Artificial Intelligence in Big Data

Artificial Intelligence in Big Data

As businesses around the world become able to more reliably depend on AI-driven insights from Big Data analysis, the two have become inextricably intertwined with a growing Internet of Things (IoT); and with each other. This interdependence implies that each spurs development in the other, as in, the data input in AI leads to a smarter AI engine; and a smarter AI engine means less human intervention. AI cannot be useful without data, and big data analysis becomes overwhelming without AI.

This interdependence indicates that if businesses mean to utilize big data analytics to its full potential, efficient artificial intelligence is indispensable to their processes. Armed with an enhancing human input, artificial intelligence in big data analytics can work wonders with big data. With online profiles, website cookies, social media accounts, tagged interests, product reviews, loyalty reward programs and apps, ‘liked’ and ‘shared’ content, and CRM (customer relationship management) systems contributing to big data, businesses have the opportunity to understand consumer habits like never before. Artificial intelligence is the tool needed to tap into this significant resource provided by democratized data and the very vast IoT.

How AI uses Big Data

Employing Machine Learning (ML) algorithms can enable companies to identify patterns in big data and draw conclusions about the industry and consumers that data pertains to.

To understand machine learning, it is imperative that one defines AI first. Artificial Intelligence (AI) refers to any computer system that can perform tasks that would ordinarily be perceived to require human intelligence. Machine Learning (ML) consists of a subset of AI techniques that can use algorithms to analyze historical data and discover patterns and predict outcomes that are lost to the human eye, the most effective of big data analysis tools.

ML algorithms use statistical analysis to learn by example from historical data, therefore they can be called self-learning algorithms. They have applications across a number of industries, for example, they can be used to predict likely loan defaulters, likely fraudulent transactions and insurance claims, predicting hospital readmission, personalizing products and services, finding duplicate database records, identifying the most impactful marketing, understanding customer churn, blockchain, counterterrorism, cybersecurity and so on.

The operation of ML algorithms can best be understood through the ‘big data cycle’. The big data cycle is a structure prescribed by experts to the consumption of big data in businesses and can help identify how AI is most effectively applicable at each stage.

The big data cycle has seven critical components:

  1. Data Management

Data management entails acquiring, storing, validating, protecting and processing required datasets from big data and ensuring its reliability, timeliness and accessibility for a variety of users. This part of the process has recently become faster (near real-time) and more complex due to diverse data sources (images, videos, text and voice).

AI can help here with the introduction of hyper-personalization using ML and adaptable profiles. It can also discern useful data from big data using relationship capture and Natural Language Processing (NLP) categorization, observe images or videos to locate similar data, and find outlier events, all in a matter of seconds. It is also flexible in the sense that it can perform through the cloud or the IoT.

  1. Pattern Management

AI can empower organisations to be proactive rather than reactive, by distinguishing both expected and unexpected patterns, and pointing out anomalies. AI also evolves through the self-learning properties of ML, adapting to patterns, identifying opportunities for big decisions, and instances for additional responses and further action.

  1. Context Management

The significance of data always changes in accordance with the context. AI is capable of interpreting information about context by taking its various elements into consideration, for example, market, process, industry or application. It can relate these factors with connected data points and grasp the evolution of its meaning in a numerous variety of contexts, whether the ‘subjects’ of the data interact with other data points or not. This enables AI to comprehend conversations and human interactions with NLP, as there may be differing interpretation grids.

  1. Decision Management

A critical component of the big data cycle is decision management, which entails all the aspects of designing, constructing and managing automated decision-making frameworks inside and outside an organization. AI systems can be used to govern the soaring number of outside interactions with suppliers, customers, retailers, vendors, communities and other stakeholders that may overwhelm human labor.

Artificial intelligence can also help businesses upscale and match the rapidly expanding scope of operational requirements. The implementation of artificial intelligence, especially in managing human interactions, can help improve customer experiences on the user interface and increase the speed of resolution of issues. Artificial intelligence can also spot decision opportunities, create a predictive model for outcomes and monitor the organization’s performance against certain standards.

  1. Action Management

In action management, AI can help organizations plan out and organize all the tasks of employees, bot applications, processes and devices used by an organization. It can coordinate and choreograph every task into a project plan, and monitor performance against predetermined parameters.

AI can orient tasks that logically follow in various business processes, by flexibly choosing an action from its inventory, adapting some of the parameters of an inventoried action to circumstance, or suggest altogether new actions as required. AI algorithms can also test the outcome and suggest any changes.

  1. Goal Management

The process of goal management is to define and track the goals that guide the individual business processes of an organization. Artificial intelligence has revolutionized this through its capability to learn in real-time and automatically adjust goals of the many autonomous elements in business processes by depending on feedback loops and logs.

  1. Risk Management

Risk management is the approximation and prioritization of risk to the functioning of an organization. AI can manage and intelligently apply company resources to mitigate risks by planning out a response. AI can identify anomalies in patterns, events, feedback and system logs and also pinpoint internal threats to prepare pertinent defenses

The Artificial Intelligence of NetFriday

Artificial intelligence is varied and complex, and its applicability to each businesses’ needs is different. NetFriday’s team of expert data scientists can become a lucrative partner to your business by bringing creative, applied AI solutions that can revolutionize the way you conduct your business. We bring to you the following AI solutions:

  • Machine Learning
  • Predictive Modeling
  • Natural Language processing
  • AI based Chat-bot development
  • Image-Recognition
  • Deep Learning
  • Recommendation System
  • Real-time data stream analytics
  • Object identification
  • Anomaly Detection

We can make the big data streaming into your business meaningful and significant to your executive personnel, by providing straightforward input not obscured by ‘consultant-speak’. In order to boost your business, we commit ourselves to delivering quality services in line with your organizational objectives. We understand our role as an augmenting service to your business and enthusiastically invest ourselves in helping your business reach its full potential.

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