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#653: Think Twice About Data You Use in Your Recommendations

Posted By Mark Haas CMC FIMC, Wednesday, September 14, 2011
Updated: Wednesday, September 14, 2011
We are a quantitatively-oriented and capable consulting firm but not so much that we would be considered a heavy-duty analytics one. Here's my question. Our clients can vary considerably on the extent to which they are convinced by numbers. At the risk of sounding harsh, are those that discount quantitative analysis not being responsible managers?

We shouldn't be quick to criticize a client's reluctance to adopt our quantitative analyses. As much as fact-based decision making is a good practice, it implies that those decisions are based on valid and reliable data. Reluctance to base decisions on your data can be a competent approach by managers. Do your clients consider the data you use to develop your recommendations valid or not? Your analytical methods? Whose data and models should you, or your clients, trust?

It is widely accepted that data series used for public policy and private decision making are less than perfect. However, it is increasingly recognized that some of these data and the concepts on which they are based are fundamentally flawed. Research over the past decade shows several macro scale financial concepts (e.g., CAPM, VAR, shareholder wealth) fail to stand up to empirical analysis, despite still being taught in business school. At the national policy level, GDP has fallen from favor because it excludes the majority of asset value and infrastructure investment, compelling some countries like the UK to develop a replacement measure. The World Bank admits that 80% of the wealth of nations is left out of asset accounts, even as those flawed accounts are used for policymaking. The unemployment rate, used by economists and media to track the state of the job market, is understated by about half because it measures people looking for work who can't find a job, not real unemployment. See Shadow Stats as one of many emerging alternative statistics sources.

At the company level, executives complain about distorted financial and tax measures they have to contend with. An investment in training is counted, and taxed, as an expense not the investment it is, thus never shows up on the balance sheet. Uncompensated overtime by salaried employees is considered free labor. Data series, business concepts and tax laws all add to this distorted view of the world. As a result, portfolio managers and consultants are adopting ESG (environment, social, governance factors) triple bottom line asset valuation models, for which there is expanding evidence of being a better predictor of financial success than traditional models.

If you are trying to lose weight and the scale was off by 5 (or maybe 10) pounds in one direction (or maybe the other) every few days, to what extent would you base your diet on that scale? We ask our executives to make fact-based decisions but we also should let them be responsible for judging the validity of the data on which they make those decision.

Tip: This is a good reminder to review with our clients at the beginning of an engagement our assumptions about data sources, analytical models and philosophies, and the extent to which we will base our recommendations on analytical vs. other findings.

© 2011 Institute of Management Consultants USA

Tags:  analysis  data visualization  management theory  recommendations 

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