IT is in the crosshairs of a major data center architecture transition driven by a phenomenal rise in factors including Internet of Things (IoT), Big Data, and real-time analytics, finding a deeper relevance in the current business landscape. Over the last year,
Intel introduced SDI primarily to make IT more flexible, agile, and cost-effective, but there may be other ways to utilize its capabilities as well. One of the ways SDI could have a tremendous impact, is by optimizing the IoT value chain. Think of the IoT value chain as a collection of products and solution providers, right at the core of a device (such as a processor or OS) to the cloud, including all of the data analytics and application software that comes in between. Data analytics driven by the Internet of Things value chain could be an effective use case for cloud data centers looking to implement SDDC practices that encompass both SDI and SDN (software defined networking). I’ll talk about this aspect of SDI briefly in this article, but if you want more information on the topic, you can read more here.
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In December of last year at an event I attended in San Francisco, Intel announced its IoT platform, with a goal of achieving the three-pronged goal of controlling and coordinating connectivity and security of networked devices; delivering trusted data to cloud; and offering insight-driven value through analytics. Until now, SDI has been more of a concept, but Intel is working to enable real-world usage models and I think IoT presents a terrific case for the use of SDI. Before we discuss how, it’s important to point out one of the most pertinent concerns in terms of IoT generated data – accuracy of insights.
We know that data flow in an IoT network is initiated by the sensor, or actuator, at the endpoint, or “edge,” where the data is collected. Currently, the quality and action-ability of the insights generated depends on many factors, including the ability of the endpoints to capture the most relevant data, collection of data across multiple end-points, and analytics/processing horsepower at the endpoints to pre-filter data and provide real-time insights and actions. The real challenge lies in making the end-points, or the “edge,” more intelligent. Until that happens we won’t really be able to trust the accuracy of the insights generated. This is where SDI could step in and make things better. It could help refine those insights, and make them more suitable for business cases. However, that’s just one part of SDI’s contribution in the IoT value chain.
Here’s more on how Intel’s SDI influences analytics:
- Data Management - Intel SDI allows cloud data centers to create the right pools of resources for different kinds of analytics, backend services with multiple devices, and management and control of different sensor inputs.
- Services Frameworks - Intel has partnered with Cloudera and the open source community to understand requirements and create accelerated solutions to deploy Hadoop and Spark analytics on top of both SQL and NoSQL databases.
- Edge Analytics - SDI enables intelligent gateways and, in some applications, even intelligent distributed endpoints, which include processing and aggregation capabilities closer to the data.
- Cloud Management - ISDI provides a horizontal cloud management framework and policies to fulfill specific workload/tenant requirements for multi-tenant cloud datacenters.
- Security and Edge Management - Intel SDI framework can help manage intelligent “micro clouds” at the “edge” by providing acceleration, aggregation, control, and policy management capabilities.
When it comes to real-life use cases, SDI could have transformative impacts on the manufacturing sector, as well as on retail. In the not-too-distant future, manufacturers will be utilizing smart factories where we’ll see the use of real-time analytics that will help improve product quality, increase productivity, identify chief manufacturing issues, and reduce machine failure and downtime, while making faster decisions. Intel’s SDI can effectively optimize the manufacturing processes while controlling the challenges using real-time analytics on the factory floor, all the way through to big data analytics based on performance in the market within the datacenter.
The retail industry will definitely be impacted by SDI, where we’ll see more responsive retail outlets. This will be personified by data and real-time analytics generated and processed within the retail environment to create actionable and predictive insights that companies will be able to use to market to their customers. To allow these responsive stores to communicate effectively with the data center located at corporate headquarters, SDI- and SDN-backed intelligent network capabilities will be put in place.
If you’re as fascinated by this topic as we are and are interested in exploring this topic further, these use cases are discussed in greater depth in our whitepaper.
As IoT becomes more and more significant in real life applications, SDI’s usage models will branch out in different directions. If you think IoT is still just over the horizon, you’re making a mistake. It’s already here, although it is in its early stages of maturity. If you’re digging in and learning more about SDI and its future implications for business, you’re already ahead of the curve.
You can find Patrick Moorhead, President & Principal Analyst of Moor Insights & Strategy on the web, Twitter, LinkedIn and Google+.
Disclosure: My firm, Moor Insights & Strategy , like all research and analyst firms, provides or has provided research, analysis, advising, and/or consulting to many high-tech companies in the semiconductor industry, including Intel, cited in this article. No employees at the firm hold any equity positions with any companies cited in this column.
Note: Daniel Newman, adjunct professor at North Central College School of Business and Economics, contributed to this article.
