Creating Your Own Reality

November 7, 2011 § 2 Comments

On my white board sits a list of topics that are near and dear to my heart; topics that I think about often and want to espouse, pontificate and illuminate.  Most often, I think I have original ideas on these subjects and while I don’t feel I have the time to get it all out at once, I keep this list with the intention of banging them out slowly – one by one.  And almost without fail, in my regular reading or research, I’ll come upon an article or book on one of these topics and then suddenly, like a bolt of revelation, someone’s beaten me to the punch; made the key insights that I thought were my domain.

The Surety of Fools

One such happening this past weekend as I perused the New York Times Magazine, a gentleman by the name of Daniel Kahneman wrote an article entitled “The Surety of Fools”, an adaptation from his upcoming book entitled “Thinking, Fast and Slow”.  He hit on a key observation that is at the core of what I’ve been writing about over these last few months; misperceptions of risk.  I won’t rehash the whole article, but in essence, Mr. Kahneman points out how we often hypothesize based on logic, but when empirical evidence belies our theories, we simply don’t believe the facts.  He calls this phenomenon the “illusion of validity.”  I love this premise as I see it so often with investment managers, news reporters, mortgage brokers, sport team coaches, politicians, voters and prognosticators in general – they all create their own reality. 

Creating Your Own Reality

We ALL do it to some degree.  We watch the news channel that validates our set biases.  We befriend people who support and validate our opinions and views.  On the topic of investment risk, operational risk and risk in general, how does that phenomenon play out?  Do we see the facts and are we able to evaluate data without bias? Mr. Kahneman illustrates the reality of investment bias with examples of studying investment managers and how their performance is measured.  The vast majority of investment managers he studies do not perform better than a purely random pick of stocks.  Yet, the illusion of validity causes the management of the largest investment firms to bonus and commission those managers as if they are keenly skilled; as if the fund managers have brought tremendous value to their client’s interests.  They create their own reality – instead of accepting that the unbiased data shows no value in their management of investment assets.

Life Sciences’ High stakes

There are even greater risk examples.  Life Sciences companies such as pharmaceuticals, biotechnology and medical device firms have huge investments and pressures to produce new products.  Each development stage requires rigorous testing and massive volumes of data.  While the FDA enforces regulations and these companies are regularly audited both internally and externally, the pressure to produce is high.  Time is of the essence when it comes to bringing a new drug to market; both for the sake of patients as well as profits.  How well is the data reviewed and scrutinized before passing each validity stage?  Is there a bias that errs on the side of validation ahead of rejection?  Absolutely.  Kahneman’s Illusion of validity is at play and the consequences are immense. 

The Supply Chain Fog

For Life Sciences companies the risks involve patient health as well as immense risks to the company including product recalls, regulatory findings, lawsuits, and ultimately, reputation damage.  The organizations I’ve worked with over these last few years are extremely diligent in their processes and methods for R&D, trials, manufacturing as well as distribution.  But other operational risks do exist.  In a post last year by Daniel R. Matlis entitled, “Life Science Executives Concerned about Outsourcing and Globalization Unintended Consequences”, Mr. Matlis notes, “In the drive to lower costs, manufacturing and sourcing of ingredients and components in countries such as China and India are playing a more prominent role. Yet, according to the research, outsourcing to manufacturers in developing economies carries significant operational risks.  Industry Executives surveyed for the research said that Raw Materials sourced outside the US represented the greatest risk to the Value Chain, with 94% of those who responded seeing it as a significant or moderate risk.  When comparing the risk profile of US vs. foreign raw material Suppliers, United States Suppliers were classified as low risk nearly 10 times as often as foreign Suppliers.”  Any Life Science company’s ability to define, monitor and track each and all of their third party providers adds a level of complexity and difficulty.  This difficulty stems from what consultants at Nimbus have labeled the “fog of process accountability, control and oversight.” 

To be certain, this fog exists to some degree everywhere and obviously with supply chain partners even more so, but how well an organization tries to create clarity of process definition and clarity of quality both from within and beyond the enterprise is critical when managing operational risk.  Perhaps the biggest concern I have with the phenomena of “creating your own reality” is the fact that the “fog of accountability” provides a condition for pushing forward; an excuse for not accepting what the data is revealing; and a scenario wherein doubt can always be cast on outliers.

Focus on the Facts

I spent part of last week with a biotechnology firm’s scientific directors, their CIO and colleagues from TIBCO, briefing them on my company’s software technologies and how they apply to the wide variety of process areas they represent.  The volume of data and the complexity of that data as it applies within their product trials is tremendous.   Next week I’m with a medical device company who’s in the process of a major transformation and will need to address most every operational area as part of a corporate spinoff.  These are just a couple of quick snapshots, but they epitomize the speed with which organizations change, adapt, and grow.  Speed and volume is only increasing – further escalating the demands for validation of each initiative. 

I can only hope that Mr. Kahneman’s “illusion of validity” is tempered when organizations manage operational risk and the key decisions that drive product development.  The stakes are indeed high when it comes to Life Sciences, but every industry is predisposed to this condition.  In short, we can never be to too sure.  Let’s not fall too in love with our own marketing slogans.  Let’s understand the complexity that we’re faced with, make our best, valid judgments and do the best with the facts we have.  While there is never purity in our judgments, we can at least try to be aware of the propensity to fulfill objectives through maintaining a blindness to the facts.

Real Time vs Analysis

September 19, 2011 § 4 Comments

It happens every day, every hour, minute and second.  Stuff.  Stuff happens, and lots of it.  Every so often, something happens that make us go, “oh, that’s big”.  And sometimes so “big” that we scramble to react to either take advantage or take cover; to move money in or out; run for higher ground or head out to sea.  Sometimes we have a bit of notice, but other times we don’t.

Previously, I wrote about risk, fraud and how Barings Bank was brought down by a single rouge trader.  Well, it happened again just a few days ago.  UBS AG, a large Swiss bank appears to have lost somewhere in the neighborhood of $2 Billion.  The news caused its stock to promptly drop; closing 11% lower than the previous day’s close.  Moody’s Investor Service quickly reacted suggesting they would review UBS for a possible downgrade; citing concerns that it’s not adequately managing risk.

It’s much too early to determine how this trader pulled off his scheme.  Early information suggests he may have manipulated back-office operational systems as he previously worked in back-office operations and would have had that knowledge.  Did UBS have a policy to restrict back-office workers from transferring to front-office trader positions?  They didn’t comment.

There is much that needs to come to light.  Was this the work of the single trader, Kweku Adoboli, that is currently being implied or were others involved?  What controls were in place to prevent these type of trades and why did they fail?  How long did it take for monitors to catch the rouge activity and did they prevent additional potential damage? 

To give a sense of size, it only took Nick Leeson’s $1.3B cheat to bring down Barings in 1995.  Jerome Kerviel devised a scheme that cost Societe Generale $7.16 Billion in 2008.  Other scandals have impacted banks over the years and the fraudulent events don’t seem to end.  Regulations can be implemented and made more stringent; auditors can review organization’s processes for compliance to those regulations, but still big stuff happens.  It’s the kind of big stuff that wipes out all other assumptions.  You can be the finest analyst in the universe, performing all the due diligence necessary to make the most prudent investments.  You believe in UBS, the fact that they brought back senior leadership that they are serious about reform.  Oswald Grubel were supposed to be turning around the troubled UBS, but it appears he and his leadership team was just not that concerned about managing operational risk.  Simple bottom line is: one event can be catastrophic erasing all other assumptions.

So, the questions that are most pertinent:  Which operational events need real-time monitoring?  What events need process controls in place to automatically prohibit additional risk exposure?  How can managers respond in real-time to both opportunities and adverse situations?  As Pete Seeger adapted from the Book of Ecclesiastes, “there’s a time to gain, a time to lose, a time to rend, a time to sew”.  Similarly, there is a time for analysis and there is a time for real-time response.  All the analysis in the world cannot determine the future.  As the Heisenberg Uncertainty Principal states; the more precisely one property is measured, the less precisely the other properties can be controlled or determined.   In other words, the mere act of observance imposes yet another factor into the set of conditions.  There are no absolutes about tomorrow and there is no such thing as risk-free.  So, while I point out the immense advantage that doing your homework will bring with a previous blog post in interconnectedness, at the end of the day, a single event can wipe out all of your assumptions. 

Well, I know what you’re thinking…. that sucks.  First you tell me that I should do fantastic amounts of due diligence to identify opportunities, but then you say, “ahhh, it’s all a waste once a single unexpected event strikes.”  Okay, I can see that paradox or contradiction, but really what I’m saying is: you have to do both.  Good operational process management is about analysis of the details; of every single activity; every single owner, reviewer, regulation and risk.  And yet, it’s also about agility.  What do we do when things don’t go as planned?  What do we do when the proverbial poop hits the fan?  Can we analyze each activity for its risk exposure?  Can we find methods and control activities to mitigate against adverse events… especially the catastrophic ones?  And can we buy insurance to position ourselves for gain if adverse events strike?  Absolutely, I say!  Why some organizations don’t, especially financial institutions that are particularly vulnerable, is beyond me.  Sometimes, it’s just incompetent management, but often it’s a simple lack of appreciation for how solid operational process management requires a sizable investment in process thinking, risk management and development of a process improvement culture. 

Fortunately, a lot is being done during this generation to advance process-based thinking and to raise the level of consciousness about business process management and its impact on corporate governance and risk.  But, it’s happening slowly.  Maybe events like last week’s UBS debacle will open a few eyes…. let’s hope so.

The Interconnectedness of Things

September 7, 2011 § 6 Comments

This past week the company I work for, Nimbus Partners, was purchased by a larger software company, TIBCO.  I can’t comment on the process of due diligence of the deal, but as any large acquisition is considered, a great amount of analysis must be performed.  To value any software company,  the acquirer must value the assets for product technology, position in the market, product position within the existing family of assets, the company’s existing financial state as well as projected earnings potential.   

Minimizing Risk

This acquisition is one of many major decisions that executives at TIBCO and other corporations go through every year.  Some investment options require incredibly in-depth analysis while other investment decisions may be made quickly with far less due diligence.  There are plenty of reasons for performing an analysis on an investment to a given level and not to a finer level.  When purchasing a stock or making a trade on an existing holding, how much information is driving your decision?  Did you read the prospectus of the latest 10-Q?  Did you attend the recent investor conference calls with management?  Did you get all the answers to your concerns of the latest one-time charge to net income?  The odds are you didn’t.  The odds are you’re trading on gut feel of the situation or you’re trading on some limited understanding and you accept that risk based on the fact that simply don’t have the time to do all of the research you would have liked.  Now, you might also put your trust with money managers or fund managers; expecting they are doing all the analysis required to make good value judgments that are in line with your risk profile and your investment objective.  Again, are you sure they are going down to a depth of analysis that ensures risk is minimized?

A Hedge Fund Legend

Recently, I read about a very successful investor named Michael Burry.  For those of you who haven’t heard of Mr. Burry, he gained a degree of notoriety for wisely betting against banks’ mortgage holdings and cashing in massive returns for his hedge fund when the credit crises hit full tilt in 2007.  His brilliance wasn’t just that he recognized a good bubble when he saw one, it’s the way he figured out how to capitalize on this realization that a spectacular amount of mortgages were doomed to fail.  The fact is, when Mr. Burry first became convinced that the type of lending that banks were engaged in was destined to result in large numbers of defaults, there was no real instrument for wagering against the performance of these notes.  The various tranches of subprime mortgage bonds could not be sold short.  Even with his conviction that the subprime mortgage bond market was doomed, he could not capitalize on it.

Then came Mr. Burry’s discovery of the credit-default swap.  It was basically an insurance policy that could be purchased against corporate debt, but that was only useful for betting against the companies that would likely default such as home builders.  Ultimately, he convinced a number of big wall street firms to create them including Deutsche Bank and Goldman Sachs.  Now, what made his work absolutely brilliant was the fact that he would spend untold hours poring over each bond prospectus, only investing in the most risky of those assets.  He was performing the due diligence on each of the loans, such as analyzing the loan to value ratios, which had second liens, location, absence of income documentation, etc.  Within each bond, he could sort out the riskiest of the lots and incredibly enough, Deutsche and the other banks didn’t care which bonds he took positions against.  He essentially cherry-picked the absolute worst loans (best for him) and found the bonds that backed them.

Mr. Barry would ultimately bring his investors and himself astronomical returns at a time when the vast majority of investors lost roughly 50% during this crisis.  If you read about Mr. Burry, you’ll find there is much more to his story as he is unique in many ways, but one key point that separates him from the pack is that he does his homework.  Details matter.  How these loans were structured matter to all that were connected to them.  In these bonds were real loans that represented real value.  Understanding the risk factors would immediately point to a very low valuation on these bonds.

I’m not going to delve into the full issue of responsibility relative to loan originators, banks, Fannie Mae, borrowers, etc, but suffice it to say that solid due diligence reduces the risk of any transaction.  The more you understand about the asset under consideration, the better you can predict its performance.  It’s as simple as that.

Oneness

So, what’s with my title, “The Interconnectedness of Things?”  Well, it got me thinking about just how interconnected we all are.  Without getting all Jean Paul Sartre on you, let me point out the most common difficulty in all of management: interconnectedness.  That’s right, interconnectedness.  The fact is; executives hate it.  But it exists.  We have the tendency to measure performance of an exact metric; of an exact process step, or an exact person.  We like to think that sorting out the specific items of measurement can enable us to understand what is strong and what is weak.  Fix the weak bits, keep the strong bits, and voila, you have Lean.  But, from the work I’ve been involved in, it’s not so simple.  Similar to the difficulty of sorting out all the bits that make up a good loan from a bad loan; a good mortgage bond from a bad mortgage bond; business processes can be extremely complex and highly interdependent.

How do we get our arms around the complexity of process?  Mostly, in very distinct ways.  How many of us love to look at organizational charts, value chain analysis diagrams, system architecture diagrams?  If you are shaking your head “yes”, I’m deeply sorry.  The fact is, we are trying to ensure we understand the interconnectedness of things, but we often do that work in silos.  In efforts to diagram process or entity relationships of systems or people relationships, this work is most often performed as one-off attempts with a singular purpose or project in mind.  They are not done to ensure a wider scope of understanding is gained and maintained.  And therein lies a serious shortcoming of those efforts.  With islands of understanding, there may be some level of interconnected understanding, but the silos remain silos and whenever we look at those groupings within a map or chart or a diagram, there is too much lost information.  The value of what you have is just as quickly defined by what it does not have.  (Perhaps some Camus?)

Devil’s in the Details

So, how do we connect all these silos and how do we know when we have enough detail?  These are big questions to which there are no silver bullets.  During a recent engagement, I was working with a global IT organization who brought together four business units to define standard global processes.  Ultimately, the idea was to consolidate where possible, but initially they needed to capture how each unit was operating.  I’ve done this type of work a number of times and what still amazes me each time is how often we find gaps in processes, areas that are not understood, as well as overlaps where steps are replicated and no one knew what the other one was doing.  As we embarked on the journey of process design, the key question that this team asked of me was, “how many levels down do we need to go?”  My answer was pretty simple: go down to the level of detail that someone from outside this process area can read and understand what is happening without any ambiguity.

Imagine if you will an organization that has documented down to that level in a consistent way across their organization.  Further, imagine a singular map with diagrams that connect to all appropriate related process steps, to all related electronic content and within a platform that provides instant feedback from the personnel that perform the operations.  Now, that’s getting your arms around complexity and it tells the story of the interconnectedness of things.

Finally, once we gain perspective on this interconnectivity we can truly understand what is working and where risk lies.  For it is risk that we are constantly managing.  The banks that held large amounts of mortgage credit were blind to what was in the big bag of bonds that contained smaller bags of loans that contained all kinds of facts, some of which were never gathered (such as income verification).  Did they completely understand the interconnectedness of things?  Did they get down to a low enough level of detail to really understand the assets that so much was riding on?  To reduce operational risk, the devil’s in the details.  Get your arms around process, get your arms around the details and know what you’re buying into.

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