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Data objective

As explained in Data for All

An objective is a specified goal that guides how data is managed, offered, combined, or priced.

What do you want your data to do for you? A data objective is a goal that guides choices about how your data is offered, combined, priced, or made available. The goal might be to earn money, support work you value, or prevent uses you oppose. Choosing one starts with a more personal question: which of your values and needs should your data serve? section 8.6 section 8.6.3

In the envisioned system, you would set a primary objective in an account with a DI, a data company that helps manage and offer your data. If your priority were revenue, the DI would assess prices for individual data elements and for combinations. For example, combining online and in-store grocery purchases could give a more complete picture of your spending. The objective tells the DI what you want to achieve; the grouping and pricing are ways it might pursue that goal. section 8.6.2

One objective need not govern every use of your data. You could set different terms for different purposes:

  • Support disease research with genetic-sequencing data while expressing a preference between work on a treatment and work on a possible cure. section 8.6.3
  • Limit access by company or region, or allow broad use of transaction data without a fee. section 8.6.3
  • Arrange for use by a chosen clean-water charity to trigger a payment from your DI account. section 8.7

Objectives can change as your needs and life stage change. You might add, remove, or revise them rather than make one permanent choice. Working out how your values translate into data settings may take thought; DIs are envisioned as helping with that work. The practical starting point is to decide what matters to you before deciding who may use which data, on what terms. section 8.6 section 8.6.2

How Data for All covers it

How deep it goes
Outlines selecting a primary goal and revising multiple goals, with choices about access and price but no detailed interface procedure.
Examples
Illustrates objectives through grocery-purchase grouping, genetic data for disease research, company access restrictions, and clean-water charity payments.
What it assumes
Starts with personal goals and values, while anticipating DI help in translating them into data objectives.
What you can do after
Evaluate/understand how to express changing priorities as proposed terms for data use, access, and payment.
How it connects
Connects personal values to data monetization, access rights, data grouping, and charitable giving.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Data for All. The section links point to the parts of the book it draws on.