Data Generations: Segmenting The Industry’s Data Professionals

Data-Driven Thinking" is written by members of the media community and contains fresh ideas on the digital revolution in media.

Today’s column is written by Lindsey Harju, co-founder at Blinc Digital Group.

At recent industry events, I’ve noticed distinct differences in conversations.

The more seasoned industry vets understood consumer data, especially offline data, better than anyone.

When talking to those newer to the marketing data space, I was surprised to learn some who didn’t understand how to read deciles yet were in the driver’s seat for millions of dollars in data-driven media spend.

As someone who frequently sits between the two worlds, I recognized a disconnect that is keeping organizations from working seamlessly together. We have mountains of information used to communicate with millennials or boomers, but what about those with different histories in our own industry?

This is my anecdotal and unscientific attempt to segment our fellow data-driven colleagues. Segmentation will never perfectly reflect everyone. You may look more like a different data generation depending on where your mentors and teachers started. Thinking about the backgrounds of your colleagues and clients can make your conversations faster and clearer. Isn’t that the goal of every data-focused exercise?

Data seniors

Approximate start of career in data: before 1995.

The industry vets I’ve met were using data before it was #bigdata. Direct mail was the primary channel and selling and brokering lists was big business. They are hungry to apply their knowledge and experiences but understand the world is changing around them.

It’s cliché for a reason: The tech side tends to be the most challenging element for this group. Many may drastically overestimate the power of capabilities but are self-aware enough to realize this weakness. Behind closed doors, some data seniors admit they don’t understand how most technology-based data companies make money. But they understand it is the future, and they need to learn it.

In my experience, many data seniors seem to confuse data management platform capabilities with those offered by other vendors, such as onboarders or demand-side platforms (DSPs).

Many are familiar with deterministic identities like name and home address. If a project is based on probabilistic or even deterministic digital identifiers, expectations should be set for accuracy and match rates up front.

Technical capabilities may need to be explained in straightforward terms. They seem hungry to learn about new capabilities, and direct mail can be a good reference for analogies if it is understood well enough by the presenter to do it right.

Data juniors

Approximate start of career in data: 1996-2009.

Data juniors probably understand data-specific technology best because they saw the industry’s foundational elements being built and improved upon. This can mean very successful technical conversations, but also the danger of false assumptions.

When they got their start, the industry was more segmented in channels such as display, search and email, so data juniors often converse through that lens. Knowing in which industry niche a data junior got his or her start may help a pitch by being built from the industry they think of as “home.”

This group is also used to working with offline data, but only after the data was compiled. Many have a tenuous understanding of how large consumer data sets are created, so they may not have as critical of an eye for good vs. bad data as data seniors. As they age, they may rely on data freshman and data sophomores to keep them up to date with consumer trends in order to spot hot new opportunities and platforms for data application.

I’ve noticed that some data juniors consider ideas to be “new” when, really, they are a modified data product so old it may have been retired. This is especially prevalent today as companies increasingly activate offline data in an online environment. Many tools launched today are similar to list tools used by data brokers in the past.

Some data juniors may have a less than functional grasp of logic statements. They should have a solid understanding of the difference between “AND” and “OR” before being put behind the wheel of an expensive new self-service tool.

Data sophomores

Approximate start of career in data: 2010-2014.

When they started their careers, DSPs were already heavily used, so programmatic buying can be taken for granted to the point that they may not understand why anyone would buy media without optimized bidding. As they move up in their respective organizations, their desire for training and learning opportunities is similar to data seniors. Their on-the-job experience may have revealed just how little they understand about data and the technology they use every day. They are especially drawn to certifications they can share publicly as a means to prove their knowledge and hopefully advance their careers.

Data sophomores may already be in their second or third role in the space, so they are increasingly becoming subject matter experts in more niche areas. Encouraging them to pass their knowledge onto data freshman could cement their understanding of complex topics and make them more effective in front of clients so they can break down complicated processes.

I get the impression that some data sophomores may not understand the value of different programmatic vendors. When the digital advertising was born, publishers first sold digital impressions directly to brands. Data juniors saw the emergence of new middlemen and understand the value of the layers created as the daisy chain got longer. Data sophomores started their careers when most layers already existed. Through acquisition, many layers disappeared but may still exist inside the acquirers’ walls. As a result, data sophomores don’t always have the same foundational knowledge of the path of an impression and when an additional layer is needed and adding value.

“Addressable” is a new thing. Some data sophomores don’t realize that targeting a person based on offline data has been done for decades via direct mail and are surprised that using offline data is even legal.

Data freshman

Approximate start of career in data: 2015 to present.

Newest to data, many data freshmen started their careers by learning self-serve platforms and buying media around the clock. Audience segment titles and descriptions are their most-used resources when selecting data variables for campaigns. They are also the first generation to start in a cross-channel marketing environment. Even if their responsibilities focused on one channel, their opportunities for advancement likely covered multiple channels. This foundation of marketing in diverse channels allows them to more easily transition from role to role, ideal for a group that tends to change companies more frequently than prior generations.

Unfortunately, I get the sense that some don’t truly understand the data or the technology side. Yes, this group uses technology around the clock and is tech dependent, many even addicted. But a side effect of the cleaner and more intuitive interfaces is that users can simply use them without first learning how they work.

Before we judge this group too harshly, remember how much you understood about the inner workings of the industry when you began. This audience tends to be fresh-faced and creative. Raised with role models from successful startups with visions of IPOs dancing in their heads, data freshmen are actively looking for the next big idea and are more likely to take a risk with a startup.

Many data freshmen see segments as binary: Either someone falls in the segment or they don’t. With modeled data, deciles can be useful but this group should be given clear examples of what a 1 looks like versus a 10 or they may use levels that are too low and be disappointed with performance.

CPM means cost per thousand but it’s not clear per thousand of what. If a company sells data products based on cost per record, a data freshman buyer may assume per thousand impressions.

The data and digital worlds operate on a mostly made-up language of acronyms and buzzwords, so it’s no wonder we all share more than a few lost-in-translation moments.

Data, and its application in advertising, isn’t going anywhere. In fact, LinkedIn found that data science roles have increased by more than 650% since 2012. Clearly organizations value data and are investing in the people that can put it to work. More and more data focused conversations will continue to happen every day. By considering each other’s backgrounds, we can communicate in a clearer and more productive way and take advantage of this rising tide.

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1 Comment

  1. jim hodgkins

    Lindsey, interesting segmentation and there are definitely big differences, one caused by so many data scientists having developed through a technical skills path. I notice many don't know what an index is, we always thought that was the most basic measure of relative strength. They love talking about uplift as the alternative. At a very high level the more experienced have better perspective but less technical strength - in fact can't actually process the data personally whilst the less experienced can do amazing things with e data but don't necessarily realise the power of what they are discovering, Jim

    Reply

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