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AI & Tech

What Big Data and AI Can Do for Your Business

MM
Matt Montenegro Founder & CEO
04/03/2023 4 min
TL;DR

Big Data alone isn't enough anymore. Companies need AI-powered analytics to extract actionable insights from massive datasets and drive real business outcomes across operations, marketing, and strategic decisions.

  • Why it matters: Manual data interpretation becomes impossible at scale, leaving valuable business insights buried in unused datasets.
  • By the numbers: Starbucks personalizes emails for 400,000+ customers; UPS routes 19 million daily packages with data analytics.
  • Case in point: Shell reduces drilling costs by analyzing failure patterns across 3,000 machinery parts to optimize spare inventory placement.
  • The big picture: AI transforms raw data collection into predictive analytics that inform everything from personalized marketing to supply chain efficiency.
  • What's next: Deloitte's 2026 AI report cites 66% of organizations reporting improved productivity from enterprise AI adoption.

Your business requires information and insights that will lead to successful outcomes, from executive decisions to marketing campaigns.

In the past, companies amassed Big Data to improve decisions and sales, gathering data on processes and customer behavior.

But what happens when Big Data becomes too big or too cumbersome to make any sense by those who collect it?

Just storing more data isn’t a solution. You need big-time analytics to help you make sense of it all in less time.

More and more today, the emerging technologies of artificial intelligence (AI) and machine learning are coming to the rescue of those getting bogged down in their data and manual interpretations.

Somewhere deep down in those numerous data sets of Big Data are revelations you need to know to improve various aspects of your business offerings and marketing strategy. AI can help.

In this blog post, find out how Big Data and AI can join forces and positively impact your business.

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AI and Big Data Applications

More and more today, companies are beginning to realize the value of utilizing AI to power their Big Data. 

A sampling of how certain businesses in different sectors are accomplishing this already includes the following.

Big Data and AI in the Retail/food and beverage sector: Starbucks

Starbucks has long been innovative in its business model and marketing strategy. 

Today, they go the extra mile to personalize emails utilizing data collected about customers’ past purchases on their app and rewards program.

At the heart of this strategy is their usage of Big Data and AI. 

The company infuses AI-enabled capabilities to help generate personalized emails for over 400,000 customers, featuring their latest offers and promotions.

Big Data and AI in the Retail/energy sector: Shell

To find ways to reduce costs and maintain inventory in relation to managing their oil drilling machines, Shell relies heavily on their data collection and analysis.

The data consists of information on the 3,000 parts of oil drilling machinery prone to failure at some point and vendor data on when that might occur. 

This data is at the core of their spare parts inventory strategy, helping them keep the most needed parts in stock and selecting strategic locations to store them.

Big Data and AI in the supply chain sector: UPS

Handling over 19 million packages a day is no small feat. 

UPS optimizes its operations in this sphere by relying on its Big Data to reveal necessary information to make informed decisions.

Collection of data from multiple sources, including vehicles and customers, leads to decisions on delivery routes made with the help of an algorithm in their On-Road Integrated Optimization and Navigation system.

UPS also uses Big Data to determine where to place more vehicles (higher demand) and identify ways to speed up delivery times.

Wrap Up

Big Data, with the help of AI technology, can provide insights for your business that can then be incorporated in a variety of ways to accomplish your most important goals. 

In other words, by joining forces, Big Data and AI can positively impact your business.

Are you ready to find out what is hiding deep within your Big Data with AI?

Frequently Asked Questions

How does AI make Big Data more valuable for businesses?

AI processes massive datasets faster than human analysts, identifying patterns and correlations that would take months to discover manually. Machine learning algorithms can analyze customer behavior, operational inefficiencies, and market trends simultaneously, transforming raw information into actionable business strategies. This automation allows companies to respond to opportunities and threats in real-time alongside relying on outdated quarterly reports.

What specific business outcomes can companies expect from AI-powered Big Data?

Companies see improvements in customer personalization, operational efficiency, and predictive maintenance. Retailers increase sales through targeted recommendations, logistics companies optimize delivery routes to reduce costs, and manufacturers prevent equipment failures before they occur. These applications directly impact revenue growth and cost reduction across multiple business functions. Implement pilot programs in your highest-impact areas first to demonstrate measurable ROI.

Which industries benefit most from combining Big Data and AI?

Retail, energy, logistics, and manufacturing lead adoption due to their data-rich operations and clear performance metrics. These sectors generate continuous streams of transactional, operational, and customer data that AI can immediately optimize. However, any business collecting substantial customer or operational data can benefit from AI-powered analysis. Evaluate your current data collection processes to identify optimization opportunities specific to your industry.

How can small businesses leverage Big Data and AI without massive investments?

Cloud-based AI platforms and software-as-a-service solutions make advanced analytics accessible to smaller companies. Many tools offer pay-per-use pricing models that scale with business growth, eliminating large upfront costs. Focus on specific use cases like customer segmentation or inventory optimization alongside enterprise-wide implementations. Research affordable AI tools that integrate with your existing business systems and data sources.

MM Matt Montenegro

Matt Montenegro

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