Integrating AI and Big Data for Enterprises


What is Big Data: Big data refers to complex, massive, and high velocity datasets. It is the fuel for the development of Artificial Intelligence (AI). Big data analytics is the use of technologies and processes, including Machine Learning (ML) and to combine and analyze massive datasets with the aim of detecting patterns and creating actionable insights.

What is AI: AI is set of technologies that allow computers to simulate human intelligence. Examples of AI are speech recognition, instructing virtual assistants such as Alexa to perform image recognition for identification, tasks, and autonomous driving. AI also helps to make augmented analytics tools more accessible, and powerful helping you explore and analyze sprawling, unstructured data to gain greater clarity and understanding of the many factors influencing your business.

Relationship between Big Data and AI

AI and big data have a collaborative relationship. AI needs a massive scale of data to learn and enhance decision-making processes and big data analytics optimizes AI for better data analysis. Thus, one can easily make use of advanced analytics capabilities such as predictive or augmented analytics. Also, this enables one to gain actionable insights from their vast stores of data easily. With big data AI-powered analytics, one can empower their users with the robust technologies and tools required to extract high-value insights from data. Thus, fostering data literacy across the organization and reaping the advantages of becoming a data-driven organization.

By integrating AI technology and big data, organizations can enhance business performance and efficiency by:

  • Capitalizing on emerging industry and market trends.
  • Automating customer segmentation and assessing consumer behavior
  • Customising and optimizing the performance of digital marketing campaigns
  • Using intelligent decision support systems powered by big data, predictive analytics, and , AI.

AI Big Data Analytics.

AI can help users in the entire big data cycle, or all the processes involved in the collation, storage, and recovery of different types of data from different sources. These include pattern management, data management, decision management, context management, goal management, risk management, and action management.

AI can detect data types, find connections among datasets, and identify knowledge by optimizing natural language processing. It can help to automate and accelerate data preparation tasks, comprising the development of data models, and assisting in data detection. It can learn common human error patterns, detect and also resolve potential flaws in information. And it can learn by observing the manner in which a user interacts with an analytics program, surfacing insights from massive datasets quickly. AI can also understand subtle differences in context-specific nuances or meaning to help users understand numeric data sources. It can alert users about anomalies or unforeseen patterns in data, actively monitoring events. It can also recognize possible threats from social networking data or system logs .

Wrapping Up

Big data and AI can work together to accomplish more. First, data is provided to the AI engine, thus, making the AI smarter. Next, minimal human intervention is required for the AI to run properly. As fewer people are required to run AI, we can realize the full potential of integrating AI and big data.