Modernize your IT Infrastructure Monitoring by Combining Time Series Databases with Machine Learning

#artificialintelligence

Let's explore the complexity and vulnerability of IT infrastructure and how to build a modern IT infrastructure monitoring solution, using a combination of time series databases with machine learning. Check out ZDNet's series of articles detailing the outages. The outage caused disruptions to the likes of YouTube, Snapchat, and Gmail, among others. We have quickly embraced the cloud as more resilient than on-premise infrastructure, so this news is sobering. It also shows the vulnerability of the IT infrastructures, both cloud-based and on-premise, that power much of our software-dependent world -- a world that now includes entertainment and personal, as well as professional connections.


How Software-Defined Storage Empowers Business Advantage

Forbes - Tech

In today's business environment, technology has become the foundation on which innovation is built. As a result, companies are looking into digital transformation as a key strategic priority. It seems as if every chief information officer (CIO), regardless of company size, geography or industry is going through some kind of digital transformation journey. The reason for this is that digital transformation has the power to change organizational processes and capabilities and ultimately transform how a business delivers value to customers. In this article, we'll explore how real-time data is essential for powering digital transformation as providing faster response and continuous availability to critical data becomes a key competitive advantage for businesses to keep up in today's dynamic and always-on world.


Disaster recovery in the age of data and AI

#artificialintelligence

As data becomes the only real competitive advantage feeding increased operational efficiencies, better customer intimacy and constantly improving customer experience, it is imperative that enterprises shift their disaster recovery efforts from just focusing on availability and reliability of services to ensure that their data assets are recoverable and re-integratable into various data powered scenarios backing their business. Modern enterprises require data in many shapes and forms across the board for powering planning, ideating, experimenting and designing/developing new products and services. These business-critical scenarios are often dependent on data that has been transformed, processed and made suitable to meet the requirements. As the "distance" between raw data and transformed data that drives products and services increases due to increasingly complex techniques of transformation, disaster recovery needs to include the not just the time to bring up the copy of lost data online but the time it takes to retransform the data. AI techniques such as Machine Learning, NLP, Anomaly Detection etc. produce "models" that can be leveraged to drive predictions, classifications and categorization.


Dataworks Summit - Big Data meets multi-cloud

#artificialintelligence

'The network is the computer' was the mantra of the early days of connected systems, but it took the Internet to fully realize the concept. In today's era of smart sensors, cheap storage and sophisticated algorithms, an apt aphorism might be'the data is the business' in that business decisions, new services and product strategies are fueled by the analysis of massive amounts of mundane data. The ability to collect, store and analyze such routine data as transaction records, system logs, sensor readings and location information with increasing granularity has the potential to turn what was formerly lost or ignored information into valuable business assets. The organizations that are most adept at spinning the digital straw into gold find themselves at a significant competitive advantage. Aside from the advances in core infrastructure, perhaps nothing has been as responsible for the rise of data-inspired business decisions as the Hadoop ecosystem of open source distributed data storage and processing software.


Security and networking were industrial IoT's top challenges. Now there's a third: Practical AI

#artificialintelligence

Sponsored Some would have us believe that the whole of Internet of Things will soon be artificially intelligent. Not only do we not think that's true, we think the Industrial Internet of Things (IIoT) – the part that does the meaningful work – will take longer than the rest of the connected device industry to acquire those AI features. IDC in November 2016 made a lofty prediction: some form of AI would make its way into all IoT deployments by this year. "By 2019, 40 per cent of all digital transformation initiatives, and 100 per cent of all effective IoT efforts, will be supported by cognitive/AI capabilities." A Skynet crafted from smart kettles and door locks by 2019?