A Quality-Based Methodology For Digital Projects

The Case of the Methodology of Convergence

Man is the only creature who refuses to be what he is.

Albert Camus

It is unanimously agreed that digital transformation will be more significant imperative for organizations in the short-term future. The Economist[1] recently noted that one of the most obvious consequences of the current pandemic crisis will be the infusion of data-enabled services into ever more aspects of life. In the context, contrary to traditional understanding, digital transformation is less about technology and more about people[2]. Everybody can acquire any technology, but the ability to rapidly adapt to a performant digital future depends on developing the next generation of skills, on accessing knowledge and on generating logical processing methodologies.

A few thoughts in brief

Projects are evolutionary frameworks that aim to build knowledge or to access dynamic mechanisms of “logical leaps,”[3] in order to reposition specific groups of people on superior levels of understanding the reality.

This definition can be applied to any project.  At the same time, projects are designed and executed by human beings. Therefore, the evolution of human behavior, the processing mechanisms of the human brain, education, technology, and social dynamics cannot be ignored if you want to create a reliable and sustainable project process. This is particularly true in the digital realm.

Isaac Newton’s personality thoroughly fascinates me; while I studied almost all of available information about Newton’ scientific becoming, I cannot entirely grasp his logical mechanisms. Still, Isaac Newton understood “the mystery” realm and applied a strict methodological framework to his entire work. His strict methodology was based on getting information, setting hypotheses, testing them, and eventually validating or invalidating the framework. The most compelling aspects arguably reside in his ability to build mathematical tools and algorithms in order to validate sets of hypotheses. As a matter of fact, this is the method in innovation project management. However, its mechanisms should be continuously adapted to the complex context that human beings live in and to the transformation of connections between humans and knowledge.

This article aims to explore the rationale behind failures of almost all digital ventures (projects and transformation). It is obvious that everything is related to the understanding and interpretation of theory of knowledge, as well as to aligning a possible methodology to the new behavioral status of the human being. The article proposes a structural rapport between qualitative and quantitative outcomes of any digital venture. Methodology of convergence is an approach based on developing visual artefacts and prototypes. It has been developed, tested, and improved by a group of business consultants, supported by organizational psychologists from Axiobit (an Irish incorporated consulting company, with operations in Europe). 

Digital projects: a brief (un)evolutionary history

The Standish Group is a group of experts supported by a platform[4] that mines information related to IT projects. In their 2018 CHAOS Report (a biannual study) their experts stated that, in 2017, only 32% of IT projects succeeded, whereas the rest 44% completely failed and 24% were challenged, because they did not meet all the agreed requirements. Furthermore, according to Standish Group, only 16.2% of projects were deemed successful by being completed on time and on budget, with all the promised functionality. A majority of projects, or 52.7%, were over cost, over time, and/or lacking promised functionality. That leaves 31.1% to be classified as failed, which means they were abandoned or cancelled.

In terms of digital transformation, the other digital venture we explore here, the Harvard Business Review states that in 2018, $1.4 trillion were reportedly invested in digital transformation initiatives; more than $900 billion were wasted for failed or irrelevant projects.[5] After a decade of efforts around digital transformation and huge allocated budgets, the results are insignificant; more than 70% of invested money was wasted because of inappropriate work effort.

Statistics show that things have been even worse. More and more projects have had a “challenged” status or have failed; further, the customer/user satisfaction has been dramatically decreasing with each implemented project or digital transformation venture. When considering this rather scary part of the story, the question that arises is the obvious WHY

The official causes of the declared failure of digital projects/ventures 

The literature focused on failures in the IT realm, and the reasons behind them, points to various causes. Most of them are biased due to specialists belonging to a specific profession that has been involved in part of the business process. I captured some of the opinions below:

  1. CIOs report that communication with business teams is the problem of any digital transformation.
  2. COOs report that project management is the main reason for any problem during the digital implementation process.
  3. Communication, complexity, or lack of skills among software providers are some other reasons mentioned in reports.

Many issues invoked by practitioners are symptoms and truisms. Therefore, we focus on three main reasons that can support the process of building a solution.

  1. The poor or inappropriate enterprise architecture (in terms of Zachman framework)[6] represents one of the issues. This is based on the lack of vision of integration, of goals, and of an objective plan based on common understanding; further, there could be a permanent dispute and misunderstanding around goals. Enterprise Architecture (EA) is a discipline which has evolved to structure the business and its alignment with IT systems. The Zachman Framework is an enterprise-based ontology and is a fundamental structure for Enterprise Architecture; this, in turn, provides a way of viewing an enterprise and its information systems from different perspectives and shows how the components of the enterprise are related. Enterprise Architecture is the process used by a business to make explicit representations of enterprise operations and resources, rather than rely on implicit notions or understand individual managers’ thinking.
  2. A gap between the capabilities required for business analysis (including pilot construction) and those required for scaling digital platforms. This may open the discussion about gaps between the existent business analysis methods, software implementation, and live actions.
  3. Reasons rooted in the decision latency theory.[7] The Standish Group has studied this decision latency for over a decade and has concluded that a project will create one decision for every $1,000 in project labor cost. If it takes many hours to make a decision, there is probably a lot of overhead involved and it would be difficult to stay within time and budget. Simply reducing decision latency can improve a project performance by 25% (The Standish Group).

All the reasons presented above are valuable approaches for creating an understanding related to the failure of IT projects. However, after more than 30 years in the field of designing, managing, and implementing IT projects, I can argue that majority of reasons behind failures are rooted in the changes suffered by the educational paradigm, as well as in the impact of the internet on people’s minds and related knowledge behaviors. While knowledge is accessed differently, business analysts are using the same methods for processing information and exposing the results.

The bridge over the gap: business analysis

In an HBR article published in 2013, Donald Marchand and Joe Peppard argue  that “organizations that want employees to be more data oriented in their thinking and decision making must train them to know when to draw on data and how to frame questions, build hypotheses, conduct experiments, and interpret results.” [8]  This is a reaction to IT problems. New categories of thinkers are necessary in order to improve delivery models. They will not just write code. Rather, they will include new thinking models within the methodology of project implementation.

We believe that organizations involved in any digital venture should focus on the business analysis methodology as a thinking model. This is, in fact, crucial and it is the moment when the quality of outcomes is developed. Our approach is that in a digital project, business analysis methodology sets the rules up and creates the language to be used in the project. Therefore, explorations among methodologies, as well as capabilities are fundamentally important.

There are three core business analysis methods:

  1. ITIL produces text outcomes based on endless interviews sessions that can lead to confusing narratives.
  2. Agile Method aims to combine business analysis and IT skills. The issue, however, is that the budgets of these projects exceed initial estimations with more than 100%.
  3. Design Thinking represents the business design based on outstanding idea and a generous approach. However, the implementation of theory (design thinking methodology) has its own share of problems. It may last forever, and attempts to shorten the processes (design sprint) are focused more on fail fast and often more than on the design of sustainable projects outcomes.

It is obvious that today, ITIL and other text-based methods do not work because of the shift in people’s attention span and behaviors toward knowledge. Further, the Agile Method does not achieve promised results because of the overuse of professional languages and continuous conflicts. Lastly, Design Thinking is still lacking in terms of a sustainable approach. For sure, all of them have problems in providing reliable quality and quantity benefits. The Scoping Framework is never included and the quality aspects are never provided.

Due to internet-driven transformations, using visual instruments for project communication has become critical nowadays. It is a required shift in approaching business analysis from the text-based traditional model to an exclusive visual model. In this new knowledge paradigm, only a visual model of business analysis allows for a common understanding and a communication platform between business people and IT experts.

Axiobit consultants have defined, designed, implemented, and tested a visual-based methodology named the methodology of convergence. This methodological approach has been successfully applied in order to implement digital projects and digital transformation requirements. It bridges the gap between management consulting and software execution by creating a common visual language and process for business people and technologists.

The optimal delivery model for digital projects

What does the methodology of convergence entail? This methodology proposes a new logic for the quality/quantity of perceived benefits for the duration of the project. In this context, this methodological framework sets up a different qualitative framework of the project. The scope is to ensure the process in which users can get more than 90% of the planned qualitative benefits during the initial stages of the project delivery approach. This maximization of qualitative outcomes creates the managerial circumstances for reaching the planned scope at the end of IT implementation.

Methodology of Convergence vs. ITIL based methodologies

The figure above presents the differences between the quality/quantity rapport in the case of methodology of convergence and the classical implementation approach driven by ITIL business analysis method.

Concluding notes

Due to the increasing complexity of projects, changes in the structures for knowledge development, the establishment of new education systems and related behavioral framework, as well as implementation methodologies will be crucial, especially in the digital realm. The shift from the rather irrelevant text-based analysis documents to a visually-driven approach as a knowledge creation tool is fundamental.

The methodology of convergence is the result of over 15 years of concentrated effort of the Axiobit team of consultants.  The rapport between quality/quantity benefits has been recently tested for more than 10 complex projects for which the principles of methodology of convergence have been applied.

[1] https://www.economist.com/briefing/

[2] Bencky Frankiewicz and Tomas Chamorro-Premuzic. Digital Transformation Is About Talent, Not Technology. May 06, 2020. Harvard Business Review.

[3] Roger Martin, The Design of Business (Harvard Business Press, November 2009).

[4] See https://www.standishgroup.com

[5] Behnam Tabrizi, Ed Lam, Kirk Girard and Vernon Irvin, “Digital Transformation Is Not About Technology,” Harvard Business Review, 13 March 2019.

[6] For more see www.zachman.com.  

[7] On this see Henny Portman, “Review Chaos Report 2018,” Blog Entry, 3 February 2020. Accessed at Portman expands on the Decision Latency Theory, by claiming that“the value of the interval is greater than the quality of the decision. Or with other words, if you want to improve project success, you have to speed-up your decision-making.”

[8] Donald A. Marchand and Joe Peppard, “Why Your IT Project Needs a Cognitive Scientist,” Harvard Business Review, 17 January 2013.  


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