The Internet of Things (IoT) is in position to become one of the greatest application performance management challenges faced by IT. APMdigest asked experts across the industry – including analysts, consultants and vendors – for their recommendations on how to ensure performance for IoT applications. Part 2 covers data and analytics.
7. REAL-TIME DATA
The IoT is still too new and the technologies and protocols too diverse to ensure anything, let alone performance – but that doesn't mean you can't get started. The first step: realizing the IoT operates in real-time. Any performance management for the IoT will have to deal with an ongoing deluge of real-time data.
User engagement is fundamentally changing. The broad scale onset of smart sensors, voice interaction, AR/VR is creating an increasingly connected world where customer engagement spans across digital and physical touch points. To ensure optimal business outcomes, it is imperative for businesses to measure near-live time performance of software across devices, connecting microservices, and the clouds supporting the uniform experience.
GM, Emerging Technologies, AppDynamics
We scaled orders of magnitude when we transformed from Client-Server to Internet. This caused dramatic changes how we built, tested, measured, and maintained our systems. With IoT, it's about to happen again. Sensors and tags aren't clients. They're emitters. IoT will demand capture, analytics, and querying millions of data points an hour, in real time. Anything less would be like claiming data that fits on a laptop is a big data problem.
Product Manager, SOASTA
Big Data flowing from IoT-connected devices helps organizations be more responsive, adaptive and competitive in a constantly changing business environment. The ability to analyze massive volumes of data as they are collected allows businesses to predict and respond to trends with superior accuracy and precision. Data becomes more actionable and reliable the closer it is analyzed to real-time, and for this reason, organizations cannot afford bottlenecks anywhere in the IoT data collection and analysis process.
CEO and Founder, Catchpoint
8. ADVANCED ANALYTICS
People wrongly assume that connectivity is the biggest challenge facing IoT initiatives, when in fact, this is getting easier everyday. The real challenge isn't accessing data, it's gaining knowledge from the data. The more devices we connect, the more noise we create, and — effectively — the more garbage we churn out. Without establishing an intelligent way to make sense of this information, we're simply going to drown in noise.
Advanced analytics is not only critical to maintaining IoT performance, it also influences business, technology and investment decisions. The best way to help IT teams learn what is happening with edge computing and IoT — such as what devices are interacting with others, what levels of performance are normal, and what are anomalies—is to gather operational data from these log files, and use advanced analytics to move from reactive to proactive problem solving. Log files are a source of the truth and advanced analytics can be used to identify pattern, decrease mean time to identification and predict potential issues before they happen. By understanding critical usage system trends, proactive decisions can be made that positively influence the business and ensure the best customer experiences.
Ramin Sayar, CEO of Sumo Logic
President & CEO, Sumo Logic
9. ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING
IoT is going to make Big Data into Giant Data. It's the next level of scale, but what will the impact be? Companies will no longer be able to manage the hundreds of millions of connected devices … you simply can't hire enough people to chase down that many alarms. This is where AI and machine learning becomes a "must have" for IT operations tools. AI learns what is normal and abnormal behavior, then will be able to heal itself before an anomaly causes an incident. AI and machine learning will power the growth of IoT and vice versa.
JF Huard, Ph.D.
Founder and CTO, Perspica
10. DATA BATCHES
One of the biggest challenges of building an IoT application is collating the data from various sources. But when an application makes many repetitive requests to different IoT devices to obtain data, it can slow app performance. As such, the best way to ensure performance of IoT applications is to consolidate data into batches. Data can be pushed to the application at low latency in small chunks, as it becomes available. At the same time, deploying an application through a web browser makes it's usable across an extremely wide variety of devices.
Sales Engineer, Sencha
11. ADHERE TO LAWS OF DATA GRAVITY
IoT is a big contributor to Big Data, generating massive real-time data streams. Therefore, in order to build high-performing IoT applications, it's important to adhere to the laws of data gravity. Data gravity refers to the nature of data and its ability to attract additional applications and services. Developers must bring their applications as close to the (IoT) data as possible, versus the other way around. Cloud and open, extensible platforms are absolutely key to doing this in a quick and cost-effective manner.
12. LINK DATA TO BUSINESS GOALS
Organizations that link their IoT sensor data to a specific business process or target ensure that their results will gain visibility with the most important IoT champions in an organization – Operational Teams. These OT groups are focused on the delivery and improvement of the operational activities associated with an organization. For energy companies, this takes the form of efficient and predictable distributed energy production. By using sensor information associated with solar collection and daylight hours, or wind speed and direction associated with turbine performance, an IoT initiative provides information directly to the OT team operating the distributed power production and managing the efficient use of non-renewal energy sources. With this context, IoT initiatives link directly to operation productivity and OT team goals for maximum value.
John L Myers
Managing Research Director, Enterprise Management Associates (EMA)
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