Paycor Releases Workforce Insights to Provide Business Intelligence Derived from Workforce-related Data Using Advanced Data Visualization

Paycor has announced the availability of Workforce Insights, a platform that delivers workforce-related business intelligence to HR and Operational leaders about employees within an organization. Workforce Insights provides analytics into data related to topics such as overtime pay, turnover, contract labor and resource management. Customers can use Workforce Insights to understand discrepancies between scheduled and overtime pay per business unit as well as correlations between OSHA safety incidents and overtime as illustrated below:

overtime_insight

Paycor’s Workforce Insights absolves customers of the challenge of extracting data from disparate platforms and subsequently cleansing, integrating and analyzing the resultant data to produce actionable insights. The platform’s interactive dashboards accelerate time to insight by delivering visually rich analytics that enable users to identify trends and root causes of people-related issues in small to mid-size businesses. Users have the ability to customize dashboards to enhance access to insights trends within the workforce that they manage. The platform delivers a treasure trove of analytics into people-related issues that facilitate more effective management of a workforce, with opportunities to segment analytics and focus on areas of the organization of interest. As such, Workforce Insights represents a powerful complement to HR data about salary and attrition because of its rich visualization capabilities and focus on actionable business intelligence for operational leaders.

Merck Aims to Harness Capabilities of Amazon Alexa to Tackle Type 2 Diabetes with Alexa Diabetes Challenge

On April 10, pharmaceutical giant Merck announced the launch of the Alexa Diabetes Challenge, a competition aimed at encouraging the development of software applications that use Amazon’s Alexa technology to help patients with a recent diagnosis of Type 2 diabetes. The competition builds upon Merck’s exploration of the capabilities of Amazon Lex, the machine learning technology that undergirds Amazon Alexa by facilitating the development of “conversational interfaces” for applications, to enhance and enrich capabilities to manage and ameliorate chronic diseases. Kimberly Park, Vice President, Customer Strategy & Innovation, Global Human Health, Merck, remarked on the significance of Merck’s usage of Amazon Web Services to address chronic diseases as follows:

Merck has a deep heritage of tackling chronic diseases through our medicines, and we have been expanding into other ways to help, beyond the pill. We are excited to leverage the AWS Cloud to find innovative ways to leverage digital solutions, such as voice-activated technology, to help support better outcomes that could make a difference in the lives of those suffering from chronic conditions like diabetes.

Here, Park comments on Merck’s expansion into modalities of treatment that range “beyond the pill” in what amounts to a disruptive expansion of the company’s traditional business model. Powered by Luminary Labs and sponsored by Merck, the Alexa Diabetes Challenge will provide incentives for selected applicants to use Amazon Alexa as well as Amazon Web Services. In the first round of the competition, five entrants will be awarded $25,000 in addition to $100,000 in AWS credits. The entrants will subsequently receive access to mentoring resources about their proposed solutions and have the opportunity to further develop and refine their apps before vying for the grand prize of $125,000. The Alexa Diabetes Challenge illustrates increased interest in exploration of the intersection of machine learning, cloud computing and healthcare on the part of both technology companies as well as healthcare organizations and pharmaceutical firms. Moreover, in the case of AWS, the collaboration with Merck underscores Amazon CEO Bezos’s interest in embracing the contemporary trend of machine learning and artificial intelligence as elaborated in a recent letter to AWS shareholders. Learn more about the Alexa Diabetes Challenge here.

Mist Releases Asset Visibility Service Powered by Bluetooth Low Energy Technology

Mist recently announced the release of its Asset Visibility Service, a platform that empowers organizations to track mobile devices by means of the Mist wireless platform. The Mist Asset Visibility Service enables companies in any vertical to identify the location of mobile devices for mobile device management, supply chain optimization, workforce management and inventory management use cases. By leveraging Mist’s revolutionary Bluetooth Low Energy (BLE) technology, companies can use Mist to track the location of strategic assets such as laptops, vehicles, machinery and associates. Mist’s Asset Visibility Service empowers companies to not only pinpoint the location of strategic assets of interest, but to also more effectively make operational decisions based on data about the location of the devices in question by means of analytics as shown below:

BLE Asset visibilty - location history (1)

In the example above, companies can use the Mist Asset Visibility Service to locate employee badges as well as the location of hardware. Using the platform’s indoor location data, companies can optimize traffic-related workflows for machines and related assets in a warehouse or docking facility. Sudheer Matta, VP of products at Mist, remarked on the significance of the company’s Asset Visibility Service as follows:

Mist is the first vendor to offer an open, scalable wireless networking platform that brings connectivity, indoor location services, asset tracking and Internet of Things (IoT) together in a seamless and cost-effective manner. The Mist platform leverages machine learning and modern cloud technologies to simplify wireless operations and deliver amazing new location based services.

Here, Matta elaborates on the innovation of an open “wireless networking platform” that stands at the intersection of internet of things, asset tracking, machine learning and cloud computing. The confluence of asset tracking technology with machine learning and IoT technology means that companies can step beyond merely tracking device location data and transition to the development of actionable business intelligence to inform strategic and operational decision making. For example, airline customers tracking the global trajectory of luggage can use Mist’s Asset Visibility Service to iteratively optimize the identification and retrieval of lost luggage. The launch of the platform signals the entry of an important player in the indoor location and asset tracking space whose technology taps into the scalability of the Mist Intelligent Wireless Cloud in collaboration with cutting edge analytics and business intelligence. Mist’s Asset Visibility Service is generally available through select, strategic partners that include Bluevision, Cetani, Kontak.io and AiRISTA.

DigitalOcean’s Newly Released Free Monitoring Service Empowers Customers to Track Operational Health of their Droplets

On April 4, DigitalOcean announced the availability of a monitoring service that gives developers enhanced insight into the operational health of their DigitalOcean Droplets. Capabilities of the monitoring service include the ability to monitor the CPU of each Droplet in addition to disk utilization, network traffic and top processes. The new, free monitoring service means that DigitalOcean customers now have access to real-time data about the health of their deployments by means of the platform’s automated capabilities to deliver performance-related data regarding their Droplets. Available via an intuitive visual interface that empowers customers to create alerts and notifications, DigitalOcean’s monitoring service enables developers to create alerts based on upper and lower bounds for metrics of interest as illustrated below:

DigitalOcean Monitoring 2The user friendly monitoring interface enables resources without scripting skills to define and set parameters for monitoring each Droplet, thereby democratizing the ability of teams to monitor the operational health of Droplets. Moreover, the monitoring service absolves customers of the need to write custom code to monitor the health of their deployments by relying, instead, on an intuitive platform that accelerates the ability of customers to identify and troubleshoot performance issues. The release of DigitalOcean’s monitoring service follows-up on the company’s release of load balancing functionality in February and marks yet another significant step in DigitalOcean’s transformation from an IaaS platform geared primarily toward developers and startups, to one that can accommodate the needs of larger applications, workloads and organizations. Given that 2017 already features the release of load balancing and monitoring for its IaaS platform, existing and prospective customers can look forward to further enhancements from DigitalOcean that strengthen its ability to cater to a more diverse set of customers and application workloads by means of enhanced analytic and operational functionality.

Qumulo Finalizes $30M in Funding to Support Global Expansion of its Scale-Out Storage Platform at Petabyte Scale

Qumulo today announced the finalization of $30M in funding led by Northern Light Venture Capital, a new investor, with additional participation from existing investors Kleiner Perkins Caufield & Byers (KPCB), Madrona Venture Group, Top Tier Capital Partners, and Tyche Partners. The oversubscribed funding round brings the total capital raised by Qumulo to over $130M. The funding will be used to accelerate Qumulo’s market expansion and global growth in geographies such as North America, Europe and Asia in recognition of an intensified need amongst enterprises to replace legacy storage platforms with Qumulo’s data-aware, scale-out storage infrastructure. Qumulo’s scale-out storage platform delivers the ability to deploy and manage petabyte-scale storage and subsequently provide insight into the usage of billions of files as well as the data stored within those files. Qumulo’s ability to empower enterprises to manage multi-petabyte storage deployments, in conjunction with the granular visibility it provides into storage trends, renders it a disruptive force in the battle to transform legacy storage to accommodate the business needs of big data storage for on-premise and cloud-based infrastructures. Qumulo’s Series C funding raise comes soon after the February 2017 release of Qumulo Core 2.6, a new version of its data aware storage platform in addition to QC 360, a hybrid storage appliance and the November 2016 appointment of former EMC executive Bill Richter as the company’s CEO. With a roster of investors that include Amazon Web Services, Isilon, Microsoft and Google, Qumulo stands poised to continue to shake up the legacy storage landscape by delivering a solution that differentiates by way of its ability to manage petabyte-scale storage with a high degree of performance in addition to keen visibility into storage patterns. The latest funding raise promises to inaugurate the next wave of Qumulo’s growth as Qumulo expands globally and correspondingly enriches its product in relation to customer feedback and the unfolding of the company’s larger strategic vision.

Weaveworks Launches Weave Cloud Enterprise Edition Featuring Enhanced Container Management Functionality

On March 29, Weaveworks announced the launch of the Weave Cloud Enterprise Edition (EE) tier for its container and microservices management platform. Compatible with all major container platforms and orchestration frameworks, the Weave Cloud Enterprise Edition simplifies, streamlines and accelerates the deployment and ongoing operational management of container-based applications. Developers can use the Weave Cloud Enterprise Edition (EE) to manage application releases for container-based applications. Additionally, Weaveworks allows developers to visualize the inter-relationship between different containers and monitor application performance as it relates to either individual containers or amalgamations of containers. Moreover, the Weave Cloud Enterprise Edition gives developers the capability to monitor application performance as measured by a multitude of metrics and to subsequently perform root cause analytics to understand the drivers of performance degradation or improvement. The platform also empowers developers to connect containers via its networking functionality that enables the creation of secure networked relationships between containers.

The recently launched platform features the availability of incident management functionality marked by the ability to obtain a granular understanding of the nexus of root causes responsible for an incident, the history of similar incidents as well as dashboards that elaborate on the timing and impact of the incident. In addition, the Weave Cloud Enterprise Edition (EE) boasts release automation functionality as well as the ability to roll-back releases to earlier points in time. Furthermore, the Weave Cloud Enterprise Edition (EE) features advanced analytics for the troubleshooting of Kubernetes that includes resource container mappings. Taken together with the delivery of its container management functionality via the cloud, the Weave Could Enterprise Edition empowers developers to focus on monitoring and improving container-based applications without the hassle of attention to the underlying infrastructure in which the containers are hosted.

The availability of incident management, release automation and Kubernetes troubleshooting functionality in this version of the Weave Cloud Enterprise Edition (EE) bolsters its positioning within the container management space by delivering enterprise-grade functionality that enable enterprises to track metadata associated with container-based applications and automate application releases.  But the larger story, here, is the narrative of an enterprise-grade container management platform that delivers on its promise of monitoring as well as ongoing operational management of container-based applications and infrastructures. Notable about Weaveworks is the sophistication of its advanced analytic capabilities for troubleshooting performance issues in container-based applications in conjunction with its unique visualization and secure networking capabilities for container-based infrastructures. As such, the platform differentiates in the container-management space by way of an end to end container management solution with strengths in monitoring and ongoing operational management.

Alibaba Cloud Releases PAI 2.0 for Artificial Intelligence and Machine Learning in the Cloud

Alibaba Cloud has announced the release of PAI 2.0, an updated version of a platform designed to facilitate the deployment of “large-scale data mining and modeling,” with a specific focus on artificial intelligence and machine learning. Alibaba Cloud’s PAI 2.0 represents China’s first publicly available machine learning platform that encompasses use cases for its “ET Industrial Brain” related to manufacturing, optimized device and sensor configuration, energy utilization management and analytics related to the industrial internet of things, more generally. Separately, Alibaba has announced details of an “ET Medical Brain” that specializes in use cases related to drug discovery, patient management, hospital and clinical facility management as well as the deployment of virtual medical assistants to help patients interact with clinical protocols and tests. PAI 2.0 features over 100 pre-configured machine learning algorithms that can be adapted for different use cases and scenarios.  The announcement of PAI 2.0 underscores the intersection between cloud platforms and machine learning technologies as cloud infrastructures increasingly seek to differentiate their platforms with value-driven analytic, coding and data management capabilities. PAI 2.0 allows Alibaba Cloud to claim parity with the likes of AWS, Azure, Google Cloud and IBM SoftLayer with respect to advanced machine learning functionality although the sophistication and ease of use of its algorithms and deep learning technologies remains in the process of discovery and realization by its customers.