are also being employed for the purpose of data cleaning which is saving a lot of time, and machine learning is one of the important areas that are contributing to the advancements in predictive analytics.
With the rise in machine learning technology, a number of data-based tasks are getting automated. This is providing analysts access to broader and more substantial data and helping them to generate more effective insights. The accessibility to meaningful intelligent insights is further assisting decision-makers with predictive analytics in order to make better decisions to enhance their productivity.
4. Implementation of Technology-driven Data Science Tools for Data Privacy and Security
While the implementation of data analytics software is increasing at a rapid pace across industries, concerns about the security and privacy of sensitive data have also heightened at the same time. Data is one of the most important assets for businesses today and keeping it secure is a major challenge.
With the help of data science and machine learning tools, analysts can reveal trends and patterns related to the data breaches and security attacks. This could be helpful in predicting and preventing any potential attacks in the future. Anomaly detection is one of the most trusted ways that helps data scientists identify anomalous and potentially malicious activities.
With data security becoming an incredibly hot topic in the data science industry, the integration of next-generation technologies to improve security protocols of data analytics tools will soon become the hottest trend in the data science industry.
5. Cloud-Data Warehouses are Taking the Data Engineering Industry by Storm
Most businesses used data warehouses that ran on-premises, for the longest time. This required businesses to constantly look after the maintenance of these warehouses to maintain ease of accessibility and prevent any breakdowns. However, the process of creating a database in the cloud is quite easy and is way cheaper than that of on-premise warehouses.
Cloud-data warehouses with more flexibility, improved connectivity and better security, are replacing the on-premises and hybrid warehouses. With efficient tools provided by cloud vendors, database administration has become quite efficient. With cloud-data warehousing, data engineering can only become more relevant for businesses in the near future as agile businesses require efficiency and organization that comes with proper data engineering.
Data Engineering & Data Science: Aiding Business Growth in all Shapes and Forms
The rising usage of technology coupled with the availability of data has resulted in data engineering & data science gaining a lot of importance. The demand for data-driven decisions is increasing as businesses are relying on unique insights generated with the help of data analytics, to make important business decisions.
With technological advancements and innovations, data engineering & data science technologies are now getting embedded with more and more industries, working with both large and small businesses. The increasing use of cloud-based data warehouses is ensuring increased synchronization of data, which is speeding up processes and hence, aiding the growth of businesses.
Thus, the adoption of data engineering & data science technologies is helping businesses streamline their processes and thereby, businesses are witnessing substantial growth.
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