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For the past couple of years, we’ve surveyed our customers about their ecommerce platform. Initially, it was a pragmatic activity: We wanted to know what direction our customers were taking. But… Continue reading
With Enterprise Business Intelligence and analytics leaders in mind, this article summarizes the main styles of reporting and analysis to serve as a reference for evolving enterprise BI strategies and solution portfolios so… Continue reading
Return on investment (ROI) is an important factor for new technology purchase decisions. However, having invested in many security and network… Continue reading
To choose between native and third-party low-code tools, cloud application development teams need to weigh the benefits of consolidation against the risk of lock-in.
s the adoption of no-code and low-code platforms grow, some… Continue reading
Implementing better network segmentation to improve security is a significant project for network operations, data center ops, and security teams. From dividing IoT from IT using micro-segmentation to avoiding over-segmentation, we call out best practices for maximizing success in this task.
• The segmentation requirements for an enterprise call for a highly customized design.
• Avoiding either over-segmenting or under-segmenting the network is achievable but requires a formal project.
• Outsourcing segmentation project planning tends to result in poor outcomes. Too often, trust is placed in less trusted components, often resulting in segmentation projects being delayed or restarted, or with results that place the enterprise at undue risk.
• Segment based on data sensitivity, location, and criticality.
• For virtualized environments, change the technology, but not the security principles.
• Create a segmentation architecture that will accommodate short-term technology changes, and will best allow for housing new resources, applications and data within the existing framework.
• Create zones to proactively house Internet of Things (IoT) and operational technology (OT). Continue reading
IT managed services based on cloud computing are catching on with many small and medium-sized businesses as a way to offload the burdens of buying and maintaining software and hardware.
The idea is simple: instead of buying and managing your own IT assets, your company pays to use a system owned by a third party. A simple free version of the idea is Google Drive, which allows users
anywhere to work and collaborate on word processing, spreadsheet and other applications hosted on Google’s servers.
The cloud’s key benefit is that it saves companies money because they don’t have to purchase, implement, maintain and update hardware or software. For example, some experts estimate a company can save about 65% on an ERP system by implementing it through the cloud rather than buying the software.
The top three IT initiatives for 2018 include business intelligence, artificial intelligence, and big data, according to 451 Research. Digital transformation
Public clouds are the future of enterprise big data analytics, and their use is creating the unified platform needed to fully gain its value.
Today’s big data analytics market is quite different from the industry or even a few years ago. The coming decade will see change, innovation, and disruption ripple through at every segment of this global industry.
In the recently published annual update to its market study, Wikibon, the analyst group of SiliconAngle Media, found that the worldwide big data analytics market grew at 24.5 percent in 2017 from the year before. (I work for Wikibon.)
This was faster than forecast in the previous year’s report, owing largely to stronger-than-expected public cloud deployment and utilization as well as accelerating the convergence of platforms, tools, and other solutions. Also, enterprises are moving more rapidly out of the experimentation and proof-of-concept phases with big data analytics and are achieving higher levels of business value from their deployments.
Going forward, Wikibon forecasts that the overall big data analytics market will grow at an 11 percent annual growth rate by 2027, reaching reach $103 billion globally. Much of the market growth in later years will be sustained by the adoption of big data analytics in the internet of things (IoT), mobility, and other edge-computing use cases. Continue reading
Machine learning isn’t only in the cloud. Microsoft is bringing it to PCs in the next Windows 10 release. Here’s how to get started now.
We’re not far away from a new release of Windows 10, and with it plenty of new APIs for your applications. One big change is support for running trained machine learning models as part of Windows applications, taking advantage of local GPUs to accelerate machine learning applications.
Building a machine learning application can be a complex process. Training a model can require a lot of data, and a considerable amount of processing power. That’s fine if you’ve got access to a cloud platform and lots of bandwidth, but what if you want to take an existing model from GitHub and run it on a PC?
Trained machine learning models are an ideal tool for bringing the benefits of neural networks and deep learning to your applications. All you should need to do is hook up the appropriate interfaces, and they should run as part of your code. But with many machine learning frameworks and platforms, there’s a need for a common runtime that can use any of the models out there. That’s where the new Windows machine learning tools come into play, offering Windows developers a platform to run existing machine learning models in their applications, taking advantage of a developing open standard for exchanging machine learning models. Continue reading
Cloud-native apps built on Kubernetes can run anywhere. Now, with Open Service Broker, they can also use services hosted in public clouds such as Azure. – Cloud Software Development 
Back in the early 2000s, while working as an architect in an IT consulting company, I became fascinated by the promise of service-oriented architectures. Taking an API-first approach to application development made a lot of sense to me, as did the idea of using a message- and event-driven approach to application integration.
But that dream was lost in a maze of ever-more complex standards. The relatively simple SOAP’s take on remote procedure calls vanished as a growing family of WS- protocols added more and more features.
It’s not surprising, then, that I find much of what’s happening in the world of cloud-native platforms familiar. Today, we’re using many of the same concepts as part of building microservice architectures, on top of platforms like Kubernetes.
Like SOAP, the underlying concept is an open set of tools that can connect applications and services, working in one public cloud, from on-premises systems to a public cloud, and from cloud to cloud. It’s that cross-cloud option that’s most interesting: Each of the three big public cloud providers does different things well, so why not build your applications around the best of Azure, AWS, and Google Cloud Platform? Continue reading