Category Archives: Management Models

Model Thinking

Model Framework

The fundamental tenet of theory is the concept of “empiria“. Empiria refers to our observations. Based on observations, scientists and researchers posit a theory – it is part of scientific realism.

A scientific model is a causal explanation of how variables interact to produce a phenomenon, usually linearly organized.  A model is a simplified map consisting of a few, primary variables that is gauged to have the most explanatory powers for the phenomenon being observed.  We discussed Complex Physical Systems and Complex Adaptive Systems early on this chapter. It is relatively easier to map CPS to models than CAS, largely because models become very unwieldy as it starts to internalize more variables and if those variables have volumes of interaction between them. A simple analogy would be the use of multiple regression models: when you have a number of independent variables that interact strongly between each other, autocorrelation errors occur, and the model is not stable or does not have predictive value.

thinking

Research projects generally tend to either look at a case study or alternatively, they might describe a number of similar cases that are logically grouped together. Constructing a simple model that can be general and applied to many instances is difficult, if not impossible. Variables are subject to a researcher’s lack of understanding of the variable or the volatility of the variable. What further accentuates the problem is that the researcher misses on the interaction of how the variables play against one another and the resultant impact on the system. Thus, our understanding of our system can be done through some sort of model mechanics but, yet we share the common belief that the task of building out a model to provide all of the explanatory answers are difficult, if not impossible. Despite our understanding of our limitations of modeling, we still develop frameworks and artifact models because we sense in it a tool or set of indispensable tools to transmit the results of research to practical use cases. We boldly generalize our findings from empiria into general models that we hope will explain empiria best. And let us be mindful that it is possible – more so in the CAS systems than CPS that we might have multiple models that would fight over their explanatory powers simply because of the vagaries of uncertainty and stochastic variations.

Popper says: “Science does not rest upon rock-bottom. The bold structure of its theories rises, as it were, above a swamp. It is like a building erected on piles. The piles are driven down from above into the swamp, but not down to any natural or ‘given’ base; and when we cease our attempts to drive our piles into a deeper layer, it is not because we have reached firm ground. We simply stop when we are satisfied that they are firm enough to carry the structure, at least for the time being”. This leads to the satisficing solution: if a model can choose the least number of variables to explain the greatest amount of variations, the model is relatively better than other models that would select more variables to explain the same. In addition, there is always a cost-benefit analysis to be taken into consideration: if we add x number of variables to explain variation in the outcome but it is not meaningfully different than variables less than x, then one would want to fall back on the less-variable model because it is less costly to maintain.

problemsol

Researchers must address three key elements in the model: time, variation and uncertainty. How do we craft a model which reflects the impact of time on the variables and the outcome? How to present variations in the model? Different variables might vary differently independent of one another. How do we present the deviation of the data in a parlance that allows us to make meaningful conclusions regarding the impact of the variations on the outcome? Finally, does the data that is being considered are actual or proxy data? Are the observations approximate? How do we thus draw the model to incorporate the fuzziness: would confidence intervals on the findings be good enough?

Two other equally other concepts in model design is important: Descriptive Modeling and Normative Modeling.

Descriptive models aim to explain the phenomenon. It is bounded by that goal and that goal only.

There are certain types of explanations that they fall back on: explain by looking at data from the past and attempting to draw a cause and effect relationship. If the researcher is able to draw a complete cause and effect relationship that meets the test of time and independent tests to replicate the results, then the causality turns into law for the limited use-case or the phenomenon being explained. Another explanation method is to draw upon context: explaining a phenomenon by looking at the function that the activity fulfills in its context. For example, a dog barks at a stranger to secure its territory and protect the home. The third and more interesting type of explanation is generally called intentional explanation: the variables work together to serve a specific purpose and the researcher determines that purpose and thus, reverse engineers the understanding of the phenomenon by understanding the purpose and how the variables conform to achieve that purpose.

This last element also leads us to thinking through the other method of modeling – namely, normative modeling. Normative modeling differs from descriptive modeling because the target is not to simply just gather facts to explain a phenomenon, but rather to figure out how to improve or change the phenomenon toward a desirable state. The challenge, as you might have already perceived, is that the subjective shadow looms high and long and the ultimate finding in what would be a normative model could essentially be a teleological representation or self-fulfilling prophecy of the researcher in action. While this is relatively more welcome in a descriptive world since subjectivism is diffused among a larger group that yields one solution, it is not the best in a normative world since variation of opinions that reflect biases can pose a problem.

How do we create a representative model of a phenomenon? First, we weigh if the phenomenon is to be understood as a mere explanation or to extend it to incorporate our normative spin on the phenomenon itself. It is often the case that we might have to craft different models and then weigh one against the other that best represents how the model can be explained. Some of the methods are fairly simple as in bringing diverse opinions to a table and then agreeing upon one specific model. The advantage of such an approach is that it provides a degree of objectivism in the model – at least in so far as it removes the divergent subjectivity that weaves into the various models. Other alternative is to do value analysis which is a mathematical method where the selection of the model is carried out in stages. You define the criteria of the selection and then the importance of the goal (if that be a normative model). Once all of the participants have a general agreement, then you have the makings of a model. The final method is to incorporate all all of the outliers and the data points in the phenomenon that the model seeks to explain and then offer a shared belief into those salient features in the model that would be best to apply to gain information of the phenomenon in a predictable manner.

business model

There are various languages that are used for modeling:

Written Language refers to the natural language description of the model. If price of butter goes up, the quantity demanded of the butter will go down. Written language models can be used effectively to inform all of the other types of models that follow below. It often goes by the name of “qualitative” research, although we find that a bit limiting.  Just a simple statement like – This model approximately reflects the behavior of people living in a dense environment …” could qualify as a written language model that seeks to shed light on the object being studied.

Icon Models refer to a pictorial representation and probably the earliest form of model making. It seeks to only qualify those contours or shapes or colors that are most interesting and relevant to the object being studied. The idea of icon models is to pictorially abstract the main elements to provide a working understanding of the object being studied.

Topological Models refer to how the variables are placed with respect to one another and thus helps in creating a classification or taxonomy of the model. Once can have logical trees, class trees, Venn diagrams, and other imaginative pictorial representation of fields to further shed light on the object being studied. In fact, pictorial representations must abide by constant scale, direction and placements. In other words, if the variables are placed on a different scale on different maps, it would be hard to draw logical conclusions by sight alone. In addition, if the placements are at different axis in different maps or have different vectors, it is hard to make comparisons and arrive at a shared consensus and a logical end result.

Arithmetic Models are what we generally fall back on most. The data is measured with an arithmetic scale. It is done via tables, equations or flow diagrams. The nice thing about arithmetic models is that you can show multiple dimensions which is not possible with other modeling languages. Hence, the robustness and the general applicability of such models are huge and thus is widely used as a key language to modeling.

Analogous Models refer to crafting explanations using the power of analogy. For example, when we talk about waves – we could be talking of light waves, radio waves, historical waves, etc.  These metaphoric representations can be used to explain phenomenon, but at best, the explanatory power is nebulous, and it would be difficult to explain the variations and uncertainties between two analogous models.  However, it still is used to transmit information quickly through verbal expressions like – “Similarly”, “Equivalently”, “Looks like ..” etc. In fact, extrapolation is a widely used method in modeling and we would ascertain this as part of the analogous model to a great extent. That is because we time-box the variables in the analogous model to one instance and the extrapolated model to another instance and we tie them up with mathematical equations.

 

The Law of Unintended Consequences

The Law of Unintended Consequence is that the actions of a central body that might claim omniscient, omnipotent and omnivalent intelligence might, in fact, lead to consequences that are not anticipated or unintended.

The concept of the Invisible Hand as introduced by Adam Smith argued that it is the self-interest of all the market agents that ultimately create a system that maximizes the good for the greatest amount of people.

Robert Merton, a sociologist, studied the law of unintended consequence. In an influential article titled “The Unanticipated Consequences of Purposive Social Action,” Merton identified five sources of unanticipated consequences.

Ignorance makes it difficult and impossible to anticipate the behavior of every element or the system which leads to incomplete analysis.

Errors that might occur when someone uses historical data and applies the context of history into the future. Linear thinking is a great example of an error that we are wrestling with right now – we understand that there are systems, looking back, that emerge exponentially but it is hard to decipher the outcome unless one were to take a leap of faith.

Biases work its way into the study as well. We study a system under the weight of our biases, intentional or unintentional. It is hard to strip that away even if there are different bodies of thought that regard a particular system and how a certain action upon the system would impact it.

Weaved with the element of bias is the element of basic values that may require or prohibit certain actions even if the long-term impact is unfavorable. A good example would be the toll gates established by the FDA to allow drugs to be commercialized. In its aim to provide a safe drug, the policy might be such that the latency of the release of drugs for experiments and commercial purposes are so slow that many patients who might otherwise benefit from the release of the drug lose out.

Finally, he discusses the self-fulfilling prophecy which suggests that tinkering with the elements of a system to avert a catastrophic negative event might in actuality result in the event.

It is important however to acknowledge that unintended consequences do not necessarily lead to a negative outcome. In fact, there are could be unanticipated benefits. A good example is Viagra which started off as a pill to lower blood pressure, but one discovered its potency to solve erectile dysfunctions. The discovery that ships that were sunk became the habitat and formation of very rich coral reefs in shallow waters that led scientists to make new discoveries in the emergence of flora and fauna of these habitats.

unitended con ahead

If there are initiatives exercised that are considered “positive initiative” to influence the system in a manner that contribute to the greatest good, it is often the case that these positive initiatives might prove to be catastrophic in the long term. Merton calls the cause of this unanticipated consequence as something called the product of the “relevance paradox” where decision makers thin they know their areas of ignorance regarding an issue, obtain the necessary information to fill that ignorance gap but intentionally or unintentionally neglect or disregard other areas as its relevance to the final outcome is not clear or not lined up to values. He goes on to argue, in a nutshell, that unintended consequences relate to our hubris – we are hardwired to put our short-term interest over long term interest and thus we tinker with the system to surface an effect which later blow back in unexpected forms. Albert Camus has said that “The evil in the world almost always comes of ignorance, and good intentions may do as much harm as malevolence if they lack understanding.”

An interesting emergent property that is related to the law of unintended consequence is the concept of Moral Hazard. It is a concept that individuals have incentives to alter their behavior when their risk or bad decision making is borne of diffused among others. For example:

If you have an insurance policy, you will take more risks than otherwise. The cost of those risks will impact the total economics of the insurance and might lead to costs being distributed from the high-risk takers to the low risk takers.

Unintended-Consequences cartoon

How do the conditions of the moral hazard arise in the first place? There are two important conditions that must hold. First, one party has more information than another party. The information asymmetry thus creates gaps in information and that creates a condition of moral hazard. For example, during 2006 when sub-prime mortgagors extended loans to individuals who had dubitable income and means to pay. The Banks who were buying these mortgages were not aware of it. Thus, they ended up holding a lot of toxic loans due to information asymmetry. Second, is the existence of an understanding that might affect the behavior of two agents. If a child knows that they are going to get bailed out by the parents, he/she might take some risks that he/she would otherwise might not have taken.

To counter the possibility of unintended consequences, it is important to raise our thinking to second-order thinking. Most of our thinking is simplistic and is based on opinions and not too well grounded in facts. There are a lot of biases that enter first order thinking and in fact, all of the elements that Merton touches on enters it – namely, ignorance, biases, errors, personal value systems and teleological thinking. Hence, it is important to get into second-order thinking – namely, the reasoning process is surfaced by looking at interactions of elements, temporal impacts and other system dynamics. We had mentioned earlier that it is still difficult to fully wrestle all the elements of emergent systems through the best of second-order thinking simply because the dynamics of a complex adaptive system or complex physical system would deny us that crown of competence. However, this fact suggests that we step away from simple, easy and defendable heuristics to measure and gauge complex systems.

Emergent Systems: Introduction

The whole is greater than the sum of its parts. “Emergent properties” refer to those properties that emerge that might be entirely unexpected. As discussed in CAS, they arise from the collaborative functioning of a system. In other words, emergent properties are properties of a group of items, but it would be erroneous for us to reduce such systems into properties of atomic elements and use those properties as binding elements to understand emergence Some common examples of emergent properties include cities, bee hives, ant colonies and market systems. Out thinking attributes causal effects – namely, that behavior of elements would cause certain behaviors in other hierarchies and thus an entity emerges at a certain state. However, we observe that a process of emergence is the observation of an effect without an apparent cause. Yet it is important to step back and regard the relationships and draw lines of attribution such that one can concur that there is an impact of elements at the lowest level that surfaces, in some manner, at the highest level which is the subject of our observation.

emergent

Jochenn Fromm in his paper “Types and Forms of Emergence” has laid this out best. He says that emergent properties are “amazing and paradox: fundamental but familiar.” In other words, emergent properties are changeless and changing, constant and fluctuating, persistent and shifting, inevitable and unpredictable. The most important note that he makes is that the emergent property is part of the system and at the same time it might not always be a part of the system. There is an undercurrent of novelty or punctuated gaps that might arise that is inexplicable, and it is this fact that renders true emergence virtually irreducible. Thus, failure is embodied in all emergent systems – failure being that the system does not behave according to expectation. Despite all rules being followed and quality thresholds are established at every toll gate at the highest level, there is still a possibility of failure which suggests that there is some missing information in the links. It is also possible that the missing information is dynamic – you do not step in the same water twice – which makes the study to predict emergent systems to be a rather difficult exercise. Depending on the lens through which we look at, the system might appear or disappear.

emergent cas

There are two types of emergence: Descriptive and Explanatory emergence. Descriptive emergence means that properties of wholes cannot be necessarily defined through the properties of the pasts. Explanatory emergence means laws of complex systems cannot be deduced from the laws of interaction of simpler elements that constitute it. Thus the emergence is a result of the amount of variety embodied in the system, the amount of external influence that weights and shapes the overall property and direction of the system, the type of resources that the system consumes, the type of constraints that the system is operating under and the number of levels of sub-systems that work together to build out the final system. Thus, systems can be benign as in the system is relatively more predictable whereas a radical system is a material departure of a system from expectation. If the parts that constitute a system is independent of its workings from other parts and can be boxed within boundaries, emergent systems become more predictable. A watch is an example of a system that follows the different mechanical elements in a watch that are geared for reading the time as it ultimate purpose. It is a good example of a complex physical system. However, these systems are very brittle – a failure in one point can cascade into a failure of the entire system. Systems that are more resilient are those where the elements interact and learn from one another. In other words, the behavior of the elements excites other elements – all of which work together to create a dance toward a more stable state. They deploy what is often called the flocking trick and the pheromone trick. Flocking trick is largely the emulation of the particles that are close to each other – very similar to the cellular automata as introduced by Neumann and discussed in the earlier chapter. The Pheromone trick reflects how the elements leave marks that are acted upon as signals by other elements and thus they all work together around these signal trails to behave and thus act as a forcing function to create the systems.

emerg strategy

There are systems that have properties of extremely strong emergence. What does Consciousness, Life, and Culture have in common? How do we look at Climate? What about the organic development of cities? These are just some examples of system where determinism is nigh impossible. We might be able to tunnel through the various and diverse elements that embody the system, but it would be difficult to coherently and tangibly draw all set of relationships, signals, effectors and detectors, etc. to grapple with a complete understanding of the system. Wrestling a strong emergent system would be a task that might even be outside the purview of the highest level of computational power available. And yet, these systems exist, and they emerge and evolve. Yet we try to plan for these systems or plan to direct policies to influence the system, not fully knowing the impact. This is also where the unintended consequences of our action might take free rein.

Comparative Literature and Business Insights

Literature is the art of discovering something extraordinary about ordinary people, and saying with ordinary words something extraordinary.” – Boris Pasternak

 

It is literature which for me opened the mysterious and decisive doors of imagination and understanding. To see the way others see. To think the way others think. And above all, to feel.” – Salman Rushdie

  nobel

There is a common theme that cuts across literature and business. It is called imagination!

Great literature seeds the mind to imagine faraway places across times and unique cultures. When we read a novel, we are exposed to complex characters that are richly defined and the readers’ subjective assessment of the character and the context defines their understanding of how the characters navigate the relationships and their environment. Great literature offers many pauses for thought, and long after the book is read through … the theme gently seeps in like silt in the readers’ cumulative experiences. It is in literature that the concrete outlook of humanity receives its expression. Comparative literature which is literature assimilated across many different countries enable a diversity of themes that intertwine into the readers’ experiences augmented by the reality of what they immediately experience – home, work, etc. It allows one to not only be capable of empathy but also … to craft out the fluid dynamics of ever changing concepts by dipping into many different types of case studies of human interaction. The novel and the poetry are the bulwarks of literature. It is as important to study a novel as it is to enjoy great poetry. The novel characterizes a plot/(s) and a rich tapestry of actions of the characters that navigates through these environments: the poetry is the celebration of the ordinary into extraordinary enactments of the rhythm of the language that transport the readers, through images and metaphor, into single moments. It breaks the linear process of thinking, a perpendicular to a novel.

comp literature

Business insights are generally a result of acute observation of trends in the market, internal processes, and general experience. Some business schools practice case study method which allows the student to have a fairly robust set of data points to fall back upon. Some of these case studies are fairly narrow but there are some that gets one to think about personal dynamics. It is a fact that personal dynamics and biases and positioning plays a very important role in how one advocates, views, or acts upon a position. Now the schools are layering in classes on ethics to understand that there are some fundamental protocols of human nature that one has to follow: the famous adage – All is fair in love and war – has and continues to lose its edge over time. Globalization, environmental consciousness, individual rights, the idea of democracy, the rights of fair representation, community service and business philanthropy are playing a bigger role in today’s society. Thus, business insights today are a result of reflection across multiple levels of experience that encompass not the company or the industry …but encompass a broader array of elements that exercises influence on the company direction. In addition, one always seeks an end in mind … they perpetually embrace a vision that is impacted by their judgments, observations and thoughts. Poetry adds the final wing for the flight into this metaphoric realm of interconnections – for that is always what a vision is – a semblance of harmony that inspires and resurrects people to action.

interconnect

I contend that comparative literature is a leading indicator that allows a person to get a feel for the general direction of the express and latent needs of people. Furthermore, comparative literature does not offer a solution. Great literature does not portend a particular end. They leave open a multitude of possibilities and what-ifs. The reader can literally transport themselves into the environment and wonder at how he/she would act … the jump into a vicarious existence steeps the reader into a reflection that sharpens the intellect. This allows the reader in a business to be better positioned to excavate and address the needs of current and potential customers across boundaries.

“Literature gives students a much more realistic view of what’s involved in leading” than many business books on leadership, said the professor. “Literature lets you see leaders and others from the inside. You share the sense of what they’re thinking and feeling. In real life, you’re usually at some distance and things are prepared, polished. With literature, you can see the whole messy collection of things that happen inside our heads.” – Joseph L. Badaracco, the John Shad Professor of Business Ethics at Harvard Business School (HBS)

Transparency in organizations

“We chose steel and extra wide panels of glass, which is almost like crystal. These are honest materials that create the right sense of strength and clarity between old and new, as well as a sense of transparency in the center of the institution that opens the campus up to the street.”

Renzo Piano

What is Transparency in the context of the organization?

It is the deliberate attempt by management to architect an organization that encourages open access to information, participation, and decision making, which ultimately creates a higher level of trust among the stakeholders.

The demand for transparency is becoming quite common. The users of goods and services are provoking the transparency question:

  1. Shareholder demand for increased financial accountability in the corporate world,
  2. Increased media diligence
  3. Increased regulatory diligence and requirements
  4. Increased demand by social interest and environmental groups
  5. Demands to see and check on compliance based on internal and external policies
  6. Increased employees’ interest in understanding how senior management decisions impact them, the organization and society

There are 2 big categories that organizations must consider and subsequently address while establishing systems in place to promote transparency.

  1. External Transparency
  2. Internal Transparency

 

External Transparency:

Some of the key elements are that organizations have to make the information accessible while also taking into account the risk of divulging too much information, make the information actionable, enable sharing and collaboration, managing risks, and establishing protocols and channels of communication that is open and democratic.

For example, it is important that employees ought to able to trace the integrity, quality, consistency and validity of the information back to the creator. In an open environment, it also unravels the landscape of risks that an organization maybe deliberately taking or may be carrying unknowingly. It bubbles up inappropriate decisions that can be dwelt on collectively by the management and the employees, and thus risks and inappropriateness are considerably mitigated. The other benefit obviously is that it enables too much overlap wherein people spread across the organizations may be doing the same thing in a similar manner. It affords better shared services platform and also encourages knowledge base and domain expertise that employees can tap into.

 

 Internal Transparency:

Organization has to create the structure to encourage people to be transparent. Generally, people come to work with a mask on. What does that mean? Generally, the employees focus on the job at hand but they may be interested to add value in other ways besides their primary responsibility. In fact, they may want to approach their primary responsibility in an ingenious manner that would help the organization. But the mask or the veil that they don separates their personal interest and passions with the obligations that the job demands. Now how cool would it be if the organization sets up a remarkably safe system wherein the distinction between the employees’ personal interest and the primary obligations of the employee materially dissolve? What I bet you would discover would be higher levels of employee engagement. In addressing internal transparency, what the organization would have done is to have successfully mined and surfaced the personal interests of an employee and laid it out among all participants in a manner that would benefit the organization and the employee and their peers.

Thus, it is important to address both – internal and external transparency. However, implementing transparency ethos is not immune to challenges wherein increased transparency may distort intent, slow processes, increase organizational vulnerabilities, create psychological dissonance among employees or groups, create new factions and sometimes even result in poor decisions. Despite the challenges, the aggregate benefit of increased transparency over time would outweigh the costs. At the end, if the organization continues to formalize transparency, it would also simultaneously create and encourage trust and proper norms and mores that would lay the groundwork for an effective workforce.

Reputation is often an organization’s most valuable asset. It is built over time through a focused commitment and response to members’ wants, needs, and expectations. A commitment to transparency will increasingly become a litmus test used to define an association’s reputation and will be used as a value judgment for participation. By gaining a reputation for value through the disclosure of information, extensive communications with stakeholders, and a solid track record of truth and high disclosure of information, associations will win the respect and involvement of current and future members.

Kanter and Fine use a great analogy of transparency like an ocean sponge. These pore bearing organisms let up to twenty thousand times their volume in water pass through them every day. These sponges can withstand open, constant flow without inhibiting it because they are anchored to the ocean floor. Transparent organizations behave like these sponges: anchored to their mission and still allowing people in and out easily. Transparent organizations actually benefit from the constant flow of people and information.

 

Plans to implement transparency

Businesses are fighting for trust from their intended audiences. Shel Holtz and John Havens, authors of “Tactical Transparency,” state that the realities associated with doing business in today’s “business environment have emerged as the result of recent trends: Declining trust in business as usual and the increased public scrutiny under which companies find themselves thanks to the evolution of social media.” It is important, now more than ever, for organizations to use tools successfully to be sincerely but prudently transparent in ways that matter to their stakeholders.

“Tactical Transparency” adopted the following definition for transparency:

Transparency is the degree to which an organization shares the following with its stakeholder publics:

▪   Its leaders: The leaders of transparent companies are accessible and are straightforward when talking with members of key audiences.

▪   Its employees: Employees or transparent companies are accessible, can reinforce the public view of the company, and able to help people where appropriate.

▪   Its values: Ethical behavior, fair treatment, and other values are on full display in transparent companies.

▪   Its culture: How a company does things is more important today than what it does. The way things are done is not a secret in transparent companies.

▪   The results of its business practices, both good and bad: Successes, failures, problems, and victories all are communicated by transparent companies.

▪   Its business strategy: Of particular importance to the investment community but also of interest to several other audiences, a company’s strategy is a key basis for investment decisions. Misalignment of a company’s strategy and investors’ expectations usually result in disaster.

Here are some great links around transparency.

According to J.D. Lasica, cofounder of Ourmedia.org and the Social Media Group, there are three levels of transparency that an organization should consider when trying to achieve tactical transparency.

▪   Operational Transparency: That involves creating or following an ethics code, conflict-of-interest policies, and any other guidelines your organization creates.

▪   Transactional Transparency: This type of strategy provides guidelines and boundaries for employees so they can participate in the conversation in and out of the office. Can they have a personal blog that discusses work-related issues?

▪   Lifestyle Transparency: This is personalized information coming from sites like Facebook and Twitter. These channels require constant transparency and authenticity.

 

Create an Action Plan around policies and circumstances to promote transparency:

Holtz and Havens outline specific situations where tactical transparency can transform a business, some of which are outlined in this list.

▪   Major Crises

▪   Major change initiatives

▪   Product changes

▪   New regulations that will impact business

▪   Financial matters

▪   Media interaction

▪   Employee interaction with the outside world

▪   Corporate Governance

▪   Whistleblower programs

▪   Monitoring corporate reputation internally and externally

▪   Whistleblower programs

▪   Accessibility of management

 

The Big Data Movement: Importance and Relevance today?

We are entering into a new age wherein we are interested in picking up a finer understanding of relationships between businesses and customers, organizations and employees, products and how they are being used,  how different aspects of the business and the organizations connect to produce meaningful and actionable relevant information, etc. We are seeing a lot of data, and the old tools to manage, process and gather insights from the data like spreadsheets, SQL databases, etc., are not scalable to current needs. Thus, Big Data is becoming a framework to approach how to process, store and cope with the reams of data that is being collected.

According to IDC, it is imperative that organizations and IT leaders focus on the ever-increasing volume, variety and velocity of information that forms big data.

  • Volume. Many factors contribute to the increase in data volume – transaction-based data stored through the years, text data constantly streaming in from social media, increasing amounts of sensor data being collected, etc. In the past, excessive data volume created a storage issue. But with today’s decreasing storage costs, other issues emerge, including how to determine relevance amidst the large volumes of data and how to create value from data that is relevant.
  • Variety. Data today comes in all types of formats – from traditional databases to hierarchical data stores created by end users and OLAP systems, to text documents, email, meter-collected data, video, audio, stock ticker data and financial transactions. By some estimates, 80 percent of an organization’s data is not numeric! But it still must be included in analyses and decision making.
  • Velocity. According to Gartner, velocity “means both how fast data is being produced and how fast the data must be processed to meet demand.” RFID tags and smart metering are driving an increasing need to deal with torrents of data in near-real time. Reacting quickly enough to deal with velocity is a challenge to most organizations.

SAS has added two additional dimensions:

  • Variability. In addition to the increasing velocities and varieties of data, data flows can be highly inconsistent with periodic peaks. Is something big trending in the social media? Daily, seasonal and event-triggered peak data loads can be challenging to manage – especially with social media involved.
  • Complexity. When you deal with huge volumes of data, it comes from multiple sources. It is quite an undertaking to link, match, cleanse and transform data across systems. However, it is necessary to connect and correlate relationships, hierarchies and multiple data linkages or your data can quickly spiral out of control. Data governance can help you determine how disparate data relates to common definitions and how to systematically integrate structured and unstructured data assets to produce high-quality information that is useful, appropriate and up-to-date.

 

So to reiterate, Big Data is a framework stemming from the realization that the data has gathered significant pace and that it’s growth has exceeded the capacity for an organization to handle, store and analyze the data in a manner that offers meaningful insights into the relationships between data points.  I am calling this a framework, unlike other materials that call Big Data a consequent of the inability of organizations to handle mass amounts of data. I refer to Big Data as a framework because it sets the parameters around an organizations’ decision as to when and which tools must be deployed to address the data scalability issues.

Thus to put the appropriate parameters around when an organization must consider Big Data as part of their analytics roadmap in order to understand the patterns of data better, they have to answer the following  ten questions:

  1. What are the different types of data that should be gathered?
  2. What are the mechanisms that have to be deployed to gather the relevant data?
  3. How should the data be processed, transformed and stored?
  4. How do we ensure that there is no single point of failure in data storage and data loss that may compromise data integrity?
  5. What are the models that have to be used to analyze the data?
  6. How are the findings of the data to be distributed to relevant parties?
  7. How do we assure the security of the data that will be distributed?
  8. What mechanisms do we create to implement feedback against the data to preserve data integrity?
  9. How do we morph the big data model into new forms that accounts for new patterns to reflect what is meaningful and actionable?
  10. How do we create a learning path for the big data model framework?

Some of the existing literature have commingled Big Data framework with analytics. In fact, the literature has gone on to make a rather assertive statement i.e. that Big Data and predictive analytics be looked upon in the same vein. Nothing could be further from the truth!

There are several tools available in the market to do predictive analytics against a set of data that may not qualify for the Big Data framework. While I was the CFO at Atari, we deployed business intelligence tools using Microstrategy, and Microstrategy had predictive modules. In my recent past, we had explored SAS and Minitab tools to do predictive analytics. In fact, even Excel can do multivariate, ANOVA and regressions analysis and best curve fit analysis. These analytical techniques have been part of the analytics arsenal for a long time. Different data sizes may need different tools to instantiate relevant predictive analysis. This is a very important point because companies that do not have Big Data ought to seriously reconsider their strategy of what tools and frameworks to use to gather insights. I have known companies that have gone the Big Data route, although all data points ( excuse my pun), even after incorporating capacity and forecasts, suggest that alternative tools are more cost-effective than implementing Big Data solutions. Big Data is not a one-size fit-all model. It is an expensive implementation. However, for the right data size which in this case would be very large data size, Big Data implementation would be extremely beneficial and cost effective in terms of the total cost of ownership.

Areas where Big Data Framework can be applied!

Some areas lend themselves to the application of the Big Data Framework.  I have identified broadly four key areas:

  1. Marketing and Sales: Consumer behavior, marketing campaigns, sales pipelines, conversions, marketing funnels and drop-offs, distribution channels are all areas where Big Data can be applied to gather deeper insights.
  2. Human Resources: Employee engagement, employee hiring, employee retention, organization knowledge base, impact of cross-functional training, reviews, compensation plans are elements that Big Data can surface. After all, generally over 60% of company resources are invested in HR.
  3. Production and Operational Environments: Data growth, different types of data appended as the business learns about the consumer, concurrent usage patterns, traffic, web analytics are prime examples.
  4. Financial Planning and Business Operational Analytics:  Predictive analytics around bottoms-up sales, marketing campaigns ROI, customer acquisitions costs, earned media and paid media, margins by SKU’s and distribution channels, operational expenses, portfolio evaluation, risk analysis, etc., are some of the examples in this category.

Hadoop: A Small Note!

Hadoop is becoming a more widely accepted tool in addressing Big Data Needs.  It was invented by Google so they could index the structural and text information that they were collecting and present meaningful and actionable results to the users quickly. It was further developed by Yahoo that tweaked Hadoop for enterprise applications.

Hadoop runs on a large number of machines that don’t share memory or disks. The Hadoop software runs on each of these machines. Thus, if you have for example – over 10 gigabytes of data – you take that data and spread that across different machines.  Hadoop tracks where all these data resides! The servers or machines are called nodes, and the common logical categories around which the data is disseminated are called clusters.  Thus each server operates on its own little piece of the data, and then once the data is processed, the results are delivered to the main client as a unified whole. The method of reducing the disparate sources of information residing in various nodes and clusters into one unified whole is the process of MapReduce, an important mechanism of Hadoop. You will also hear something called Hive which is nothing but a data warehouse. This could be a structured or unstructured warehouse upon which the Hadoop works upon, processes data, enables redundancy across the clusters and offers a unified solution through the MapReduce function.

Personally, I have always been interested in Business Intelligence. I have always considered BI as a stepping stone, in the new age, to be a handy tool to truly understand a business and develop financial and operational models that are fairly close to the trending insights that the data generates.  So my ear is always to the ground as I follow the developments in this area … and though I have not implemented a Big Data solution, I have always been and will continue to be interested in seeing its applications in certain contexts and against the various use cases in organizations.

 

Pivots – The Unholy Grail of Employee Engagement !

Most of you today have heard the word “pivot”. It has become a very ubiquitous word – it pretends to be something which it is not.  And entrepreneurs and VC’s have found oodles of reasons to justify that word.  Some professional CXO’s throw that word around in executive meetings, board meetings, functional meetings … somehow they feel that these are one of the few words that give them gravitas. So “pivot” has become the sexy word – it portrays that the organization and the management is flexible and will iterate around its axis quickly to accommodate new needs … in fact, they would change direction altogether for the good of the company and the customers. After all, agility is everything, isn’t it? And couple that with Lean Startup – the other Valley buzz word … and you have created a very credible persona. (I will deal with the Lean Startup in a later blog and give that its due. As a matter of fact, the concept of “pivot” was introduced by Eric Ries who has also introduced the concept of Lean Startup).

Pivots happen when the company comes out with product that is not the right fit to market. They assess that customers want something different. Tweaking the product to fit the needs of the customer does not constitute a pivot. But if you change the entire product or direction of the company – that would be considered a pivot.

Attached is an interesting link that I came across —

http://www.readwriteweb.com/start/2012/10/when-is-it-time-to-pivot-8-startups-on-how-they-knew-they-had-to-change.php

It gives examples of eight entrepreneurs who believe that they have exercised pivot in their business model. But if you read the case studies closely, none of them did. They tweaked and tweaked and tweaked along the way. The refined their model.  Scripted.com appears to be the only example that comes closest to the concept of the “pivot” as understood in the Valley.

Some of the common pivots that have been laid out by Eric Ries and Martin Zwilling  are as follows 😦http://blog.startupprofessionals.com/2012/01/smart-business-knows-8-ways-to-pivot.html). I have taken the liberty of laying all of these different pivots out that is on Mr. Zwilling’s blog.

  1. Customer problem pivot. In this scenario, you use essentially the same product to solve a different problem for the same customer segment. Eric says that Starbucks famously did this pivot when they went from selling coffee beans and espresso makers to brewing drinks in-house.
  2. Market segment pivot. This means you take your existing product and use it to solve a similar problem for a different set of customers. This may be necessary when you find that consumers aren’t buying your product, but enterprises have a similar problem, with money to spend. Sometimes this is more a marketing change than a product change.
  3. Technology pivot. Engineers always fight to take advantage of what they have built so far. So the most obvious pivot for them is to repurpose the technology platform, to make it solve a more pressing, more marketable, or just a more solvable problem as you learn from customers.
  4. Product feature pivot. Here especially, you need to pay close attention to what real customers are doing, rather than your projections of what they should do. It can mean to zoom-in and remove features for focus, or zoom-out to add features for a more holistic solution.
  5. Revenue model pivot. One pivot is to change your focus from a premium price, customized solution, to a low price commoditized solution. Another common variation worth considering is the move from a one-time product sale to monthly subscription or license fees. Another is the famous razor versus blade strategy.
  6. Sales channel pivot. Startups with complex new products always seem to start with direct sales, and building their own brand. When they find how expensive and time consuming this is, they need to use what they have learned from customers to consider a distribution channel, ecommerce, white-labeling the product, and strategic partners.
  7. Product versus services pivot. Sometimes products are too different or too complex to be sold effectively to the customer with the problem. Now is the time for bundling support services with the product, education offerings, or simply making your offering a service that happens to deliver a product at the core.
  8. Major competitor pivot. What do you do when a major new player or competitor jumps into your space? You can charge ahead blindly, or focus on one of the above pivots to build your differentiation and stay alive.

Now please re-read all of the eight different types of “pivot” carefully! And reread again. What do you see? What do you find if you reflect upon these further? None of these are pivots! None! All of the eight items fit better into Porter’s Competition Framework. You are not changing direction. You are not suddenly reimagining a new dawn. You are simply tweaking as you learn more. So the question is – Is the rose by any other name still a rose? The answer is yes!  Pivot means changing direction … in fact, so dramatically that the vestiges of the early business models fade away from living memory.  And there have been successful pivots in recent business history.  But less so … and for those who did, you will likely have not heard of them at all. They have long been discarded in the ash heap of history.

Great companies are established by leaders that have vision. The vision is the aspirational goal of the company. The vision statement reflects the goal in a short and succinct manner.  Underlying the vision, they incorporate principles, values, missions, objectives … but they also introduce a corridor of uncertainty. Why? Because the future is rarely a measure or a simple extrapolation of expressed or latent needs of customers in the past.  Apple, Microsoft, Oracle, Salesforce, Facebook, Google, Genentech, Virgin Group, Amazon, Southwest Airlines etc. are examples of great companies who have held true to their vision. They have not pivoted. Why? Because the leaders (for the most part- the founders) had a very clear and aspirational vision of the future! They did not subject themselves to sudden pivots driven by the “animal spirits” of the customers. They have understood that deep waters run still, despite the ripples and turbulence on the surface. They have honed and reflected upon consumer behavior and economic trends, and have given significant thought before they pulled up the anchor. They designed and reflected upon the ultimate end before they set sail. And once at sea, and despite the calm and the turbulence, they never lost sight of the aspirational possibilities of finding new lands, new territories, and new cultures. In fact, they can be compared to the great explorers or great writers – search for a theme and embark upon the journey …within and without.  They are borne upon consistency of actions toward attainment and relief of their aspirations.

Now we are looking at the millennial generation. Quick turnarounds, fast cash, prepare the company for an acquisition and a sale or what is commonly called the “flip” … everything is super-fast and we are led to believe that this is greatness. Business plans are glibly revised. This hotbed of activity and the millennial agility to pivot toward short-term goal is the new normal — pivot is the concept that one has to be ready for and adopt quickly. I could not disagree more.  When I hear pivots … it tells me that the founders have not deliberated upon the long-term goals well. In fact, it tells me that their goals are not aspirational for the most part. They are what we call in microeconomic theory examples of contestable agents in the market of price-takers. They rarely, very rarely create products that endure and stand the test of time!

So now let us relate this to organizations and people. People need stability. People do not seek instability – at least I can speak for a majority of the people. An aspirational vision in a company can completely destabilize a certain market and create tectonic shifts … but people gravitate around the stability of the aspirational vision and execute accordingly. Thus, it is very important for leadership to broadcast and needle this vision into the DNA of the people that are helping the organization execute.  With stability ensured, what then happens are the disruptive innovations!  This may sound counter-factual! Stability and disruptive innovations!  How can these even exist convivially together and be spoken in the same breath!  I contend that Innovation occurs when organizations allow creativity upon bedrock of discipline and non-compromising standards.  A great writer builds out the theme and let the characters jump out of the pages!

When you have mediocrity in the vision, then the employees have nothing aspirational to engage to. They are pockets sometimes rowing the boat in one direction, and at other times rowing against one another or in a completely direction. Instability is injected into the organization.  But they along with their leaders live behind the veil of ignorance – they drink the Red Bull and follow the Pied Piper of Hamelin.  So beware of the pivot evangelists!