Processing Data to Grow Business Via Big Data & Machine Learning

Write 2.5 and add 18 zeros. That’s the amount of data generated on a daily basis (the Big Data). By the way, that number is called 2.5 quintillions, and since we are talking about data, we’ll use bytes as the measuring unit of data.

With such a hefty amount of data, it wasn’t possible for the computing devices to interpret Big Data and make something useful out of it. The traditional way of data processing wasn’t enough to handle that colossal amount of unstructured data until a computer scientist and cognitive psychologist, Geoffrey Hinton, used his expertise in neural networks to work on what’s known as Machine Learning today.

What is Machine Learning (ML)?

Machine Learning is a division of Artificial Intelligence (AI) that DOES NOT use explicit programming to generate results. That means that once you feed a program with the basic input, you will start getting results based on the primary input.

Since the world is generating so much data and there’s no reduction expected in the next 5-10 years, instead of that, the data generated will increase by 12-14%. So how will large-scale businesses cope with such a situation?

Here comes the role of ML in dealing with Big Data. To store the huge unstructured data, we have storage devices to do. Businesses don’t hesitate to invest in storing but reading and extracting useful information from that large collection of unorganized data. The data scientists are well-qualified, no doubt, but to find the criticality and give something productive to the company is what matters the most. And when it comes to processing all that, there are programs that use ML to do the following tasks:

  • Manage unstructured data
  • Eliminate useless data
  • Record useful data
  • Discover key patterns
  • Analyze the data
  • Give evaluation

Let’s understand this technology with an example.

Machine Learning in Online Businesses

The trend of online shopping and transactions is at its peak. People have found out that e-commerce has really eased their shopping experience. By giving them cost-less virtual shopping mall visits and the facility to pay online, people are now spending a good time finding their desired items on particular websites. While they are scrolling hither and tither, an AI-based software is analyzing their activity online and gathering what they are doing. Here, you have to accept that your online activities are never hidden. Companies silently gather such data and apply ML algorithms to get a pattern of your browsing and buying behavior. Hence ML is a dominating factor that allows totally unrelated ads to haunt you.
That’s a common example of how online businesses are retaining customers.

Machine Learning in AI-based Devices

Let’s move to an AI-based device that also recently became a trend in households. A litter robot that got attention in the last couple of years due to its efficiency and ease of use. You can read more about it from a product’s perspective at the litter robot review. But if you observe how it works, there’s nothing new but the same mechanism of AI and ML.
 
A litter robot is smart enough to identify whether there’s litter on the floor or not. Sounds simple, isn’t it? But the real challenge occurs when you bring a litter robot to a new place or change your furniture’s setting. This makes the litter robot struggle in cleaning your abode because it has no idea where to move. One inch forward, and here it comes, the couch!
 
A litter robot functions using ML algorithms that record and recognize the movement pattern in the first attempt. The system learns where the pathway is in search of litter. Once it’s ready to roll, your litter robot will start sucking the litter from the floor. Apart from that, there are micro identifiers that notify you when the litter robot becomes full or when there is something unusual stuck in its pump. It also carries a trash can that collects all the litter, and you have to empty that can once it reaches a threshold.

Summery

AI gave birth to ML through which a large amount of unstructured data (Big Data) is interpreted. With such data interpretation, ML gives useful information from that data by eliminating futile elements. The information is then used to make productive decisions for the growth of the online business. Numerous industries have adapted AI-based business operations, and they now use ML programs to predict what step to take in the future.

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