Removing the technology fear factor from digital transformation
- Barry Dwolatzky and Mark Harris
Digital transformation: Technology shouldn’t be a source of fear, but rather an ally and key business tool.
To all but the most technically astute executives, digital technology is seen as something of a minefield. Many large organisations fail in their digital transformation strategies because they embark on massive projects that take too long, cost too much and fail to yield any business value. In this article, we unravel the layers of technology that need to be navigated.
“Digital Transformation” – these words have featured prominently in boardroom meetings and strategy sessions in organisations large and small in recent years.
“Going digital”, closely associated with the buzzwords “Fourth Industrial Revolution”, or 4IR, has become a business imperative for almost every industry, company and government entity. Executives are having to deal with the strategy, execution and impact of implementing – or not implementing – a digital transformation programme.
In an article in Forbes magazine in September 2019, Blake Morgan notes that 70% of digital transformation programmes fail. While the reasons for this vary, she identifies a common cause: namely, a lack of buy-in by key stakeholders. At the root of the reluctance from senior executives is uncertainty and fear about the technology aspects of digital transformation.
One of the biggest challenges that executives face is that there are so many layers of technology and platforms that need to be evaluated and navigated.
Executives ask, “How do I deal with this complexity? How do I make the right selections to maximise the business benefits? What is affordable?” They fear the risks inherent in having to fund and manage massive technology projects that are expensive and could take years before the business benefits are derived.
To all but the most technically astute executives, digital technology is seen as something of a minefield. Many large organisations fail in their digital transformation strategies because they embark on massive projects that take too long, cost too much and fail to yield any business value.
In this article we unravel the layers of technology that need to be navigated. We offer some broad suggestions and a useful framework for understanding and assessing the technology choices faced by those tasked with running a business in the 2020s.
In an article titled “Four keys to successful digital transformations in healthcare”, Sastry Chilukuri and Steve Van Kuiken present the very useful diagram shown in Figure 1 below.
They explain that key drivers for digital transformation are: developing a closer relationship with customers; redesigning and innovating key business processes; building “smarter” products and services; and being willing to take on greater levels of risk.
These objectives are delivered by redesigning the customer experience; using the tools and methods from data science; and being “agile at scale”.
It is this latter attribute that many organisations fail to appreciate. It advises against the “big bang” approach to digital transformation, i.e. large, expensive and risky initiatives, and calls for small, iterative and incremental steps in the adoption of digital technologies.
Figure 1 also highlights laying down a strong foundation of technology to support a “data backbone” for the organisation, supported by good cybersecurity.
Organisations should also consider migrating to the cloud, using “software as a service”, and adopting the Internet of Things (IoT) as a way of enriching its access to relevant real-time information.
There are several specific business drivers that may necessitate digital transformation. Three of the most important of these are:
Increasing revenue and accessing new markets
If a company is focused on increasing revenue, accessing new markets and improving its “Know Your Customer” environment, it would need to focus on digital technologies that would create a strong digital presence. This would require creating world-class websites, maximising social media presence, digital marketing and potentially an e-commerce platform. There are specific technologies and platforms that underpin each of these.
Efficiencies and reducing costs
If a company is aiming to improve effectiveness and reduce costs, many executives fear that implementing new digital technology has the opposite effect. They believe that new digital platforms are costly and create huge capex requirements. This risk may be significantly mitigated by opting to use Cloud-based platforms and easy-to-use RPA (Robotic Process Automation) technologies.
Innovating and becoming a data-driven organisation
A more advanced technology-driven approach to digital transformation is required for improving functions like supply chains, asset management, changing business models and gaining deeper customer insights.
Some businesses already have huge amounts of information that can be utilised to innovate and support new business models and product innovations. Other businesses would need to create and access data that could dramatically improve the visibility of their operational environment.
Technologies such as IoT (Internet of Things) and Scada (Supervisory Control and Data Acquisition) can facilitate the collection of relevant data.
For example, by implementing an IoT platform and adding sensors to remote devices, businesses can gain enormous insights into supply chains, customer behaviour, asset control and the management and control of remote environments.
Depending on the specific business driver, digital technologies are deployed to achieve that driver. Figure 2 below shows some of these technologies in relation to the three drivers listed above. All of these technologies are, however, underpinned by a common core of “enabling technologies”.
Most digital transformation strategies are undertaken to drive some level of automation. In the modern context, automation is usually based on implementation of Artificial Intelligence (AI) in some shape or form. But what needs to be in place to support automation and AI? In Figure 2 these are listed as “enabling technologies”.
Some form of “machine-to-machine” (M2M) communication usually underpins AI and automation. This is necessary because automation usually involves reducing or eliminating human engagement in business processes and operations.
Automating an insurance claim, for example, might mean that information currently filled in by the claimant on a paper form and then entered into a computer system by a human clerk, would be replaced by an app on the claimant’s phone that communicates directly with the insurance company’s computer.
While M2M communication is almost as old as computing itself, modern protocols and technologies have introduced opportunities to exchange an increasing amount of information between systems and devices.
“Big Data” is the next important “enabling technology”. Introducing M2M communication within an organisation’s business processes results in the conversion of information flows into a digital format.
Whereas an old-fashioned analogue business might have information flows consisting of paper records, hand-written memos, telephone conversations and cash transactions, a digital business will convert all of these into a digital format. The quantity and variety of digital data that moves around a modern organisation, even a relatively small organisation, is large.
Organisations also have the ability to draw in and use digital data from other sources. This falls under the heading “Big Data”.
Rather than investing in its own on-site hardware infrastructure to store and process data, the modern digital business uses resources in the Cloud. This is another layer of technology that an organisation needs to manage.
Deploying AI to drive automation in a business depends on successfully managing and using this Cloud-based Big Data repository. This is where “Analytics”, and particularly “Predictive Analytics”, comes into play. This not only requires technology, but also depends heavily on appropriate skills.
“Data Scientist” is the modern label attached to a person with the prerequisite set of skills required to use predictive analytics and AI to automate a business’s operations.
Other technologies deployed in digital businesses are RPA (i.e “Robotic Process Automation”), IoT and robotics. These latter two are particularly necessary when business operations need to interact with the physical world.
Removing the technology fear factor from digital transformation
As we noted in the introduction, many executives responsible for managing a digital transformation strategy for their organisation fear that it will unleash a fearsome ogre into their business. This ogre comes in the form of a big disruptive technology project with budgets and timelines that soon spiral out of control.
In this article we have identified some of the core enabling technologies that need to be selected and implemented. Our key message is that the technology aspect of the digital transformation journey should be “right-sized” and implemented in an agile, incremental and appropriate manner.
Technology should not be a source of fear, but rather an enabler to rapidly achieve tangible benefits.
Professor Barry Dwolatzky is Emeritus Professor at the University of the Witwatersrand and Director of the Joburg Centre for Software Engineering. Mark Harris is CEO of Altron Nexus. This article first appeared in Daily Maverick/Business Maverick.