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By combining a clear business plan with streaming analytics, organizations can optimize their internal operations and identify growth opportunities.
The Industrial Internet of Things (IIoT) is flooding today’s industrial sector with data. Information is streaming in from many sources — from equipment on production lines, sensors in products at customer facilities, sales data, and much more.
Organizations that can find meaning in these rich data sources will gain a competitive edge. But harvesting insights means filtering out the noise to arrive at actionable intelligence.
To do that, industrial leaders need an analytical platform that generates intelligence in lockstep with business needs. Understanding the value of the analytical life cycle — and capitalizing on its potential — is one of the key elements of a successful IIoT strategy.
This report shows how to craft a strategy that will turn IIoT data into competitive advantage. It explains how to evaluate IIoT solutions, including what to look for in end to-end analytics solutions. Finally, it shows how SAS has combined its analytics expertise with Intel’s leadership in IIoT information architecture to create solutions that turn raw data into valuable insights.
The Data Paradox to see how the IIoT is affecting business, just look
at the numbers. Analysts predict the global IIoT market could top $151 billion by 2020 as it registers an 8% compound annual growth rate.
Along with this breakneck market growth comes blistering acceleration in data volumes. According to IDC,the universe of digital information created and distributed each year is doubling every two years and is expected to reach 44 trillion gigabytes by 2020.
Driving IIoT adoption is the underlying value of data.Manufacturers and other industrial firms are turning data into insights that help make production processes more efficient and improve product quality, while reducing maintenance and energy costs and operational risks.
The IIoT is even helping organizations transform their business models. For example, an aerospace company can do more than make jet engines — with IIoT data it can provide high-value services that continuously monitor an airline’s fleet to improve equipment performance.
Five Steps to IIoT Success But success requires more than simply accumulating data. To achieve the full benefits of the IIoT, industrial organizations must determine what information is truly
valuable and then act on it quickly to realize the intended business outcomes.
Five key steps can help your organization succeed.
STEP 1:
Define IIoT Business Goals While IIoT capitalizes on digital innovation and analytics, success doesn’t start with technology decisions. Business and technology leaders should collaborate to identify IIoT use cases with the potential to deliver benefits to their
organizations.
What opportunities exist? Early adopters are using IIoT to help develop new products and services; others eye the chance to mitigate risks by reducing errors in production systems or avoiding machine downtime.
Once managers create a short list of use cases, they refine them by establishing the high-level requirements. For example, project leaders determine which data sources and processes are required for each use case.
This upfront planning will enable the project team to quantify the potential impact on the organization — whether that’s product innovation, risk reduction, or some other benefit — and what new investments and change-management efforts will be required to achieve the desired results. Analyses such as these provide the
foundation for estimating the overall return on investment (ROI) of the projects being considered. The IIoT development team can then prioritize funding requests to senior executives for projects that demonstrate significant ROI potential without requiring massive change management to bring them to fruition.
Step 2:
Define an Analytics Strategy With a clear business case in hand, the IIoT team can scope out a project’s analytics requirements.Choosing the right analytics platform is essential for turning large amounts of data into insights that support the business case.
When evaluating platforms, assess candidates for how well they deliver a holistic analytical life cycle. In particular, look for solutions that:
Emerging technologies can further enhance analysis efforts. For example, artificial intelligence capabilities such as machine learning, deep learning, and cognitive computing use self-learning algorithms to model new trends and identify potential problems before their impact is felt.
SAS Analytics for IoT illustrates the advantages of a comprehensive solution. This platform combines SAS’s streaming analytics with Intel’s IIoT architecture to cover the full spectrum of IIoT requirements. The platform delivers a full suite of analytics capabilities, end-to-end security, and integration with storage platforms such as Hadoop. The result is a full-fledged IIoT solution designed for easy implementation.
Step 3:
Assess the Need for Edge Analytics IIoT users must do more than analyze information. They need to turn analyses into action, which requires a management structure designed to ope-rationalize the
insights.
Edge analytics can capture value in real time, and it deserves special consideration by IIoT planners. Edge analytics processes the data stream close to the source of the data. This allows the analytics system to stem impending problems by shutting down machinery,
triggering alerts, or taking other actions. This capability for immediate, automated response is not possible if analysis has to wait until data reaches back-end storage systems.
Edge analytics offers another important benefit — it filters data at the source so that only relevant data is sent to the cloud. This keeps irrelevant information from overloading networks and storage systems, and it helps managers focus on what’s most important to the business.
IIoT gateways are an important underlying technology for supporting edge analytics. An IIoT gateway provides a bridge between industrial sensors and the existing IT infrastructure. Thus, the system can communicate status and performance information to management systems that monitor the industrial environment for predictive maintenance and other activities.
Intelligent gateways enable predictive analytics at the edge for fast responses to potential production failures or other events. For example, if such a gateway detects excess vibration in a piece of equipment, the machinery can be idled or slowed and operators alerted so they can address the problem before an equipment failure occurs. While valuable in many IIoT applications, edge analytics
is not required for all use cases. Edge analytics will likely be needed if project managers answer yes to any of these questions:
Step 4:
Choose the Right Analytics Solutions Analytics is at the heart of successful IIoT deployments, but setting up analytics often presents one of the biggest hurdles for new IIoT efforts. For this reason, it’s important to evaluate analytics technologies as much for their ease
of deployment and ability to minimize project risk as for the sophistication of their insight tools.
To do this, work closely with those in business and operations units who will benefit most from IIoT intelligence. Not all organizations employ data scientists, which is why the analytics tools should be designed so these users can slice and dice available information and see visualizations of the results without having to pull in experts.
When choosing IIoT analytics, decision-makers should also look for solutions with a track record for minimizing long-term risk. For example, SAS Analytics for IIoT built on Intel and SAS technology provides a trusted environment that IT and operations-technology staffs can depend on for the long-life requirements of IIoT.
Consider the Intel IIoT gateway architecture used by SAS Analytics for IIoT. This architecture leverages Intel’s expertise in manageability to simplify integration with IT infrastructure and to enable scaling across a multitude of factory assets.
Organizations also need high-performance cloud servers and storage systems to efficiently process massive data volumes. SAS and Intel are working together to help these organizations select the best platforms for their applications, including high-performance solutions expressly designed to handle and store big data.
While speed is essential, performance isn’t the only consideration when choosing analytics platforms for IIoT. To avoid malicious access, IIoT needs enterprise-class security across all the data gathering, communications, and analytics components. To avoid costly manual maintenance, manufacturers need a centralized environment that can manage all of their IIoT devices. With these requirements in mind, Intel and SAS worked closely together to provide the highest levels of protection and manageability, starting with the gateway and extending into the cloud.
Finally, the IIoT environment should provide a scalable platform. As IIoT matures and business goals evolve, industrial organizations may need analytics in new ways. Care must be taken to ensure that the analytics platform and architecture can scale to support expanding data and computing requirements. Tapping solutions designed by technology leaders such as Intel and SAS ensures the ability to deploy and grow intelligence where it is needed.
Step 5:
Focus on Continuous Improvement Because IIoT continues to evolve, industrial organizations should regularly assess their use cases and analytics performance, and update these areas as new capabilities and business opportunities arise. At the same time, they
should re-examine existing deployments to ensure that analytics continues to achieve use-case goals.
There is little doubt that IIoT has the potential to transform industrial organizations. But to fully realize IIoT’s potential, organizations may require simultaneous changes in thinking and in culture. The process of gaining insight from data, including IIoT data, is by its nature iterative. It takes a mixture of analytics capability and domain expertise, combined with vision and imagination, to achieve success. But when this happens, organizations see a valuable opportunity to operate more efficiently,
serve their customers more successfully, and establish true competitive differentiation in their markets.
Experience has shown that moving to IIoT by chasing the biggest, most impressive use case often results in failure. Many organizations do better by starting with smaller steps such as easy, quickly deployed use cases, or dividing larger projects into multiple parts and then iterating toward larger goals over time. This allows them to build on success, gain confidence, develop internal skills, and cultivate wider organizational support for IIoT.
Most importantly, manufacturers should take advantage of end-to-end analytics solutions from leaders such as Intel and SAS. By working with established experts, manufacturers can simplify deployment and instead focus on uncovering insights and transforming their business practices. In this way, organizations will increase their chances of becoming an IIoT success story.
