Machine Learning, Artificial Intelligence, and Data Science – you’ve likely heard these terms being used interchangeably, but the key connector behind them is Big Data. In simple terms, when you think of Machine Learning and AI, it’s about writing algorithms and creating models used for validation, testing, and producing results. The models are then cleaned up and applied to real life.
These models depend on Big Data. Big Data is linked to job automation in the economy, and a McKinsey Study revealed that by 2036, 90% of jobs would get automated out of which 30% will be leveraging Big Data and its associated technologies. Japan faces the highest potential for job automation while India ranks second with an expected automation potential of 52%.
Looking at these figures, we realise that Big Data is in high demand, but why?
1. Robust Data Models
The more data and the more variety you feed into Machine Learning models, the more robust they become. Big Data influences the outcome and results produced by the models, and if you want real-life accuracy, then it’s a must.
2. Lack of Skilled Professionals
Big Data experts are in high demand and the job market is expected to stay open at least for the next few years. Big Data analysts require to don many hats and be skilled in business acumen, analytics, math, programming, communicative and collaborative skills. Companies want recruits who can analyse multiple data sources and extract valuable insights for them which lead to growth and more sales.
3. Influences Project Outcomes
The direction of corporate or company projects hinges on Big Data. Lack of Big Data causes unforeseen project management failures, leading to pitfalls or losses. Big Data analysis reveals patterns, trends, and various parameters or outliers related to the project being worked on. This helps overcome any unexpected variables which crop up at the last minute. Thus impacting and improving performance.
4. Predictive Analytics
Predictive analysis uses many techniques from data mining, statistics, modeling, machine learning and AI to analyze current data to make predictions about the future. The patterns found in historical and transactional data can be used to identify risks and opportunities for future. Big companies like Amazon and Flipkart use Predictive Analytics to evaluate any unforeseen mechanical failures, defaults, or compromises at the supply chain level can be spotted by studying past and present data and trends. By successfully applying predictive analysis the businesses can effectively interpret big data for their benefit
5. User Experience
One of the biggest reasons big data is in high demand is because of the user or customer experience. Taking in data from call logs, social media channels, customer feedback forms and surveys, website visit logs, and product/service reviews lets businesses improve their lineup of products and improve customer experience. Big Data also reveals customers’ likes, dislikes, sentiments, opinions, and any thought-perspectives related to their services which helps identify new target groups and expand their user-base too.
6. Fraud Detection and Prevention
With more data being available on the internet, you’re not just dealing with customers but hackers and unethical individuals who are keen on stealing valuable information. This leads to the rise in demand of Cybersecurity experts who are required to analyse the growing volumes of data and frame regulatory policies and measures to prevent data theft or hacking. Companies don’t like losing billions, but the availability of unstructured data sets to the public makes security and data protection a tough job. This causes a surge in demand for Big Data Professionals.
Big Data isn’t just tied to jobs or data protection. From the way businesses operate to preventing bottlenecks in logistics and various departments, it influences how businesses evolve and meet the requirements of their customers.
Without a doubt, as technology advances and user bases grow, Big Data and associated professional career openings will continue to grow.