Vicki Jauron, Babylon and Beyond Photography/Getty Images
The top trophy hire in data science is elusive, and it’s no surprise: a “full-stack” data scientist has mastery of machine learning, statistics, and analytics. When teams can’t get their hands on a three-in-one polymath, they set their sights on luring the most impressive prize among the single-origin specialists. Which of those skills gets the pedestal?
Today’s fashion in data science favors flashy sophistication with a dash of sci-fi, making AI and machine learning the darlings of the job market. Alternative challengers for the alpha spot come from statistics, thanks to a century-long reputation for rigor and mathematical superiority. What about analysts?
Analytics as a second-class citizen
If your primary skill is analytics (or data-mining or business intelligence), chances are that your self-confidence has taken a beating as machine learning and statistics have become prized within companies, the job market, and the media.
But what the uninitiated rarely grasp is that the three professions under the data science umbrella are completely different from one another. They may use some of the same methods and equations, but that’s where the similarity ends. Far from being a lesser version of the other data science breeds, good analysts are a prerequisite for effectiveness in your data endeavors. It’s dangerous to have them quit on you, but that’s exactly what they’ll do if you under-appreciate them.
Instead of asking an analyst to develop their statistics or machine learning skills, consider encouraging them to seek the heights of their own discipline first. In data science, excellence in one area beats mediocrity in two. So, let’s examine what it means to be truly excellent in each of the data science disciplines, what value they bring, and which personality traits are required to survive each job. Doing so will help explain why analysts are valuable, and how organizations should use them.
Excellence in statistics: rigor
Statisticians are specialists in coming to conclusions beyond your data safely — they are your best protection against fooling yourself in an uncertain world. To them, inferring something sloppily is a greater sin than leaving your mind a blank slate, so expect a good statistician to put the brakes on your exuberance. They care deeply about whether the methods applied are right for the problem and they agonize over which inferences are valid from the information at hand.
The result? A perspective that helps leaders make important decisions in a risk-controlled manner. In other words, they use data to minimize the chance that you’ll come to an unwise conclusion.
Excellence in machine learning: performance
You might be an applied machine learning/AI engineer if your response to “I bet you couldn’t build a model that passes testing at 99.99999% accuracy” is “Watch me.” With the coding chops to build both prototypes and production systems that work and the stubborn resilience to fail every hour for several years if that’s what it takes, machine learning specialists know that they won’t find the perfect solution in a textbook. Instead, they’ll be engaged in a marathon of trial-and-error. Having great intuition for how long it’ll take them to try each new option is a huge plus and is more valuable than an intimate knowledge of how the algorithms work (though it’s nice to have both). Performance means more than clearing a metric — it also means reliable, scalable, and easy-to-maintain models that perform well in production. Engineering excellence is a must.
The result? A system that automates a tricky task well enough to pass your statistician’s strict testing bar and deliver the audacious performance a business leader demanded.
Wide versus deep
What the previous two roles have in common is that they both provide high-effort solutions to specific problems. If the problems they tackle aren’t worth solving, you end up wasting their time and your money. A frequent lament among business leaders is, “Our data science group is useless.” And the problem usually lies in an absence of analytics expertise.
Statisticians and machine learning engineers are narrow-and-deep workers — the shape of a rabbit hole, incidentally — so it’s really important to point them at problems that deserve the effort. If your experts are carefully solving the wrong problems, your investment in data science will suffer low returns. To ensure that you can make good use of narrow-and-deep experts, you either need to be sure you already have the right problem or you need a wide-and-shallow approach to finding one.
Excellence in analytics: speed
The best analysts are lightning-fast coders who can surf vast datasets quickly, encountering and surfacing potential insights faster than those other specialists can say “whiteboard.” Their semi-sloppy coding style baffles traditional software engineers — but leaves them in the dust. Speed is their highest virtue, closely followed by the ability to identify potentially useful gems. A mastery of visual presentation of information helps, too: beautiful and effective plots allow the mind to extract information faster, which pays off in time-to-potential-insights.
The result is that the business gets a finger on its pulse and eyes on previously-unknown unknowns. This generates the inspiration that helps decision-makers select valuable quests to send statisticians and ML engineers on, saving them from mathematically-impressive excavations of useless rabbit holes.
Sloppy nonsense or stellar storytelling?
“But,” object the statisticians, “most of their so-called insights are nonsense.” By that they mean the results of their exploration may reflect only noise. Perhaps, but there’s more to the story.
Analysts are data storytellers. Their mandate is to summarize interesting facts and to use data for inspiration. In some organizations those facts and that inspiration become input for human decision-makers. But in more sophisticated data operations, data-driven inspiration gets flagged for proper statistical follow-up.
Good analysts have unwavering respect for the one golden rule of their profession: do not come to conclusions beyond the data (and prevent your audience from doing it, too). To this end, one way to spot a good analyst is that they use softened, hedging language. For example, not “we conclude” but “we are inspired to wonder”. They also discourage leaders’ overconfidence by emphasizing a multitude of possible interpretations for every insight.
As long as analysts stick to the facts — saying only “This is what is here.” — and don’t take themselves too seriously, the worst crime they could commit is wasting someone’s time when they run it by them.
While statistical skills are required to test hypotheses, analysts are your best bet for coming up with those hypotheses in the first place. For instance, they might say something like “It’s only a correlation, but I suspect it could be driven by …” and then explain why they think that. This takes strong intuition about what might be going on beyond the data, and the communication skills to convey the options to the decision-maker, who typically calls the shots on which hypotheses (of many) are important enough to warrant a statistician’s effort. As analysts mature, they’ll begin to get the hang of judging what’s important in addition to what’s interesting, allowing decision-makers to step away from the middleman role.
Of the three breeds, analysts are the most likely heirs to the decision throne. Because subject matter expertise goes a long way towards helping you spot interesting patterns in your data faster, the best analysts are serious about familiarizing themselves with the domain. Failure to do so is a red flag. As their curiosity pushes them to develop a sense for the business, expect their output to shift from a jumble of false alarms to a sensibly-curated set of insights that decision-makers are more likely to care about.
Analytics for decision-making
To avoid wasted time, analysts should lay out the story they’re tempted to tell and poke it the from several angles with follow-up investigations to see if it holds water before bringing it to decision-makers. The decision-maker should then function as a filter between exploratory data analytics and statistical rigor. If someone with decision responsibility finds the analyst’s exploration promising for a decision they have to make, they then can sign off on a statistician spending the time to do a more rigorous analysis. (This process indicates why just telling analysts to get better at statistics misses the point in an important way. Not only are the two activities separate, but another person sits in between them, meaning it’s not necessarily any more efficient for one person to do both things.)
Analytics for machine learning and AI
Machine learning specialists put a bunch of potential data inputs through algorithms, tweak the settings, and keep iterating until the right outputs are produced. While it may sound like there’s no role for analytics here, in practice a business often has far too many potential ingredients to shove into the blender all at once. One way to filter down to a promising set of inputs to try is domain expertise — ask a human with opinions about how things might work. Another way is through analytics. To use the analogy of cooking, the machine learning engineer is great at tinkering in the kitchen, but right now they’re standing in front of a huge, dark warehouse full of potential ingredients. They could either start grabbing them haphazardly and dragging them back to their kitchens, or they could send a sprinter armed with a flashlight through the warehouse first. Your analyst is the sprinter; their ability to quickly help you see and summarize what-is-here is a superpower for your process.
The dangers of under-appreciating analysts
An excellent analyst is not a shoddy version of the machine learning engineer; their coding style is optimized for speed — on purpose. Nor are they a bad statistician, since they don’t deal at all with uncertainty, they deal with facts. The primary job of the analyst is to say: “Here’s what’s in our data. It’s not my job to talk about what it means, but perhaps it will inspire the decision-maker to pursue the question with a statistician.”
If you overemphasize hiring and rewarding skills in machine learning and statistics, you’ll lose your analysts. Who will help you figure out which problems are worth solving then? You’ll be left with a group of miserable experts who keep being asked to work on useless projects or analytics tasks they didn’t sign up for. Your data will lie around useless.
When in doubt, hire analysts before other roles. Appreciate them and reward them. Encourage them to grow to the heights of their chosen career (and not someone else’s). Of the cast of characters mentioned in this story, the only ones that every business needs are decision-makers and analysts. The others you’ll only be able to use when you when you know exactly what you need them for. Start with analytics and be proud of your newfound ability to open your eyes to the rich and beautiful information in front of you. Data-driven inspiration is a powerful thing.
One of the most damaging myths about creativity is that there is a specific “creative personality” that some people have and others don’t. Yet in decades of creativity research, no such trait has ever been identified. The truth is that anybody can be creative, given the right opportunities and context.
If you don’t believe me, take the least creative person in your office out for lunch — someone who doesn’t seem to have a creative bone in their body. Chances are, you’ll find some secret passion, pursued outside of office hours, into which they pour their creative energies. They just aren’t applying those energies to their day jobs.
The secret to unlocking creativity is not to look for more creative people, but to unlock more creativity from the people who already work for you. The same body of creativity research that finds no distinct “creative personality” is incredibly consistent about what leads to creative work, and they are all things you can implement within your team. Here’s what you need to do:
One of the things that creativity researchers have consistently found for decades is that expertise is absolutely essential for producing top-notch creative work — and the expertise needs to be specific to a particular field or domain. So the first step to being creative is to become an expert in a particular area.
The reason expertise is so important is that you need to be an expert in a specific field to understand what the important problems are and what would constitute an important new solution. Einstein, for instance, studied physics intensely for years to understand the basic physical model for time and space before he understood that there was an inherent flaw in that model.
So how do you cultivate expertise? Performance expert Anders Ericsson has studied that problem for decades and found that the crucial element is deliberate practice. You need to identify the components of a skill, offer coaching, and encourage employees to work on weak areas. That goes far beyond the intermittent training that most organizations do.
Any company can replicate Amazon’s memo-writing policy. What’s not so easily replicated is the intense commitment to cultivating writing expertise that the company has prioritized for years.
While deep expertise in a given field is absolutely essential for real creativity, it is not sufficient. Look at any great body of creative work and you’ll find a crucial insight that came from outside the original domain. It is often a seemingly random piece of insight that transforms ordinary work into something very different. For example, it was a random visit to a museum that inspired Picasso’s African period. Charles Darwin spent years studying fossils and thinking about evolution until he came across a 40 year-old economics essay by Thomas Malthus that led to his theory of natural selection. The philosophy of David Hume helped lead Einstein to special relativity.
More recently, a team of researchers analyzing 17.9 million scientific papers found that the most highly cited work is far more likely to come from a team of experts in one field working with a specialist in something very different. It is that combination of expertise, exploration, and collaboration that leads to truly breakthrough ideas.
That is how Google’s “20% time” policy is able to act as a human-powered search engine for new ideas. By allowing employees to work on projects unrelated to their formal job descriptions 20% of the time, people with varied experiences and expertise can combine their efforts in a way that would be extremely unlikely in a planned company initiative.
Empower Your People with Technology
In Walter Isaacson’s recent biography of Leonardo da Vinci, he recounts how the medieval master would study nature, from anatomy to geological formations, to guide his art. Now Leonardo was clearly a genius of historical proportions, but think about how much more efficient he would have been with a decent search engine.
One of the most overlooked aspects of innovation is how much technology can enhance productivity. Part of the reason is because it makes the two factors noted above, acquiring domain expertise and exploring adjacencies, so much easier. However, another reason is because it frees up time to allow for more experimentation.
You can see this at work at Pixar, which was originally a technology company that began shooting short films to demonstrate the capabilities of its original product, animation software. However, as they were experimenting with the technology, they also found themselves experimenting with storytelling, and those experiments led them to become one of the most highly acclaimed studios in history.
As Pixar founder Ed Catmull put it in his memoir, Creativity Inc., “Every one of our films, when we start off, they suck…Our job is to take it from something that sucks to something that doesn’t suck. That’s the hard part.” It is that kind of continual iteration that technology makes possible, and that makes truly great creative work possible.
Far too often, we think of creativity as an initial, brilliant spark followed by a straightforward period of execution, but as Catmull’s comment above shows, that’s not true in the least. In his book, he calls early ideas “ugly babies” and stresses the need to protect them from being judged too quickly. Yet most organizations do just the opposite. Any idea that doesn’t show immediate promise is typically killed quickly and without remorse.
One firm that has been able to buck this trend is IBM. Its research division routinely pursues seemingly outlandish ideas long before they are commercially viable. For example, a team at IBM successfully performed the first quantum teleportation in 1993, when the company was in dire financial straits, with absolutely no financial benefit.
However, the research wasn’t particularly expensive, and the company has continued to support the work for the last 25 years. Today, it is a leader in quantum computing — a market potentially worth billions — because it stuck with it. That’s why IBM, despite its ups and downs, remains a highly profitable company while so many of its former rivals are long gone.
Kevin Ashton, who first came up with the idea for RFID chips, wrote in his book, How to Fly a Horse, “Creation is a long journey, where most turns are wrong and most ends are dead. The most important thing creators do is work. The most important thing they don’t do is quit.”
Yet all too often, organizations do quit. They expect their “babies” to be beautiful from the start. They see creation as an event rather than a process, don’t invest in expertise or exploration, and refuse to tolerate wrong turns and dead ends. Is it any wonder that so few are able to produce anything truly new and different?
Today we have more platforms and data available than ever before to best help us choose and reach target consumers. Is it possible, though, to zoom in too narrowly? Are we potentially missing opportunities to reach customers because we are not using enough channels, especially when it comes to mobile marketing?
CodeBroker found some intriguing and valuable answers from its recent Consumer Mobile Engagement research. The survey polled 1,552 American consumers, 53 percent of whom were female and 47 percent were male. Before getting into details about the results and what they mean, it can be helpful to take a step back and remember a few things about best practice marketing.
In an article in Business.com, Ryan Ayers, a consultant to Fortune 500 companies, said that while marketing has changed significantly through the years, some basics have not. He reminds us that among other things, marketing requires a balance between what is best for consumers and what is best for advertisers. He uses the example that free products may be terrific for consumers, but giving away merchandise may not work for an advertiser (ignoring free samples, of course).
We should listen to consumers and do what will be most effective in getting them to take action, as long as their desires fall within our own goals. That's where knowing consumers' attitudes toward mobile can help. Consumers remind us that mobile is a multi-channel platform and each consumer has her engagement preference – some may respond best to text messages, while others to mobile email or app. To reach the broadest range, we have to appeal to each individual's preferred mobile channel. If we don't use all the available channels, we may be marketing too narrowly.
One example of this possibility was shown when survey respondents were asked: "What is your preferred way to receive retailer messages promoting sales, discounts, and coupons on your mobile device?" While mobile email comes out on top at 41 percent, significant numbers prefer text (38 percent) or an app push message (21 percent).
Here's another way to look at the data: If you were to rely solely on mobile email marketing, you would forego 59 percent of available mobile options. Likewise, if you just utilized texting to reach consumers, you'd be ignoring 62 percent of available outreach options.
The survey digs even deeper, asking participants, "Which type of text message do you find most valuable to receive from a retailer?" More than half said they found receiving coupons most valuable, whereas 35 percent said they prefer "sale or discount information," and the remaining 12 percent said they liked to learn about new products and special events via text message.
The CodeBroker survey may provide the answer to marketers and advertisers who are not getting the desired results from their rewards programs.
When asked, "How do you prefer to access loyalty rewards from your mobile device?" survey participants chose a wide variety of channels, with a "link in a text message" leading the preferred way at 37 percent. An additional 32 percent of those consumers polled prefer accessing loyalty rewards information through an app, while 29 percent mostly prefer email. It may be good to point out that most companies believe that they have mobile loyalty covered with an app – but they're missing the majority of the market with an app-only mobile strategy.
Yet many loyalty reward programs only offer a single channel to access loyalty rewards. That may be easier or more self-serving for marketers or retailers but does not offer the "balance" Ryan Ayers discussed in his article mentioned earlier. It also may be the reason why a rewards program isn't performing to expectations.
The survey may also hold the key for retailers looking to build their text message marketing lists. When asked, "Have you ever opted into a retailer or brand text message marketing list?" a whopping 60 percent said yes. While that 60 percent is impressive, it may be more impressive than 100 percent of millennials who responded said they have opted in to a text message marketing list.
Mobile is a platform with multiple channels; consumers have a range of channel options for engaging with retailers over mobile.
Retailers can drive higher consumer engagement by employing a channel marketing mix over mobile.
SMS/text has surpassed both mobile email and mobile app push as consumers' preferred mobile communications channel, including accessing loyalty rewards.
Though many retailers and brands make their loyalty programs available through a mobile app, many consumers indicate a preference for multiple mobile channels to access rewards.
In short, consumer preferences vary, therefore retailers must be able to address them all. When it comes to mobile marketing, give consumers a choice. If we focus too narrowly on the single best way to reach consumers, we may lose sight of the mark. Use all available mobile channels to reach and market to your desired audience.
Full contents of the Consumer Mobile Engagement research can be downloaded here.