The essential
- Decision bias: Relying solely on data to make decisions often confirms biases rather than optimizing outcomes. Seek diverse sources of data to challenge preconceived notions.
- Consumer vs producer: The impact of data varies widely between consumers and producers, affecting decision-making processes differently depending on perspective.
- The role of AI called into question: Rebecca Haddix urges decision-makers to ask tough questions of AI, making the case for its role in challenging, not just confirming, business strategies.
A few months ago, I was doing research for a blog I wrote about data-driven decisions. Regular CMSWire Readers know that there is a lot of information out there on the subject, from pseudo-scientific research into the effectiveness of human intuition to worrying predictions that humanity will soon cede all control to AI.
What is clear, however, is that when it comes to using data in decision-making, analytics are too often used to confirm preconceived ideas rather than optimize outcomes. Let’s take a look at some issues related to decision-making bias.
Misuse of data: skepticism about statistics is healthy
The idea that data can be misleading and misused is not new: Mark Twain, quoting British statesman Benjamin Disraeli, spoke of “lies, damn lies and statistics.” Most attribute the quote to Twain – which is false, but it actually is. doubtful, Diraeli was the first to say it either. What I mean is that if you get your information from a tabloid newspaper, from a chatbotor an analytical dashboard integrated into your DXP, skepticism is healthy.
Related article: Are your business decisions failing because they are biased?
Data-driven decisions: more harm than good?
As I was searching for opinions on the subject, an article in Forbes written by Rebecca Haddix jumped out at me. I found it refreshing for its honesty and authoritative tone – I also loved the title: “Your Data-Driven Decisions Are Probably Wrong.” Rebecca has been contributing to Big Data technology for Forbes for nearly a decade.
Her paper, published in 2020, didn’t mention generative AI at all, but the strategy and clear guidelines she suggested seemed so relevant to the debate we’re currently having over decision-making bias and the data that I felt compelled to reach out. I wanted to know if his advice to decision-makers in 2024 would be different now that the topic of AI is present in every conversation.
She was kind enough to set up a video call with me. What follows is a summary of some of his deep thoughts from this discussion. Let’s consider what Rebecca has to say about data-driven decisions, decision bias and more.
Related article: 3 Ways to Reduce Bias in Customer Survey Data for an Effective Customer Experience
The impact of Big Data, good and bad, is a question of perspective
The large amounts of data we collect can be good or bad depending on our perspective as a consumer or producer. In any study you read, you will learn that we now measure daily data creation in hundreds of terabytes per day and this collection rate is increasing at an exponential rate.
When I asked Rebecca whether the vast amounts of data we collect were good or bad for us, her response was nuanced and surprising, drawing a distinction between consumer and producer decision-making. She replied: “I guess it depends on who we are in this equation. RIGHT? So, as consumers, we have the choice between products created by producers, companies.
Related article: Managing AI bias, part 1: Recognizing bias
Consumers delegate their choices, trust takes precedence over optimal decisions
She went on to explain that consumers want to delegate to a trusted source, an influencer or a search engine. Consumers are excited by the prospect of optimal decisions, but this is often not what they actually want or need.
“The research is based on a number of factors, as well as marketing and SEO, which may not give the optimal purchasing decision for you as a consumer for a number of factors that you do not consider , that’s why global sources that people trust come into play.” She adds: “As a consumer, we’ve never made the optimal decisions. Having more data means we analyze less.
“When you say we, as a producer, a company, creators of these products…I think more data is really exciting. As long as we’re intentional in how we process it and analyze it.
“We’re already sort of at the saturation point, with the human brain as a consumer not consuming much more. So I’m really excited about the rise of Big Data. And the impact that that has on companies that are making new products, whatever they may be, and really optimizing the way that we’re going to work, have the ability to act on the basis of a better understanding of the relationship real between different things. That’s exciting.”
Related article: The imperative of data mastery in business decision-making
It is the responsibility of humans to ask the right questions
Rebecca once wrote: “The results will only be actionable if the inputs are relevant.
“We get an answer. We don’t necessarily get an answer to the question you’re asking based on the training data that it has. So our responsibility is to ask the right questions and ensure that (the AI) is trained on the correct dataset.
If we were looking for information on a medical topic, for example, she suggested: “We might say look only at JAMA, the Journal of the American Medical Association, from these dates, by these authors who have at least quotes and replies. a question like this: “What is the most effective treatment for this disease?” » So go validate that.
Related article: Overcoming AI Bias in CX with Latimer
Challenge AI: Avoid Bias and Improve Your Marketing Strategy
“So with marketing technology, in particular, if we say, ‘Well, the campaigns that we have the ability to run, the data that we have the ability to collect, only reflect the visitors to our site who have arrived here through the existing channels that we have, there could be optimal implementations or experiments that we don’t have yet.
She goes on to suggest that the process begins by developing a clear problem statement and making four or five hypotheses about the optimal solution before asking the question (to the AI), and at this point: “ Don’t ask the AI to prove you are right. – dare AI to prove you wrong. This helps avoid decision-making bias.
Decision-making bias: it’s human nature to want to be right
Rebecca highlighted the importance of having a “continuous instinctive check of our own biases, because when we have an idea, we would like to be right, so we like to look at the data ourselves? In the scientific method, we don’t achieve this. We look for evidence that our assumptions are wrong. This is how we can get around this cyclical bias loop that I often see. We want to be right;
Rebecca added: “We want to say that our intuition is as important as the data, (but) it’s by asking the right questions and then using the data to disprove hypotheses – or not – and not just delegate the taking decision-making on dashboards. »
(Author insight: Rebecca Haddix is not a fan of analytics dashboards.)
Isaac Asimov was a visionary
Towards the end of our interview, Rebecca recited science fiction writer Isaac Asimov’s Three Laws of Robotics almost verbatim, which I found quite impressive. This happened when I told him my thoughts that the AI should adopt the posture of a guide dog for his humanit is more about protecting us from danger than accelerating our progress.
For reference, Asimov’s three laws of robotics are as follows: (1) A robot may not injure a human being or, through inaction, allow a human being to come to harm. (2) A robot must obey orders given to it by human beings, except where those orders would conflict with the First Law. (3) A robot must protect its own existence so long as such protection does not conflict with the First or Second Law.
She talked a little about how these rules ripple out, each building on the last. It’s a powerful concept, but one worth noting, that faster and better cognition, at our request, should put guardrails in place to protect and enforce restraint – be trained to discern danger and to protect themselves.
Final Thoughts
It was enlightening to hear a thought leader build on his already strong ideas from years past. The new information did not change their previous advice, but improved it with more in-depth information.
As do notable thinkers such as Isaac Asimov and Rebecca Haddix. I look forward to future articles and other discussions on data-driven decisions, decision-making bias, and other topics covered by her.
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