Top Ten Data Science Trends of this Decade
We are living in the age of massive data, which can generate useful insights for businesses. The use of advanced technologies has helped the harnessing of data to realize its massive impact on businesses. Data Science involves collating, sorting, and analyzing data to make decisions related to business growth through algorithms and frameworks. Since the pandemic, more companies are relying on these insights to make decisions which has strengthened certain data science trends.
Below are top data science trends which have shaped this decade.
Big Data on the Cloud: Cloud computing gives users access to storage, files, software and servers via the Internet. Such devices may be laptops, smartphones, tablets and wearables. Unlike traditional hardware and software, cloud technology helps companies to enhance their performance and increase efficiency without the need for investment in purchasing, maintaining and servicing advanced hardware. According to IDC, the global spending on cloud services is growing at a Compound Annual Growth Rate of 16.9%. It is expected to cross $1.3 trillion by 2025.
Speech Recognition: The speech recognition technology identifies and converts human speech into readable text with the help of Artificial Intelligence and Machine Learning. It is different from voice recognition technology which recognises an individual’s voice. The algorithms in the speech recognition processes organize audio into text by identifying different speech patterns, speaking tones and dialects. Research by MarketsandMarkets estimates that the global speech and voice recognition market was worth nearly $8.3 billion in 2021 and would reach $22 billion by 2026.
Use of Natural Language Processing: Businesses use volumes of unstructured, text-heavy data which must be efficiently processed to generate outcomes. Many of this data is stored in databases as natural human language, and this is where Natural Language Processing comes in handy. Natural Language Processing involves the application of Artificial Intelligence (AI) to extract written or spoken narratives from a data set. Natural Language Processing (NLP) is a multi-step process which consists of content analysis, data understanding, document structuring, sentence aggregation, grammatical structuring and language presentation. Natural Language Processing improvises the quality of insights and enables organizations to make informed decisions.
The rise of Machine Learning platforms: This is among the prominent data science trends. Machine Learning Platforms aid businesses in developing Machine Learning platforms which focus on either one or more of the following components- i) managing data, ii) building models and iii) serving predictions.
Massive amounts of data generated by businesses have fuelled the rise of machine learning platforms. ML helps software applications to become more accurate in predicting outcomes without being programmed to do so. These Machine Learning platforms provide the requisite tools to source and prepare data, build, deploy and train models to generate actionable insights for business growth.
Use of predictive analysis: Businesses must foresee problems and understand their weaknesses. This helps them plan better ahead. Among the recent trends in data science is the use of Predictive analytics. It uses statistical techniques like machine learning, predictive modeling to predict future outcomes. Some day-to-day applications of Predictive analytics include weekly weather forecasts and R&D in healthcare.
Internet of Things: We are living in an advanced world where we can control appliances like TV, AC, etc., with a click of a smartphone. According to IDC, global spending on cloud services has been growing at a CAGR of 16.9% to be worth $1.3 trillion by 2025. Thanks to IoT, Siri and Google Assistant are the recent trends in data science which have impacted our lives. Integrating the Internet of Things (IoT) with Machine Learning and Data Analytics gives flexibility to the system. It improves the accuracy of responses generated by ML algorithms.
Virtual Agents: Virtual agents use artificial intelligence and natural language processing to cater to customers’ requests. These agents mimic human conversation, understand user language better, ask additional questions to understand the language better and pass on the conversation to a human agent if there is a need. These virtual agents use Machine Learning platforms, enabling them to improve the quality of responses and customer interactions. For instance, web chatbots are among the recent trends in data science, which are programmed to address customer queries outside business hours.
Robotic process automation: Robotic process automation consists of several software applications to build, deploy and manage robots and is governed by business logic and structured inputs. RPA tools enable companies to configure software or a robot to automate several processes. These robots mimic certain human functions, such as digital systems and software, to execute tasks at high volume and speed and minimize chances of human error. Robotic process automation enables CIO and the top business leaders to adopt and speed up digital transformation in business and generate a higher return on investment. According to Forrester Research, the RPA software market is expected to be worth from $2.4 billion in 2021 to $6.5 billion by 2025.
Deep Learning platforms: Deep Learning is a subset of Machine Learning, a neural network with three or more layers. Deep Learning powers many applications and services that automate manual services and perform specific logical and physical tasks with minimal human intervention. Although Deep Learning has been theorized since the 1980s, it requires large amounts of labeled data and uses tremendous computing power. High-performance GPUs have a parallel architecture which enables development teams which cuts the training time for deep learning platforms from weeks to hours. Some applications that use Deep Learning are voice-enabled TV remotes, credit card fraud detection and digital assistants.
Focus on Edge Intelligence: Edge computing is among the latest trends in data science, which is expected to strengthen further in the years to come. It is a computing paradigm which refers to a range of networks and devices at or near the user. According to Gartner, edge computing is defined as a process “part of a distributed computing topology in which information processing is located close to the edge—where things and people produce or consume that information”. Edge computing speeds up the processing and storing of data, thereby multiplying the efficiency of real-time applications. Edge computing is used in Virtual and Augmented Reality platforms, self-driving cars, smart cities and building automation systems. Gartner predicts that by 2025, more than 50% of enterprise-critical data will be created and processed outside the datacenter or cloud.
High-tech advancements such as Quantum Computing, DataOps, Blockchain, etc., which now figure among the latest news in data science, are expected to transform our lives greatly. These trends are a testimony to the bright prospects in data science. LinkedIn predicts that the data science market will grow from $37.9 billion in 2019 to $230.80 billion by 2026.
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