It’s hard to avoid the phrase “digital transformation” in technology articles these days. The thing is we’ve been in the digital age for at least 40 years. Companies have been transforming with the use of digital technology for a long time now. For example, in 1986 the New York Times covered “Digital’s Surprising Revival” which looked into the business transformation of Digital Equipment Corporation.
Why is digital transformation so important right now for companies? What’s different?
We’ve Been Digitally Transforming for Years Now
First, let’s define digital transformation, as it could mean different things to different people. Gartner defines it as, “Digital transformation can refer to anything from IT modernization (for example, cloud computing), to digital optimization, to the invention of new digital business models.”
Examples of the types of technology driving digital transformation in 2020 are cloud computing, machine learning, internet of things, among many others. The foundations of these technologies have been around for many years. I took courses in pattern recognition, machine intelligence and image analysis at University of Waterloo almost 30 years ago (I think I just dated myself). One of my professors was doing consulting on detecting cracks in ice flows using pattern recognition algorithms. Another professor and two grad students were doing facial recognition and facial animation (believe it or not to do low-bandwidth video conferencing). When it comes to internet of things, the use of collecting data from and controlling edge devices started in the 1970s with technologies such as supervisory control and data acquisition (SCADA) for engineering and X10 used for home automation.
What’s different now? Two major areas have surfaced: the democratization of technology and the availability of data. Cloud vendors have enabled both of these areas which is making it possible for businesses to embark on their digital transformation journey. Here’s how.
Cloud-Driven Digital Transformation: The Democratization of Technology
When Amazon launched Amazon Web Services (AWS) in 2006, it gave customers access to the same cloud technology it had built to serve millions of shoppers on Amazon.com. It made it more accessible for the person in their garage or dorm room to build the next big software company. All you needed was a credit card and you could launch the next big software application. This technology enablement of developers has continued as seen by the rich set of services within the AWS platform.
Microsoft also had a historic focus on developers, but in recent years has expanded its focus to anyone using its technology. “Democratization improves the overall digital capability of an organization,” said Satya Nadella, CEO of Microsoft. Microsoft would like to make everyone a software developer. This is often referred to as “citizen access,” leading to citizen data scientists and citizen programmers. In addition to all the services in Azure, Microsoft has built the Power Platform to support citizen programmers. As Microsoft advertises on the PowerApp landing page, “Turn bright ideas into brilliant apps.”
Google also sought to broaden its customer base by putting artificial intelligence in reach for a wider audience. With Google’s AutoML, Google CEO Sundar Pichai wrote, “Today, designing neural nets is extremely time intensive, and requires an expertise that limits its use to a smaller community of scientists and engineers. That’s why we’ve created an approach called AutoML, showing that it’s possible for neural nets to design neural nets. We hope AutoML will take an ability that a few PhDs have today and will make it possible in three to five years for hundreds of thousands of developers to design new neural nets for their particular needs.”
Cloud AutoML is a suite of machine learning products that allows developers with limited machine learning expertise to train high-quality models specific to their business needs. It relies on Google’s state-of-the-art transfer learning and neural architecture search technology.
Historically, algorithms for artificial intelligence were contained in expensive, difficult to use software libraries. These are now available as Platform as a Service with AWS, Azure and GCP. Developers can now perform AI activities such as translate between hundreds of different languages, convert text to speech and find faces in images with ease.
Cloud-Driven Digital Transformation: Availability of Data
The other area pushing the current drive towards digital transformation is data. It shows up in a couple ways. First, storing data is incredibility cheap, making it affordable to keep lots of data around and easily accessible. As well, acquiring data and processing it is even easier than before. This provides the ability to do many kinds of analytics enabling business decisions.
Data is also the foundation of artificial intelligence. The power of the AI models are only as good as the data they are trained on. Historically, with a limited catalogue of data, the accuracy and usefulness of AI algorithms was limited. That is different today. With the ability to acquire data easier than ever before and store it more cheaply makes AI more of a possibility for organizations. You don’t even have to have your own data set to take advantage of AI. Google and Microsoft have an incredibly rich set of image and text data due to their search engines. These cloud vendors have used that data to train AI models that anyone can use as Platform as a Service. The quality of these services to recognize patterns, such as faces, has in some cases exceeded that of human ability.
Digital transformation is different today because of the ease of building applications and availability of data. The two big questions then are:
What applications should be built powered by what data to transform business outcomes?
How can an organization manage the change from how they do business today to their vision of the future?
Great questions, which is why we will devote two separate upcoming articles to those topics.