
BMC introduced TrueSight Intelligence, a cloud-based big data analytics platform that collects and analyzes IT, business, and operational data, from virtually any source, enabling fast, data-driven decisions for digital service improvement and innovation.
The TrueSight Intelligence solution is a key component of BMC’s Digital Enterprise Management strategy, which is designed to make digital business fast and seamless and optimize every environment from mainframe to mobile to cloud.
“Optimizing digital business potential requires new levels of leadership and insight from IT,” said Dennis Drogseth, VP, Enterprise Management Associates. “Advanced analytics can and must play a critical role in delivering insights enabling IT and business performance to become more proactively and creatively aligned. BMC’s TrueSight Intelligence provides an excellent platform for delivering on this promise in terms of both capabilities and time to value.”
“The needs of the digital business fundamentally challenge the boundaries of traditional IT operations management,” said Bill Berutti, President of the Cloud, Data Center and Performance Businesses at BMC. “TrueSight Intelligence delivers an actionable path to the data-driven future of IT operations by expanding the range of what IT can and should consider as they improve digital services and expedite innovation.
TrueSight Intelligence is a cloud-based big-data analytics solution that easily discovers, organizes and analyzes diverse operational, application, service and business data. The platform scales to support the seamless collection and contextualization of high-volume, real-time data from applications, services, and digital business.
The initial release of the TrueSight Intelligence solution will stream metric data at web-scale from any source, empowering IT leaders, application owners, DevOps teams and data scientists to:
- Visualize large collections of data to quickly gain insights
- Explore data to find answers with intuitive search
- Measure performance baselines for better product operations
- Compare normal versus abnormal behavior for any metric
Built on an open-source, micro-services architecture, with easy-to-use API’s and out of the box integrations, TrueSight Intelligence will quickly evolve so IT executives, application owners, and data scientists can collect, correlate, and contextualize structured and unstructured data to derive unprecedented insight into their business.
TrueSight Intelligence will enter public beta in October 2015 and be available for commercial sales in Q1 2016.
The Latest
In MEAN TIME TO INSIGHT Episode 14, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud network observability...
While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...
Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...
As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...
Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...
AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...
Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...
A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...
IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...
A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...