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What is Big Data Analytics?

Big data analytics is the technique that automates the process of collecting, processing, contextualizing and analyzing large sets of data, commonly referred to as big data, to uncover patterns that help a business make better decisions. Big data analytics differs from traditional data analytics because it has the ability to capture and analyze data sets that are very large, move fast and lack a common structure.

The digital revolution runs on data

The digital business revolution infuses technology into every step of an organization's value chain, creating a complex and fast-changing technology environment comprised of new digital business systems and sources that generate an explosion of data. Big data analytics makes sense of this data and generates actionable insights for IT, business, and operations teams to leverage when tuning business performance, managing cost, and mitigating risk.

Improving decision speed and accuracy

In the modern digital business, data is the currency that guides all decisions and actions. Complete and accurate analysis empowers IT operations to make fast, data-driven decisions that support continuous digital service improvement and innovation.

With the TrueSight Intelligence analytics platform you can discover, organize, and analyze high volumes of volatile IT operations, service, and business data, in real time.

Explore TrueSight Intelligence ›

"TrueSight Intelligence is a big data platform with strong evolutionary strengths and a clear roadmap for broader functionality and use case versatility."

— Enterprise Management Associates
Read the EMA Impact Brief ›

Better visibility and knowledge

Big data analytics break down information silos that commonly cause businesses to miss the big picture. Prepare to meet the demands of the business with a complete picture by applying big data analytics in three sequential steps:
  • Data capture and ingestion: real-time streaming, transformation, storage and indexing
  • Analytics and machine learning: automated baselines and abnormality detection, pattern discovery, statistics and probability, recommendation engine
  • Exploration: visualize, search, correlate and compare, trend, predict, and collaborate
Follow three steps to achieve big data analytics maturity ›

Web-scale, real-time streaming requires a modern approach

The analytics tools and processes most businesses rely on were not designed to analyze the volume, velocity, and variety of big data in today’s modern business. As such, businesses are ill-equipped to makes sense of the explosion of data generated by the digital business. To harness the power of big data, organizations should adopt a single architected big data analytics platform that is optimized to handle high volume, real-time data streaming and offers easy-to-use APIs and out-of-the-box integrations to pull data from any source.
Contatta l’ufficio vendite ›

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