On the previous article about A Social Platform Definition, I presented a framework about the elements of such Platform. The following articles I will expand each of the layers. This one is dedicated to the Search and Analysis component.
Before we dig into the component content, I would like to bring some background about its significance.
An important introduction to Social Network Analysis
Last week, I had a meeting with a college headmaster to figure it out if there was alignment between me and the headmaster’s expectations and values regarding how students will be prepared for the forthcoming decades, taking into consideration the shift we are facing regarding work patterns, information overload and technology disruption.
The institution is catholic oriented and have strong roots with the Catholic Church. Let me say that I do not consider myself catholic as by the book definition, but probably I’m more catholic that others that go to the church every day and don’t have ethics and values. This means I did not choose to evaluate the institution because it is linked with my religious beliefs, but because they are the best institution according to the evaluation program that was created by the Portuguese Government some years ago.
During the interaction with the headmaster (a religious person), we talked about two vectors I introduced into the conversation: values and student preparation for the forthcoming decades (how we prepare people to interpret and act on information and how they improve reasoning in the knowledge era). When the headmaster was talking about values, introduced an amazing characteristic from the human being point of view (sorry by the religious background I’m putting into the discussion but I consider that it’s worth for the sake of clarification about social network analysis).
God created humans as a single and unique entity. There are no equal human beings (even perfect twins) and God created animals and all the other living organisms differently that belong to a system (let us call planet earth that belongs to other system called the universe) made by diversity in constant balance and adaptation.
This point of view opens and reinforces the main characteristic that we humans who belong to families, communities, organizations, arrangements that are part of a super system called the universe whose foundations rely on the top of diversity and complexity, not on standardization. Somehow, we keep pushing in into an ordered regime because it is much simpler to understand concepts, interactions and our own existence in an controlled manner rather than in a complex one.
The world is complex and we cannot change that as much we would like to
Ashby’s law teaches us that any system must match the complexity of its elements in an actively and adaptive way to survive and prosper.
In addition, Ashby pointed out other important conclusion: any attempt to limit part of the variety (because it is considered noise by the humans) that constitutes the system will lead that the system will lose the capacity to adapt and lead into implosion. This reflects in the way some business processes cannot respond to exception handling, because the misleading adaptation consists into fighting against the process model rather than adapt to changing executing conditions. If we consider a different organization layer like strategy management, think when external signs are ignored that can lead the organization to bankruptcy or financial loss.
In the social era we are being misleading about what is Social Network Analysis, one of the reasons it is about the semantics, the meaning of Social, broadly understood connected people, but a Social Network is much more than that. In very general terms a Social Network can be described as a graph whose nodes (vertices) identify the elements of the system. The set of connecting links (edges) represents the presence of a relation or interaction among these elements. With such a high level of generality it is easy to perceive that a wide array of systems can be approached within the framework of network theory .
Social Networks can be made of Organizational Units, Business Units, Roles and Functions, Individuals, Data, Technology consumption (what part of the IT solution is used), Technology interaction (how IT solutions communicate), Business Processes, Traffic, Biological, Physics (these last two categories lend so much of its properties to business analysis) etc.
All the networks are self organizing systems, but there are important patterns that can be identified anywhere from the self organization, despite randomness, patterns are critical for humans to understand how data can be transformed into information, that ultimately is transformed into knowledge used to understand the behavior of such networks (see note below).
Self-organization refers to the fact that a system’s structure or organization appears without explicit control or constraints from outside the system. In other words, the organization is intrinsic to the self-organizing system and results from internal constraints or mechanisms, due to local interactions between its components  (that can be put on top of a business process). These interactions are often indirect thanks to the environment. The system dynamics modifies also its environment, and the modifications of the external environment influence in turn the system, but without disturbing the internal mechanisms leading to organization  (think for example social interaction with customers that change the course of the business process, or events during product research and development that makes to alter the characteristics and features). The system evolves dynamically either in time or space, it can maintain a stable form or can show transient phenomena. In fact, from these interactions, emergent properties appear transcending the properties of all the individual sub-units of the system  (and these emergent properties are the ones than be understood using a combined set of discovering techniques like process mining, social network analysis and data mining).
I tend to agree that with argument that looking for patterns into a complex landscape is a waste of time for the reason that into complex domains any attempt to take a snapshot is a distorted version of the reality. Nevertheless, the objective of patterns discovery and understanding is not to predict behavior but to infer trends or in Jason Silva’s words “to understand is to perceive patterns” http://vimeo.com/34182381 .
The objective of Social Network Analysis is not to predict outcomes, but to understand, to construct knowledge around emergence self-organization and adaptation in scenarios like for example decision making or distributed systems that are becoming real enterprise challenges as business complexity and interactions grow exponentially.
Huge amount of data is being recorded today (see image bellow) that allow us to make discovery and analysis of complex interactions. The argument that does exist and it cannot be done only fits in a category like airport security information that typically relies on paper.
The Internet of Things – new infographics – Source: Bosch
On part two, I will explore techniques to analyze social networks.
On Fastcompany’s article: “IBM’s Watson Is Learning Its Way To Saving Lives” is said that “Watson is poised to change the way human beings make decisions about medicine, finance, and work” […] “They believed Watson could help doctors make diagnoses and, even more important, select treatments”. I argue that IT can help humans to process and show data to help humans to make better decisions. Last weekend, a family member stood at a hospital during a day making analysis on what could have been a heart attack. Diagnosis were automatic: they make a 1 minute electrocardiogram (considered insufficient by experts) combined among others with measurement of troponin levels (diagnostic marker for various heart disorders). The results found correlation between the results and the family member was told a cardiologist should immediately see him. When the cardiologist looked to the results he said that there was no correlation at all, the results of the electrocardiogram were insufficient and the troponin level was 1/100 of the danger threshold and was unlikely to raise suddenly. In the end the diagnostic was wrong and the cause of sickness was nervous system. This evidence like many others should make us think as Einstein said: “Information is not knowledge, the only source of knowledge is experience”; I would add information cannot be stored.
 Preliminaries and Basic Definitions in Network Theory – Guido Caldarelli and Alessandro Vespignani – Large Scale Structure and Dynamics of Complex Networks: From Information Technology to Finance and Natural Science – World Scientific Publishing Company – ISBN 978-9812706645
 Self-Organisation: Paradigms and Applications – Giovanna Di Marzo Serugendo, Noria Foukia, Salima Hassas, Anthony Karageorgos, Soraya Kouadri Mostéfaoui, Omer F. Rana, Mihaela Ulieru, Paul Valckenaers, and Chris Van Aart – Engineering Self-Organising Systems – Springer – ISBN – 3-540-21201-9