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The 5 game changing big data use cases

While much of the big data activity in the market up to now has been experimenting and learning about big data technologies, IBM has been focused on also helping organizations understand what problems big data can address.


We’ve identified the top 5 high value use cases that can be your first step into big data:

  • Big Data Exploration

    Big Data Exploration

    Find, visualize, understand all big data to improve decision making. Big data exploration addresses the challenge that every large organization faces: information is stored in many different systems and silos and people need access to that data to do their day-to-day work and make important decisions.

  • Enhanced 360º View of the Customer

    Enhanced 360º View of the Customer

    Extend existing customer views by incorporating additional internal and external information sources. Gain a full understanding of customers—what makes them tick, why they buy, how they prefer to shop, why they switch, what they’ll buy next, and what factors lead them to recommend a company to others.

  • Security/Intelligence Extension

    Security Intelligence Extension

    Lower risk, detect fraud and monitor cyber security in real time. Augment and enhance cyber security and intelligence analysis platforms with big data technologies to process and analyze new types (e.g. social media, emails, sensors, Telco) and sources of under-leveraged data to significantly improve intelligence, security and law enforcement insight.

  • Operations Analysis

    Operations Analysis

    Analyze a variety of machine and operational data for improved business results. The abundance and growth of machine data, which can include anything from IT machines to sensors and meters and GPS devices requires complex analysis and correlation across different types of data sets. By using big data for operations analysis, organizations can gain real-time visibility into operations, customer experience, transactions and behavior.

  • Data Warehouse Augmentation

    Data Warehouse Modernization

    Integrate big data and data warehouse capabilities to increase operational efficiency. Optimize your data warehouse to enable new types of analysis. Use big data technologies to set up a staging area or landing zone for your new data before determining what data should be moved to the data warehouse. Offload infrequently accessed or aged data from warehouse and application databases using information integration software and tools.


  • The Forrester Wave™: Big Data Streaming Analytics Platform

    Read report to understand why IBM was named a leader in big data streaming analytics platforms and why InfoSphere Streams received the highest score possible in the implementation support and ability to execute categories.

What is a use case?

A use case helps you solve a specific business challenge by using patterns or examples of technology solutions. Your use case, customized for your unique issue, provides answers to your business problem.


  • Operations Analysis Use Case

    Operations analysis is about using big data technologies to enable a new generation of applications that analyze large volumes of multi-structured, often in-motion machine data. Read this paper to learn how you can gain real-time insights, become more proactive, identify and investigate anomalies and monitor end-to-end infrastructure.

    Get the solution sheet


  • Top 5 Big Data Use Cases

    Conversations around big data are shifting from "What is big data?" to "What can I do with big data?" This podcast describes five uses—with real-world examples—that hold high potential value for many organizations.


  • IBM Big Data Stampede

    Expedite your Big Data success by leveraging IBM’s innovation, leadership and skills


TDWI. Big Data Maturity Model Sponsor.

TDWI Big Data Maturity Model and Assessment Tool

Sponsored by IBM, this big data maturity online assessment tool enables organizations to objectively measure the maturity of an enterprise’s big data analytics program across five dimensions that are key to deriving value from big data analytics: organization, infrastructure, data management, analytics, and governance.


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