Post-Apocalyptic Advertising. (And Life.)

I’ll admit, like many people of my generation, I grew up in an advertising business that was infinitely less-complicated than the one I work in now. In fact, not only was it less complicated, it was more effective. 

Great brands—the brands we remember, admire and use today—were mostly built with great headlines, art and design. So agencies were heavy in copywriters, art directors and account people to sell the work. In the words of a notable adman I grew up with, agencies “Put their money where they made their money.” In creative.

Of course today, in the age of big data, where we make our money is anything but creative. Creative is told to be faster and cheaper. We behave as if "availability" is a "capability." We sling cliches and we call them insights.

Where agencies make their money today is in data. They make it in analyzing data. In packaging data. In manipulating data. And in selling to the easily-gulled the idea that we know what all this data means.

You only have to spit these days to hit a data-conspiracy theorist who believes that the data-surveillance-state being built by Google, Facebook, Amazon, Russia, the US, the NSA, the Chinese and a few dozen other entities will render us easily-manipulated by those companies. So easily-manipulated that our every action and reaction will be predicted, controlled and monetized by those companies.

They’ll know what we’re going to buy, where we’re going to eat, who we’re going to vote for and who we’re likely to sleep with. They’ll know more about us than we know about ourselves and will, in time, use their knowledge to turn us into docile dotards who will turn over our entire paycheck to anyone who can use predictive analytics like a Borscht Belt comedian uses a whoopie cushion.

That’s fine and apocalyptic.

Only, I don’t believe it. 

Because for all the precision and brilliance of big data as it exists today when our every keystroke or eye-movement is being tracked, 37% of the non-work email I get is for baldness remedies, 12% are for home loans or chimney-sweeping services and 51% are for brides, Asian, Ukrainian or Russian. Presumably certified pre-owned virgins.

In fact, I can’t think of a single brand, with the possible exception of Amazon (which in reality is built not on a data strategy but on a tax-avoidance strategy) whose eminence you can remotely attribute to data strategy.

Nevertheless, it seems the current superstars of our non-creative “creative” industry are data scientists or data strategists.

Just now in the nearly moribund and always un-insightful “Agency Spy” there’s an article about a “customer experience agency” that’s just hired a new svp of data and insights.

Again, I’ll confess to my ignorance. I have virtually no idea what a customer experience agency does. About 97% of the customer experiences I have are bad experiences. Most times when I buy something, I do it in spite of my customer experience.

Then I wondered, about what this new svp would be doing. According to the agency’s CEO, “he will deliver more customer loyalty and business growth to clients.” And his “deep knowledge and expertise in leading data and analytics…will better serve our clients to create customer experiences that matter.” The new svp himself adds, I committed “to data and finding the intersection between customer and business truths that inspires thoughtful human experiences…”

For the life of me I have no idea what a customer truth, business truth or thoughtful human experience is, at least as it pertains to a company.

The sad fact is, I think as an industry we have almost completely forgotten what a "thoughtful human experience" is. Most depictions of carbon-based bipeds I see on television have as much emotional truth as a birthday email from Spirit Airlines.

If you ask me, our industry should TM the portmanteau Fauxthenticity. That's something that looks about as real as Donald Trump's toupee. Below is what I mean. Do either of these pull on any of your heartstrings that haven't been blowtorched by a lifetime of saccharine-steeped Hallmark sentiments?

Because Ad Aged has the journalistic integrity of a Woodward and a Bernstein, I decided to ask a coterie of my friends in the business a simple, non-rhetorical question. “Explain in simple terms what a data strategist does. Explain it so my mom (who’s dead) would understand. Then give me one tangible example of one of the following. 1) The intersection of customer and business truths that inspires thoughtful human experiences. 2) Data delivering a customer experience that matters. 3) Data that delivers more customer loyalty.

I quickly decided that was way too much work. And my friends way too cynical.

Instead, I decided to Google, “What Does a Data Strategist Do?”

I found these answers.

Answer 1:

What is a data strategist? Quite simply it is the person who is the business owner of the big data space at a company. From my experience, IT is really great at what IT does. So you have IT architects who can put together a hadoop cluster and have it up and running smoothly. And you have data scientists who can analyze the data and write the algorithms. But the type of people who fill these roles don’t have their eye on the customer experience or the running of the business like a business manager would. That’s where the data strategist comes into play. This person is the business person on the team. The data strategist is the role I fill. 

My job is to manage the space like I am growing a company with in a company or a market. Big data is a growing function in a lot of companies and someone needs to understand how to grow it so that it adds value to the customer experience and the business’s bottom line. Data scientists and IT architects, don’t often do this. But a data strategist does. In fact that is what the data strategist needs to be the most grounded in.I think a good data strategist is someone with an MBA who understands the business side of the equation yet knows the technology and analytics well enough to know what the IT and the data scientist are doing and the tools they are using. In essence, a data strategist can speak the language of IT and analytics. Now being able to write an algorithm in Mahout would be nice but not required. I don’t write the algorithms, but I know what Mahout is and how to use it to grow the business. 

I also happen to know R and have spent a few years of my career in analytics. I know the difference between REST and SOAP (I know the IT guys think this is basic but trust me, most of the business people reading this have no clue what I just wrote). I also know how to code in some language and those languages that I don’t know how to code in, I know what they can do and what their limits are as well as understanding the tools IT is using and why they are being used from both a business and IT perspective. This person needs to be well rounded in finance, marketing, leadership and other areas of the business. Essentially acting as a business owner for data.

Answer 2:

As a Lead Data Strategist, you will help identify and deploy data-driven solutions that demonstrate the value of internal or external data assets and technologies with active client or business needs. You will work closely across client strategy, data partnerships, and the product owners to identify opportunities for high-impact development initiatives.

You will help articulate and communicate the roadmap of development and initiatives and consolidate existing solutions into a globally scalable catalog of executable intellectual property. This team will facilitate the development of new data-driven innovation by leveraging the latest technologies and methodologies across AI, machine learning, and data science to benefit __________ and our clients through collaborative co-development, commoditization of existing solutions, and proactive innovation. 

Answer 3:

Drew Conway’s famous venn diagram describes the skill set of a data scientist as mixture of hacking skills, math & statistics knowledge and substantive expertise. Conway’s diagram is certainly helpful when thinking about data scientists – but it does not bring to the table all skills required to make data science projects succeed. A productive data science team must cover more ground than conway’s diagram.

What is needed? Three things, in essence: (a) a non-stop and rigorous big picture & conceptual view, (b) excellent client-facing communication skills and (c) a passion for painstakingly untangling the idiosyncrasies of the application domain. We have found that these talents are found rarely in (excellent) data scientists. What is more, taking care of the big picture – and being the gateway to the client and her domain requires an entirely different way of working. Constant interruptions are the norm, which non-syncable with data science work, which often requires methodological deep dives and focused work. Hence, the role of the data strategists, which focuses on dealing with client issues and allows the data scientists to work productively.

Again, I’ll confess my ignorance.

Not only do I have no idea what any of this means, I doubt anyone else does either, least of all people who buy things.

I also don’t care. And never will.

When I see a real-live example of data science working on a real-live person, we can have a conversation.

You bring the disposable syringes. 

I’ll bring the heroin.

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