Top Technology Trends & their Impact on Performance Testing – Part One

In October 2017, Gartner published its list of the top 10 strategic technology trends that it believes will shape the enterprise landscape over the next several years. As they define it, a strategic technology trend describes technology with the potential to disrupt businesses and remake the customer experience over the long term. Intended for CIO and senior IT leadership, the list is organized to address the “Intelligent Digital Mesh” – Gartner’s vision of the foundation for future digital business and ecosystems. The listing details the value that these technologies will deliver to competitive and brand positioning.

As a group, many technology trends impact how artificial intelligence (AI) and machine learning have infiltrated nearly every application and system and how they represent a significant battleground for technology providers over the next five years. Several trends focus on blending the digital and physical worlds to create an immersive, digitally enhanced environment. Other patterns concentrate on making the connections between people, content, devices, and services more integrated and secure to ensure optimal digital business outcomes.

In this two-part blog, we’ll explore the implications of these strategic technology trends and, where applicable, discuss the challenges posed to performance testing. Part one examines the first five trends.

1. AI Foundation

Universities, such as MIT and Carnegie Mellon, have been exploring the potential of artificial intelligence (AI) for decades. Recently, AI technologies have evolved sufficiently for businesses to apply their capabilities to their enterprise operations.

At its core, AI is the simulation of human intelligence processes by machines and computers. At a high level, these processes include learning (acquiring information and applying rules for using it), reasoning (using the rules to reach approximate or definitive conclusions) and self-correction. Specific applications of AI include expert systems, speech recognition, and machine vision.

One of the most common areas of AI, machine learning, is enjoying massive adoption based on vast amounts of reliable data, new algorithms, and incredible computing processing power. This will lead to many new applications and changes to existing applications.

Gartner’s AI Foundation describes technologies that enhance data preparation, integration, algorithm and training methodology selection, and model creation. In layman’s terms, some examples of these AI-enabled technologies include chatbots on websites, call routing based on a customer’s verbal commands, speech and voice analysis that analyzes a callers tone to predict emotion and satisfaction, and predictive analysis to assess anticipated customer behavior.

Creating systems that learn, adapt and potentially act autonomously will be a major battleground for technology vendors through at least 2020. The vendors that integrate AI capabilities into their platforms will establish themselves as the dominant technology leaders in the coming decade. The enterprises that implement these platforms will reinvent their business models and ecosystems, remake the customer experience, and expand their brand reach.

2. Intelligent Apps and Analytics

Gartner asserts that within the next few years, most apps, applications, and services will incorporate some form of AI component to make them smarter. Many of these apps will themselves be intelligent, imbued with capabilities of AI and machine learning, and be layered between people and systems to augment human activity. Others will be “discrete users” of AI and will offer intelligence behind the scenes.

Today, AI includes packaged applications, such as SAP ERP analytics, where the software can determine the probability of deal close; offer predictive profit and loss based on the impact of pending orders, and automate the matching of invoices to orders – it can learn from users who do this manually. Other businesses use AI techniques to create virtual customer assistants (VCAs), such as Sense.ly’s Virtual Nurse, a virtual nurse avatar that simplifies the provisioning of outpatient services and helps physicians communicate with patients to prevent hospital re-admissions. Other VCAs improve existing applications, such as performance analyses of employees, sales, marketing, and security. The intent is for the software to perform routine tasks and free the human to deliver more strategic activities. Says Gartner, “Augmented analytics is a particularly strategic growing area which uses machine learning to automate data preparation, insight discovery and insight sharing for a broad range of business users, operational workers, and citizen data scientists.”

Gartner predicts that over the next few years, 90 percent of business intelligence tools will add natural language processing, so users can verbally ask rather than type queries. These types of capabilities will transform the workplace. Gartner encourages packaged software and service providers to consider how to implement AI to add the business value that promotes advanced analytics, intelligent processes and elevated user experiences.

3. Intelligent Things

Intelligent things are physical things that use AI to proffer the sophisticated behavior that enables more natural interaction with surroundings, especially with people. AI is the key that drives autonomous vehicles, robots, and drones and offers immense opportunity to connect the consumer and industrial sectors, such as with IoT.

Gartner expects that swarms of “Intelligent Things” will function together. In this concept, free-standing intelligent devices will interact without human influence. A great example is a smart home where independent devices communicate seamlessly while being managed from a single point. Another interesting concept is “swarm intelligence” where multiple drones and other devices work together toward a common goal.

4. Digital Twin

A digital twin is a digital representation of a real-world entity, such as a jet engine. A digital twin is useful in applications that involve the simulation, monitoring, or analysis of the real-world object; it helps stakeholders understand how the object state responds to different variables, changes, and impacts. Gartner postulates that digital twins will evolve to help organizations improve their ability to collect and visualize the right data, apply the right analytics and rules, and respond effectively to business objectives. Back to the jet engine example, a digital twin can provide insight into how various weather conditions and wind speeds impact engine performance and metal fatigue.

As digital twins become more integrated into business planning and operations, they will impart valuable insight to improve enterprise decision-making, drive “what-if” scenarios, and add value. The digital twin concept will have massive implications for healthcare, city and industrial planning, and digital marketing.

5. Cloud to the Edge

As Gartner explains, “While it’s common to assume that cloud and edge computing are competing approaches, it’s a fundamental misunderstanding of the concepts. Edge computing speaks to a computing topology that places content, computing, and processing closer to the user/things or ‘edge’ of the networking. Cloud is a system where technology services are provided using Internet technologies, but it does not dictate centralized or decentralized service delivering services.”

Edge computing moves data collection, processing, and delivery away from core systems or “nodes” and places them closer to the “edge,” where sources of this information reside. By keeping traffic and related processing at a local level, edge computing resolves basic connectivity, latency, and bandwidth constraints. In the IoT world, edge computing can streamline the flow of traffic from IoT devices and provide real-time local data analysis. Cloud computing offers elastically scalable technology capabilities, delivered as a service, for enterprises to “pay as they go.” Essentially, Cloud enables ubiquitous access to shared system resources and services that are rapidly provisioned with minimal effort.

Says Gartner, “When used as complementary concepts, the cloud can be the style of computing used to create a service-oriented model and centralized control and coordination structure with the edge being used as a delivery style allowing for disconnected or distributed process execution of aspects of the cloud service.” Translated – Gartner expects to see more of a balance between edge and cloud where cloud provides centralized data management and acts as a point of coordination and control for the edge which enables speedy information processing and delivery. They recommend that enterprises with substantial IoT elements begin incorporating edge design patterns into their infrastructure architectures.

The Implications of Gartner’s First Five Trends on Performance Testing

When reading about these first five trends, several common themes emerge; the intertwining of people, technology, and devices; the impending learning capabilities of software; and the evolution of the customer’s digital experience. Although each of these themes warrants a blog of its own – what I want to explore is the implication on the performance test.

Increased Complexity: As the digital mesh evolves, it will manifest as increased complexity in enterprise testing ecosystems. Businesses will be hard pressed to acquire a sufficient threshold of hardware and software to emulate the stressors that intelligent things and IoT components will invoke on their applications. Performance testing vendors will need to integrate powerful virtualization and emulation capabilities into test solutions to meet these requirements.

In the long-term, economies of scale may demand that some application vendors outsource their testing to either the cloud or to specialized testing vendors able to approximate the impact of the digital mesh on their applications.

Smarter Applications and Analytics Everywhere: The features and functionality that make applications smarter are also making them more involved. As packages applications enhance business value with background AI capabilities, they use more third-party interfaces and APIs. This, in turn, requires more testing to maintain usability and satisfy user expectations.

The prevalence of analytics, and the real-time insight and business value it delivers, emphasizes the need for automating data preparation as well as insight discovery and sharing for business information consumers. The integration of analytics into business applications requires rigorous testing and validation.

Architectures on the Edge: The IoT and its related swarms mean more sensors, controllers, and connected devices gathering data and acting on instructions that guide them to analyze it themselves or send it to other computing devices for processing. To engender trust with end users, both the data’s handling and its movement among various nodes must be flawless. Thus, testing must be comprehensive and rigorous.

Learn More

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Deb Cobb
Deb Cobb has deep expertise in enterprise product management and product marketing. She provides fractional product marketing, go-to-market strategy, and content development services for software and technology companies of all sizes. To learn more about Deb, her passion for collaborative thought leadership delivery, review her portfolio, or to connect directly, click here.

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