Sunday, February 26, 2017

The Impact of Loss Aversion on Project Management

by David Ashley

Here’s something interesting.  When faced with the choice of avoiding a loss or pursing a gain, most people have a strong tendency of putting energy toward protecting against losses rather than concentrating on increasing gains.  This is because losses have a greater psychological impact than do gains of equal magnitude. It’s a theoretical principle referred to as loss aversion. (Ariely D., 2005) (Baumeister R.F., 2001)

I’ll offer a project management example.  After spending a good amount of time and resources on a project that is not producing the desired outcome, decision makers tend to keep going the course for the sake of protecting the project investment instead of ditching the project (accepting the loss) for another project promising better gains. 

Complementary studies on Status Quo Bias indicate that people largely prefer the status quo over alternative options that have some uncertainty. The greater the number of available options the greater the volume of uncertainty and the greater likelihood of staying with the status quo. This is true even in cases where the status quo is not positive. It generally requires a disproportionate effort to dissolve uncertainties in alternative options for the scales to tip in favor of those alternative options. (Samuelson, 1988) (Fleming, 2010)

Overcoming these psychological forces is a challenge in any project team.  Managers should first do everything possible to avoid getting into these situations. But even with the best intentions and planning often there is no guarantee that the team won’t eventually need to deal with the exact situation they were trying to avoid with all the planning. 

What does this mean for project teams? I have two considerations:

1.     The most critical decision making happens early.  Since these natural aversions and biases against change are so strong and difficult to overcome, the importance of correctly exercising initial decision making processes is all that much more important early in the project.  

2.     Risk management skills are essential to reducing the negative impact of changing direction. Recall that risk management is about predicting and evaluating both positive and negative events that could happen during the course of a project. It’s a proactive exercise. Risk management tools help define these potential events, postulate outcomes, determine the most promising path, and reduce uncertainty in alternative options.  The degree to which uncertainty can be reduced is the degree to which the effects of loss aversion and status quo bias can be reduced.  Use qualitative and quantitative measurements to define and reduce uncertainty.  Probability and impact matrices will help rate the importance of risks.  The Delphi technique can reduce bias and help create a consensus.   (Institute, 2013)

There is much more to be said about how to make good early decisions and how to lay out plans to deal with uncertainty should it arise.  There are volumes of books that speak on these topics, so I won’t go into all that here. But I’ll leave you with a quote from Henry Ford who seemed to be immune to Status Quo Bias and Loss Aversion.

Failure is only the opportunity to begin again more intelligently. 
- Henry Ford


References

Ariely D., H. J. (2005). When do losses loom larger than gains? Journal of Marketing Research, 134-138.
Baumeister R.F., B. E. (2001). Bad is stronger than good. Review of General Psychology, 323-370.
Fleming, S. T. (2010). Overcoming Status Quo Bias in the Human Brain. Proceeding of the National Academy of Sciences of the United States of America (pp. 6005-6009). Massachuesetts: National Academy of Sciences.
Institute, P. M. (2013). A Guide to the Project Management Body of Knowledge. Newtown Square, Pennsylvania: Project Management Institute.

Samuelson, W. Z. (1988). Status Quo Bias in Decision Making. Journal of Risk and Uncertainty, 7-59.

Sunday, February 12, 2017

Data Driven Decisions? Only if Growth Matters

by David Ashley

Program and project managers are often consulted because of their intimate involvement with improving business growth through the projects they manage. Recently I have been facilitating meetings and decision making among a team of managers with regard to some strategic planning. While doing some research for this project I ran across an interesting stat:

When data is used to drive decisions, it is paying off to the tune of 5-6% higher productivity and also increasing asset utilization, return on equity, and market value.  This is enough to separate those who win and those who lose. (Brynjolfsson, 2011)

One zettabyte is one billion terabytes. That's a number almost too big to comprehend. Cisco predicts that by 2019, global traffic is expected to hit 2 zettabytes per year. (Pappas,2016) That's like streaming Netflix's entire catalog 6,300 times. That's another number beyond me; I'll watch maybe a dozen movies per year. Continuing the mind-blowing, growth is expected to be at a pace that will more than double every two years. (IDC, 2011)  Bottom line: world data growth is huge... very huge.

Most forward looking companies are expanding their business data at a similar rate through the inclusion and integration of data that is normally outside of their core business data to include cloud, social media, and mobile.  This is the new world of 'big data'.

The business opportunity is the glaring question. What should managers be doing with business data? (no matter how big it is)

I’ve created a 5-step production improvement model that can be used to get most managers started along the path of using data to increase competitiveness and growth. It's a model that can be used during project initiation or even during project execution.

1.    Define Key Performance Indicators.  What specific measurements define success for your business?  At the very least, determine the most critical measurements that should be used to drive major company decisions.

2.    Create Metrics.  Measure and track the key performance indicators defined in step 1.  Do this in periodic (monthly/quarterly) reports with graphs and charts. Be disciplined and consistent when collecting and measuring. Be transparent about the metrics by publicizing and making them available to the entire company.  Help everyone understand that these measurements represent the health and future of the company (everyone’s jobs) and that everyone contributes to these successes and failures.

3.    Analyze Trends. Conduct deep dives into the data to determine causes and effects. Understand variances and anomalies. Definitively prove what causes growth and what causes declines.  Don’t guess; opinions aren't important here. This should be an unbiased, non-emotional, objective analysis. 

4.    Act on Analysis.  Promote those things that cause growth, reject (don’t repeat) those things that cause declines or negative results.  Do limited tests if necessary when analysis is not conclusive, but resist the temptation to act on suspicion or speculation.

5.    Measure Again. Repeat steps 2-4. 

Data can be just a bunch of numbers if left unused.  However, successful businesses use data within a continuous improvement model to grow and become even more successful.



References

Pappas, Stephanie, How Big Is the Internet, Really? (2016, March), Life Science, Website: http://www.livescience.com/54094-how-big-is-the-internet.html

IDC. (2011, December). In Frank Gens (Ed.), Top 10 Predictions, IDC Predictions 2012: Competing for 2020 (IDC #231720, Volume 1). Retrieved January 5, 2012, from CDN.IDC.com Web site: http://cdn.idc.com/research/Predictions12/Main/downloads/IDCTOP10Predictions2012.pdf

Brynjolfsson, Erik, Hitt, Lorin M. and Kim, Heekyung Hellen, Strength in Numbers: How Does Data-Driven Decisionmaking Affect Firm Performance? (April 22, 2011). Available at SSRN: http://ssrn.com/abstract=1819486 or http://dx.doi.org/10.2139/ssrn.1819486