Future Technology Will Combine AI, Big Data and the Cloud
PRODUCTIVE

AI, Big Data and the Cloud in Future Technology

“Everything is moving to the cloud and Big Data is one of the enablers of AI, blockchain, and other emerging technologies,”

So says Mandeep Singh, Senior Technology Analyst at Bloomberg. In a recent presentation at the Value of Data industry event, Mandeep highlighted the explosive growth of Big Data and the cloud. His teammate, Anand Srinivasan—also a Senior Technology Analyst at Bloomberg—detailed use cases of Big Data analytics in action for a variety of industries.

AI, Big Data and the Cloud in Future Technology

Big Data, artificial intelligence, and machine learning aren’t exactly new, but as the cost of data storage continues to go down over time, servers and other technology equipment can finally handle the diverse workloads of huge datasets. Companies in all types of industries, from manufacturing to retail and telecom, are predicted to increase spending on specialized servers1 that will help them store and work with Big Data.

Watch these two presenters from Bloomberg share their insights on future technology:

  • Mandeep Singh, Senior Technology Analyst, Bloomberg
  • Anand Srinivasan, Senior Technology Analyst, Bloomberg

How Data Will Be Stored in Future Technology

AI, Big Data and the Cloud in Future Technology
Mandeep Singh, Senior Technology Analyst at Bloomberg.

Machines, especially from mobile and Internet of Things devices, are generating more data than ever before. In fact, Mandeep spoke about possible projections of nearly 163 Zettabytes of data to be produced each year by 20252. That would be nearly double the amount of data that was produced in 2018 and four times the volume produced in 2016.

But, much of this data is unstructured. It comes from logs, sensors, and other sources that lack a defined structure. This presents a problem. Traditional databases are relational, which means that they were designed to easily categorize and store data that can be accessed efficiently at a later time.

In other words, relational databases were purpose-built to store structured data.

Non-relational databases, on the other hand, are better suited to identify patterns in unstructured data. That’s why Mandeep predicts that while companies might use a mix of these two databases to store data, the market for non-relational databases could grow faster annually than relational databases by 2021.

Still, the largest high-growth market remains the cloud. With a potential market size of $277 billion by 2021—according to Bloomberg—the public cloud market3 is larger than the size of the AI, Big Data, security, and blockchain markets combined.

Big Data Analytics Built into the Cloud

Storing zettabytes of data is only half of the story, though. Companies and organizations still need to create actionable insights from their stored data. And, some of the world’s largest companies are providing this value by building big data analytics on top of their public cloud infrastructure. Pure-play vendors, on the other hand, are offering cloud-based subscription to boost their market share.

“Security and fraud detection, supply-chain optimization and improved customer engagement have emerged as the top use cases of Big Data initiatives.” – Bloomberg Intelligence Presentation4, 9/13/2018

The industries that are taking advantage of these Big Data analytics services include banking, manufacturing, and the federal government. They are joined by other sectors that are slow to change due to the inability of traditional data warehouses to process unstructured data.


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Real-World Applications for Future Technology

AI, Big Data and the Cloud in Future Technology
Anand Srinivasan, Senior Technology Analyst at Bloomberg.

Following Mandeep’s presentation on Big Data storage and analysis trends, Anand Srinivasan spoke about a few problems that had been addressed through the unique combination of AI, Big Data, and the cloud. Here is a list of just a few practical applications of future technology being made possible through data.

  • Construction and mining: Automation of dangerous manual labor tasks
  • Manufacturing: Predictive analysis to prevent industrial machine breakdowns, which improves utilization and reduces downtime
  • Precision agriculture: Improve land and farming tool use while increasing crop yields

“The volume, variety, and depth of data, coupled with new technologies and rapidly falling technology costs, is an incredible convergence,” Anand Srinivasan, Senior Technology Analyst at Bloomberg

Learn More about the Value of Data

Sources:

  1. https://www.idc.com/getdoc.jsp?containerId=prUS44259518 
  2. https://www.forbes.com/sites/andrewcave/2017/04/13/what-will-we-do-when-the-worlds-data-hits-163-zettabytes-in-2025/#f86103e349ab
  3. https://www.idc.com/getdoc.jsp?containerId=prUS43511618
  4. https://youtu.be/lZ0cmlxXQ0M

 

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