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The Obvious Synergy Between Machine Learning and Big Data

In the last few years, there has been a huge paradigm shift in the way in which data sets are stored and processed by companies.  Big Data has become the new buzzword in the industry and is regarded to be the next big thing in computing.

As per the latest statistics, there are over 6 million developers working worldwide using big data and analytics. The spending on big data technology is set to reach $57 billion this year. It has also been estimated that the analytics and business intelligence market will be worth around $18.3 billion by the end of this year.

Traditional Data Processing Systems v/s Big Data Tools

  • The challenges of using traditional system of data processing has been overcome with the use of big data and its tools. Thus, large amounts of data can be stored and analysis and processing of data is much faster than before using big data. Traditional data systems with a centralized database architecture can be more expensive and ineffective in processing large amounts of data. Big Data offers a more viable solution and works out to be a cost-effective option for companies as it makes use of a distributed database architecture with better computing capabilities for a superior performance.
  • The focus in traditional systems is mainly on structured data, which are stored in fixed formats in the file. As a result, they can focus on issues at a smaller level in the organization and fail to provide deep insight into the data. On the other hand, big data uses both structured and semi-structured data from a wide variety of sources providing a more comprehensive view of the data to be used as information in later stages.
  • Traditional systems of data processing require more complex and expensive software and hardware to manage large amounts of data. Also, it may not be possible to store all the data so lesser data may be available for analysis which may reduce the accuracy of the results. In big data, this is taken care with the segregation of the huge amounts of data across different systems which results in a decrease in the amount of data with more accurate results.

Machine learning is opening up a world of new possibilities

  • The rise of e-commerce platforms, availability of smartphones and multiple channels of communication has led a massive shift in methods used for customer engagement today. At the same time, there is another big revolution taking place in the field of technology in the form of machine learning and artificial intelligence. It is believed that machine learning technologies have the ability to augment and increase the reach of data analytics to such an extent, where algorithms can be used to fully eliminate the possibilities of human error for enhancing the customer service experience.
  • This can allow support teams to use their valuable time and energy elsewhere to focus on more critical issues that need more human intervention. This technology can now offer more predictive and prescriptive analytics to offer customized solutions to customers depending on their past shopping experiences and patterns. With machine learning, it’s possible to keep track of the individual preferences of each customer, which is stored in the memory of the centralized database.
  • This provides for more collective intelligence which may be utilized by the customer service team more efficiently for providing a personalized and efficient service to their valuable customers. It has been envisaged that machine learning may have the unique capability of replacing the cognitive functions of the human mind. Many global giants such as Google, Uber, Facebook, and Apple already have machine learning and artificial intelligence departments within their enterprises to aid them in providing the best solutions to their customers.

The Convergence of Artificial Intelligence and Big Data – Some Exciting Possibilities

Big data and artificial intelligence are set to be game changers in the industry and with easy access to cloud technologies such as Azure and AWS made possible, computing power is no more restricted.  High bandwidth and access to enormous amount of structured and unstructured data are changing the way in which businesses can access information.

  • This new transformation will be first seen in the field of Lifesciences, where the use of these technologies will help in understanding the complexities arising due to combining of data from various plants and animal genomics – it could provide innovative ways for treating different diseases. It is believed that the deciphering process of human genome, which earlier took 10 years, can now be achieved in less than a week’s time.
  • With businesses start using exabytes for communication, the data infrastructure sector will witness a change too leading to its tremendous growth. It has been projected that by 2020, internet transactions will touch 450 billion each day with content generated by enterprises set to increase by 240 billion gigabytes every day.
  • As a result, there will be a greater need for information management in areas such as data storage, fraud detection and prevention, compliance reporting and risk management. As per Gartner reports, by 2020, customers will have the ability to manage 85% of their interactions with the enterprise without the need for human intervention.

The Combination of Machine Learning and Big Data 

Machine learning models are successful and effective mainly due to the availability of high speed, high volume and a wide variety of data, where big data perfectly fits the bill. At present, businesses are generating enormous amounts of data which is not fully utilized through data analytics. Traditional techniques and tools of analytics do not have the best capabilities to make the best use of such complex data. This is where machine learning can help in tapping into the huge resources of the Big data.

The rise of big data has helped data scientists to extract values from large amounts of data and when it comes to machine learning, the greater the volume of data, the higher the chances of getting more accurate information.  Microsoft’s Azure, Google’s Deepmind, and MIT’S ConceptNet5 are some of the enterprises using machine learning for big data analytics.

Limitations of Machine Learning

There are many applications of machine learning and potential benefits, but at the same time, there are many drawbacks and challenges that businesses need to know and be prepared ahead of their journey.

  • The conclusions with different machine learning techniques may vary even with unbiased and good quality of data. The variations can be mainly observed on account of different assumptions, irrespective of whichever machine learning algorithm may be used. Analysis of data would still require some subjective decisions to be made about the use of the best models.
  • Interpretation of results can also pose a major challenge for deciding on the effectiveness of the machine learning algorithms.
  • Depending on which action and when it needs to be taken, different machine learning techniques may need to be applied.

Big data and machine learning are shaping the future of how enterprises will derive maximum value from their data and analytics capabilities. With the availability of large volumes and sources of data, machine learning has been able to take advantage of the datasets to provide meaningful insights and more accurate results. This has enabled companies to work with large sets of data, without posing any restriction – this has helped them to move and progress faster by aiding in their decision-making process.

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