Category Archives: Business Process
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.
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.
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.
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.
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.
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.
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.
There are two models in complexity. Complex Physical Systems and Complex Adaptive Systems! For us to grasp the patterns that are evolving, and much of it seemingly out of our control – it is important to understand both these models. One could argue that these models are mutually exclusive. While the existing body of literature might be inclined toward supporting that argument, we also find some degree of overlap that makes our understanding of complexity unstable. And instability is not to be construed as a bad thing! We might operate in a deterministic framework, and often, we might operate in the realms of a gradient understanding of volatility associated with outcomes. Keeping this in mind would be helpful as we deep dive into the two models. What we hope is that our understanding of these models would raise questions and establish mental frameworks for intentional choices that we are led to make by the system or make to influence the evolution of the system.
Complex Physical Systems (CPS)
Complex Physical Systems are bounded by certain laws. If there are initial conditions or elements in the system, there is a degree of predictability and determinism associated with the behavior of the elements governing the overarching laws of the system. Despite the tautological nature of the term (Complexity Physical System) which suggests a physical boundary, the late 1900’s surfaced some nuances to this model. In other words, if there is a slight and an arbitrary variation in the initial conditions, the outcome could be significantly different from expectations. The assumption of determinism is put to the sword. The notion that behaviors will follow established trajectories if rules are established and the laws are defined have been put to test. These discoveries by introspection offers an insight into the developmental block of complex physical systems and how a better understanding of it will enable us to acknowledge such systems when we see it and thereafter allow us to establish certain toll-gates and actions to navigate, to the extent possible, to narrow the region of uncertainty around outcomes.
The universe is designed as a complex physical system. Just imagine! Let this sink in a bit. A complex physical system might be regarded relatively simpler than a complex adaptive system. And with that in mind, once again …the universe is a complex physical system. We are awed by the vastness and scale of the universe, we regard the skies with an illustrious reverence and we wonder and ruminate on what lies beyond the frontiers of a universe, if anything. Really, there is nothing bigger than the universe in the physical realm and yet we regard it as a simple system. A “Simple” Complex Physical System. In fact, the behavior of ants that lead to the sustainability of an ant colony, is significantly more complex: and we mean by orders of magnitude.
Complexity behavior in nature reflects the tendency of large systems with many components to evolve into a poised “critical” state where minor disturbances or arbitrary changes in initial conditions can create a seemingly catastrophic impact on the overall system such that system changes significantly. And that happens not by some invisible hand or some uber design. What is fundamental to understanding complex systems is to understand that complexity is defined as the variability of the system. Depending on our lens, the scale of variability could change and that might lead to different apparatus that might be required to understand the system. Thus, determinism is not the measure: Stephen Jay Gould has argued that it is virtually impossible to predict the future. We have hindsight explanatory powers but not predictable powers. Hence, systems that start from the initial state over time might represent an outcome that is distinguishable in form and content from the original state. We see complex physical systems all around us. Snowflakes, patterns on coastlines, waves crashing on a beach, rain, etc.
Complex Adaptive Systems (CAS)
Complex adaptive systems, on the contrary, are learning systems that evolve. They are composed of elements which are called agents that interact with one another and adapt in response to the interactions.
Markets are a good example of complex adaptive systems at work.
CAS agents have three levels of activity. As described by Johnson in Complexity Theory: A Short Introduction – the three levels of activity are:
- Performance (moment by moment capabilities): This establishes the locus of all behavioral elements that signify the agent at a given point of time and thereafter establishes triggers or responses. For example, if an object is approaching and the response of the agent is to run, that would constitute a performance if-then outcome. Alternatively, it could be signals driven – namely, an ant emits a certain scent when it finds food: other ants will catch on that trail and act, en masse, to follow the trail. Thus, an agent or an actor in an adaptive system has detectors which allows them to capture signals from the environment for internal processing and it also has the effectors that translate the processing to higher order signals that influence other agents to behave in certain ways in the environment. The signal is the scent that creates these interactions and thus the rubric of a complex adaptive system.
- Credit assignment (rating the usefulness of available capabilities): When the agent gathers experience over time, the agent will start to rely heavily on certain rules or heuristics that they have found useful. It is also typical that these rules may not be the best rules, but it could be rules that are a result of first discovery and thus these rules stay. Agents would rank these rules in some sequential order and perhaps in an ordinal ranking to determine what is the best rule to fall back on under certain situations. This is the crux of decision making. However, there are also times when it is difficult to assign a rank to a rule especially if an action is setting or laying the groundwork for a future course of other actions. A spider weaving a web might be regarded as an example of an agent expending energy with the hope that she will get some food. This is a stage setting assignment that agents have to undergo as well. One of the common models used to describe this best is called the bucket-brigade algorithm which essentially states that the strength of the rule depends on the success of the overall system and the agents that constitute it. In other words, all the predecessors and successors need to be aware of only the strengths of the previous and following agent and that is done by some sort of number assignment that becomes stronger from the beginning of the origin of the system to the end of the system. If there is a final valuable end-product, then the pathway of the rules reflect success. Once again, it is conceivable that this might not be the optimal pathway but a satisficing pathway to result in a better system.
- Rule discovery (generating new capabilities): Performance and credit assignment in agent behavior suggest that the agents are governed by a certain bias. If the agents have been successful following certain rules, they would be inclined toward following those rules all the time. As noted, rules might not be optimal but satisficing. Is improvement a matter of just incremental changes to the process? We do see major leaps in improvement … so how and why does this happen? In other words, someone in the process have decided to take a different rule despite their experiences. It could have been an accident or very intentional.
One of the theories that have been presented is that of building blocks. CAS innovation is a result of reconfiguring the various components in new ways. One quips that if energy is neither created, nor destroyed …then everything that exists today or will exist tomorrow is nothing but a reconfiguration of energy in new ways. All of tomorrow resides in today … just patiently waiting to be discovered. Agents create hypotheses and experiment in the petri dish by reconfiguring their experiences and other agent’s experiences to formulate hypotheses and the runway for discovery. In other words, there is a collaboration element that comes into play where the interaction of the various agents and their assignment as a group to a rule also sets the stepping stone for potential leaps in innovation.
Another key characteristic of CAS is that the elements are constituted in a hierarchical order. Combinations of agents at a lower level result in a set of agents higher up and so on and so forth. Thus, agents in higher hierarchical orders take on some of the properties of the lower orders but it also includes the interaction rules that distinguishes the higher order from the lower order.
A lean financial infrastructure presumes the ability of every element in the value chain to preserve and generate cash flow. That is the fundamental essence of the lean infrastructure that I espouse. So what are the key elements that constitute a lean financial infrastructure?
And given the elements, what are the key tweaks that one must continually make to ensure that the infrastructure does not fall into entropy and the gains that are made fall flat or decay over time. Identification of the blocks and monitoring and making rapid changes go hand in hand.
The Key Elements or the building blocks of a lean finance organization are as follows:
- Chart of Accounts: This is the critical unit that defines the starting point of the organization. It relays and groups all of the key economic activities of the organization into a larger body of elements like revenue, expenses, assets, liabilities and equity. Granularity of these activities might lead to a fairly extensive chart of account and require more work to manage and monitor these accounts, thus requiring incrementally a larger investment in terms of time and effort. However, the benefits of granularity far exceeds the costs because it forces management to look at every element of the business.
- The Operational Budget: Every year, organizations formulate the operational budget. That is generally a bottoms up rollup at a granular level that would map to the Chart of Accounts. It might follow a top-down directive around what the organization wants to land with respect to income, expense, balance sheet ratios, et al. Hence, there is almost always a process of iteration in this step to finally arrive and lock down the Budget. Be mindful though that there are feeders into the budget that might relate to customers, sales, operational metrics targets, etc. which are part of building a robust operational budget.
- The Deep Dive into Variances: As you progress through the year and part of the monthly closing process, one would inquire about how the actual performance is tracking against the budget. Since the budget has been done at a granular level and mapped exactly to the Chart of Accounts, it thus becomes easier to understand and delve into the variances. Be mindful that every element of the Chart of Account must be evaluated. The general inclination is to focus on the large items or large variances, while skipping the small expenses and smaller variances. That method, while efficient, might not be effective in the long run to build a lean finance organization. The rule, in my opinion, is that every account has to be looked and the question should be – Why? If the management has agreed on a number in the budget, then why are the actuals trending differently. Could it have been the budget and that we missed something critical in that process? Or has there been a change in the underlying economics of the business or a change in activities that might be leading to these “unexpected variances”. One has to take a scalpel to both – favorable and unfavorable variances since one can learn a lot about the underlying drivers. It might lead to managerially doing more of the better and less of the worse. Furthermore, this is also a great way to monitor leaks in the organization. Leaks are instances of cash that are dropping out of the system. Much of little leaks amounts to a lot of cash in total, in some instances. So do not disregard the leaks. Not only will that preserve the cash but once you understand the leaks better, the organization will step up in efficiency and effectiveness with respect to cash preservation and delivery of value.
- Tweak the process: You will find that as you deep dive into the variances, you might want to tweak certain processes so these variances are minimized. This would generally be true for adverse variances against the budget. Seek to understand why the variance, and then understand all of the processes that occur in the background to generate activity in the account. Once you fully understand the process, then it is a matter of tweaking this to marginally or structurally change some key areas that might favorable resonate across the financials in the future.
- The Technology Play: Finally, evaluate the possibilities of exploring technology to surface issues early, automate repetitive processes, trigger alerts early on to mitigate any issues later, and provide on-demand analytics. Use technology to relieve time and assist and enable more thinking around how to improve the internal handoffs to further economic value in the organization.
All of the above relate to managing the finance and accounting organization well within its own domain. However, there is a bigger step that comes into play once one has established the blocks and that relates to corporate strategy and linking it to the continual evolution of the financial infrastructure.
The essential question that the lean finance organization has to answer is – What can the organization do so that we address every element that preserves and enhances value to the customer, and how do we eliminate all non-value added activities? This is largely a process question but it forces one to understand the key processes and identify what percentage of each process is value added to the customer vs. non-value added. This can be represented by time or cost dimension. The goal is to yield as much value added activities as possible since the underlying presumption of such activity will lead to preservation of cash and also increase cash acquisition activities from the customer.
Financial awareness of key drivers are becoming the paramount leading indicators for organizational success. For most, the finance department is a corner office service that offers ad hoc analysis on strategic and operational initiatives to a company, and provides an ex-post assessment of the financial condition of the company among a select few. There are some key financial metrics that one wants to measure across all companies and all industries without exception, but then there are unique metrics that reflect the key underlying drivers for organizational success. Organizations align their forays into new markets, new strategies and new ventures around a narrative that culminates in a financial metric or a proxy that illustrates opportunities lost or gained.
Having been cast in operational finance roles for a good length of my career, I have often encountered a high level of interest to learn financial concepts in areas such as engineering, product management, operations, sales, etc. I have to admit that I have been humbled by the fairly wide common-sense understanding of basic financial concepts that these folks have. However, in most cases, the understanding is less than skin deep with misunderstandings that are meaningful. The good news is that I have also noticed a promising trend, namely … the questions are more thoroughly weighed by the “non-finance” participants, and there seems to be an elevated understanding of key financial drivers that translate to commercial success. This knowledge continues to accelerate … largely, because of convergence of areas around data science, analytics, assessment of personal ownership stakes, etc. But the passing of such information across these channels to the hungry recipients are not formalized. In other words, I posit that having a formal channel of inculcating financial education across the various functional areas would pay rich dividends for the company in the long run. Finance is a vast enough field that partaking general knowledge in these concepts which are more than merely skin-deep would also enable the finance group to engage in meaningful conversations with other functional experts, thus allowing the narrative around the numbers to be more wholesome. Thus, imparting the financial knowledge would be beneficial to the finance department as well.
To be effective in creating a formal channel of disseminating information of the key areas in finance that matter to the organization, it is important to understand the operational drivers. When I say operational drivers, I am expanding that to encompass drivers that may uniquely affect other functional areas. For example, sales may be concerned with revenue, margins whereas production may be concerned with server capacity, work-in-process and throughput, etc. At the end, the financial metrics are derivatives. They are cross products of single or multiple drivers and these are the elements that need to be fleshed out to effect a spirited conversation. That would then enable the production of a financial barometer that everyone in the organization can rally behind and understand, and more importantly … be able to assess how their individual contribution has and will advance organization goals.
Tags: conversation, employee engagement, finance, financial barometer, financial drivers, financial knowledge, financial metrics, organization, organization architecture, organization behavior, value management
Where the mind is without fear and the head is held high
Where knowledge is free
Where the world has not been broken up into fragments
By narrow domestic walls
Where words come out from the depth of truth
Where tireless striving stretches its arms towards perfection
Where the clear stream of reason has not lost its way
Into the dreary desert sand of dead habit
Where the mind is led forward by thee
Into ever-widening thought and action
Into that heaven of freedom, my Father, let my country awake.
– Rabindranath Tagore
Among the many fundamental debates in philosophy, one of the fundamental debates has been around the concept of free will. The debates have stemmed around two arguments associated with free will.
1) Since future actions are governed by the circumstances of the present and the past, human beings future actions are predetermined on account of the learnings from the past. Hence, the actions that happen are not truly a consequent of free will.
2) The counter-argument is that future actions may not necessarily be determined and governed by the legacy of the present and the past, and hence leaves headroom for the individual to exercise free will.
Now one may wonder what determinism or lack of it has anything to do with the current state of things in an organizational context. How is this relevant? Why are the abstract notions of determinism and free will important enough to be considered in the context of organizational evolution? How does the meaning lend itself to structured institutions like business organizations, if you will, whose sole purpose is to create products and services to meet the market demand.
So we will throw a factual wrinkle in this line of thought. We will introduce now an element of chance. How does chance change the entire dialectic? Simply because chance is an unforeseen and random event that may not be pre-determined; in fact, a chance event may not have a causal trigger. And chance or luck could be meaningful enough to untether an organization and its folks to explore alternative paths. It is how the organization and the people are aligned to take advantage of that random nondeterministic future that could make a huge difference to the long term fate of the organization.
The principle of inductive logic states that what is true for n and n+1 would be true for n+2. The inductive logic creates predictability and hence organizations create pathways to exploit the logical extension of inductive logic. It is the most logical apparatus that exists to advance groups in a stable but robust manner to address the multitude of challenges that that they have to grapple with. After all, the market is governed by animal spirits! But let us think through this very carefully. All competition or collaboration that occurs among groups to address the market demands result in homogenous behavior with general homogeneous outcomes. Simply put, products and services become commoditized. Their variance is not unique and distinctive. However, they could be just be distinctive enough to eke out enough profits in the margins before being absorbed into a bigger whole. At that point, identity is effaced over time. Organizations gravitate to a singularity. Unique value propositions wane over time.
So let us circle back to chance. Chance is our hope to create divergence. Chance is the factoid that cancels out the inductive vector of industrial organization. Chance does not exist … it is not a “waiting for Godot” metaphor around the corner. If it always did, it would have been imputed by the determinists in their inductive world and we would end up with a dystopian homogenous future. Chance happens. And sometimes it has a very short half-life. And if the organization and people are aligned and their mindset is adapted toward embracing and exploiting that fleeting factoid of chance, the consequences could be huge. New models would emerge, new divergent paths would be traduced and society and markets would burst into a garden of colorful ideas in virtual oasis of new markets.
So now to tie this all to free will and to the unbearable lightness of being! It is the existence of chance that creates the opportunity to exercise free will on the part of an individual, but it is the organizations responsibility to allow the individual to unharness themselves from organization inertia. Thus, organizations have to perpetuate an environment wherein employees are afforded some headroom to break away. And I don’t mean break away as in people leaving the organization to do their own gigs; I mean breakaway in thought and action within the boundaries of the organization to be open to element of chance and exploit it. Great organizations do not just encourage the lightness of being … unharnessing the talent but rather – the great organizations are the ones that make the lightness of being unbearable. These individuals are left with nothing but an awareness and openness to chance to create incredible values … far more incredible and awe inspiring and momentous than a more serene state of general business as usual affairs.
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:
- What are the different types of data that should be gathered?
- What are the mechanisms that have to be deployed to gather the relevant data?
- How should the data be processed, transformed and stored?
- How do we ensure that there is no single point of failure in data storage and data loss that may compromise data integrity?
- What are the models that have to be used to analyze the data?
- How are the findings of the data to be distributed to relevant parties?
- How do we assure the security of the data that will be distributed?
- What mechanisms do we create to implement feedback against the data to preserve data integrity?
- How do we morph the big data model into new forms that accounts for new patterns to reflect what is meaningful and actionable?
- 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:
- 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.
- 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.
- 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.
- 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.
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 —
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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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!
Tags: boundaries, choice, core, creativity, employee engagement, extrinsic motivation, intrinsic motivation, lean startup, learning organization, mass psychology, organization architecture, pivot, platform, talent management
MECE is a thought tool that has been systematically used in McKinsey. It stands for Mutually Exclusive, Comprehensively Exhaustive. We will go into both these components in detail and then relate this to the dynamics of an organization mindset. The presumption in this note is that the organization mindset has been engraved over time or is being driven by the leadership. We are looking at MECE since it represents a tool used by the most blue chip consulting firm in the world. And while doing that, we will , by the end of the article, arrive at the conclusion that this framework alone will not be the panacea to all investigative methodology to assess a problem – rather, this framework has to reconcile with the active knowledge that most things do not fall in the MECE framework, and thus an additional system framework is needed to amplify our understanding for problem solving and leaving room for chance.
So to apply the MECE technique, first you define the problem that you are solving for. Once you are past the definition phase, well – you are now ready to apply the MECE framework.
MECE is a framework used to organize information which is:
- Mutually exclusive: Information should be grouped into categories so that each category is separate and distinct without any overlap; and
- Collectively exhaustive: All of the categories taken together should deal with all possible options without leaving any gaps.
In other words, once you have defined a problem – you figure out the broad categories that relate to the problem and then brainstorm through ALL of the options associated with the categories. So think of it as a mental construct that you move across a horizontal line with different well defined shades representing categories, and each of those partitions of shades have a vertical construct with all of the options that exhaustively explain those shades. Once you have gone through that exercise, which is no mean feat – you will be then looking at an artifact that addresses the problem. And after you have done that, you individually look at every set of options and its relationship to the distinctive category … and hopefully you are well on your path to coming up with relevant solutions.
Now some may argue that my understanding of MECE is very simplistic. In fact, it may very well be. But I can assure you that it captures the essence of very widely used framework in consulting organizations. And this framework has been imported to large organizations and have cascaded down to different scale organizations ever since.
Here is a link that would give you a deeper understanding of the MECE framework:
Now we are going to dig a little deeper. Allow me to digress and take you down a path less travelled. We will circle back to MECE and organizational leadership in a few moments. One of the memorable quotes that have left a lasting impression is by a great Nobel Prize winning physicist, Richard Feynman.
“I have a friend who’s an artist and has sometimes taken a view which I don’t agree with very well. He’ll hold up a flower and say “look how beautiful it is,” and I’ll agree. Then he says “I as an artist can see how beautiful this is but you as a scientist takes this all apart and it becomes a dull thing,” and I think that he’s kind of nutty. First of all, the beauty that he sees is available to other people and to me too, I believe. Although I may not be quite as refined aesthetically as he is … I can appreciate the beauty of a flower. At the same time, I see much more about the flower than he sees. I could imagine the cells in there, the complicated actions inside, which also have a beauty. I mean it’s not just beauty at this dimension, at one centimeter; there’s also beauty at smaller dimensions, the inner structure, also the processes. The fact that the colors in the flower evolved in order to attract insects to pollinate it is interesting; it means that insects can see the color. It adds a question: does this aesthetic sense also exist in the lower forms? Why is it aesthetic? All kinds of interesting questions which the science knowledge only adds to theexcitement, the mystery and the awe of a flower! It only adds. I don’t understand how it subtracts.”
The above quote by Feynman lays the groundwork to understand two different approaches – namely, the artist approaches the observation of the flower from the synthetic standpoint, whereas Feynman approaches it from an analytic standpoint. Both do not offer views that are antithetical to one another: in fact, you need both to gather a holistic view and arrive at a conclusion – the sum is greater than the parts. Feynman does not address the essence of beauty that the artist puts forth; he looks at the beauty of how the components and its mechanics interact well and how it adds to our understanding of the flower. This is very important because the following dialogue with explore another concept to drive this difference between analysis and synthesis home.
There are two possible ways of gaining knowledge. Either we can proceed from the construction of the flower ( the Feynman method) , and then seek to determine the laws of the mutual interaction of its parts as well as its response to external stimuli; or we can begin with what the flower accomplishes and then attempt to account for this. By the first route we infer effects from given causes, whereas by the second route we seek causes of given effects. We can call the first route synthetic, and the second analytic.
We can easily see how the cause effect relationship is translated into a relationship between the analytic and synthetic foundation.
A system’s internal processes — i.e. the interactions between its parts — are regarded as the cause of what the system, as a unit, performs. What the system performs is thus the effect. From these very relationships we can immediately recognize the requirements for the application of the analytic and synthetic methods.
The synthetic approach — i.e. to infer effects on the basis of given causes — is therefore appropriate when the laws and principles governing a system’s internal processes are known, but when we lack a detailed picture of how the system behaves as a whole.
Another example … we do not have a very good understanding of the long-term dynamics of galactic systems, nor even of our own solar system. This is because we cannot observe these objects for the thousands or even millions of years which would be needed in order to map their overall behavior.
However, we do know something about the principles, which govern these dynamics, i.e. gravitational interaction between the stars and planets respectively. We can therefore apply a synthetic procedure in order to simulate the gross dynamics of these objects. In practice, this is done with the use of computer models which calculate the interaction of system parts over long, simulated time periods.
The analytical approach — drawing conclusions about causes on the basis of effects – is appropriate when a system’s overall behavior is known, but when we do not have clear or certain knowledge about the system’s internal processes or the principles governing these. On the other hand, there are a great many systems for which we neither have a clear and certain conception of how they behave as a whole, nor fully understand the principles at work which cause that behavior. Organizational behavior is one such example since it introduces the fickle spirits of the employees that, at an aggregate create a distinct character in the organization.
Leibniz was among the first to define analysis and synthesis as modern methodological concepts:
“Synthesis … is the process in which we begin from principles and [proceed to] build up theorems and problems … while analysis is the process in which we begin with a given conclusion or proposed problem and seek the principles by which we may demonstrate the conclusion or solve the problem.”
So we have wandered down this path of analysis and synthesis and now we will circle back to MECE and the organization. MECE framework is a prime example of the application of analytics in an organization structure. The underlying hypothesis is that the application of the framework will illuminate and add clarity to understanding the problems that we are solving for. But here is the problem: the approach could lead to paralysis by analysis. If one were to apply this framework, one would lose itself in the weeds whereas it is just as important to view the forest. So organizations have to step back and assess at what point we stop the analysis i.e. we have gathered information and at what point we set our roads to discovering a set of principles that will govern the action to solve a set of problems. It is almost always impossible to gather all information to make the best decision – especially where speed, iteration, distinguishing from the herd quickly, stamping a clear brand etc. are becoming the hallmarks of great organizations.
Applying the synthetic principle in addition to “MECE think” leaves room for error and sub-optimal solutions. But it crowd sources the limitless power of imagination and pattern thinking that will allow the organization to make critical breakthroughs in innovative thinking. It is thus important that both the principles are promulgated by the leadership as coexisting principles that drive an organization forward. It ignites employee engagement, and it imputes the stochastic errors that result when employees may not have all the MECE conditions checked off.
In conclusion, it is important that the organization and its leadership set its architecture upon the traditional pillars of analysis and synthesis – MECE and systems thinking. And this architecture serves to be the springboard for the employees that allows for accidental discoveries, flights of imagination, Nietzschean leaps that transform the organization toward the pathway of innovation, while still grounded upon the bedrock of facts and empirical observations.
Posted in Business Process, Employee Engagement, Innovation, Leadership, Learning Organization, Learning Process, Management Models, Model Thinking, Motivation, Organization Architecture, Recognition, Risk Management, Social Dynamics, Social Systems
Tags: Analysis, creativity, employee engagement, employee recognition, innovation, learning organization, mass psychology, Mental Construct, Mental Models, organization architecture, social network, social systems, Synthesis, Systems Thinking, talent management, uncertainty
The Balanced Scorecard Model (BSC) was introduced by Kaplan & Norton in their book “The Balanced Scorecard” (1996). It is one of the more widely used management tools in large organizations.
One of the major strengths of the BSC model is how the key categories in the BSC model links to corporate missions and objectives. The key categories which are referred to as “perspectives” illustrated in the BSC model are:
Kaplan and Norton do not disregard the traditional need for financial data. Timely and accurate data will always be a priority, and managers will do whatever necessary to provide it. In fact, often there is more than enough handling and processing of financial data. With the implementation of a corporate database, it is hoped that more of the processing can be centralized and automated. But the point is that the current emphasis on financials leads to the “unbalanced” situation with regard to other perspectives. There is perhaps a need to include additional financial-related data, such as risk assessment and cost-benefit data, in this category.
Recent management philosophy has shown an increasing realization of the importance of customer focus and customer satisfaction in any business. These are leading indicators: if customers are not satisfied, they will eventually find other suppliers that will meet their needs. Poor performance from this perspective is thus a leading indicator of future decline, even though the current financial picture may look good. In developing metrics for satisfaction, customers should be analyzed in terms of kinds of customers and the kinds of processes for which we are providing a product or service to those customer groups
Internal Business Process Perspective
This perspective refers to internal business processes. Metrics based on this perspective allow the managers to know how well their business is running, and whether its products and services conform to customer requirements (the mission). These metrics have to be carefully designed by those who know these processes most intimately; with our unique missions these are not necessarily something that can be developed by outside consultants. My personal opinion on this matter is that the internal business process perspective is too important and that internal owners or/and teams take ownership of understanding the process.
Learning and Growth Perspective
This perspective includes employee training and corporate cultural attitudes related to both individual and corporate self-improvement. In a knowledge-worker organization, people — the only repository of knowledge — are the main resource. In the current climate of rapid technological change, it is becoming necessary for knowledge workers to be in a continuous learning mode. Metrics can be put into place to guide managers in focusing training funds where they can help the most. In any case, learning and growth constitute the essential foundation for success of any knowledge-worker organization.
Kaplan and Norton emphasize that ‘learning’ is more than ‘training’; it also includes things like mentors and tutors within the organization, as well as that ease of communication among workers, the engagement of the workers, the potential of cross-training that would create pockets of bench strength and switch hitters, and other employee specific programs that allows them to readily get help on a problem when it is needed. It also includes technological tools; what the Baldrige criteria call “high performance work systems.”
This perspective was appended to the above four by Bain and Company. It refers to the vitality of the organization and its culture to provide the appropriate framework to encourage innovation. Organizations have to innovate. Innovation is becoming the key distinctive element in great organizations, and high levels of innovation or innovative thinking are talent magnets.
Taking the perspectives a step further, Kaplan and Cooper instituted measures and targets associated with each of those targets. The measures are geared around what the objective is associated with each of the perspectives rather than a singular granule item. Thus, if the objective is to increase customer retention, an appropriate metric or set of metrics is around how to measure the objective and track success to it than defining a customer.
One of the underlying presumptions in this model is to ensure that the key elements around which objectives are defined are done so at a fairly detailed level and to the extent possible – defined so much so that an item does not have polymorphous connotations. In other words, there is and can be only a single source of truth associated with the key element. That preserves the integrity of the model prior to its application that would lead to the element branching out into a plethora of objectives associated with the element.
Objectives, Measures, Targets and Initiatives
Within each of the Balance Scorecard financial, customer, internal process, learning perspectives and innovation perspectives, the firm must define the following:
Strategic Objectives – what the strategy is to achieve in that perspective
Measures – how progress for that particular objective will be measured
Targets – the target value sought for each measure
Initiatives – what will be done to facilitate the reaching of the target?
As in models and analytics, the information that the model spouts could be rife with a cascade of metrics. Metrics are important but too many metrics associated with the perspectives may diffuse the ultimate end that the perspectives represent.
Hence, one has to exercise restraint and rigor in defining a few key metrics that are most relevant and roll up to corporate objectives. As an example, outlined below are examples of metrics associated with the perspectives:
Financial performance (revenues, earnings, return on capital, cash flow);
Customer value performance (market share, customer satisfaction measures, customer loyalty);
Internal business process performance (productivity rates, quality measures, timeliness);
Employee performance (morale, knowledge, turnover, use of best demonstrated practices);
Innovation performance (percent of revenue from new products, employee suggestions, rate of improvement index);
To construct and implement a Balanced Scorecard, managers should:
- Articulate the business’s vision and strategy;
- Identify the performance categories that best link the business’s vision and strategy to its results (e.g., financial performance, operations, innovation, and employee performance);
- Establish objectives that support the business’s vision and strategy;
- Develop effective measures and meaningful standards, establishing both short-term milestones and long-term targets;
- Ensure company wide acceptance of the measures;
- Create appropriate budgeting, tracking, communication, and reward systems;
- Collect and analyze performance data and compare actual results with desired performance;
- Take action to close unfavorable gaps.
The link above contains a number of templates and examples that you may find helpful.
I have discussed organization architecture and employee engagement in our previous blogs. The BSC is a tool to encourage engagement while ensuring a tight architecture to further organizational goals. You may forget that as an employee, you occupy an important place in the ecosystem; the forgetting does not speak to your disenchantment toward the job, neither to your disinclination toward the uber-goals of the organization. The forgetting really speaks to potentially a lack of credible leadership that has not taken the appropriate efforts to engage the organization by pushing this structure that forces transparency. The BSC is one such articulate model that could be used, even at its crudest form factor, to get employees informed and engaged.
Posted in Business Process, Employee Engagement, Employee retention, Financial Metrics, Financial Process, Innovation, Leadership, Learning Organization, Learning Process, Management Models, Organization Architecture, Recognition, Risk Management, Social Dynamics, Talent Management
Tags: bain and company, balanced scorecard, business process, communication channel, employee engagement, employee recognition, extrinsic motivation, financial process, innovation, intrinsic motivation, learning organization, management tools, mass psychology, model, organization architecture, rewads, risk management, social systems, strategy, talent management, value, value management