Software Industry 2019 Decoded: The Benefits of Doing Big Data Analytics
By 2020, software and SaaS industry would depend totally on the advanced tools for big data analytics. There’s a huge scope of development in big data specialization, including Predictive and Prescriptive analytics, Data Modeling and Dark Data Discovery. It’s imperative for a professional to get a clear understanding of how and what tools for big data analytics have to offer, and how data scientists can use them for building a big data analytics software stack for efficient business economy.
Let’s decode the software industry around Big Data Analytics.
Big Data Analytics: An Emerging Enterprise Capability
What makes big data influential and attractive?
Size, scale and diversity would be top answers you would learn in Big Data Analytics Course and in the industry roles.
With high-speed internet and mobility tools, the big data game is much larger than anticipated. Between 2019 and 2022, the industry is pegged to grow at more than 35% CAGR, bringing in $4.8 billion annual revenue to the SaaS and overarching cloud and IT infrastructure sectors.
With data governance and data management policies at an all-time high risk, businesses are looking to hire and empower more data scientists and analysts with hands-on experience in big data analytics.
Types of Data: The Big Sources
With scalable SaaS computing platform available to most businesses, it is highly speculated that Big Data would be the go-to solution for all decision-making processes, including for vendor management and Point-of-Scale ecommerce and retailing.
The opportunities in Big Data Analytics are largely sprouting from the current data sources, including these popular ones –
- Mobile Data,
- Multi-orchestration of Sales and Marketing data
- Human-Generated Data, like emails, documents, social media, Text and SMS
- Web and Internet click stream/ Search Engine (also referred to as Transactional Data)
- Machine-generated data, also referred to as IoT and connected devices
In 2020, mobile data alone would attribute to 35% of the total SAS data available for Big Data Analytics. With growing use of connected devices and gadgets like Alexa and Siri, and self-driving cars, the mobility-based data would grow by 50%. That opens up a huge scope of improvement in the way tools could be leveraged to measure scalability and productivity of Big Data Analytics models. That’s where, I think, AI and machine learning would come handy. Automation and gamification of big data analytics would allow data scientists to run auto-pilot test runs on Data Governance and Data accuracy.
Once you visualize the opportunities in gaining hands-on experience in dealing with these different types of data and SaaS tools for analytics, you won’t think twice about choosing a Big Data Analytics course in 2019-2020.