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Business Intelligence

Combining Big Data with Machine Learning

This article will explain about Combining Big Data with Machine Learning. Machine learning comes in handy as it goes further to unveil the hidden potentials of big data by producing and implementing solutions to complex business problems.

The importance of machine learning and big data to businesses cannot be overemphasized; both are revolutionizing business operations and consistently providing lots of new opportunities.

Big data enables businesses to not grope in the dark but make wise real-time decisions by providing them with insights into various market situations and ensuring a better understanding of consumers’ behaviors and preferences.

Ways for Combining Big Data with Machine Learning helps the Business

Facilitating Customer Segmentation:
  • It is not uncommon to find distinct groups — each comprising individuals who share a wide range of similarities – within a business’s customer base.
  • Fortunately, machine learning clustering algorithms are perfect for achieving this kind of segmentation. Many such algorithms are unsupervised in that they don’t require special human direction to operate.
  • Rather, an unsupervised clustering algorithm requires only data for exploration, so as to discover similarities and differences (where they exist), and come up with distinct clusters based on a number of features.
  • Your business can also harness the power of machine learning and big data to achieve segmentation. But, first, you need to discover whether segmentation holds any potential benefit for your organization. If you believe it does, then it will become necessary to invest heavily in data analytics, make your business machine learning ready, and then employ a machine learning team.
Making Targeting Feasible and Effective:
  • Knowing that your customer base is composed of different groups doesn’t cut it -– you have to devise means to cater to divergent needs.
  • On the other hand, it’s sometimes necessary to view one’s customer base as comprising different individuals with various preferences rather than a conglomeration of different groups.
  • This perspective will make it more pragmatic to tailor products to each individual based on his or her specific behavior and perceived preferences. Again, machine learning, under the aegis of big data, facilitates this.
  • Google, for example, uses big data to better understand your preferences and combines it with complex (machine learning) algorithms to provide supposedly relevant results for every query you make.
  • In other words, business owners need to understand that targeting consumers differently makes a lot of sense, and that machine learning makes personalization, which is key to providing a better user experience, possible. Say you run an e-commerce business, machine learning can help you personalize your ads so that people see only products that are most likely suited to their needs.
Fostering Predictive Analysis:
  • After gaining insight into consumer behavior from big data, you’ll want to use machine learning to develop generalizations and thus make predictions regarding various business issues.
  • In other words, machine learning models can learn behavior patterns from data and determine how likely it is for a person or a set of people to take certain actions, such as subscribing for a service. This makes it possible to anticipate events and make futuristic decisions.
  • To make the predictions, you must employ machine learning expertise to help grapple with your business’s data. Classification algorithms are usually used as the foundation for such predictions.
Providing Foundations for Risk Analysis and Regulation:
  • The machine learning system is considered to differ from the previously existent fraud detection systems which included only manually created rules and is better off because it’s likely to improve with more data inputs.
  • Your business can also make use of machine learning to decrease financial irregularities. Many organizations are, in fact, developing systems to make the process easier.
  • Employing machine learning models can go a long way in ensuring anti-money laundering compliance, detecting rogue trading and other trade anomalies, so it’s best to not starve your business of these elements of sanity.

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