Big Data involves implementing technologies like Artificial Intelligence, Machine Learning, and Natural Language Processing on a virtual platform.
It integrates both structured and unstructured data to speed up data processing capability, insights for business needs, and real-time virtualization which are faster than traditional processes. It has the following perceptions that make big data processing is inevitable and useful for any business.
- Strong focus on data governance: It helps the business or organization for analyzing and diagnosing to derive the right insights from raw data as per the data governance tools that make sure confidentiality and proper use of customer data.
- Augmented Analytics: It is implemented to speed up decision-making and recognizing trends with big data using trending technologies like Artificial Intelligence, Natural Language Processing, and Machine Learning.
- Utilize as Supplement: It replaces human’s hardworking by big data processing using applications and tools to assist market research. It provides patterns, dependencies, networks, and data value to understand the context of information.
- Cloud data for improved customer experience: The integration of cloud computing with big data through automation and digital assistance enhances customer experience. It gives a cloud-first mindset for professionals that help in brand interactions to launch products with continuous updates and value-added services.
- Coexistence of Public and Private Clouds: Multicloud IT strategies became reality in 2020 during a pandemic for continuous workflow and data processing. It ensures the accessibility and security of data on the internet to provide quality data to customers whenever they require it. The deployment of 5G and edge enhances real-time visibility and data management through cloud providers like Azure, AWS, and so on.
- Accessible data using complete cloud computing: Cloud computing with big data makes information always accessible for a data scientist. It provides simplified solutions for managers through interfaces and computing power with advanced analytics.
Important Market Movement of Big Data Processing till 2020
The following trends of data processing until the year 2020 marks important impression in big data history
- Big Data analytics market is expected the improve at 12.3% CAGR in the market from 2019 to 2027.
- Large enterprises dominate the big data market with more than 70% of share by the adoption and inclination of trending technologies.
- SME sector is expected to grow at the remarkable pace that is associated with digitalization and big data technologies.
- North America is the leading region that implements Big Data Analytics with 35% of successful revenue generation through numerous technological advancements.
- Asia-pacific is anticipating the highest growth of big data processing and technical advancements with expertise through the countries like Japan, China, and India.
Expected Growth of Big Data in 2021
Big Data Analytics is improved rapidly for managing the following processes and technologies in the day-to-day life of millions of people around the world.
Increased data volumes and need for cloud migration
The exponential growth of data usage is expected to reach 175GB in 2025 as most of the world turns to digitalization.
It depends on the increased internet users worldwide and all the business communications and shopping happening through social networking apps.
The number of connected devices and embedded systems are increasing for creating, collecting, and sharing data analytics to implement in IoT device developments.
It is used for an organization to store and analyze large volumes of data for managing individual customers as per their desired search. It requires hybrid and multi-cloud data environments for complete business needs.
Machine learning for continuous change
The development of technologies depends on machine learning technologies for augmenting everyday business operations and requirements.
It combines with AI technology in application development, algorithm implementation, and smart robot development, computer vision operation, natural language processing, virtual assistants, speech recognition, gesture control, and face recognition, image processing, and video recognition.
The Machine Learning model improves the advanced unsupervised algorithms with cognitive services and deeper personalization. It is used to develop self-driving cars, space exploration, and treat patients.
It helps in the banking sector for a transaction with an original research report that is confidential and secure.
Data Science Professionals are in high demand
The knowledge gap of skilled data analysts, data scientists, and data engineers in the industries leads to the requirement of comprehensive study with hands-on exposure and certification.
Nearly 70% of companies around the globe are facing a shortage of skilled professionals and they require people with the following range of demand as per the report of the CIO survey.
- Big Data or Analytics Skills with 44% of skill demand
- Cybersecurity Knowledge with 39% of skill demand
- Artificial Intelligence with 39% of skill demand
- Enterprise Architecture with 34% of skill demand
- Business Analytics with 31% of skill demand
To bridge the above-mentioned skill gap, one must have the following skills:
- Data Platform and Tools
- Programming Languages
- Machine Learning Algorithms
- Data Manipulation Techniques
- Data Pipeline Building
- Manage ETL process
- Data Analytics Preparations
Top companies are ready to pay a high salary for qualified and certified data science professionals for developing advanced data analytical models through trending technologies and complete data processing.
Business owners around the world realize the importance of a data science role that stays fit-for-all skills for restructuring and evolving data-driven solutions.
Data privacy is a key issue
The security and privacy of data have always been an issue for providing potential growth of data to the customers.
This growth of data creates challenges for protecting confidential information from cyberattacks.
The following are the reasons for data security issues.
Security Gap: Lack of training and education to handle big data and this skill gap reaches 3.5 Billion in 2021 according to the report of Cybercrime Magazine.
Evolution of cyberattacks: Threats of hackers increasing and it becomes more complex for data protection.
Less regulation of security standards: Government takes multiple measures to standardize data protection as per the regulations and most of the companies are ignoring such security standards.
The data security and protection through Big Data Analytics processing increases the investments of organizations on the following platforms:
- Cybersecurity application developments
- Performance reporting of big data
- Digital documents and electronic signature verification
- CRM (Customer Relationship Management) tools implementation
- Financial Planning of a global organization
- Implementing social media tools
- Rebalancing software applications
- Robo-advice solutions through AI
Faster and actionable data
Fast data and actionable data is the rising demand for a better future of big data. It relies on tools like Hadoop and NoSQL to analyze information that allows data processing in real-time streams.
Big Data processing brings more value to the organization for better decision-making and taking actions as quickly as the data arrival. It provides users the real-time interactions through digitized solutions for improving customer satisfaction and personalized results.
Big Data processing helps the organization for retrieving accurate information in a standardized format that is actionable and ready-to-use for improving business decisions as per the market trends.
The market trend of Big Data in February 2021
The future growth of big data depends on the investment as the world is turning to data-driven solutions for every real-time problem.
Global companies are investing huge capital in “Big Data Analytics” for development, smart decisions, and high-valued data retrieval that brings higher ROI and useful insights. Following are the companies who are invested in big data till February 2020:
- TigerGraph, a graph database provider raised $105 Million
- Matillion, a data transformation solution provider raised $100 Million
- Leadspace, a B2B customer data platform raised $46 Million
- Lusha Systems, a B2B engagement platform raised $40 Million
- Peak, a software platform bagged $21 Million
- Prefect, a workflow management system raised $11.5 Million
These investments bring high-volume ROI for the companies and proving that the big data platform holds a bright future for students.
Learning big data brings a promising future as per the details that are collected from various sources. It helps the learners to build an unfading career through continuous updates on trending technologies.
Big data platform allows students and working professionals with easy adoption from their core technologies as it is working in all departments like software development, automation, cloud computing, and IoT developments. Gain expertise in big data processing for continuous career growth.