Each day, executives in Federal agencies and departments balance the public’s growing need for services with budget discipline -- "doing more with less".  They rely on predictive analytics and machine learning to make government work better, ensuring tax compliance, enforcing the law, detecting fraudulent claims, and understanding public needs.

The most demanding analytics work is often ad-hoc and time-sensitive, and requires an ability to scale up quickly. Consider the following scenarios:

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Rightly or wrongly, telemetry in Windows 10 has been roundly and soundly criticized. But while the feature may be a privacy concern for some, Microsoft says that it is using the data gathered to provide advice to would-be Windows 10 users about driver and application readiness.

This is something that is aimed at enterprise users for whom Microsoft recognizes that certain apps are mission-critical for businesses. This is why the company has launched Upgrade Analytics to "provide customers with insights which allow them to [...] mitigate potential problems".

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Both virtual and augmented reality, machine learning and big data analytics, as well as the Internet of Things, are the most in-demand skills, according to a new report by Packt.

The report, titled "Skill Up", polled more than 11,500 worldwide developers and IT professionals. Besides identifying the most sought-after skills, it also says that security is one of the highest-paying industries in 2016, especially for freelancers.

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Analytics is something that many enterprises are keen to make use of, but a new report finds that many businesses are approaching it in the wrong way.

The report from decision science and big data specialist MuSigma identifies gaps and shortcomings in traditional approaches to analytics and problem solving but also highlights that a majority of senior leadership believes analytics is affecting company strategy and results in positive ways.

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Ransomware is one of the most important security threats for business to deal with as it has the potential to cause serious damage and financial loss.

User behavior specialist Exabeam is launching its Analytics for Ransomware, a new application designed for early detection across the corporate network. Unlike other security products, Exabeam can detect ransomware movement and activity in the network, servers, workstations, BYOD devices, and cloud services.

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With virtualized environments performance issues can be hard to pinpoint. IT departments can find it difficult to spot whether the cause is in the application, network, storage, or virtualization layer of the infrastructure.

Software optimization specialist SIOS is bringing machine learning to bear on this problem with the latest release of SIOS iQ, its analytics software for VM environments.

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There is a difference between knowledge and understanding. Knowledge typically comes down to knowing facts while understanding is the application of knowledge to the mastery of systems. You can know a lot while understanding very little. Just as an example, IBM’s Watson artificial intelligence system that defeated the TV Jeopardy champs a few years ago knew all there was to know about Jeopardy questions but didn’t really understand anything. Ask Watson to apply to removing your appendix its knowledge of hundreds of medical questions and you’d be disappointed and probably dead. That’s the problem with most analytics, which is why it can be a hard sell.

The answer to this problem, we’re told, is not just machine learning but Deep Machine Learning, the difference between the two being that plain old machine learning is a statistical process that could be (and used to be) replicated by hand, while the deeper variety looks several generations deep in a longitudinal analysis that quickly grows too big for mere mortals to comprehend. Deep machine learning will, theoretically, find all the interconnections and dependencies that until now we’ve had to rely on domain experts to provide, yet even then it can only happen if you happen to be gathering the right data.

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Companies are increasingly turning to analytics to gain a competitive advantage and help with customer retention. But a recent study from cloud analytics specialist Birst reveals that half of the companies surveyed that attempted to create in-house analytics products reported being unsatisfied with the results.

To meet demand for reliable analytics products, Birst has announced new capabilities that boost time-to-market and deliver improved user experiences.

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There’s a misconception in the cyber security industry that many IT, security executives and vendors subscribe to.  They equate security analytics to SIEM and user and entity behavior analytics (UEBA). They use the three terms interchangeably as if they are all one of the same and solve the same problems. As a result, companies waste time, leave gaps in their visibility, ability to execute and ultimately fail to minimize their cyber risk.

In a report released this month, analyst firm Forrester states, "Security analytics has garnered a lot of attention during the past few years. However, marketing hype and misunderstandings regarding security analytics have confused the market, making it difficult for security and risk leaders to make information decisions". This statement couldn’t be truer. UEBA and SIEM tools are supporting components of security analytics, but are not equivalent.

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A new report from data quality management company Blazent and 451 Research shows a disconnect between businesses' desire to pursue machine learning analytics and the quality of their data.

The poll, of 200 C-level and senior IT leaders, reveals that despite the 67 percent of respondents that have a strong appetite for advanced analytics technologies, 60 percent have low confidence in their organization's data quality management practices.

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The idea of the cloud is finally starting to take off amongst the masses. No longer are consumers afraid of the security risks that could possibly lie within the cloud, for there are countless measures that can be taken, which actually make the cloud more secure than traditional systems. That being said, we can expect that a widespread adoption of cloud computing is just around the corner -- as it should be.

Backing up sensitive information to the cloud, data storage, cloud computing -- regardless of the use, the basic premise of the cloud remains the same: a system of virtualized network servers that combined create a centralized ecosystem which enables access to various resources and services, as well as data storage. At the heart of the cloud -- the ability to store, organize access and manage data in an effortless fashion.

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Healthcare costs for people diagnosed with mental illness and common chronic health conditions are 75 percent higher than for those without a mental health diagnosis according to the Centers for Medicare and Medicaid Services (CMS).

This is because there’s often a lack of coordination between multiple care providers, different state policies and diverse payment structures which can result in poor health outcomes and higher costs across health systems.

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Leader in open source databases MariaDB is announcing the release of its new big data analytics engine, MariaDB ColumnStore.

It unifies transactional and massively parallelized analytic workloads on the same platform. This is made possible because of MariaDB's extensible architecture that allows the simultaneous use of purpose built storage engines for maximum performance, simplification, and cost savings. This approach sets it apart from competitors like Oracle, and removes the need to buy and deploy traditional columnar database appliances.

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In modern fast-moving business environments, marketers need to be able to respond quickly to changes and deliver appropriate content.

To help deliver this ability, business transformation specialist Progress is releasing new versions of its Telerik Sitefinity platform for content management and customer analytics.

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Increased use of big data in IT management and other areas means that companies are on the look out for new tools to give them better insights from their information.

Behavioral analytics company Prelert is making it easier for Elasticsearch users to automate the analysis of large data sets with the launch of Behavioral Analytics for the Elastic Stack.

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