These days, data is everything. Businesses and organizations use data to uncover vital insights, make processes smoother, and guide strategy.
Thanks to technological advancements and increasingly digital operations, organizations can collect, store, and analyze more information than ever. But with so much data being generated every second, it’s all too easy for valuable insights to fly under the radar.
That’s where big data and artificial intelligence (AI) come in. Organizations can use big data and AI to gain real-time insights, predict outcomes, act faster and smarter, and ultimately stay profitable and competitive.
What is Big Data?
While there’s no official threshold for big data, the term typically refers to massive, complex datasets that traditional database tools and methods can’t efficiently process, store, or analyze. You’ll often hear big data defined by the “three Vs”:
- Volume: Big data usually involves huge amounts of information, often hundreds of terabytes or more.
- Velocity: It’s not just about size. The speed at which new data is created and ingested also matters.
- Variety: Big data comes in all shapes and forms, from spreadsheets and text files to images, videos, and other unstructured formats. That diversity makes normalization, storage, and analysis difficult.
What is Artificial Intelligence?
At its core, AI is technology that learns from data to make smarter decisions over time, helping machines learn, adapt, and solve problems in ways that mirror human thinking.With so much going for AI, it’s hardly surprising that businesses and organizations are using AI in everything from self-driving cars and fraud detection to healthcare diagnostics. In fact, 78% of organizations in a recent survey reported using AI in at least one business function, and the global industry for AI tools and services is expected to hit $1 trillion by 2031.
The Power of Artificial Intelligence With Big Data
With so much information coming in fast and from so many places, traditional data tools can’t keep up on their own. At least, they couldn’t before AI.
AI can process massive datasets, uncover patterns, and turn large amounts of raw information into clear, actionable insights. Its fast processing power, ability to work tirelessly without losing focus, strong attention to detail, and capacity to detect connections that humans and traditional data software might overlook make it an invaluable technology for modern companies.
Simultaneously, big data provides the foundation and raw data that make AI possible. The more information an organization collects, the smarter and more accurate its AI models will become.
Ultimately, big data and artificial intelligence bring out the best in each other, completely transforming how organizations operate. Not only can businesses now automate routine tasks and reduce human error, but they can also predict trends, respond to changes in real time, optimize and personalize the customer experience, and take data-based action.
AI and big data have the power to transform:
- Healthcare: Hospitals and healthcare providers must analyze massive volumes of patient data in various forms, ranging from patient charts to imaging. AI enables them to quickly identify patterns, more accurately diagnose patients, predict outbreaks, and even develop personalized treatment plans. It can also optimize staff scheduling, streamline operations, and monitor medical equipment to catch issues.
- Manufacturing: Accurate maintenance forecasts powered by AI and sensor data can prevent equipment failures and downtime. Additionally, AI and data can help manufacturers improve quality control processes, understand trends that could impact their supply chain, and streamline operations for safer, more efficient workflows.
- Finance: AI algorithms can detect data anomalies and trends, allowing organizations to uncover fraud and other risks in real time. Plus, financial institutions can utilize AI to power customer service chatbots that handle routine inquiries and answer common questions instantly or develop machine learning models for credit risk assessment.
- Energy: In the energy sector, AI can forecast demand, optimize power distribution, and detect grid and pipeline faults before they escalate. Energy companies rely on AI-driven insights to reduce waste, improve sustainability, and manage resources more effectively.
- Marketing: With AI and big data, marketers can quickly understand audience behavior and customer sentiment. That means marketers can personalize outreach using customer preferences and past behavior, ensuring messages reach the right people at the right time for stronger engagement and better returns.
- Retail: When it comes to retail, data-driven insights and AI tools help businesses improve products, anticipate customer needs, make automated, personalized recommendations, and optimize pricing and inventory in real time.
- IT: By quickly analyzing data collected from across the network, AI can detect unusual activity, flag cybersecurity risks, predict hardware maintenance needs, and even automate routine fixes to help prevent costly downtime and keep systems running efficiently and securely.
What can this look like in real life? Netflix uses big data and AI to understand viewers’ behavior and make personalized movie and show recommendations and even optimizes thumbnails and trailers based on user preferences. Meanwhile, McDonald’s uses AI and big data to predict sales, determine pricing, create a personalized app experience, and verify orders. It’s no wonder they’re planning to double down on AI investments by 2027.
Challenges of Big Data and Artificial Intelligence
Artificial intelligence with big data have plenty to offer, but they also come with significant challenges. After all, managing massive, fast-moving, and diverse datasets while keeping them secure, accurate, and ethical isn’t easy.
Data Integration and Retrieval Issues
With so much data coming from sensors, transactions, customer surveys, webpages, sales transactions, social media, and countless other sources, organizing and accessing data can be a time-consuming, complicated, and expensive process.
Many organizations wind up with siloed data spread across multiple formats or platforms. In some cases, they may even need to pay object storage providers egress fees for data retrieval.
Data Quality Issues
Retrieving data is only the start. Even with vast amounts of information at your fingertips, it won’t do you much good if the data isn’t accurate. Incomplete, outdated, biased, or inconsistent data can lead to inaccurate predictions, poor decisions, and worse outcomes.
Organizations need consistent validation and cleansing processes, but these can be difficult to maintain at scale, especially when data is flowing in from multiple sources and formats.
Privacy and Security Risks
As data volumes increase, so do the risks. Big data and artificial intelligence rely on large amounts of sensitive information, which is why organizations need to protect their data with encryption, access control, strong governance, and compliance with various regulations. Not only can security lapses erode customer trust, but they can also have serious legal consequences and result in significant fines.
Infrastructure and Cost
Building and maintaining the infrastructure required for big data and AI initiatives requires time, money, and experienced personnel. Between the storage capacity and the computing power needed, costs can add up quickly. Organizations may need to cover cloud storage, high-performance CPUs, data backup and recovery systems, data engineer salaries, employee training and upskilling, ongoing maintenance, and more.
Building a Strong Big Data Strategy
Access to big data and AI is only the starting point. The impact comes from using them strategically. With the right data and knowledge management approach, you can align your work to your goals and uncover insights you can trust.
When shaping your strategy, consider the following:
1. Define Your Goals
Before making any major decisions, think through your goals. What do you want to achieve with the data?
Common goals we’ve seen include:
- Streamline or automate operations
- Improve and personalize customer experiences
- Predict trends for better forecasting, inventory management, and strategic planning
- Discover new market opportunities, product innovations, and other areas for increased revenue
Knowing your objectives will allow you to collect and analyze data that truly matters to your organization and its goals while avoiding wasting time, money, and energy on irrelevant information or low-impact initiatives.
2. Centralize and Normalize Data
When information is scattered across departments, platforms, or incompatible systems, it becomes difficult to access, trust, or analyze effectively. That’s why centralizing data is vital.
When you have a system in place capable of collecting, storing, and processing large amounts of data while keeping it secure and accessible, you can create a strong foundation for data analysis and decision-making as well as enable seamless collaboration.
Once centralized, normalizing your data is just as important. Standardizing formats, definitions, and data quality rules helps reduce inconsistencies and eliminate duplication, resulting in more accurate and trustworthy data.
3. Use AI and Machine Learning
AI and machine learning can quickly and automatically extract, organize, understand, and share information, saving your employees time and energy. These technologies will become increasingly valuable as your data grows in volume, variety, and complexity, and the more data you input, the faster, smarter, and more accurate your AI-insights will become.
4. Form a Strong Team
Having the right technology will only get you so far. You also need the right people to make it work.
Bringing executives and other leaders into the process early on ensures alignment, clear priorities, and strong organizational support from the start. Not only can this create strong momentum, but it can also pave the way for a smoother, company-wide adoption and foster a data-driven culture.
When additional expertise is required, partnering with experienced consultants or data specialists can accelerate progress and ensure your infrastructure is designed correctly the first time around.
It’s also important to ensure everyone in the organization receives proper training. Offer Q&A sessions, hands-on training, written instructions, and more. Comprehensive training on how to use data and data tools effectively and safely allows employees to apply them correctly, consistently, and to their fullest potential, helping the organization maximize value and improve decision-making.
Turn Information Into Intelligent Decisions and Real Results
Big data and AI aren’t new — they’ve been evolving for decades. What’s new is how accessible they’ve become, opening the door for more organizations to put them to work. They’re now essential tools for growth and innovation, reshaping how businesses operate. But without the right strategy, structure, and leadership, even the best technology can fall short.
At SEI, we help organizations design big data strategies so they can collect and use data efficiently and safely. Our team can help you harness the power of big data and AI to understand customer behavior, personalize communications, anticipate and capitalize on trends, make smarter decisions, and much more.
