The Overlooked Value of Digital Transformation

How Digital Transformation is freeing entrepreneurs from being tied to large, unmanageable capital investments

Alessandro F. Martins

Keywords: digital transformation, 4th Industrial Revolution, new economy business models, business/IT capabilities, technology-driven business enablement

In my experience as an Enterprise Architect and Digital Advisor, I have helped dozens of companies to better leverage their IT capabilities and align them with their business strategies. Throughout this period, there have already been several waves of approaches. Service Oriented Architecture, Enterprise Architecture, Business Process Management and Value Engineering are but a few of the main highlights.

In recent years, the watchwords are Digital Transformation. The definitive evolution and merging of business value and state-of-the-art technological capabilities. Cloud Computing, Internet of Things, Machine Learning, Big Data, DevOps, 3D Printing, Mobile Applications. At the first glance, it may seem just a new way to propose the same value: increase the importance of Information Technology in the process of business value creation. As a consultant working in large software solution providers, while it is true that the suggested practices are extremely valuable and are defining elements of competitive ability here, I felt that something was missing. That some differentiating element separated the previous takes on IT strategy from this new proposition.

However, in these last one year and three months acting and researching independently, I have arrived to some interesting conclusions. After being in contact with various startup companies, CTOs, new economy markets and customers, I realized one of the main overlooked characteristics of the so-called Digital Transformation.

For the first time in modern history, idea realization power could be decoupled from capital investment power. Let’s elaborate a bit more on the subject.

Embrace the 4th Industrial Revolution

By now, we all have already been exposed to the concept of the evolution of the business capabilities according to the upcoming of Industrial Revolutions. Here is the timeline:

Industrial Revolutions timeline

At each one of the stages, there was a new way of introducing technology to support the current business models, and to allow for completely new other models. In the first revolution, mechanization and steam engines opened possibilities for scaling up manufacturing, reducing costs with personnel, increasing productivity and making product distribution global.

In the second wave, electricity allowed for even more reduction costs and specialization. Factories didn’t have to manage their own power supplies. Energy was much more easily transportable through the shop floor to the machinery, giving rise to the organization of optimized assembly lines. On the other hand, compact, efficient internal combustion engines allowed for transportation to take place away from the railroads, reaching the urban centers directly and flooding the consumer market with the new products. Finally, electrical power itself was the basis of the new, large-scale, consumer-accessible communication media of radio, television and telephony.

With the advent of commercial computers in the mid-1960s, now was the turn of the intellectual side of the production process to be optimized. While back-end, operational, mostly manual processes such as payroll and inventory management were made less expensive and less staff-demanding, direct-value-creation processes such as shop floor automation, sourcing, sales planning/forecasting and order management could be automated, reducing TCO and being faster and less error-prone. Internal company communication was now reliable and documented. This is the birth of the Information Technology area. Not long after that, VLSI (Very Large Scale Integration) made possible a whole new world of cheaper, widespread consumer electronics.

Then in mid-to-late 1990s commercial Internet was spreading worldwide. Again, new possibilities were opening for companies such as e-commerce and global procurement and sourcing; but, differently from the previous stages, now interaction power was at the hands of end customers. Rather than being just at the final part of the value chain, customers could now be part of and influence the whole process. From e-banking to on-line shopping, now it was possible to choose and to eliminate the middlemen. This placed the customer in a more central role than ever before.

New generations of consumers were now getting used to the new way of doing things. Whichever company that made their lives easier when buying, performing business and day-to-day operations had the upper competitive hand. In the same fashion, enterprises were interacting with each other in ways which were ever faster, ever cheaper, and ever more reliable. Customers and companies would not accept nothing less than that anymore.

But it is not the whole story yet.

A New Way of Doing Things

Back in 1999, Salesforce released their first fully-web-enabled enterprise application, in a first glimpse of what would be called Cloud Computing, more specifically in the SaaS (Software as a Service) model.

Salesforce.com first website. Source: A Brief History of Salesforce.com

​Somewhere around mid 2000s and early 2010s, computer power began to get drastically less expensive. Server virtualization, a technology which had been around since the 1990s, was powering big Internet business players such as Google and Amazon.com, which had to have an optimal balance between TCO, massive processing power and storage and virtually infinite scalability. Those companies began to make exceeding computing power available for businesses to host their servers and applications for a contracted fee, which would be a function of the virtual server size and consumption, adapted to the needs of the application. The burden of taking care of providing and maintaining servers was now the third party’s responsibility, and companies’ IT departments could have their hands (and pockets) free to invest time and money in subjects more directly related to its value creation proposition.

On the software development side of the border, there was also evolution taking place. New paradigms of software construction, while being researched and sparsely applied in somewhat maverick projects, started getting the spotlight in big development initiatives. Carefully thought-out planning, which established well-drawn paths and written-in-stone deliverables for projects ahead of time, were not being able to fully meet the constantly changing business requirements. Agile software development methodologies such as Scrum, Crystal and Extreme Programming, with their iterative, “on-the-go” planning approach, got more prominent for being able to consistently deliver value to the customer, shifting the focus from the project itself to the product being used by the customer, compounding its value as it is incrementally deployed and made available in production. For reaping the most fruits from these new ways of working, new IT development capabilities were created.

The walls between the fiefdoms of Development and Operations were crumbling down. For faster time-to-market cycles, reliability on production and monitoring the whole software product lifecycle, the old-fashioned ways of almost complete separation of concerns between these domains would not be acceptable anymore. Methods for continuous integration, continuous delivery/deployment and collaborative development, as well as the tools which support them ─ collectively referred to as DevOps ─ were, as they currently are, becoming foundational for the accomplishment of these goals.

The use and dissemination of free and open source software solutions also plays a big role in this new world of possibilities. Once seen as second-grade solutions and of interest only to the academy and hacking hobbyists, it has been evolving through time with the support of numerous communities and with a new level of quality and user-friendliness. Now companies, small and big, could rely on software that is built and maintained by thousands of developers worldwide, whose code can be easily verified and improved and last, but not least, offers a cost-free alternative to a vast array of technical solutions, from containerization (such as Docker and Kubernetes) to machine learning libraries (such as NLTK and Tensorflow).

The provisioning of Cloud Computing services are available basically in three flavors. Companies may opt for just using virtual machines in the cloud, pretty much in a similar fashion as they would with an on-premises virtualized environment; this modality is called IaaS (Infrastructure as a Service). They can also rely on predefined, customer-ready solutions, where one has just to open an account and start using the application, such as ERP and CRM systems; this is the already mentioned SaaS (Software as a Service) model. In between these two models, in terms of functionality, there is one which I regard as especially important for the transformation of the business into digital. It is the PaaS (Platform as a Service) variation, where Cloud Computing service providers offer business service platforms over which their own business solutions can be created upon, such as Machine Learning, Big Data, Internet of Things hubs, collaborative development, serverless computing, video/audio streaming and many others. Its importance is twofold: by one side, it has the advantage over IaaS of not placing the concern of the maintaining and administering the environment ─ hardware, licenses, operating systems, databases, etc. ─ on the shoulders of the company; on the other hand, it provides the flexibility needed for the implementation of their strategic, differential business solutions in a faster time frame. We will return to this concept in the next section.

IaaS, PaaS and SaaS deployment models. Source: IaaS vs PaaS vs SaaS: Which Should You Choose?

​The items described above were foundational to the creation of a new way of provisioning business IT solutions. Information Technology now can respond faster, be less expensive and scale up in terms of processing power in an unprecedented manner. That’s great, but still too technical. Without a real business context, they look more like a solution searching for a problem. Still, there is a number of characteristics of Information Technology that set it apart from the prior revolutionary technologies.

Solutions à la Carte

Information Technology has a distinct nature from the mentioned infrastructural commodities of the 1st, 2nd and 3rd revolutions. It builds and evolves upon itself; more than that, in the wake of the Digital Revolution it blends itself together to the very fabric of the business, redefining its traditional models. Not only it provides the underpinnings of the next generation of its own solutions, like storage, processing power and networking, it allows ideas that were previously only theoretical to see the light of day as fully blown business functionalities. Let’s see three of the main examples, for the sake of brevity and focus on the scope of this article.

Big Data is one of the great advances in data science, revolutionizing how data is stored and utilized. It allows for the use of the whole set of data produced by companies activities (from sales to operational to social media to device generated data, and everything in between) and the extraction of meaningful, actionable, interrelated information, without having to rely to sampling and outlying data removal ─ as a matter of fact, in many cases the answer does lie on the extremes ─ in situations when the previous methods would be of no avail. Businesses can now can establish correlations between millions of user comments on, say, Facebook, and its sales performance with a timeliness and accuracy that would be simply impossible before.

Machine Learning is another data science breakthrough. Not only the business is able to research through past collected data to find patterns and make intelligent decisions, with Machine Learning it’s possible to learn from previously collected data and take actions using new, untouched data in an automated manner. Predictive analytics (ranging from industrial machinery wear-out time to credit scoring to customer behavioral analysis), product and service recommendation, computer vision, natural language interaction and robotization are but a few of the possibilities available.

Internet of Things is an aspect of the current transformation which is creating a new concept about the way the world relates to physical objects. They are not, at least potentially, unattended pieces of gadgetry which exist in a solitary existence and dedicated exclusively for their primary attributions anymore. Now these “things”, such as cell phones, vehicles, watches, wearables, clothing, household appliances, industrial machinery, street cameras, sensors of all sorts and a host of other devices can send signals through the Internet to local and remote servers, carrying data both about their behavior and its surroundings and to perform actions in their environment. This drives a whole new era of connectivity and interaction with our environment, from Smart Cities to Augmented Reality to Industrial IoT.

Adaptive Federated Machine Learning: An example of IoT, Machine Learning and Big Data solutions joining forces. Source: Avoiding industrial IoT digital exhaust with machine learning

This being said, it is noteworthy that the aforementioned transformative technologies have one thing in common: they demand humongous amounts of processing power and storage capacity (and, in some cases, network throughput). While big companies, with large investments in on-premises hardware capabilities and software licensing, development projects and operations, are the natural candidates for being the forerunners of these solutions, they are also very pressured by the business to provide the greatest value for the lowest possible Total Cost of Ownership. To be able to cater those functionalities, these companies must place (and some of them indeed have placed) quite a large sum to make them happen (a complete reframing of management mindset and business models is much more challenging than to manage the required technology investment, but it is outside of the scope of this article. For more information, please refer to the book Digital to the Core: Remastering Leadership for Your Industry, Your Enterprise, and Yourself, cited in the Bibliography).

Nothing new so far. But what is new is that the PaaS Cloud Computing service model and its offers allow companies to focus on the conception of the business applications that drive their digital transformation strategy. Their development teams can use the ready-made solutions offered by the cloud platforms and build upon them.

Let’s say that a company creates a strategic imperative to improve their relationship with their customers using a new trend on natural-language, voice-interactive chatbots; traditionally, they would have to develop the new natural language processing and speech recognition capabilities from scratch, or adapt a shelf software product, which is normally difficult to customize and has to be maintained on the company’s data center. In a PaaS-driven setup they could instead, for instance:

  • Build their new capabilities using algorithmic support platform services for natural language and speech recognition such as Amazon Web Services Comprehend/Transcribe, Microsoft Azure Cognitive Services or Google Cloud Speech API/Cloud Natural Language.
  • Deploy the main application as a serverless service using AWS Lambda, Azure Functions or Google Functions.
  • Use a managed cloud database service such as Amazon Aurora, Azure SQL Database or Google Cloud SQL.
  • Deploy the site itself on AWS Lightsail, Azure App Services or Google App Engine.

All of these solutions are tested-and-true platforms managed by the cloud providers, while enjoying the fault tolerance, scalability and availability that come by definition with the PaaS model, for a contracted fee, which may be “pay as you go” in most cases, getting it ready for production in a fraction of the time and cost of the traditional, on-the-premises approach.

The Power of the Idea

If this is great for large, incumbent companies, for startups this is tremendous. As the price for the cloud services can be adjusted as demand grows, many startups begin by proving their strategic imperatives with a scaled-down version of their products, iterate and improve as they gather more information and feedback from the market, and pivot/invest as needed. On the top of that, the Cloud-based paradigm promotes a significant staff cost reduction in terms of infrastructure, operations and development management. They can dream big and are restrained only by their imagination and ability to translate their customers’ and market needs into products and services.

Having at their disposal a computational “fire power” potentially on par with those of large, traditional enterprises, scaled just right for their needs, startups benefit from their very nature of enjoying small operations, being lightning fast and tuned to more aggressive risk-taking and pivoting abilities to compete head-to-head with them. To see how expressive the growth of the startup market is, check the increase in the venture capital volume, with some fluctuation, from 2009 to 2017 in the United States, which is mostly directed to new economy and innovation-related entrepreneurship (please note that the high numbers in 2000, followed by a stark decline in 2001/2002, correspond to the apex and the burst of the .com bubble, respectively):

Value of venture capital investment in the United States from 1995 to 2017 (in billion U.S. dollars) Source: Statista

And if this arrangement is powerful for the digital service providing companies themselves, be them traditional or startups, on the other hand it opens an ocean of new possibilities for their customers. As the new economy enterprises are becoming ever more customer-focused, being able to rapidly identify their needs and evolve through their innovation cycles, they demand and have at their disposal a plethora of digital products services, from which they can find something just right for their needs, be them end consumers or entrepreneurs. Especially when talking about enterprise customers, this takes partnerships to a whole new level, building new, innovative business models where communities of enterprises with smaller operations form ecosystems that encompasses whole value chains, occupying business niches once only available to large corporations. Some of those possible models for these companies are:

  • Freemium: offers basic service for free, with additional premium functions or services only available for a fee.
  • Sharing Economy: private customers share access to products or services with other private customers ─ a platform serves as intermediary.
  • Customer Data Monetization: the user gets the service (for free) and the company sells the data to a partner.
  • Open Sourcing: openness of a company’s research and innovation processes to external groups through licensing, joint entrepreneurship or branching.
  • “Prosumerism”: from “producers” + “consumers”, it denotes a collaborative, co-creation partnership where customers help design and create the products.
  • Long Tail: focus on selling a large number of niche products, each one with relatively infrequent sales.
  • Multi-sided Platforms: creates value primarily by enabling direct interactions between two (or more) distinct types of affiliated customers, providing each other network benefits.

Now the tide is flowing towards the type of agility and prompt market response displayed by the startups. Traditional companies not only are rethinking the way they are doing business, they are changing their business models altogether, and to deal with this innovation some of them are acknowledging that they must act as startups themselves. Many of those companies are creating “startup-like” organizations within their structures, incubating new economy businesses and developing a new managerial mindset, like MasterCard’s MasterCard Labs, Coca-Cola’s Lean Startup, General Electric’s FastWorks, Mondelez’s Mobile Futures and Tyco’s Growth and Innovation System.

True Entrepreneurship Democratization in the New Economy

In April, 2018, I had the pleasure of participating in the Alboom Summit, an event organized by their CEO Marcelo Moscato for their partners and customers. Alboom is an on-line digital platform which provides intelligent, 360º solutions for the photography, videomaking and visual arts industry, including specialized professional websites, on-line event photobooks, vertical Customer Relationship Management solutions, digital album layout lab and Augmented Reality.

Born as a startup, Alboom was presenting, along with the news about the industry and the new technologies for the sector, some interesting case studies. One of them caught my attention for being, for me, one of the most beautiful and representative examples of the transformational power of the Digital Revolution.

This is the story of a photographer who lives in a small town in the countryside of Northeastern Brazil. His job was to take pictures of tourists and passersby in the town’s central square. His earnings were not very expressive, but he had decades of experience and was known and appreciated by all the citizens. He wanted to be able to grow his little business, covering events like weddings and proms, but the infrastructure needed for such tasks is quite large. He would have to open a photo studio, invest in hardware and software to put together the album layout lab, hire people to deal with customer acquisition, among other time- and capital-consuming activities.

When he was presented to the Alboom solution suite, he found what he needed. Being a one-stop-shop for photography professionals, it provided him all the process backbone he needed to redefine his business model and grow his customer base. He created a fully-fledged virtual photo studio with all the necessary photo editing, layout, album creation and CRM functionalities for a fraction of the traditional cost demand, with an easily accessible front-end, and no worries with non-value-adding IT processes such as infrastructure maintenance and a dedicated IT team. He was then able to grow his business to his expectations and reach customers not only in his hometown, but also within the nearby region.

Small entrepreneurship has been around since the dawn of Humanity; what is changing now is that the possibility to establish exponentially growing businesses with the new forms of digitally-driven, spread-out, thriving partnerships.

Conclusion

Digital Transformation is about value chain and business model redefinition given the new technology capabilities, and one of its main corollaries is that it doesn’t always take a huge amount of upfront investment to provide products and services which can be razor-sharply tuned for both the consumer market and enterprise customers. These new economy firms take advantage of the “information technology backbone democratization” to provide customer-centricity, agility and new, affordable partnership models which, in turn, allows for a whole new perspective for new market entrants.

Large, incumbent companies are also surfing the wave of the new economy, rethinking and rebuilding their business praxes, sometimes even reproducing the startup business model within their own organizations. This is a shining sign that the new way of conducting business with technology is, instead of being the “tech fad of the day”, here to stay and evolve.

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About the author

Alessandro F. Martins is a Senior IT Business Strategist with 20 years of experience in IT and business in areas ranging from Enterprise Architecture and Business Process Management to Program and Project Management, C-Level consulting for large companies and advisory for Digital Innovation.

Sources and Bibliography

[1]: Keith D. Foote : A Brief History of Cloud Computing

[2]: Ben McCarthy: A Brief History of Salesforce.com

[3]: John Moavenzadeh: Survey of Industry Strategy Officers, Sept. 2015 in World Economic Forum 2015: The 4th Industrial Revolution: Reshaping the Future of Production

[4]: Jeanne Ross: 2017–20 Designing for Digital (Video)

[5]: David L. Rogers: David Rogers on The Digital Transformation Playbook (Video)

[6]: Cynthia Harvey: IaaS vs PaaS vs SaaS: Which Should You Choose?

[7]: Dean Hamilton: Avoiding industrial IoT digital exhaust with machine learning

[8]: Statista Website: Value of venture capital investment in the United States from 1995 to 2017

[9]: Scott Lenet: Analyzing the spectrum of corporate innovation from R&D to VC

[10]: Andreas Bubenzer-Paim: Innovation Isn’t Just For Startups: How Big Companies Can Tap Their Creative Power

[11]: Jennifer Alsever: Startups … inside giant companies

[12]: Alboom Website: https://www.alboompro.com/

[13]: Business Model Toolbox: https://bmtoolbox.net/

[14]: Andrei Hagiu, Julian Wright: Multi-Sided Platforms

[15]: Mark Raskino, Graham Waller: Digital to the Core: Remastering Leadership for Your Industry, Your Enterprise, and Yourself ─ ISBN 978–1–62956–073–1

[16]: Viktor Mayer-Schönberger, Kenneth Culkier: Big Data: A Revolution that will Transform how we Live, Work and Think ─ ISBN 978–0–544–00269–1

[17]: Ovidiu Vermesan, Peter Friess: Internet of Things: From Research and Innovation to Market Deployment ─ ISBN 978–87–93102–95–8

[18]: Pedro Domingos: The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World ─ ISBN 978–0–241–00454–8

[19]: Eric Ries: The Lean Startup: How Today’s Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses ─ ISBN 978–0–307–88789–4

[20]: Alexander Osterwalder, Yves Pigneur: Business Model Generation: A Handbook for Visionaries, Game Changers, and Challengers ─ ISBN 978–0–470–87641–1

[21]: Thomas H. Davenport, Marius Leibold, Sven Voelpel: Strategic Management in the Innovation Economy ─ ISBN 3–89578–263–7

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