Long-term thinking, systems of intelligence, and the dangers of sloppy evidence.

1. Long view → Better financial performance.
A McKinsey Global Institute team sought hard evidence supporting their observation that “Companies deliver superior results when executives manage for long-term value creation,” resisting pressure to focus on quarterly earnings (think Amazon or Unilever). So MGI developed the corporate horizon index, or CHI, to compare performance by firms exhibiting what they call long-termism vs. short-termism.

Findings are relevant to executive decision makers: “Companies that operate with a true long-term mindset have consistently outperformed their industry peers since 2001 across almost every financial measure that matters.” Average revenue and earnings growth were 47% and 36% higher, respectively, and market capitalization also grew faster. Yet short-term thinking appears to be on the rise: “We can all see what appear to be the results of excessive short-termism in the form of record levels of stock buybacks in the U.S. and historic lows in new capital investment.”

Developing the CHI required systematic measurement of 5 indicators for 615 large and mid-cap US public firms. For example, “investment” was evaluated based on the ratio of capital expenditures to depreciation. Read about the methodology in Where companies with a long-term view outperform their peers by Dominic Barton et al.

Additional reading. The full research report, Measuring the economic impact of short-termism, is available as a pdf. See a recap of these insights in the Harvard Business Review: >Finally, Evidence That Managing for the Long Term Pays Off.

2. Analytics maturity → Systems of intelligence.
The advanced analytics platform is dead, long live the advanced analytics platform. On Data Science Central, William Vorhies has a nice writeup about how machine learning and data science are evolving. In
Data Science is Changing and Data Scientists will Need to Change Too, he explains why technology vendors must focus on an end-user or customer problem: Show them the evidence, not the sausage-making.

Most people don’t want or need to know the details of the “invisible secret sauce middle layer” – Experts say the “next movement will see the advanced analytic platform disappear into an integrated enterprise stack as the critical middle System of Intelligence…. Suddenly Systems of Intelligence is on everyone’s tongue as the next great generational shift in enterprise infrastructure, the great pivot in the ML platform revolution.” Data scientists must feed Systems of Engagement, where people consume insights and findings.

3. Sloppy evidence → Rethinking the clearinghouse
Patrick Lester of the Social Innovation Research Center (@SIRC_tweets) examines the recent ‘evidence-based’ crisis in Canary in a Coal Mine? SAMHSA’s Clearinghouse Signals Larger Threat to Evidence-based Policy. First we heard the US government prohibited use of the term ‘evidence-based’. But there’s lots more going on: The Substance Abuse and Mental Health Services Administration (SAMHSA) caused a stir with concerns about the validity of evidence recommended by its clearinghouse. The administration revoked the contract of the National Registry of Effective Prevention Programs, which reviews studies of mental health and drug treatment programs.

An independent review highlighted substantial problems with clearinghouse ratings, including potential conflicts of interest. One reviewer of the 113 newly listed programs found that 50%+ were approved on the basis of a single published article, non-peer-reviewed online report, or unpublished report. Plus, many of the studies had design flaws such as small samples or and brief follow-up. Alas, this casts a shadow over sanctioned, evidence-based policymaking.

4. Strong perceptions → Strong placebo effect
The excellent Knowable Magazine did a piece on the placebo effect and imagination. Many studies of placebo effects show them to be strongest in conditions where perceptions are key, such as pain, anxiety and depression. “American anesthesiologist Henry K. Beecher observed that some wounded men from the battlefields of World War II often fared well without morphine.” Thanks to @Koenfucius.

Posted by Tracy Allison Altman on 

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Pondering the places where people interact with artificial intelligence: Collaboration on evidence-based decision-making, automation of data-driven processes, machine learning, things like that.

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