Michael.Walker's blog

Economists apply theory to complex reality yet fail to accurately interpret the past attempting to forecast the future.

Lawyers define and manipulate words yet fail to define justice within legal architectures.

Physicians apply medical science to cure disease yet fail to define health and accept unknown unknowns.

Accountants count yet fail to calculate future uncertainty.

Mathematicians apply logic yet fail to factor human fallibility.

Psychologists apply academic theory to complex human behavior yet fail to understand their own minds.

Seasoned data scientists avoid the quantitative fallacy trap where you focus solely on certain quantitative metrics while ignoring other non-quantifiable variables. While the old saw that you cannot improve and manage what you cannot measure is true - what you decide to measure and not measure matters a great deal for understanding complex static, situational and fluid reality.

Data science forensics is a hot growing field for professional data scientists. Usually employed to detect financial and business fraud, it is now being used to detect voting fraud in the US. Teaming with data engineers, computer scientists, lawyers, law enforcement and regulators, forensic data scientists look for anomalies to flag for further investigation.

Recent events suggest a smart upgrade of election voting systems. I propose an intelligent e-voting system architected to prevent vote fraud and protect personal privacy while allowing optimal convenience and ease-of-use for voters.

Biometric technology allows voter identification to grant access to voting systems (using facial recognition, iris retina scanning, fingerprint or voice identifiers).

COVID19 exposed the danger of relying on models to make policy decisions. It appears in vogue with certain academics that models are "science" and fit for forecasting and guiding policy decision making. Yet as any successful real world executive, trader or surgeon will tell you, models are NOT appropriate for decision making but may or may not be useful for understanding phenomena.

Models are a useful tool, NOT an accurate forecaster.

COVID19 has exposed many hidden conflicts of interest in the scientific community. An ancient proverb holds that he who serves two masters has to lie to one. Public scientists have an interest in obtaining funds for research. Private scientists have an interest in holding gainful employment in organizations that may profit from exaggerated disease risks and public fear. Incentives matter.

Professional data scientists are goal oriented and understand there are many ways to skin a cat.

One school of thought is to NOT aim directly at the goal but rather focus on process and NOT care about the outcome. This mindset provides great success for professional athletes, big mountain powder skiers, traders, surgeons, CEO's, fighter pilots and other elite performing people. It reduces anxiety and calms the mind.

Seasoned data scientists understand that leading a high performance organization demands both discipline and freedom. Discipline to apply logic, avoid biases and follow scientific method. Freedom to explore a diversity of conceptual frameworks and allow cross pollination of subject matter expertise.

Seasoned data scientists understand that some data is more important than others.

Great data scientists are able to acquire high value awesome data and interpret to find new truths.

Are you preparing to become a great data scientist?

Professional data scientists understand that algorithm source codes is the secret sauce to significant competitive advantages, better public policy and massive profits. Ownership and control of the algorithm source code is of critical importance.

Data scientists who work as an employee directly for an employer concede legal ownership and control of any and all work products to the employer.