Tag Archives: data analytics

2013-09-25 by: James Bone Categories: Risk Management TheGRCBlueBook Editor’s Letter – September, 2013

free_249263  Autumn waterfall

September represents change in New England.  October is the peak of fall foliage season with its brilliant colors, crisp air, fresh apple orchards and the transition from fall to the last remnants of summer fade in distant memory.   Markets also appear to be experiencing a great deal of change as well.  Historically, fall has ushered in volatility in the markets and this year appears to be no different!

 Chairman Ben Bernanke and the Federal Reserve Bank Governors surprised the markets and did not begin to taper the so called “Quantitative Easing” or purchases of mortgage backed and treasury bonds.  Unfortunately, the “Taper” Caper signals that the economy is not improving as much as expected even though we are now half way through the second half of the year. 

 Most analysts concluded that the Fed would begin to taper its purchases and many had predicted a reduction in the range of $10 – $20 billion.   The assumption was that the economy was improving enough to begin to taper but was not strong enough to pull back too quickly. 

 To Taper or Not to Taper!  The process of making decisions is complex and includes many variables.  Ben Bernanke has been consistent in his speeches that the decision to taper will be based on the “data”.   It appears that the markets misread which metrics would be most influential in the decision making process to taper.

 What lessons can be learned from Ben Bernanke about risk management and dealing with uncertainty?  First, let’s look at decision-making under uncertain conditions.  The Federal Reserve has a range of data points at its disposal which inform its decision making process.   Chairman Bernanke does not make decisions in a vacuum as he chairs and guides the Board of Governors.  Building consensus on key risk decisions is critical to the operation of the Federal Open Market Committee in directing monetary policy. 

Consensus building does not mean that everyone on the committee agrees.  Reasonable people disagree that data considered from their perspective may point out risks containing multiple variables that play into how outcomes unfold or materialize.  The uncertainty of outcomes leaves room for disagreement.  In fact, the discipline of the process may be more important than the actual decision itself.

 What are the important strategic lessons learned from the decision by Ben Bernanke to continue quantitative easing?

 First, the process is time-constrained.  The FOMC meets 8 times per year and must make a decision each meeting.  Markets wait for the Fed decision on monetary policy and reacts accordingly.   People need the certainty of a decision, good or bad, to guide their actions.

 Secondly, data is used as a guide to inform decision making yet the interpretation of the analysis and final conclusions are based on objective and subjective analysis of  perceived outcomes.   Data facilitates but does not govern decision making.

 Third, the markets and members of the FOMC monitor their decisions in real-time and make adjustments along the way as circumstances change.   Build in and anticipate corrective action steps as conditions dictate.

 The final lesson may be that risk management is a process that is responsive to change.  The processes used by Ben Bernanke and the Board of Governors are the result of lessons learned over many years through various market and business cycles.   

Not everyone agrees with Bernanke’s candid disclosures of Fed policy or the decisions made by the FOMC but the process has worked arguably well through the financial crisis.  The FOMC is one example of a live demonstration of effective risk management replete with its successes and failures. 

 Going forward TheGRCBlueBook will highlight additional examples of the art and science of decision-making under uncertain conditions.  I hope that you find the articles informative.

Executive Director, TheGRCBlueBook

TheGRCBlueBook mission is to become a global risk and compliance community site and resource portal for sharing best practice across all highly regulated industries.  A one stop source for all things risk and compliance related.

2013-09-20 by: James Bone Categories: Risk Management Relationships Matter more than ever in a Data Driven World by Keith Lynn

Keith LynnToday, we are inundated with data and it drives most management decisions.  Due to the quantity of data available from a myriad of sources many managers and companies have discarded traditional relationship management and make decisions wholly on data driven statistics.  “If your numbers don’t add up, you might, and probably will be replaced, given a bad review or fail to receive a promotion.”  How can there be an argument against managing with data?  Statistics bear out the validity of the decisions.

However, there can be negative consequences:

Management decisions based wholly on data can cause disenfranchisement among your key employees.  When they determine they are not valued beyond the numbers they produce the work environment quickly can become toxic with interpersonal relationships and company loyalty quickly disappearing; especially if the change is sudden and made without proper preparation and orientation of employees.

In this data only environment, local market conditions are often ignored as the data from the larger market fosters the decisions made locally.  The ignoring of this local information often creates false goals, either high or low; damaging the relationships between the local producers, management and “corporate.”

A second byproduct that is even more disturbing is a quickly learned lesson: “If numbers are the only thing that matter, then it does not matter how they are obtained.”  In the sales world, this can lead to questionable sales practices and eventually damage to the company’s reputation as the sales force struggles to remain relevant.   In a team environment it can and often does destroy cooperation as each participant tries to obtain the credit in order to survive.

So, if these are the dangers, how can they be avoided?  

First: don’t bury your head in the sand and ignore the data.  This will lead to failure quickly. 

Second: Don’t make long term decisions on short term data.  I have found that many managers wake up in a new world at the beginning of each week or month, often reacting to the numbers instead of looking at what caused them.  I have found that measuring positive activity rather than short term sales results is a much more effective way of determining successful results and one which all participants can agree.

Third: Trust your producers and listen to them.  Decisions made do not have to agree with all of the ideas presented, but listening and gathering input will foster respect both for the producer and for the manager.  Each will understand they have been heard and the final decision will most times be better because of this interaction.

Fourth:  There is still time in this fast paced data driven world to stop and appreciate your employees.  Just a few words of encouragement, without a hidden agenda, can go far in making a positive work environment.

Data is not going away nor should it.  Better and quicker decisions are made because of the availability of information.  Yet, time proven relationship management techniques can be used to maximize your results and ensure team loyalty.

Keith Lynn is a retired senior sales executive with expertise managing high performing sales professionals.

TheGRCBlueBook mission is to become a global risk and compliance community site and resource portal for sharing best practice across all highly regulated industries.  A one stop source for all things risk and compliance related.

2013-05-28 by: James Bone Categories: Risk Management Aligning strategic value with risk management


The vision of risk management contributing as strategic partner in the executive suite has long been a dream of most serious risk professionals and now that vision may be coming into focus.  Senior managers now view risk managers as strategic partners in the execution of corporate objectives by assessing and identifying key risks resulting from strategic plans.  That’s the good news!

However, according to a study by Marsh and RIMS “only 15% of the risk professionals and 20% of the C-Suite respondents said the risk manager is a full member of the strategic planning and/or execution teams, suggesting that risk management has yet to be fully integrated strategically.”

The study does not attempt to explain why risk managers have not made the leap to equal partners in guiding the organization to successful outcomes but one key factor may be the relevance of risk information brought to the table.  This begs the question of what defines strategic value in risk terms?  Increasingly the answer is data and the analysis of risks impacting an organization.

It is hard to argue with the collective wisdom that is forming around the quest for a better understanding of data and developing better techniques for the analysis of data.  Senior management has begun to define the value proposition in the form of data analytics therefore risk management must be responsive to these expectations. 

The problem or challenge with these surveys is the generic use of the terms data analytics and the lack of specificity regarding what firms expect. 

Blindly conducting fishing expeditions for the sake of “doing” risk management may backfire and not produce the results firms are seeking.  Many obvious risks are lying around in plain view needing attention but are ignored because there is no systemic approach to investing in risk mitigation.  Other risks are the unknown risks that are inherent in the uncertainty of launching a new and unproven initiative or line of business. 

What appears to be missing is a clear and balanced approach to risk management with a focus on setting the context for discussing risks and the tools that should be employed to understand and address risks.  Risk management is not a science project where data analysis alone will uncover some universal truth.  Good risk management is the implementation of a clear baseline from which to judge changes in the environment that may create risks and opportunities alike. 

Risks, in all its forms, evolve as the business environment evolves requiring senior management and the risk manager to think about risk as a natural byproduct of business objectives.  Risk practice, no matter how quantitatively proficient will not eliminate risk.  Therefore, risk management should be perceived as a learning process informed by data and adjusted in response to new information as it becomes available.

When everyone understands that risk management is a process like all good business processes risk managers will have earned their place in the executive suite with other senior managers.

TheGRCBlueBook mission is to become a global risk and compliance community site and resource portal for sharing best practice across all highly regulated industries.  A one stop source for all things risk and compliance related.

2013-05-12 by: James Bone Categories: Risk Management Algorithmic staff recruiting


 While you may not know the term “work-force science” your next job may be determined by Big Data.  A small but growing trend is emerging with recruiters and Silicon Valley start-ups to find top talent using analytics based on publicly available data.

The search for top talent using data and social media has been revolutionized by sites such as LinkedIn and the bar is being raised by new start-ups seeking to cash in.  Luca Bonmassar is the founder of Gild, a new entrant in the talent search industry to use proprietary analytics to find talent for highly sought after computer programmers.

Bonmassar and others in this field are turning traditional metrics of recruiting on its head by developing algorithms to determine how well someone will perform on the job.  

The traditional markers of top talent such as the college you attended, referrals from colleagues or your past career path may become less relevant, at least for high tech talent.   Gild searches for other clues to determine job performance by scouring the web and social media in search of test scores, relevance in the blogosphere, and other soft skills that may not be apparent in a resume.

Gild is not alone.  According to the New York Times author Matt Richtel, “competitors such as TalentBin, RemarkableHire, and Entelo” all perform their own version of data analytics to uncover talent for firms seeking hard to find top talent. 

Not everyone is convinced that Big Data is a huge improvement over the current process performed by human resources according to Susan Etlinger, data analyst with Altimeter Group.  “The big hole is actual outcomes,” she said. “What I’m not buying yet is that probability equals actuality.”  However, Etlinger concedes “it’s worth a try. “

The potential and risks associated with Big Data will no doubt have a profound impact of our lives in ways not yet contemplated.  The concept of privacy is evolving along with its inherent risks and opportunities for sharing data to create new opportunities.  

But don’t be surprised in the near future when the email you receive for a new career opportunity comes from a computer program instead of a human.

Originally article written By MATT RICHTEL / The New York Times
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TheGRCBlueBook mission is to become a global risk and compliance community site and resource portal for sharing best practice across all highly regulated industries.  A one stop source for all things risk and compliance related.

2013-05-08 by: James Bone Categories: Risk Management Decoding Hit Movies with Data

Marilyn MonroeHollywood is quietly using data analysis to decode the secret formula hit movies.  Data analytics has been used in medical research, Wall Street financial reports and a host of other industries now movie scripts are being mined for the “hits” and “misses” in Hollywood scripts.

“This is my worst nightmare” said Ol Parker, a writer whose film credits include “The Best Exotic Marigold Hotel.” But Hollywood executives are plowing ahead with data analysis to mitigate the risk of a box office flop. 

Vinny Bruzzese, a chain-smoking professor who has taught statistics at State University of New York at Stony Brook on Long Island, claims to be a distant relative of Albert Einstein.  Bruzzese is but one of a cadre of analytical scientist and students who may well form an entire industry preparing to find gold in the data haystack.

Hollywood, long the bastion of creative talent has come to respect and fear Dr. Bruzzese’s success.  The New York Times article by Brooks Barnes notes that movie executives are paying as much as $20,000 per script to compare the movie script and genre with recently released box office hits shows. 

Bruzzese sees a market on Broadway and with TV producers as well which suggests that data analytics will become a critical risk mitigation tool in a variety of entertainment industries. 

Solving Equation of a Hit Film Script, With Data, By BROOKS BARNES