The IoT Matures With Solutions Like SAP’s Leonardo

Image of the SAP Leonardo platform that includes The platform include modules such as IoT, analytics, machine learning and blockchain
The platform includes modules such as IoT, analytics, machine learning and blockchain

The IoT market is an ill-defined, nebulous space. While the industry refers to it as the IoT, it isn’t a single product. In reality, the IoT refers to numerous use cases and products that vary across different industries. For example, a mining company and a hospital can both benefit from predictive maintenance, but the problems and implementations are completely different.

A successful IoT implementation requires a coordinated technology and business process transformation strategy. The business process improvement strategy is often overlooked as the technology teams jump into the nuts and bolts of deploying a set of IoT tools. Technology-first thinking is precisely what leads to failed implementations. Companies, fearing they’ll be left behind, have rushed to purchase various IoT components — such as sensors, hubs, and platforms–before the business has defined the problem it’s trying to solve. Leaders in the space have sought out use cases where IoT will improve employee productivity, deliver operational efficiencies and enable new revenue opportunities.

The IoT requires more than one technology change

Once a company defines a set of potential business outcomes, the real fun begins. IoT deployments are anything but simple. Information and operational technology leaders face a morass of vendors, products and integration issues. At a minimum, an IoT solution requires multiple layers that include:

  • Connected devices and communications. The IoT vision requires connecting existing and new equipment with sensors. Additionally, there are many wireless connectivity options such as 3G, WiFI, BlueTooth and LPWAN. A complete list of IoT protocols can be found here. Most organizations get hung up on this first stage of defining the proper devices and connectivity options.
  • A set of connection platform tools. The first iteration of IoT platform solutions was a hub that could connect to data from multiple devices that used different protocols to communicate. Today a broad set of IoT software platforms solutions exist that include items such as device management that focus on configuring, provisioning, troubleshooting and operating the endpoint devices. Like mobile device management, IoT device management supports monitoring, testing, updating software, and troubleshooting connected devices. Wired and wireless connection management must be part of this suite. API management is necessary for connecting to device data and linking this data to applications and system of record/engagement. This layer continues to evolve rapidly.
  • Big data storage and analytics. IoT creates a multitude of new information that varies in volume type and frequency. Connecting and collecting sensor data is useless if you don’t have the right solutions to manage, analyze and create meaningful insights from all this data. Business must decide what data needs to be analyzed in near real-time versus batch processed. The tech team must also define what data will be analyzed centrally versus locally. In some cases, companies will need to balancing marketing’s desire for personalization with the need to maintain the privacy and confidentiality of a user’s data. Once you’ve corralled all of this data, a company also needs to design a long-term machine learning strategy to understand patterns.
  • Security. In a recent Lopez Research study, security topped the list of IoT technology concern for 2017. Companies need a security strategy that extends from the IoT devices through the application layer. This will require multiple software solutions. In some cases, the endpoint IoT devices can’t run security, such as embedded encryption, and companies will need an edge gateway to act as a security intermediary.
  • Applications that use the data & analytics insight. Once companies have collected and analyzed IoT, this data needs to be integrated into a company’s existing systems of record and engagement to create need insights and opportunities for action. For example, a cold supply chain solution can be redesigned to use IoT data such as temperature and humidity to have fact-driven information on product health through its distribution. Many IoT deployments fail because application integration strategies were an afterthought.

SAP aims to help companies run IoT simply with Leonardo 

As you can see, there is a mixture of business and technology decisions that need to be coordinated for an IoT solution to deliver value and market differentiation. On the technology front, vendors are racing to deliver more comprehensive IoT solutions to minimize customer’s implementation woes. Enter SAP’s Leonardo. At the company’s annual SAP SAPPHIRE and ASUG group meeting, a variety of executives took the stage to help SAP’s customers understand how AI, IoT and cloud computing were changing the company’s products and the future of computing.

The company’s CEO, Bill McDermott defined digital business as intelligently connecting people, things and businesses. The conference keynotes showcased how SAP was making efforts to live up to the corporate tagline “Run Simple”. While SAP made many announcements, the bell of the ball was the Leonardo system, which it defines as a digital innovation system. SAP’s Leonardo, not to be confused with the famous polymath Leonardo da Vinci, was clearly chosen as a name to invoke visions of a multi-disciplinary platform that can help its customers achieve IoT success.  The sheer volume of products in SAP’s Leonardo highlights the growing complexity of designing an IoT solution.