How Machine Learning Can Fuel Big Data? What’s The Future Ahead

Original article was published on Artificial Intelligence on Medium

Machine learning and Big Data created a huge uproar in the IT industry few years back.

Result: The world saw an upward surge in the job openings and demand for AI professionals and Big Data professionals skyrocketed.

Cut to present — A futuristic Dream!

Cited as the blue-chips of the present IT industry, both machine learning and big data are as different as chalk and cheese. Yet, AI professionals, world-over are eyeing them like a new plaything and wondering how they can combine the power of machine learning with big data.

But, is it possible? Yes!

Could this recipe be a new future dish? Maybe, let’s find it out!

Beautiful Dream or Chasing Windmills?

At the heart of it –

Big Data is about storing, analyzing, and extracting insightful information out of the bulk data sets. Machine learning, on the other hand, is how the machines learn on their own and improve their functions based on the past experiences without being programmed to do so!

Now think about the future — where they both come together and create more disruptions in the world.

Could be a wonderful dream or could it be chasing windmills…let’s delve a bit deeper into both Big Data and machine learning.

Going back in time — to recall the past; to understand the present and to create a promising future!

Travelling back in time to know more about big data, machine learning and create that heavenly recipe of success where machine learning is applied to big data to get some stupendous results.

In a nutshell — recall everything about Big Data (the five Vs, the source of data influx), machine learning and how the marriage between the two would forge a path to create more disruptions in the world.

Big Data — Definition; the Five Vs; the Source!

Big Data as the name suggests is an assortment of large and complex data sets that are difficult to store and process traditionally. It could be in a group of structured, semi-structured and unstructured data collected through various sources by an organization. The biggest challenge about Big Data is how to — capture; curate; store; search; share; transfer; analyze; and visualize it.

The Five V’s of Big Data

  1. Volume: sums up the huge amount of data
  2. Velocity: is the speed with which the data is accumulated
  3. Variety: refers to the different form of data sourced from various places
  4. Veracity: is the inconsistency and the uncertainty in the sourced data
  5. Value: is the process where the useful data is extracted from the received data

The Source: From where do we get all this Big Data?

There are numerous sources from where an organization receives Big Data. But here are some of the important sources from where each and every organization worth its business source Big Data –

  • Social Media
  • Cloud storage of any third party
  • Webpages of other organizations; Blogs and other online presence
  • Internet of Things

The received data is then mined (data mining) and analyzed (data analytics) to process the Big Data. Understand: Data mining is about collecting data from various sources and data analytics is about how logical reasoning is applied to it to make sense.

How does it help?

Analyzed and sorted data offers variety of insights and also helps in uncovering the hidden patterns — a powerful tool for industries — as Big Data is a treasure trove of business value for any organization worth its salt. The sorted and analyzed Big Data helps in predictive modelling, pattern identification, and other advanced analytics applications.

And this is where our another ingredient for the recipe of perfect ML and Big Data pie comes in!

So What is Machine Learning?

In simple words… it is an ability of machines to learn something new on its own without being programmed to do so.

Technically speaking — Machine learning is nothing but an automated data processing and decision-making algorithms, which have been designed to keep improvising their assigned task at every stage based on their past experience.

In a nutshell — it is all about ‘Evolve through Learning’

Merging the two — ML and Big Data — to create the new Futuristic Pie!

Machine learning when talked about in the context of Big Data can help in both keeping up with the continuous influx of data and also automatically improvise with that huge volume and variety of data thus delivering a continuous stream of evolved and valuable insights.

How does it all work?

With the help of machine learning algorithms, the incoming influx of data is defined and then the patterns involved with it are identified. Once that is done, it is then transfer into valuable or informational insights for organizations to implement in their business operations. The machine learning algorithms are also helpful in automating specific aspects of the decision-making process.

This is where decision trees and deep learning — other techniques of machine learning are used.

Coming Together: The Big Fusion!

As we mix both the ingredients, remember: machine learning is all about automated algorithms and big data is all about huge amount of data with challenges like how to gather, analyze and assimilate.

Machine learning helps Big Data professionals through its efficient and automated tools to gather, analyze and assimilate the received data. Note: Machine learning in collaboration with the superiority of cloud computing adds agility to the processing and integration of large data sets irrespective of its source.

While the algorithms of machine learning could be applied to almost every element of Big Data operation’ the three main operations where ML could definitely help to improve includes

  • Data Analytics
  • Simulation
  • Data Segmentation

Above-mentioned stages help in creating a bigger picture of the huge amount of data received. They help in creating insights and identifying patterns that later are categorized and packaged in a format that is easy to understand. This is in turn AI specialists in the industry

The big fusion of machine learning and Big Data is like a circle that has no ends and could only be monitored, improvised, and perfected over a period of time.

So what does the future hold?

The Future: Heading for exponential growth in IT world!

The power-packed combination of machine learning and big data has resulted in phenomenal growth in some industries.

One such example is Automobile industry wherein automobile manufacturers along with AI professionals integrated statistical models with data that helped them in identifying the strategies that would further aid the manufacturers to offer the best in class automation in the vehicles for an excellent end-user experience.

With predictive analytics manufacturers have been able to monitor and share anything related to the vehicle or the failures of its parts. Smart vehicles — that communicate with their owners — are another example of machine learning and big data fusion in the automobile industry.

Connecting the Missing Dots!

While both machine learning and big data are different, they both are interdependent to create the world where technology would do most of the work — leaving humans to play with data and technology to innovate more!

It is a given that both are necessary to connect the missing dots and create a data-based, tech-driven garden where new innovations bloom!