Harnessing Big Data for Predictive Analytics and Business Intelligence

Big data is a term that refers to vast datasets that cannot be handled by standard data processing tools, like the colossal quantities of text used to train large language models (LLMs) such as ChatGPT.¹ This kind of data is way too large and complex for an average laptop.

LLMs are trained using a form of predictive analytics. They are fed huge amounts of data and "trained" to spot context and meaning, basically by treating words as variables that can be given a value and ranked relative to one another. At a very basic level, the LLM is predicting what word comes next when it generates meaningful sentences on command. That function, processing huge amounts of data and arriving at very refined conclusions, is the basis of predictive analytics. It's the use of historical data and statistical modeling to find patterns and make predictions about the future.²

Big data and predictive analytics are important elements of business intelligence, which is the process businesses use to collect and analyze data to yield insights that direct strategy and decisions.

This post will look closely at big data analytics, which offers tremendous value to businesses as it has the potential to improve the accuracy of forecasting and deliver data-driven insights. This post will also explore how an online Master's in Data Science from New York Institute of Technology can help you launch a career in this mission-critical business function.

Foundations of Big Data and Predictive Analytics

At the core of big data analytics are the "five Vs," which describe the key characteristics that define big data and the challenges/opportunities that come with analyzing it.¹

• Volume: Volume is the amount of data generated every second, which is measured in massive units (think "petabytes") for truly big data. Managing and storing these large datasets is a core challenge of big data
• Velocity: The speed at which the data is generated, collected, and processed. Many applications require data to be analyzed instantly or near real-time
• Variety: Data comes in multiple formats: databases, spreadsheets, videos, emails, images, and social media posts
• Veracity: The quality and reliability of data. Big data can contain noise, biases, or inconsistencies
• Value: The value of the data to a business and extracting meaningful, actionable insights from data

With a Master’s in Data Science, you'll learn data mining techniques, including statistical modeling and machine learning concepts, all of which are used to turn raw data into actionable insights. Those insights are often displayed as key performance indicators (KPIs) on an interactive, visual interface: the business intelligence (BI) dashboard.³

Building a Robust Data Pipeline

Enterprise data pipelines are critical to the processing and storage of big data. They are a process for receiving, processing, and storing data.⁴

The process usually begins with collection: pulling in data from multiple sources such as apps, websites, sensors, or financial transactions. From there, the data goes through extract, transform, load (ETL)⁵. It’s extracted from the source, cleaned and reformatted into a usable structure, and then loaded into storage. Depending on the business needs, the data might flow into a data lake (a large reservoir for raw, unstructured data) or a data warehouse (a structured storage system optimized for reporting and analytics).⁶ Along the way, application programming interfaces (APIs)⁷ act like connectors, ensuring information can flow smoothly between different systems. At every step, organizations must maintain strong data governance—building safeguards for quality, privacy, and security. Without these, even the most advanced predictive analytics models could be compromised by inaccurate or unsafe data.

For aspiring data scientists, understanding pipelines is essential. A well-built pipeline ensures that predictive models are trained on reliable, high-quality data.

Analytical Techniques and Tools

When it's time to analyze data, the principal set of techniques is sometimes collectively termed the "Analytics Triad."⁸ Descriptive, predictive, and prescriptive analytics are three different analytical modes:

• Descriptive: Describe historical data
• Predictive: Look at past and present patterns to build an idea of what might happen next
• Prescriptive: Develop a recommendation for what to do next based on analysis of past, present, and predicted future performance

Analytic tools include regression and deep learning algorithms. Data scientists often use specialized big data processing software like Hadoop or Spark to power their work.

Visualization and Business Intelligence Delivery

Data analysis is intended to generate insights that can inform business decisions. Data scientists design interfaces that present information to executives in a manner that can be quickly understood and acted on.

A data scientist needs to know how to design intuitive BI dashboards that use data storytelling principles to allow executives to interrogate data and reach informed conclusions. And increasingly, those dashboards need to be mobile with embedded analytics so they can be accessed on the go.

Operationalizing Predictive Models

Data scientists increasingly turn to AI and machine learning to build predictive models that help businesses make smarter, faster decisions. This has given rise to machine learning operations (MLOps), a core function of data science.⁹

MLOps is the practice of managing the full machine learning lifecycle. This involves processes like continuous integration and continuous delivery (CI/CD) pipelines, which help teams update and deploy models quickly, and containerization, which allows multiple models to run smoothly side by side.^10,11

MLOps provides the monitoring and feedback loops needed to keep models accurate over time. Without these safeguards, an AI model can “drift,” gradually losing accuracy as data patterns change.

Advance Your Career in Data Science With an Online Master's From New York Tech

Companies that can successfully capture, process, and analyze big data for predictive analytics stand to gain a huge competitive advantage in today's data-driven business world. The online Master's in Data Science from New York Tech trains graduates for leadership roles in data science, piloting big-data projects to generate invaluable BI insights.

With a curriculum covering everything from fundamentals of data science to machine learning and principles of cybersecurity, the degree is designed to equip you with an understanding of the full range of tools available to today's data scientists. Review our admissions requirements or contact us directly for more information. When you're ready to discuss your next step toward a career in data science, schedule a call with one of our admissions outreach advisors.