Three Technologies Driving Digital Transformation in 2021

The term “digital transformation” is one of the hottest buzzwords in corporate lingo at the moment. Despite the many definitions that exist for this term, for the sake of this article, it will be defined as the innovative application of digital technologies that result in significant sustaining improvements to business performance or meaningful disruptive progress that creates access to new markets and sources of revenue.

Digital transformation is buzzed about because there is a ubiquitous recognition of its potential benefits to business and the likely detriment to business that fail to transform.

However, the types of businesses looking to transform, typically mature businesses with business model roots established prior to digital boom, are by definition those do not have much if any history of digitally driven business.

Unlike digitally native tech startups, these businesses face challenges that ironically stem from their history of success – the legacy structures of management and infrastructure are monolithic, a barrier to entrance of competitors but also to disruptive progress. Oftentimes, these businesses also operate in long-lived industries such as financial services and pharmaceuticals which are heavily regulated. These and other factors combined make for a substantial sense of inertia for established businesses to adopt the necessary changes in technical capability, organizational culture, and fundamental business model that are required to truly transform.

Despite the challenge, the drive towards digital transformation is a very strong trend and imperative in the financial services industry. Traditionally, the financial services industry has generated revenue through conventional means such as investment, management of assets, and sale of financial products.

Digital transformation is pushing the industry to innovate in both sustaining and disruptive ways – beyond simple digital advances such as moving to a new website - technologies can be applied in ways that make operations in their traditional business models more efficient but can also be used in novel ways that actually generate new revenue sources from the large amounts of financial data they have amassed over their long histories.

While the former has been the majority of the current work to date within most digitally transforming companies, there is an increasing emphasis towards the latter given the possibilities presented by new digital technologies for reimagining business models to drive new value and competitive advantages.

New Technologies Driving Transformation

Several of the most significant technologies currently driving digital transformation include cloud computing, big data, machine learning.

Cloud computing is essentially outsourced computation infrastructure that has advantages, one of the most significant being seamless, instant scalability of high-performance computational resources. This has made high performance computing accessible to everyone from individuals to enterprises, enabling users of cloud computing to perform resource intensive, large scale computation without having to make significant upfront capital investments in computational infrastructure.

Big data is a sophisticated analytics approach that draws useful insights from extremely large volume and diversity of data. It leverages the ubiquity and sheer amount of both private and public data to understand the world with a high level of precision and nuance.

Machine learning is a sub-category of artificial intelligence technologies. Machine learning is essentially the act of a computer program (the machine learning model) programming itself through a process known as “training”.

Training can be supervised – the machine learning model is provided a set of data to observe, generates an answer (e.g. categorization of the observed data) called the “inference”, and is told whether that inference is correct or incorrect. Through many iterations of this process on large datasets, a machine learning model can become extremely robust in generating accurate inferences. Popularly recognized applications of machine learning technology include the deep neural networks that power image recognition engines used by autonomous vehicles.

While applications of machine learning often get most of the spotlight, it is the cloud computing and big data technologies in the background that enable machine learning to be developed and applied meaningfully. Without computing power of cloud computing or the “raw materials” of big data, machine learning applications would not be as high performing or sophisticated as they currently are.