Big data analytics
Leverage effective big data technology to analyze the growing volume, velocity and variety of data for the greatest insights
Overview
What is big data analytics?
Big data analytics is the use of advanced analytic techniques against very large, diverse big data sets that include structured, semi-structured and unstructured data, from different sources, and in different sizes from terabytes to zettabytes.
What is big data exactly? It can be defined as data sets whose size or type is beyond the ability of traditional relational databases to capture, manage and process the data with low latency. Characteristics of big data include high volume, high velocity and high variety. Sources of data are becoming more complex than those for traditional data because they are being driven by artificial intelligence (AI), mobile devices, social media and the Internet of Things (IoT). For example, the different types of data originate from sensors, devices, video/audio, networks, log files, transactional applications, web and social media — much of it generated in real time and at a very large scale.
The amount of data in today’s world is staggering. But big data offers vast opportunities for businesses, whether used independently or with existing traditional data. Data scientists, analysts, researchers and business users can leverage these new data sources for advanced analytics that deliver deeper insights and to power innovative big data applications. Some common techniques include data mining, text analytics, predictive analytics, data visualization, AI, machine learning, statistics and natural language processing.
With big data analytics, you can ultimately fuel better and faster decision-making, modelling and predicting of future outcomes and enhanced business intelligence. As you build your big data solution, consider open source software such as Apache Hadoop, Apache Spark and the entire Hadoop ecosystem as cost-effective, flexible data processing and storage tools designed to handle the volume of data being generated today.
Examples of big data analytics in industries
Healthcare
Healthcare
Healthcare big data analytics drive quicker responses to emerging diseases and improve direct patient care, the customer experience, and administrative, insurance and payment processing.
Financial services
Financial services
Financial analytics improve customer targeting using customer analytics. Businesses can make better informed underwriting decisions and provide better claims management while mitigating risk and fraud.
Communications service providers
Communications service providers (CSPs)
CSPs can use big data analytics to optimize network monitoring, management and performance to help mitigate risk and reduce costs. They can also use analytics to improve customer targeting and service.
Big data analytics tools
Simplify collection and analysis
Build a solution
Advance your big data analytics initiatives
Resources
IBM + Cloudera
Learn how they are driving advanced analytics with an enterprise-grade, secure, governed, open source-based data lake.
Big data with IBM and Cloudera
Hear from IBM and Cloudera experts on how to connect your data lifecycle and accelerate your journey to hybrid cloud and AI.
Big data analytics courses
Choose your learning path, regardless of skill level, from no-cost courses in data science, AI, big data and more.
A data management leader
Read why IBM is listed as a leader in The Forrester Wave™: Data Management for Analytics, Q1 2020.
How to build a better data lake
Discover best practices for building a data lake, such as enterprise-grade security and governance.
Engage with an expert
Schedule a no-cost, one-on-one call to explore big data analytics solutions from IBM.