Blog Posts

The Big Problem With Big Data

Blog: Think Data Analytics Blog

The 2022 Australian Open men’s singles final between Rafael Nadal and Daniil Medvedev was epic! I watched it in Australia, which meant I was up until 2 a.m. on a Sunday night. Monday morning’s dog walk was sub-joyful.

Medvedev seemed unplayable, taking the first two sets comfortably. But Nadal is a legend for a reason. Point by point, he overran his rival to make history.

During that pivotal third set, the “match predictor” showed a 96% likelihood of a Medvedev win—which made total sense, and no sense at all. That’s the problem with big data: It all comes from the past. This poses two challenges for anyone dealing with humanity.

Firstly, the past is a flawed predictor of the present, let alone the future. Secondly, to make sense of it, we suppose or deduce patterns. But behavior isn’t linear, and even when patterns seem to emerge, I refer you to problem one.

So while technological possibility is a core pillar of growth, so too is future literacy, evolutionary psychology, the prevailing culture and complex economics. Economic value is perpetually re-constructed through the constant adaptive behavior of people. This tends to challenge the standard economics of big data . Just like Nadal did.

The Black Paint Psychology

The industrial revolution was about big everything. Big factories pumping out big numbers. It embedded a psyche of volume, efficiency and process. Henry Ford famously said, “Any customer can have a car painted any color that he wants so long as it’s black.” From 1914-1925, the Ford Model T was available in one color for efficiency and uniformity. It meant more cars on the production line.

While we have advanced technologically, economically and culturally since, much of marketing hasn’t evolved at all. Digital changed the tools—not the mindset. Marketers are still chasing the black paint when modern consumerism operates in a vastly different cultural setting. Today, we need to think in terms of choice architecture and operations that harness adaptive behavior.

Customer360 And Personalization

Behavior is a multi-dimensional world of nuance and whim, emotion and numbness, conscious and unconscious choices, rational and non-rational thoughts—much of it biased, and all of it responsive to stimuli both in the moment and over time. But that’s perceived as too complex for operational marketers and so Customer360—the notion of combining all transactional customer data—is used to justify simpler math, under the guise of personalization.

The term personalization has been misused for so long that it’s an engrained blindspot. We’re incapable of recognizing that its not personal at all and that most of our martech is designed by co-conspirators and co-victims of the same paradox.

Case in point: I know an adtech exec who ran an experiment where he browsed his most frequented websites, first in untargeted mode with browser history and cookies cleared and location tracking disabled and then in targeted mode with everything turned back on. He compared the ads being served to him and determined that to be the quality of all web experiences. Furthermore, being ad-targeted made him feel “as though I am falling into a warm bath of relevance and recognition.”

Ad people are the only souls on Earth who think other people actually want ads, which is why the folks behind truly memorable campaigns are geniuses. But that’s about the creative, not about the data or the tracking. The truth is targeted ads are less trustworthy.

Rory Sutherland got straight to the point when he said, “Indiscriminate advertising is more trustworthy than targeted advertising.” It is a position derived from the principle of messaging versus signaling. Messaging involves being able to deliver a message rapidly to an audience because brand trust is established. Signaling, however, occurs when the recipient has no established trust in the sender.

Applying this principle is Don Marti, an ex-strategist at Mozilla. He has a live model that uses “norm-enforcers,” those who punish brands they don’t trust to tell the truth, to test signal effectiveness. It’s very clever. What the model shows, consistently, is that what recipients regard as “honest” signallers tend toward long-range, non-targeted communication. Those regarded as “dishonest” apply highly targeted advertising. It is because it is targeted, that it is less trustworthy. Big data strikes out again.

The Myth Compounds

Famous as the godfather of behavioral economics with a Nobel Peace Prize to prove it, Daniel Kahneman talks a lot about how what we pay attention to affects what we think is important. It’s called “re-framing” and many in the martech sector have used it to create a lucrative niche within big data commerce.

For instance, you don’t have to know anything about a data platform to know that it’s a platform within which you store data. Yet in the customer data platform category, we are asked to accept a different proposition. Here, vendors promote an ability to create customer engagement, even though data platforms don’t cope with real behavior or complex economics any more than customer relationship management software does.

Despite this, MarketsandMarkets estimated the customer data platform market would grow from $3.5 billion in 2021 to around $15.3 billion by 2026. Certainly, re-framing of big data categories has proven highly effective for certain vendors. But for growth?

Making A Comeback

Today, many businesses talk about data, but not about customers. They announce data-first strategies, not customer-first ones. But I sense a comeback.

It is true that new advanced analytics, artificial intelligence and machine learning capabilities, when built for the behavioral paradigm, can underpin customer orientation. I work around it every day. But please understand that this is about the right data, not big data.

In a highly fragmented and hyper-connected culture, the very best of advertising is not in the data, it’s in the creative. It’s not found in the dictated buying models or segments of data platforms. It’s lurking within the behavioral, the psychological and the individual. Our mission as marketers is to drag our profession out of the industrial era. We’re only two sets down. We can do it.

Original Source

The post The Big Problem With Big Data appeared first on Big Data, Data Analytics, IOT, Software Testing, Blockchain, Data Lake – Submit Your Guest Post.

Leave a Comment

Get the BPI Web Feed

Using the HTML code below, you can display this Business Process Incubator page content with the current filter and sorting inside your web site for FREE.

Copy/Paste this code in your website html code:

<iframe src="https://www.businessprocessincubator.com/content/the-big-problem-with-big-data/?feed=html" frameborder="0" scrolling="auto" width="100%" height="700">

Customizing your BPI Web Feed

You can click on the Get the BPI Web Feed link on any of our page to create the best possible feed for your site. Here are a few tips to customize your BPI Web Feed.

Customizing the Content Filter
On any page, you can add filter criteria using the MORE FILTERS interface:

Customizing the Content Filter

Customizing the Content Sorting
Clicking on the sorting options will also change the way your BPI Web Feed will be ordered on your site:

Get the BPI Web Feed

Some integration examples

BPMN.org

XPDL.org

×