• Lokesh Rajendran

Components of Digital Transformation

Updated: Dec 12, 2021

I recently started Google's Data Analytics course, I wrote this blog to record my learnings on Digital Transformation and Data-Driven Decision making. This is for people who want to understand the essential components of digital transformation.


The digital revolution forced every organization to reinvent itself, or at least rethink how it goes about doing business. Most large companies invested in what is generally labeled as 'Digital Transformation'. The investments are projected to be 6.8 trillion by 2023. If you underestimate the various steps and stages required to successfully execute a transformation agenda, we may fail to efficiently harvest the returns. Buying technologies (tools) in hope that the organization will somehow transform will not help.


The best technology will go to waste if you don't have the right process, culture, and talent in place to take advantage of it. A major reason for the lack of productivity gains from new technologies, including AI, is the failure to invest in skills — especially the lack of reskilling and upskilling once employees are in our workforce. Just like buying a DSLR for my grandmother, buying tools with no right skills to harvest it is of no use.


The issue starts when companies don't have a clear vision of Digital Transformation. Although like humans every organization is unique, the fundamental definition of transformation is not about replacing old technologies with new ones or following what other companies do, or hiring a lot of data scientists and starting collecting high volumes of data.


The idea is to become a data-driven organization, ensuring that key decisions, actions, and processes are strongly influenced by data-driven insights, rather than by human intuition (FYI, take Google Data Analytics course on Coursera to know more on this). Simply put, we will only transform when we have managed to change how people behave, and how things are done in our organization.


Five Essential Components of Digital Transformation

The image below illustrates the essential components of digital transformation.


FYI, if you are interested, download the below file on different data analysis processes.


Origins of data analysis
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Download PDF • 39KB

People

Any transformation starts with people, Whenever we talk about valuable data there are humans at end of it. For most organizations, the people aspect of transformation refers to their clients and employees. Historically, these relationships yielded poor or dispersed records.


Let's have a look at informal small businesses, such as La Boqueria (A food market in the Belly of Barcelona - I recommend watching the episode on Curiosity Stream): the salespeople have a great deal of access to, and knowledge of, their customers and clients, but it’s all “trapped” in their minds. In the same way, a London cab driver or a Parisian bistro waiter might have in-depth knowledge of their customers and what they want, or a small business founder might know the 20 employees that make up her workforce rather well, without needing much tech or data.


But what happens when an organization becomes too large or complex to know your customers or employees on a personal basis?

Data

If you want to scale the knowledge you have about your customers and employees and replicate it across a big organization and in far more complex and unpredictable situations, you need to have data — widely accessible and retrievable records of interactions with consumers, employees, and clients.


This is where technology can have the biggest impact — in the process of capturing or creating digital records of people (e.g., what they do, who they are, what they prefer, etc.). This process is called “digitization,” or the process of datafying human behavior, translating it into standardized signals (0s and 1s). It is useful to remember this because the real benefits from technology are not “hard” (i.e., cheaper systems or infrastructure), but “soft” (i.e., capturing valuable data).


Insights

Although data has been hailed as the new oil, just like with oil, the value depends on whether we can clean it, refine it, and use it to fuel something impactful. Without a model, a system, a framework, or reliable data science, any data will be useless, just like 0s and 1s. But with the right expertise and tools, data can be turned into insights.


This is where technology gives way to analytics — the science that helps us give meaning to the data. To the degree that we have meaningful insights, a story, a notion of what may be going on and why, or a model, we will be able to test this model through a prediction. The point here is not to be right, but to find better ways of being wrong. All models are wrong to some degree, but some are more useful than others.


Action

But even getting to the insights stage is not enough. As a matter of fact, the most interesting, captivating, and curious insights will go to waste without a solid plan to turn them into actions. AI can make predictions, and data can give us insights, but the “so what” part requires actions, and these actions need the relevant skills, processes, and change management. This is why talent plays such a critical role in unlocking (or indeed blocking) your digital transformation.


Result

In the final stage of the process, you can evaluate results or impact. Except this is not really the final step — after you evaluate results, you need to go back to the data. The results themselves become part of the new, richer, dataset, which will be augmented and improved with the findings of the process. In this iterative process or retroactive feedback loop, you enable your insights to become more predictive, more meaningful, and more valuable, which itself gives more value to the data. And in that process, you enhance and develop the people skills that are needed to produce a great synergy between humans and technology.


Conclusion

The critical part of digital transformation is not “digital” but “transformation.” Simply buying new technologies and collecting data will not help organizations transform. What is needed is a shift in mindset, culture, and talent, including upskilling and reskilling your workforce so that they are future-ready. Nobody is truly a leader if they are in charge and keep things as they are. Leadership is an argument with the past, it is the duty of the present leader to create a bridge between the past and the future. Digital transformation is the name we give to today's bridge.

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