Editor’s note: In announcing an Internet of Things-focused analytics partnership, Cisco and IBM are putting together a winning program, says Technology Business Research Analyst Ezra Gottheil. WRAL Tech Wire reported on the new initiative and added a post about what IBM and Cisco execs said in pitching the new effort. Here’s Gottheil’s in-depth review.

HAMPTON, N.H. – Cisco and IBM’s new Internet of Things (IoT) partnership promises to undo a customer perception logjam by endorsing the concept of edge analytics or processing, analyzing and acting on IoT data close to the devices and/or sensors at the network “edge.” In product, it delivers capabilities for running an edge version of IBM’s Watson IoT analytics on Cisco’s networking hardware, alongside Cisco analytics.

For customers, it promises to alleviate a past IoT conundrum: where to start with analytics. The companies’ message to customers is, “You can have it both ways.” That edge analytics, when and if combined with centralized analytics such as IBM’s Watson, can result in a more powerful, flexible and cost-effective hybrid IoT analytic capability, relative to a centralized platform alone.

The integration greatly improves IBM’s and Cisco’s analytics offerings, strengthens both companies’ IoT strategies, and promises to increase their respective IoT revenues. The agreement does these things by simplifying the construction of IoT solutions that integrate the capabilities of both companies; company spokespeople have said their 25,000 mutual customers had been asking for that capability.

Edge and cloud analytics: You can have one without the other

Centralized, usually cloud-based, analytics and edge analytics have two different purposes and two different cost structures. Centralized analytics aim to discover new value in big data repositories; edge analytics aim for rapid response to incoming data.

Centralized analytics take place in the cloud or in data centers. This incurs costs for data transmission, storage and processing as well as sophisticated personnel (i.e., data scientists) to prepare the data and perform analyses. The centralized approach lies at the heart of what TBR calls “big IoT.” Big IoT follows the same assumptions as other big data applications, that there is strategic business value in large data aggregations, which companies can discover through analytics and cognitive computing. Centralized analytics is driven by the need to discover these new insights. However, it is not required for every IoT implementation.

Edge analytics is driven by the need to act on incoming data. Compared with centralized analytics, it is tactical and less expensive. It is implemented on gateways, routers, switches and near-the-edge servers to apply insights about incoming IoT data to drive rapid actions by devices or people. Based on edge analysis, summary data may be transmitted to a central location, but in any case, data transmission, storage and processing costs are lower than with centralized analytics.

Edge analysis is not looking for insight; it is looking for actionable data patterns. The patterns are dictated by rules, and the rules can be written based on domain knowledge or can be generated by centralized analytics. This is how IBM’s entry into edge analytics works; centralized Watson generates insights into data and embodies these insights into rules that can be run by Watson’s edge component. The hybrid IoT system IBM and Cisco are promoting with this announcement integrates centralized and edge analysis, but systems can run either entirely at the edge or entirely centrally. If the rules for the edge are generated by domain experts, or from separate analyses of other bodies of data, then no centralized analysis is necessary. This is the model for most of the “little IoT” point projects, which are the fastest-growing segment of the IoT market. TBR estimates little IoT accounts for 80% of IoT projects being initiated.

On the other hand, analysis for insight and action can be performed centrally, without edge analytics, with all data brought to the cloud or data center. This is the model for entirely cloud-based IoT, such as that promoted by Amazon Web Services, and is the model IBM has been promoting until now. Cisco has favored its own fog computing edge analytics but has also featured how external central analytics can be integrated.

Edge analytics promise lower costs and increased ROI

Much of the excitement about IoT in 2015 was about extracting value from the big data IoT generates, and IBM was at the forefront of generating that excitement. Vendor companies emphasized strategic transformation of companies implementing IoT. What IBM and other vendors pursuing big IoT have found over the last year, however, is that customers see IoT as strategic but are hesitant to commit to large-scale IoT projects. Customers interested in strategic transformational IoT projects often experiment with proofs of concept or tactical implementations as they evaluate their commitments to big IoT. This is a costly and time-consuming process for vendors and has resulted in slower growth rates in IoT than expected originally.

Customers see edge-based IoT as safer, with greater assurance of a more rapidly realized ROI. The problem with strategic IoT based on centralized analysis is that the cost of big IoT is large and the benefit is less certain than that for little IoT. The benefit is based on the insights generated by centralized big data analytics, and the value of those insights cannot be known until they are generated and used. With little IoT, the ROI may be more modest, but it can be estimated with more confidence. Because of the lower cost of little IoT, the risk is smaller.

With this announcement, IBM is able to offer its customers a smooth path between edge-based IoT and centralized big IoT. At the same time, the performance of analysis at the edge reduces the cost of centralized analysis by reducing the amount of data transmitted, stored and processed. Together, these benefits smooth customers’ transitions and lower their costs, making it easier for customers to move from little IoT to big IoT.

Cisco is now able to offer its customers an easy bridge to the power of centralized analytics. Cisco customers had been using edge analytics unless they created their own integration with centralized analysis platforms; the Cisco platform relied on the company’s fog computing edge analytics as its analytics platform.

Hybrid IoT: A winner for IBM and Cisco

Combining the IBM Watson brand with Cisco’s edge networking and fog computing capabilities presents a compelling message to IoT customers. IBM is a leading provider of IoT analytics software and professional services, and partnering with Cisco will help ease major adoption hurdles, thanks to a more unified solution across IBM and Cisco assets. These assets have been tested and validated and will help customers reduce implementation time and costs.

The technology paradigm underpinning this partnership is not new; vendors such as Microsoft, Dell and GE have edge and centralized analytics and computing capabilities for IoT, which provide similar cost savings and time benefits. However, this partnership will create proofs of concept and increase customer awareness of hybrid IoT, and it will allow IBM to put Watson in the driver’s seat in more situations. For Cisco, the partnership adds a vital third component to its IoT system, bringing in a powerful centralized analytics engine to pair with its platform (Jasper) and connectivity plays. TBR believes it ultimately strengthens Cisco’s class revenue driver — edge routing — while complementing its IoT-based software, security, and software revenue opportunities.

The partnership allows both companies to approach IoT with joint sales and go-to-market initiatives where it is most beneficial for both vendors. Because Cisco and IBM have separate cost structures under this agreement, we expect IBM to pursue partnerships with additional vendors, such as Juniper, to spur IoT market growth. At the same time, Cisco will explore tighter integration with other central analytics vendors, such as Microsoft and GE.

We believe the partnership will also help IBM better differentiate against IoT analytics and cloud competitors such as AWS, Google and Microsoft by keeping analytics and Watson at the center of its cloud and IoT portfolios. With AWS established as the dominant leader in public cloud infrastructure, IBM is working to promote its hybrid IT, cognitive, and cloud services abilities as part of a focused portfolio, which allows clients to use cloud-enabled technology to achieve industry-specific business outcomes. AWS struggles to provide comparable business solutions to enterprise customers because it messages a broader set of solutions, typically at the developer level. IBM’s and Cisco’s industry focus and expertise will resonate well with IoT customers who typically require customization, and ultimately provide each vendor with an additional avenue to drag their cloud solutions into the conversation.

Hybrid IoT is the emerging architecture for the broader industry

While not all implementations of IoT will have edge and centralized analytic components, the availability of both is a benefit to customers and a necessity for vendors focused on providing a comprehensive IoT solution. IBM and Cisco had hybrid solutions prior to this announcement, but they required more effort by customers. With this announcement, both companies are offering a more complete and flexible solution. They are not alone. Dell, GE, Microsoft and SAP include centralized and edge analytics. AWS remains the only major player without significant direct assets in this capability.

As this hybrid approach becomes more familiar, it will accelerate the current trend toward evolutionary IoT, where companies move toward their transformative IoT goals through a series of steps, each one providing benefits to the company. TBR believes this is the future of commercial IoT.