By Michael Fauscette
Companies are reassessing and revamping their businesses as they try to optimize for the digital economy. This business transformation, sometimes called digital transformation (DX), is very broad and impacts all areas of a business. A lot of the time, resources and budget are part of DX efforts at many companies. What is DX, what business functions are affected, and what technology can ease the transformation?
A somewhat simple definition of DX is the application of technology to modernize business processes, activities, models, and strategies with the intent of making the company more competitive and/or more profitable. Going through the modernization process requires more than technology, it also requires focused change management efforts to help employees and partners adopt new processes and behaviors. In general, the impact of DX is centered around business models, business strategy, workforce, customer interactions, and business operations.
Each of the three activity areas—workforce, customer interactions, and business operations are unique but interdependent, so modernizing one often requires efforts in the other two. Business models and strategies directly drive modernization in these activities. The modernization process can start at the business model and move to strategy, or can be a change/modification of the strategy that then drives the three activity areas.
Technology shifts associated with the availability of a pervasive technology platform—the internet—have created new behaviors in people, no matter what role or persona they happen to be in at present. With that platform you also have supporting developments like smart devices, cloud computing, online networks or communities, availability and accessibility of massive amounts of data, artificial intelligence (AI), and Internet of Things (IoT) to enable change and create new ways of doing a range of business activities.
Arguably the highest impact disruptions are created with innovative new business models, but also provide new ways to digitize and reinvigorate older models. In other words, the application of these technologies, along with process, culture, models, and strategies becomes the modern way to gain competitive advantage.
The most common examples of business model innovation come from digital native businesses like Airbnb, Amazon, and Uber, which cause disruption through the unique application of a network or marketplace business model. Many examples of traditional businesses that have accomplished the same innovative business model transition by transforming existing business lines or creating new ones. General Electric is a good example of a traditional business that has shifted its jet engine division to what it calls executing critical outcomes for its customers. The new model shifted from selling a jet engine to selling guaranteed up time by providing a digital platform that uses AI and IoT.
The internet is the underpinning of DX, but four technology areas have moved to the forefront of DX efforts over the past couple of years. These areas include AI, IoT, security, and digital platforms.
Focusing on these four areas doesn’t minimize a lot of other compelling technology developments, but instead provides four general categories that capture most—if not all—of the other developments as micro trends underneath.
An example is blockchain, a disruptive technology that can be captured in two of these technology areas—digital platforms and security.
The rapid evolution of digital platforms and the broadening of what they include is a big factor in the success of modernization efforts. Many functions move into the foundation as the technology landscape is increasingly cloud-based and composed of smaller building blocks called microservices. The modular platform provides services for other technologies and is the underpinning to a new flexible and adaptable business system architecture. The platform provides utility services like storage and collaboration and allows access to transactional frameworks like blockchain, AI, and other analytics, as well as system management.
The modular nature of digital platforms rolls up several micro trends beyond blockchain, including serverless solutions that move the compute resources and management to a third party, that removes concern from the application and its development for the most part; microservices, which are described above; containers and systems for deploying, scaling, and managing the containerization of applications like Kubernetes; and ecosystems that develop around a single platform.
AI—which includes technologies like machine learning, deep learning, neural networks, image recognition, and natural language processing—is growing in impact across the entire software stack and leading quickly to intelligent systems.
AI is quickly moving past simple automation, predictions, and decision support to prescriptive systems and autonomous action. The AI evolution is dependent on the accuracy and the availability of data, which for many companies is still very challenging and a prerequisite to taking full advantage of AI-enabled processes.
Some micro trends under AI include embedded AI—sometimes referred to as intelligent systems, individualized marketing and sales, and utilization of open data.
IoT enables innovations that involve the need for data from remote physical entities. The first and most obvious use cases were in Industrial IoT (IIoT); using the sensors to capture real-time use data of industrial machines, which when analyzed, could be used by algorithms to predict when maintenance should be done versus doing maintenance on a predetermined schedule.
Beyond the IIoT many other use cases emerge, including connected health, smart cities, smart energy, smart supply chain, connected buildings/factories, and connected vehicles. Some of the micro trends under IoT include edge computing and mesh networks.
Edge computing is the ability to move much of the processing nearer to the sensors themselves to increase compute efficiency.
Mesh networks involve the use of identical smart network devices to provide an extended network range in a consistent fashion, unlike the old network repeater approach.
2017 was a wakeup call for many businesses when it came to security. The high profile security breaches, issues with malware, ransomware, and data privacy breaches were constantly in the news. Security is broadly incorporating AI to attempt to level the playing field created by an exploding number of bad actors including those that are state sponsored. Intelligent security, among other developments, has potential to keep businesses on some par with the growing number of black hats.
Other micro trends include risk-based authentication and blockchain.
Risk-based authentication moves beyond simple passwords and multi-factor authentication to systems that can assess risk based on a number of factors like location, device, and behavioral patterns to manage the authentication process.
Blockchain, from a security perspective, is one technology framework that is a distributed trust system for transactions.
A Competitive Advantage
DX continues to be an important topic for maintaining or gaining competitive advantage for businesses. Using digital platforms as the underpinning to the technical transformation should enable new business models and allow companies to deploy flexible and adaptable systems that utilize AI and IoT as well as the micro trends under each area. Security should be a constant companion to the transformation, acknowledging the reality that mitigating the growing online and offline risks is a business imperative. SW
Michael Fauscette is the chief research officer at G2 Crowd, an online platform and community where people connect and share experiences about business software and gain user experience-based insight to support business software purchase decisions. Fauscette is responsible for strategic research, community management, and the analysis, packaging, and use of data from business to business software users.
Feb2018, Software Magazine