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Data is our story, algorithms transform our stories to the future, but it is policy and incentives that make it so

Updated: Mar 27


Data and algorithms are often confused. However, it is the organizations and people that make use of data and algorithms to fulfill their incentives.


Data represents our past reality. Algorithms are used to transform data. They are different. Data has already happened. An algorithm is a tool to transform data intended to predict and impact the future. Sometimes that data-transforming algorithm is helpful to you. More often today, that data-transforming algorithm is helpful to an organization trying to sell you something - like goods, services, or political candidates. An organization's algorithm may be helpful to you, but it often serves other purposes, including maximizing shareholder profit.


data policy algorithm

Generally, public companies have 4 major stakeholders or "bosses to please" and you are only one of the bosses. They are:

  1. The shareholders,

  2. The customers (YOU),

  3. The employees, and

  4. The communities in which they work and serve.


Company management makes trade-off decisions to please the unique needs of these stakeholder groups. In general, available capital for these stakeholders is a zero-sum game. For example, if you give an employee a raise, these are funds that could have gone to shareholder profit or one of the other stakeholders.


This means the average organizational investment and attention weight for your customer benefit is 25%. The customer weight could certainly be below 25%, especially during earnings season. Objectively, this means a commercial organization's algorithms, on average, are not explicitly aligned with customer welfare. Often, the organization's misaligned algorithm behavior is obscured from view. This obscuring is often facilitated by the organization's marketing department. Why do you think Amazon's brand image is a happy smiley face :) For more context on large consumer brands and their use of algorithms please see the next article's section 5 called "Big consumer brands provide choice architecture designed for their own self-interests."



This article - Nurture Your Numbers: Learning the language of data is your Information Age superpower - focuses on data that will help you make algorithms useful to you and identify those algorithms and organizations that are not as helpful. Understanding your data in the service of an effective decision process is the starting point for making data and algorithms useful.

While this article is focused on the data, please see the next article links for more context on algorithms:


An approach to determine algorithm and organizational alignment in the Information Age:


How credit and lending use color-blind algorithms but accelerate systemic bias found in the data:


About the author: Jeff Hulett is a career banker, data scientist, behavioral economist, and choice architect. Jeff has held banking and consulting leadership roles at Wells Fargo, Citibank, KPMG, and IBM. Today, Jeff is an executive with the Definitive Companies. He teaches personal finance at James Madison University and provides personal finance seminars. Check out his new book -- Making Choices, Making Money: Your Guide to Making Confident Financial Decisions -- at jeffhulett.com.

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