As someone who has "data-driven" in his LinkedIn profile, I co-sign a lot of what I see here as it relates to political campaigning. The issue I cut my teeth on, "climate change", has been polled in so many variants ("global warming", "climate crisis", etc.) that seasoned activists begin to sound like robots; the candidate campaigns I have chosen to work on have purposefully all been state-level races where the communications have not yet been hijacked by AI.
I also appreciate you mentioning that data is ubiquitous and something we shouldn't remove entirely. To offer something more surgical: a helpful distinction here would include the difference between using data in "politics" versus in "policy". Principled policy without data to back it up can become ideology-driven destruction.
Furthermore, although you don't speak about religious organizations in this specific piece: I do think that while a prophetic voice is necessary against abuses of data, we who work in this space also need to simultaneously demystify some of the fears religious groups have about data, as it possible to steward data in order to improve missional efficiency, accountability, and resilience. (As a fun trivia fact: the word "data", which derives from the Latin for given, was first extensively used in the early modern era to refer to "scriptural truths or revelations given by God".)
Even in politics, polling data can be good for understanding what voters actually want! It can tell Republican politicians that their voters do actually support Ukraine, so even as they fear a base they don’t understand on so many other issues, they can continue to vote in favor of that funding. And it can tell Democratic politicians that voters really care about energy costs, so reducing the carbon-emitting energy supply is a bad climate strategy compared to pursuing an abundance of cheap, renewable energy alternatives.
Now you might argue that both of those examples are really about the underlying policy, not the politics of it. But many people want politicians to put their campaign promises into action as policy, and politicians feel a need to follow through, even if conditions have changed significantly (like prominently recently with student loan forgiveness). So getting good polling data could teach Republican (and progressive) candidates not to campaign on cutting off Ukraine and Democratic candidates not to campaign on pipeline cancellations to carry those examples through.
Hmmm, I don’t think the data-driven-ness is the real problem in each of the cases you cite.
1) In the example of profanity in a campaign email, the problem was which metric they chose to maximize: open rate. Any number of other metrics could have led in a different direction, and your concern could have just as well been described quantitatively as with the “forward rate.”
Generalizing beyond the specifics of email, profanity attracts attention, and the problem was not that they had quantified attention but that this is what the team chose to maximize.
2) Slicing and dicing the electorate, and tailoring different messages to different subgroups of the population, is honestly as old as democracy itself. Sure, fine-grained data can make that potentially easier, but the decision to target your message to a subgroup in the first place is the real issue.
To take another example from 2012, the lack of micro-targeting didn’t make it any less bad for Romney to talk disparagingly of the 47 percent of the population who receive government assistance in a closed-door fundraiser with rich folks. It was the same problem of talking out of both sides of his mouth.
Data is just a tool, the real issue is what we’re trying to do with it,
As someone who has "data-driven" in his LinkedIn profile, I co-sign a lot of what I see here as it relates to political campaigning. The issue I cut my teeth on, "climate change", has been polled in so many variants ("global warming", "climate crisis", etc.) that seasoned activists begin to sound like robots; the candidate campaigns I have chosen to work on have purposefully all been state-level races where the communications have not yet been hijacked by AI.
I also appreciate you mentioning that data is ubiquitous and something we shouldn't remove entirely. To offer something more surgical: a helpful distinction here would include the difference between using data in "politics" versus in "policy". Principled policy without data to back it up can become ideology-driven destruction.
Furthermore, although you don't speak about religious organizations in this specific piece: I do think that while a prophetic voice is necessary against abuses of data, we who work in this space also need to simultaneously demystify some of the fears religious groups have about data, as it possible to steward data in order to improve missional efficiency, accountability, and resilience. (As a fun trivia fact: the word "data", which derives from the Latin for given, was first extensively used in the early modern era to refer to "scriptural truths or revelations given by God".)
Ah much good here to consider, Kaleb!
Even in politics, polling data can be good for understanding what voters actually want! It can tell Republican politicians that their voters do actually support Ukraine, so even as they fear a base they don’t understand on so many other issues, they can continue to vote in favor of that funding. And it can tell Democratic politicians that voters really care about energy costs, so reducing the carbon-emitting energy supply is a bad climate strategy compared to pursuing an abundance of cheap, renewable energy alternatives.
Now you might argue that both of those examples are really about the underlying policy, not the politics of it. But many people want politicians to put their campaign promises into action as policy, and politicians feel a need to follow through, even if conditions have changed significantly (like prominently recently with student loan forgiveness). So getting good polling data could teach Republican (and progressive) candidates not to campaign on cutting off Ukraine and Democratic candidates not to campaign on pipeline cancellations to carry those examples through.
Hmmm, I don’t think the data-driven-ness is the real problem in each of the cases you cite.
1) In the example of profanity in a campaign email, the problem was which metric they chose to maximize: open rate. Any number of other metrics could have led in a different direction, and your concern could have just as well been described quantitatively as with the “forward rate.”
Generalizing beyond the specifics of email, profanity attracts attention, and the problem was not that they had quantified attention but that this is what the team chose to maximize.
2) Slicing and dicing the electorate, and tailoring different messages to different subgroups of the population, is honestly as old as democracy itself. Sure, fine-grained data can make that potentially easier, but the decision to target your message to a subgroup in the first place is the real issue.
To take another example from 2012, the lack of micro-targeting didn’t make it any less bad for Romney to talk disparagingly of the 47 percent of the population who receive government assistance in a closed-door fundraiser with rich folks. It was the same problem of talking out of both sides of his mouth.
Data is just a tool, the real issue is what we’re trying to do with it,