Analytics insight from biological systems

Network structures and information signaling patterns gleaned from the biological world may provide more than metaphoric insight into business analytics.  With deeper insight into biology from an information systems perspective, patterns for optimal analytics architectural solution patterns may be derived.

Extrapolating from cellular signaling network research, organizations can similarly be studied using Social Network Analysis (SNA) and Multi-Agent Simulation (MAS).  Optimal biological signaling mechanism principles might then be cross-associated to design organic-efficient organizational business analytics communication networks.

If we envision firms as systems that interact within containing markets, we can model a number of strategies for communicating and managing information for decision making based on seeing firms as organisms within ecological systems.  This is particularly the case in light of steadily increasing insight into the intricate world of biology when organisms are studied as information processing mechanisms.

Network Visualization

In the past decade, advances have been made in terms of understanding sophisticated intracellular signaling processes:  the mechanisms by which cells communicate among themselves to coordinate mass behavior such as tissue differentiation during gestation or immune responses.  As well, there have been increasing insights into how microbes (i.e. bacteria and viruses) play a symbiotic role in signaling and regulating organisms.  Indeed, scientists are beginning to discover that portions of our genetic code play a role in orchestrating an interaction with an elaborate ecosystem of symbiotic microbes.

A major theory concerning the evolution of Eukaryotic cells, and thus complex organisms, is that cellular organelles such as mitochondria were originally autonomous microbes that developed closer-and-closer symbiotic relationships with primitive prokaryotic cells.  Similarly, with the notion of seeing a firm as a rich collaborative ecosystem of subsidiaries, partners, contractors, outsourcing providers, and consultants, we can also see business in a similar light.  Indeed, from one perspective, firms start as primitive systems which assimilate symbiotic functions in order to grow, or, divest and diversify complexity in order to meet changing conditions.

Where am I going with this, you begin to ask?  To bring things ‘back to earth’, with some imagination we can draw a relationship between the intricate communication and cooperation mechanisms in nature with the role of analytics in business.  In particular, analytics is a mechanism for processing and passing information in order to coordinate group decision making.  From this perspective, there are different types and levels of ‘signaling’ (sharing purposeful information) and communication, just as in the biological word.  For instance, the functional workgroup level has analytical mechanisms that are quite similar to inter-cellular signaling mechanisms:  information from the outside business and market (the organism and environment) spawns analysis which leads to discussions and decisions as to how to react, much as a cellular membrane triggers internal signals causing cellular processes to react to changing external factors.

As well, functional workgroups signal one another concerning how best to coordinate to stave off external threats, or to react to changes in the market, such as taking advantage of a new product opportunity.  This is quite similar to how cells, for instance, catalyze one another via intracellular signaling to provoke active responses such as priming the organism to search for food.  These mechanisms can also fail in business, such as when a workgroup ‘malfunctions’ and starts issuing faulty guidance.  The extreme example of this in the biological world is when a genetic malfunction causes uninhibited cellular reproduction and a failure to ‘decommission’ old cells (to provoke cell death).  We know this malfunction as the scourge of cancer.

Likewise, in the business world it can be likened to uninhibited expansion and mergers, provoked by unconstrained and ill-informed leadership, which leads to (or threatens) the bankruptcy of the firm (i.e. Enron, WorldCom, Vivendi).  Or, this can occur when internal controls are violated, such as in trading scandals when ‘rogue traders’ assume outsized market risk (i.e. Barings Bank, Société Générale, J P Morgan, etc.).

Now, this is all a ‘nice and good’ metaphor, but what is the tangible take away?  I would charge that the practical lesson we can take from the world of biology is that ‘analytics’ in the biological world is designed in a very practical fashion to balance four competing phenomenon between the organism and changing environmental dynamics:

1. continuity via ongoing operations via strict governance,
2. ability to adapt to changing conditions via exceptions,
3. constant assessments of information coming from various scales and levels in terms of importance, and
4. balancing reactive communications and actions to the appropriate levels.

From this, highly simplistic view, one begins to see that businesses, when seen as ‘sense making organisms’ which are actively assessing information, making decisions (processing information), communicating (sharing information), and acting (reacting to information), a well-designed analytical ‘nervous system’ or infrastructure is not only crucial, it is the very glue that ensures the operating firm is a ‘going concern’.

While this may seem a stretch, or worse, a misapplied metaphor, there is a solid foundation in business scholarship for examining firms as types of organisms.  From systems theory, there is the notion that firms are indeed pseudo-organic entities: they act purposefully, consume, produce waste, and even reproduce.  While having a longer timeframe than humans, they also, in most cases evidence a life cycle of aging, and, most all firms, as far as we understand it, ultimately die (or transform or are consumed).

The dominant theories of the firm, the Resource Based View (RBV) and Transactional Cost Economics (TCE) can even be framed in terms of a biological understanding of the firm.  The Knowledge Based View (KBV) of the firm, an outgrowth of the RBV, sees the firm as a bundle of unique knowledge (i.e. skills) and decision making mechanisms.

This latter model is of particular interest to analytics strategy and planning:  a powerful analytics solution is one which optimizes knowledge sharing at the correct levels in order to optimize decision making efficiency.  When we return to our learning from the biological world, it is quite similar –  firm information sharing and decision making must:

1. foremost, provide a risk-balanced continuity function,
2. provide mechanisms to respond to changing conditions and new or unusual information (in terms of deciding to react or not),
3. allow for multi-leveled information acquisition, processing, and sharing without overwhelming the firm with data processing overhead (i.e. to take a strict efficiency perspective in terms of appropriate information sharing and decision making at the correct levels), and
4. structuring communications so that hierarchies of efficiency are respected while paradoxically also providing some leeway such that lower levels gain privileged information when needed and higher levels are confronted with detailed ‘on-the-ground’ information in exceptional cases.

Thus, to bring this conceptual exercise to a close, we can assert that managing and designing analytics solutions must be a highly integrated activity which understands the operating firm as a complex whole within a dynamic environment, and thus which takes a biological view of efficient information and decision management, as appropriate to roles and governance processes.

To conclude with a suggestion for a potentially fruitful research agenda: guidance from deeper understandings of biological systems (i.e. cellular signaling networks) could provide ‘organic-efficient’ signaling network models, or patterns, which could be cross-applied in the design and deployment of organizational analytics systems (as information sharing mechanisms) in conjunction with decision making structures.  Extrapolating from cellular signaling network research, organizations can be formally studied using Social Network Analysis (SNA) and Multi-Agent Simulation (MAS).  Optimal biological signaling mechanism patterns could then be cross-associated to suggest organic-efficient business analytics communication network patterns (decision making and information sharing designs).