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InfoQ Homepage Articles What We Now Know: Digital Transformation Reaches a Point of Clarity
Jun 21, 2022 12 min read
by
reviewed by
Over the past years, there’s been much written and discussed about digital transformation. There has been a general understanding that it enables organizations to broaden their online presence and to become more disruptive in existing and new markets. However, there’s also been a great deal of ambiguity and little consistency in establishing exactly what digital transformation, as a field of practice, actually is. Nor has there been a clear indication as to what set of technology innovations primarily distinguish automation solutions in a digital transformation environment.
Without concrete definitions of practices, models and technology architectures, it is difficult for a given organization to determine whether what they are planning or undergoing is actually a genuine digital transformation.
This is important because digital transformation initiatives are typically expensive and impactful, requiring investments and changes in automation, infrastructure, education, communication and business process reengineering and optimizations. These investments are made with certain expectations that stakeholders have as to the benefits and enhancements that should result from a digital transformation effort.
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These expectations may be based on media hype, vendor hype or a legitimate analysis and assessment of an organization’s business requirements, its market and its attainable goals. Often, they are based on a combination of these and other sources. The problem is that if there is a degree of uncertainty as to what how the digital transformation is or should be carried out, then the probability of meeting expectations is low.
The good news is that the field of digital transformation has matured. There are now clear objectives, criteria and models that pinpoint exactly what a “vanilla” digital transformation is and is not. This establishes a starting point and provides us with the clarity we need to determine what parts of these general objectives, criteria and models are relevant and applicable to the unique business goals, constraints and potential of our own business.
We Now Know:
That a Digital Transformation is About More than Transforming Business and Technology
It’s been well-documented that digital transformations introduce new technologies that allow us to improve and optimize how we automate our business operations. This is the primary focal point of many of these initiatives and the business improvements being sought typically emphasize:
What often falls under the radar is the fact that other internal forms of transformations are necessary to achieve these goals.
Improving and growing a company’s online presence in existing digital markets is heavily reliant upon the creation of quality data intelligence and the successful utilization of data science systems (as explained later). These enhancements require us to significantly transform how we use, acquire, manage and store data.
To genuinely enhance customer experiences so as to make us more competitive in already highly-competitive digital markets usually require an internal transition from product-centricity to customer-centricity. This can push an organization out of its comfort zone to transform its internal structure and hierarchy to break down product-centric department silos so as to enable the extent of collaboration and cooperation needed to establish true customer-centricity within its new business and automation processes.
For example, a bank, with a portfolio of financial products, offers several of those products independently from each other. When a customer logs into their online banking portal, they have access to their account information as well as their associated credit card activity. This is good because, at a glace, they can see when they need to transfer funds from their account to pay their credit card balance.
However, another product they acquired from the bank is their retirement savings plan, to which they could contribute throughout the year. And yet another product they could also purchase with the bank is an investment product geared to those with disposable income. Neither of these products are accessible via their savings account portal, nor are they even referenced.
This is because the bank requires customers to create separate online accounts with separate login credentials for each of these products. Furthermore, these portals were designed by different (product-centric) departments and their respective online experiences are different from each other and from the savings account portal. This is burdensome for the customer and also a wasted opportunity for the bank to not only improve customer-centricity, but to also cross-market their products.
As much as the adoption of customer-centricity can require a shift in an organization’s culture and mindset, so to can the technology enhancements that come with digital transformation initiatives. Because they can significantly progress the extent to which an organization has been automating its business, human workers that have been part of established operational processes can find their roles impacted or even eliminated.
Action item: An organizational transformation is usually the most underestimated part of a digital transformation. A critical success factor is the careful planning of how the organizational culture needs to be transitioned to one that is in support of digital enablement. This falls on leadership. It requires advance planning with HR and a strong communication campaign to gain early buy-in from the workforce, as well as a solid plan to reallocate affected workers to more meaningful roles. It also requires clear foresight as to what the organizational structure needs to be transition toward.
We Now Know:
That Digital Transformation Relies on a Combination of Old and New Technologies
There specific automation technologies commonly associated with digital transformation, including Cloud Computing, Robotic Process Automation (RPA), Internet-of-Things (IoT) and Blockchain. There are also three core data science systems common utilized for data processing, analysis and incorporation within digital transformation solutions. These include Big Data Analytics, Machine Learning and Artificial Intelligence (AI) systems.
Several of these are established technologies that were in use long before digital transformation became a mainstream topic. This is a good thing, as they have evolved into robust, feature-rich platforms that can now form a solid foundation for solutions built in support of digital transformation objectives.
When building a digital transformation solution, only those technologies relevant to an organization’s business requirements are actually needed. What makes a digital transformation solution distinct is not which technologies it utilizes, but how the technologies that it does require are combined to form a solution architecture capable of achieving the pre-defined business objectives, many of which will relate to enhancing customer-centricity.
For example, an organization decides to automate more of its business tasks through the use of RPA. It further looks to delegate some low-risk decision-making responsibilities to its AI system so that a key business process is optimized in support of enhancing the customer-centricity of its outward-facing online environment. However, this raises security concerns about whether all potential risks associated with the AI have been clearly understood, especially since much of the decisioning will be in response to (potentially unpredictable) customer interactions, some of which could be fraudulent. The business benefits still appear to outweigh the security risks, but the potential for damage caused by incorrect decisions needs to be accommodated.
In addition to taking other cybersecurity measures, the organization establishes an immutable distributed ledger as part of a private blockchain implementation for the purpose of recording each customer interaction and transaction involving the AI decisioning logic. This record log is then periodically audited to provide input for the assessment of past AI decisioning (in order to determine whether the AI system is causing unreasonable damage as a result of poor decision-making) and to help identify and assess potentially fraudulent actions that may have occurred (and to also determine the damage caused by those actions in relation to the AI decisioning logic).
Action item: Technology adoption, as part of digital transformation initiative, is generally of a greater scale and impact than what most are accustomed to, primarily because we are looking not only to revamp parts of our IT enterprise, but to also introduce brand new technology architecture environments comprised of a combination of heavy-duty systems. In addition to the due diligence that comes which planning for and incorporating new technology innovations, with digital transformation initiatives we need to be extra careful not to be lured into over-automation. The reengineering and optimization of our business processes in support of enhancing productivity and customer-centricity need to be balanced with practical considerations and the opportunity to first prove that a given enhancement is actually effective with our customers before building enhancements upon it. If we automate too much too soon, it will be painful to roll back, both financially and organizationally. Laying out a phased approach will avoid this.
We Now Know:
How Much a Successful Digital Transformation Relies on Data Science
Perhaps the most distinguishing characteristic of digital transformation is that the solutions and environments we build are more data-centric and data-driven than any prior incarnation of our business operations and IT enterprise.
The scope of data we use is also no longer limited to our internal repositories. Large quantities of data are acquired from external sources and processed separately or together with our own data to produce highly insightful and meaningful analysis results. With the right skillsets and the correct balance of data science technology integration, we end up with a new type of corporate asset, known as data intelligence.
The data intelligence we produce may be presented to decision-makers to provide not only facts and options, but also accurate predictions and recommendations based on deep analysis. The data intelligence further provides us with the opportunity to delegate more business decisions to the data science logic. Specifically, we can empower AI systems to carry out decisions autonomously, resulting in instructions, formulated by the most current data intelligence, being sent to our automation solutions in real-time.
The enablement of our solutions to carry out more decisions independently, with the assurance that the quality of the data intelligence is high and the risk of damage is reasonable, provides the opportunity to outperform others. The guidance offered to human decision-makers, when correctly produced and when properly interpreted, becomes invaluable to the management of a digitally-enabled organization.
For example, a toy company that has traditionally only sold toys to retail outlets is now targeting the educational sector. It believes, as a result of its investment in digital transformation, that will not only be able to sell its building toys to schools online, but it also has plans to launch an online app for kids to share and collaborate in their building projects using their toys.
Educational markets have traditionally been inaccessible to this company because of larger organizations dominating that market, with long-standing relationships to the academic community. However, deep insights gained from data intelligence reports produced by the toy company’s new data science department have not only revealed a key gap in the market that the toy company can fill, but have also provided revealing predictions as to how existing educational vendors will continue to disregard that gap over the next year. This information gives the toy company confidence that when it enters the market with its offerings, not only will it be initially successful, but the fact that competitors won’t be prepared to respond, will give the toy company a good head start to making inroads in that market.
Action item: Data science enhancements are extremely alluring. So much so, that we can form deep dependencies on data intelligence, as it changes the way we manage and operate our business and it further changes the experiences and interactions our customers have with our organization. This reliance comes with the need to make data science technology a central part of business automation technology architecture and further make a long-term commitment to make to ensure that what we build now can be governed and evolved successfully. Otherwise, this dependency can lead our business in the wrong direction. A key critical success factor, therefore, is ensuring that your team has the skills to correctly and successfully integrate (and maintain) data science technology and product (and evolve) accurate data intelligence.
We Now Know:
What Customer-Centricity in a Digital Business Market Really Is
Ultimately, much of the data science we invest in will likely be focused on customer data intelligence. Data about what customers like, what they do, how they behave and how trends, interests and world events may be affecting them. While much of the effort we put into transforming our organizational structure and automation solution designs to become more customer-centric, all of this will rely on the successful collection and maintenance of customer-centric data.
The insights we gain from data intelligence about customers impact many aspects of our business. We focus on enhancing business processes to incorporate less transaction-type steps and more relationship-value actions that help nudge customers toward long-term loyalty. We look to add different types of “warmth” to individual customer interactions so as to further ensure long-lasting relations. Communicative warmth, proactive warmth, rewardful warmth and exceeding warmth are all characteristics we carefully incorporate into workflows that involve customer interactions, as indicated to us by our data science systems and the customer data intelligence they produce on an on-going basis.
We Now Know:
What a Digital Transformation is Supposed to Achieve
Here we return again to our expectations. Much of the hype surrounding digital transformation is legitimate. It truly can transform our business, our organization, our culture and our relationship with customers. In this article, we’ve only touched upon the concrete building legitimate blocks we can now assemble to develop and maintain the digitally-empowered environments that result from these transformations.
But a lot of what we can and cannot achieve depends on our timing. There are two primary categories of organizations successfully undergoing digital transformation:
The harsh reality of digital markets is that those that are better at transforming will outperform others that aren’t. This highlights the critical necessity of not just digital transforming to become disruptive, but continually transforming to retain or even gain market share over time.
We Also Now Know:
That There’s Much More Ahead
As much as we now know that digital transformation has reached a state of maturity, we need to be prepared for how it will undoubtedly continue to evolve. It is a hugely broad field that encompasses many practices, strategies and technologies, all of which will, individually, continue to be enhanced and optimized.
Those that fell into the pioneering category need to continue pioneering new digital innovations and new opportunities to utilize data intelligence in order to retain their status and retain their market. Those that fell into the latecomer category can now become more proactive pioneers in order to take strides ahead of those that may have become complacent.
Competition in business markets is nothing new. But we now know how much more competitive we can become in digital business markets. For many, this is a transformative realization.
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