Difference Between AI and Big Data
The widespread use of big data and artificial intelligence (AI) technology has revolutionized almost every industry by altering how businesses produce strategic insights and support data-driven decision-making.
It is not anticipated that the current rate of adoption would slow down very soon. Grand View Research projects that the global market for AI technology would expand at a rate of 36.6% per year, reaching a total market value of over $1.8 trillion by 2030.
AI and big data are bringing new capabilities and efficiencies to strategy formulation and daily operations for small firms and large organizations alike. Being the industry leader in storage, AI and big data is essential in giving companies the scalable, dependable, and high-performing data storage systems they require to integrate AI and big data into their operations.
What is AI?
Artificial intelligence can reason, learn, and solve problems logically. Machine learning, generative AI, natural language processing, and other virtual intelligence are all considered artificial intelligence. Artificial Intelligence is revolutionizing automation and personalization with smart algorithms, big data, and fast engineering.
What is big data?
Large and complicated datasets that need specialized technologies to collect, manage, and analyze their contents are referred to as “big data.” Generally speaking, big data is made up of datasets from many different sources that are connected by their ownership by or significance to a certain organization or group.
Advanced analytics, AI, and machine learning are usually needed to handle and analyze huge data efficiently and produce insightful information.
Types of data used in AI
Businesses looking to improve performance, maximize efficiency, and facilitate ongoing development across their teams and operations can benefit from the strategic insights that artificial intelligence (AI) can produce from a variety of data types.
When using AI for data analytics, everyone should be aware of the following three sorts of data:
Structured data
Processed, arranged data that is simple to search in a database is known as structured data. Customer information, inventory data, transactions, and maintenance logs are examples of common sources of structured data.
This kind of data is best suited to provide strategic insights that direct organizational optimizations and other modifications.
Unstructured data
Unstructured data is information that information that requires processing in order to yield insights and significance. Unstructured data frequently takes the shape of images, videos, and specific types of text files.
Big data analysis using AI is helping firms gain insights from unstructured sources. AI can recognize abnormalities in surveillance video that indicate certain actions. The ability to automatically contextualize and classify each individual response can also aid in the evaluation of client feedback.
Big data
Both organized and unstructured datasets are frequently included in big data, which necessitates large-scale processing and management. AI is capable of navigating both structured and unstructured data to find trends in consumer behavior. In order to help contextualize information about changes in operations, buying habits, supply chain logistics, and many other applications, it can also coordinate linkages between structured data trends and unstructured data occurrences.
How does AI help big data?
AI and big data are affecting organizations in many industries. Among the benefits:
360-degree view of the customer
AI and big data digital footprints are developing rapidly, and firms are utilizing this to gain more personal insights. Companies used to migrate data into and out of data warehouses and generate static reports that were slow to generate and edit. Smart firms now use distributed, automated, and intelligent analytics technologies on data lakes to collect and synthesize data from several sources. This is changing how organizations view customers.
Conversational interaction with data
As mentioned, generative AI apps allow people to talk to data and systems. Even casual users can derive significant benefit from their data by engaging in back-and-forth conversational cues with generative AI systems, unlike in the past when database skills and knowledge were needed.
Hyperpersonalization and recommendation systems
AI systems can learn and adapt to display relevant content, offer relevant items, provide tailored recommendations, and other uses by developing a profile of user behavior. AI-powered systems can make personalized suggestions based on user behavior and queries. These AI-powered profiles are used in marketing, education, finance, and healthcare to provide individualized products, learning, healthcare, and financial solutions.
Improved forecasting and price optimization
Companies typically predict current year sales using prior year data. Traditional forecasting and pricing optimization can be challenging because to changing patterns, worldwide pandemics, and other unpredictable events. Big data helps firms detect patterns and trends early and predict their influence on performance. It’s helping corporations make smarter decisions by providing more likely future facts. Big data and AI can reduce seasonal forecasting errors by 50% for retail companies.
Improved customer acquisition and retention
Big data and AI assist organizations understand customer wants, product usage, and why people stop buying or using them. Big data apps help organizations understand client behavior and what they want. They can use those patterns to improve products, conversions, brand loyalty, trends, and consumer satisfaction.
Cybersecurity and fraud prevention
Businesses of all sizes fight fraud constantly. Companies adopting big data-powered analytics to uncover fraud patterns can spot system anomalies and stop unscrupulous actors. Big data systems can identify, prevent, detect, and reduce fraud by analyzing enormous amounts of transactional or log data, databases, and files. These systems can also use internal and external data to notify firms to cybersecurity dangers that haven’t yet shown in their systems. Without Big data processing and analysis, this is impossible.
Identifying and mitigating potential risks
Any firm must anticipate, plan, and adapt to ongoing changes and threats to survive. Big data is improving risk management by identifying risks early, quantifying exposure and losses, and speeding up improvements. Big data models are also helping companies identify and mitigate customer, market, and natural disaster risks. Companies can synthesize data from several sources to improve situational awareness and resource allocation for new threats.
Examples of AI and big data
Many firms have recognized the power of machine learning-enhanced big data analytics and are leveraging big data and AI in many ways:
- Machine learning algorithms help Netflix understand each viewer and make more personalized recommendations. This keeps users on their platform longer and improves customer experience.
- Mayo Clinic improves patient care with AI and big data. Mayo Clinic analyzes electronic health information using machine learning to uncover patterns and trends that can forecast patient risks, illness susceptibility, and other patient care improvements.
- Machine learning gives Google users a meaningful and tailored experience. Company products include predictive email text and enhanced instructions to a location using machine learning. Google is also developing generative AI LLMs to improve search utilizing its huge big data.
- Starbucks uses big data, AI, and NLP to send personalized emails based on past purchases. Starbucks uses its “digital flywheel” with AI to send over 400,000 tailored weekly emails with promotions and offers for its broad audience.
- Amazon has long used AI and big data to personalize buying. Amazon’s recommendation engine promotes products based on customer behavior, purchase history, and browsing trends, improving the shopping experience and sales.
- Spotify customizes music recommendations with AI and big data. Individual user listening habits are analyzed by the platform. This contains played, skipped, and saved songs, which are used to create playlists. Users stay on the platform with data-driven personalization.
- The Transportation Security Administration and U.S. Customs and Border Protection are utilizing big data-powered AI face recognition to verify identities and boarding passes at checkpoints.These AI-enhanced technologies speed up verification while preserving accuracy.
