4 Signs Your Analytics Specialist Is Secretly A Joke

smiling face with white paint, red lipstick, and clown nose
What do you call a data analyst who demands a high price for their services, and gives little usable insight in return?

Back in April 2019, FullStory published an article about analytics theater: a term they coined to refer to the shallow and virtually meaningless analysis that underlies many of today’s web analytics platforms.

The article did a good job of explaining the phenomenon, but we think there’s a larger concern worth addressing: while some analytics platforms are mere analytics theater, there are plenty of specialists out there running analytics comedy –a farcical attempt at data science that’s closer to a slapstick bit than business insight.

Whenever a client hires a specialist, there’s an implicit understanding that the latter knows what they’re doing. But thanks to the knowledge gap that’s built into these exchanges, it can be exceedingly difficult to spot a faker until the joke has run its course.

To help you avoid getting screwed over, we’ve compiled a list of red flags you should watch for when working with a supposed expert in the field of big data.

1. Your analytics specialist insists that, “it’s tough to explain.”

If you’re a paying client of an analytics specialist, then you’re entitled to know where and how your budget is being spent. You deserve a breakdown of associated costs (ex. the cost of subscriptions to analytics tools, or the cost of gathering data), a briefing on any processes you’d like explained to you, and an explanation in the event of low ROI.

That’s why there’s rarely an excuse to be shrugged off with a dismissive, “What you’re asking for is hard to explain.” This kind of cop out answer is a red flag that you should definitely look out for when managing a consultant –not just in big data, but in any field of specialized knowledge that you’re outsourcing.

A specialist is only ever an expert on a given subject when they can teach it. Whether it’s through a crash course or a brief explanation in the style of ELI5, you can and should expect the specialists you hire to provide answers when asked.

This isn’t to say that you should take every chance to interrogate your consultants, but you’re definitely better off hiring a person or team that can talk you through the particulars of what you’re paying for.

2. You’re receiving a lot more visuals than insights.

Data visualization (or data storytelling, depending on who you’re asking) is the practice of drilling data down into a more accessible form like a chart, table, infographic, or whatever you call this.

Executed properly, these visualizations can be very useful tools in distilling and communicating information.

Executed poorly, they can give off a compelling illusion that the person you’ve hired to provide you with business insights has no idea what’s actually going on. That’s why the second red flag you should watch for is an empty fixation with visuals.

finger pointing at paper on wooden table, graphs
If these graphs mean as much to your business as your analyst’s last report, we have some bad news for you.

Towards Data Science published an article on why data visualization is an overrated fad, and their bottomline is salient: people need to be more critical of the difference between a graph designed to convey ideas, and a graph designed to sit still and look pretty.

The latter is a form of shock and awe that data pretenders use to give off an air of expertise. At the heart of every useless visual is the assumption that a client would believe anything you tell them if you flash some interesting media while you do the telling.

For best results, ask yourself the simple question of, “What is this chart really telling me?” when looking over a report or sitting through a presentation. If you aren’t satisfied by the conclusion you reach, you could always ask for a more useful interpretation.

3. You’re hearing a lot more numbers than sense.

The human mind wasn’t designed to think in terms of abstract numbers. For most people, having a million of anything is utterly inconceivable, and minute differences in percentages can come across as meaningless. There’s simply more work that goes into drawing insight from data than saying, “These are the numbers.”

To understand and communicate data in a way that yields results, everything needs to be contextualized: spelled out in terms that we’re naturally more adept to comprehend. The best data specialists use narratives. Spot the difference between the following two examples:

1. “Your sales are down X% in this particular region.”

2. “Your sales are down by X% in this particular region. We can examine this further, but it’s probably because of phenomenon Y for the following reasons… I recommend doing Z, because…”

Yet as anyone who’s delivered a report can tell you (after a few drinks, when they’re feeling honest), numbers can be a theatrical crutch, and dropping figures and statistics is a rhetorical device that’s been around for as long as people have needed to sound credible.

colorful alcoholic drinks fruits man crossed arms
Pictured: the secret to professional honesty.

No matter how impressive it might be that your data expert has uncovered a set of numbers and trends from your data, their job isn’t done until they’ve woven those numbers and trends into a narrative or plan that yields an effect.

Moreover, every link in the causal chain has to be accounted for. Don’t settle for a number and a recommended step –demand a logical sequence of cause-and-effect if you want to be sure that your specialist isn’t taking blind guesses.

4. Your analytics specialist over-promises.

Analytics specialists are valuable because they deliver actionable insights. After handling your data, spotting trends, conducting experiments, and forecasting results, you should be left with a series of recommendations that lead you to a certain result –spend more on A to achieve B, or do X a little differently if you want to get to Y.

The expectation that often arises here is that your expert can tell you what to do to increase your revenue by a specific amount, and by a specific date.

This expectation is false. You’re hiring a business insight consultant, not summoning a genie; they’re bound by the limits of statistics and probability. As such, any consultant who promises you fixed numbers is either an absolute genius or an absolute fraud, and you can go ahead and guess which is the likelier bet.

A consultant’s track record for increasing sales, lowering costs, and delivering ROI are fair game when pitching an engagement –after all, they have to compete and make a living just like anyone else. However, once an engagement begins, the best that any data specialist can offer is a statistically sound range and a degree of certainty.

Spotting this red flag is a non-negotiable. Data science is an exercise in probability, and there are no guarantees.

she believed he lied
Never again.


Getting screwed over in an engagement with a so-called expert is no laughing matter, and you should never settle for an analyst who fails to deliver on their rate.

The list we’ve presented should give you an idea of the profile of a consultant worth hiring: they’re quick to explain, they leave you with complete and well-reasoned recommendations, and they keep away from absolutes.

As far as industries go, big data has a lot to give. Be mindful, be critical, and make sure that comedy is left to the people who can pull it off.

The Story Behind Smart Cities, Big Data, and Sustainability

Sustainability is hard to come by. Whether it’s a sustainable organization, a sustainable way of life, or a sustainable relationship, building something that lasts is tricky.

Take a sustainable city, for instance. The modern outlook is that a city is sustainable when it’s smart, and that it’s smart when it uses technology to address problems like traffic, security, pollution, and waste management.

Building a smart city takes work, funding, infrastructure, and technical expertise. Distressingly for us Filipinos, it also takes political will and progressive thinking to get the ball rolling to the end; two things we aren’t exactly swimming in at the moment.

Many people think that all it takes to achieve widespread sustainability are good politics, advanced technology, and big capital. It makes sense to say that, at a glance, sustainability is all about resource abundance.

But that would only be telling half the story.

On the road to sustainability, your starting conditions are a major factor. However, what you have at the onset isn’t nearly as impactful as what you do with it.

There’s a method behind sustainability, and that method depends on data.

The Problem: Really Shitty Foresight

In the 1880s, the world’s most advanced cities were covered in horseshit.

I mean this literally.

Horses were essential to the urban world, once upon a time. They were widely used to transport goods, and they played a central role in early versions of public mass transit. In fact, towards the end of the 19th century, New York was home to over 150,000 horses: horses that answered the call of nature frequently, and in great volume.

As you’d expect, the amount of urine and manure produced on a daily basis brought cities to the verge of a crisis. The air was rank, vermin were thriving, and public health was at risk; even food security was threatened by the agricultural strain of feeding enough horses to support the craze.

photograph of a brown horse's butt
Pictured: the price of progress, apparently. | (Image from Paul J Everett on Flickr)

Luckily, our cities managed to avoid the equine apocalypse thanks to urgent administrative action and the rise of the automobile. City leadership addressed the symptoms of the ill, and the problem was eliminated by the eventual shift in demand from one kind of horsepower to another.

For a miraculous moment, the world shifted away from an unsustainable and environmentally disastrous method of transporting goods and people.

The world did this by embracing the combustion engine.

Boy, did that go well.

Nobody at the time could have guessed that their four-wheeled saviors would end up contributing to another catastrophe at a wider scale. Likewise, the scientific community at the time had no reason to assess the risk of global environmental degradation.

The moral of the story is that we often find ourselves worse off than when we started because none of us are capable of peering into the future, and therefore committing to solutions that are inherently flawed.

Despite our best efforts, we’re just naturally inclined to suck at predicting the future, especially when it comes to predicting a future that works against us.

The struggle to solve problems with finality is universal, but some of today’s cities have managed to do better than replacing broken things and systems —they’ve come pretty damn close to sorting certain things out for good.

Bright Lights, Smart City

Like Taleb’s black swan, a handful modern cities stand defiant of the fear that our answers are only ever bound to create more problems.

A few examples follow:

  • Smart grids allow cities like San Francisco to track energy consumption across time and geography, and make calculated adjustments for the sake of greater efficiency. SF residents can monitor their energy consumption in real time.
  • Traffic lights in cities like Pittsburgh independently calculate the optimal green time based on real-time traffic data, and coordinate with the rest of the city’s lights to keep commutes efficient.
  • A number of cities in Europe have outfitted their public parking spaces with sensors that send data on slot availability straight to motorists. The deployment of smart parking systems reduce traffic, save time, and preserve the environment all at once.
aerial photograph of san francisco in the evening
San Francisco gets it.

There are countless other examples of processes, systems, and devices that make urban living more bearable and more efficient. Upgrades to city life abound, and they range from the novel to the life-saving.

Needless to say, this is all a step up from drowning in excrement.

Smart city innovations present no visible drawbacks—but then again, neither did automobiles at the turn of the 20th century. Unlike their predecessors, however, the minds behind today’s cities have a distinct advantage: access to sprawling datasets and figures.

Data Matters (A Lot)

Any entity or organization with access to data and the expertise to use it is in a good position to keep their solutions from turning sour. There are various reasons for this, but for the sake of this article, we’ll focus on how data helps us see the future.

Data allows for the creation of predictive models and simulations. The world would be better with a little more foresight, and data can help us make smarter projections. This isn’t to say that predictive modeling is 100% accurate (you won’t find that kind of certainty anywhere in big data), but at the very least, it can indicate sharper decision-making than a shrug and a guess.

This takes into consideration the fact that many of tomorrow’s problems would be unthinkable today.

Smart cities provide an example of what being data-driven looks like in practice; in the world’s most advanced urban centers, nearly every data point that could be logged is being logged. This means that there’s a good chance an analyst could piece together the causes of a negative externality and arrive at a sound, statistically-backed recommendation about where to go next.

black and gray data mining rig servers
Server server on the wall, what the hell is going on?

Let’s play with the example of smart parking. Say that one day, we discover that a component of the sensors used to gather parking slot availability data reacts with rainwater to produce a toxic gas.

(It’s a wild idea, sure, but it would have been just as hard to convince someone in 1890 that cars would one day push us closer to the end of the world. Just bear with me for a bit.)

If it turns out that our parking sensors could end the world, then we’re in luck. Since collecting data points factors heavily in their use, we can tell where these deadly parking sensors are, and which parts of our cities are at most risk of a toxic fiasco (think: occupancy rates).

Correlate that data with rainfall predictions, wind forecasts, and other numbers that state agencies are monitoring by default, and you end up with a very manageable disaster situation.

Not a perfect example, but you get the principle: obsessive data collection and analysis can make headaches significantly easier to deal with.

At this point it also bears mentioning that even the most banal data project can be used for nefarious purposes, causing far worse problems than they were meant to solve.

An article published by The Guardian in 2014 argued that smart cities would be the death of democracy, and though I hesitate to agree outright, they raise valid concerns about privacy in an age where personal data is caught in a political and economic free-for-all.

Approach data with caution, and stay ethical.

Conclusion: Necessary But Not Sufficient

Today’s smart cities are far from perfect, but they follow a framework that brings us closer to real sustainability. They weave data into the very core of their solutions, allowing for more accurate prediction and faster responses to sudden complications.

Data in and of itself is not a cure, nor is it ever the only piece of a solution. On top of an obvious need for ethical guidance, you still need the effort, the funding, the expertise, the infrastructure, and the collective determination to correct society’s problems.

However, throwing data into the mix can drastically increase a solution’s chances to succeed, and keeps them from blowing up in people’s faces. A culture of data reliance lets us predict, assess, and improve faster than ever before in our history.

With data on our side, we stand a better chance of getting stuff right for the long-term.

Embrace it, apply it, and demand it. To settle for anything less nowadays is a load of horseshit.

black horse looking at camera, landscape in the background
Never again.

How to Tell if You’re a Data Scientist Worth Hiring

It wasn’t too long ago that the job title of data scientist was invented. In the time since then, it’d be optimistic to say that we have a unified concept of what being a data scientist entails.

There are many valid questions worth asking if you want to arrive at an ontologically satisfying conclusion: What is data? What is science? Does data science become less scientific the deeper it falls into the corporate status quo?

A co-writer of mine at CirroLytix has put his stance forward concerning the nature of data science, but I won’t be weighing in on the same terms—not for now, anyway.

Instead, I’ll be looking into a practical question: In terms of job performance, what sets a good data scientist apart from a pretender?

Grab a notepad and a mirror, folks, because we’re about to get introspective.

You Fixate on the Practical

Elegant models and cutting edge AI deployment are well and good. The world needs people who go the extra mile to chase perfection and experiment with emerging technology.

The average business, however, doesn’t.

A data scientist worth hiring puts a premium on efficiency. They know better than to condescend when presented with the occasional redundancy, and they definitely value function over form.

This isn’t to say they’re lazy or prone to cutting corners. In fact, the hallmark of a good data scientist is their ability to generate and communicate clean and usable insights, not pretty charts.

Practicality can also be demonstrated by the tools you pick. Specifically, if you’ve ever been nitpicky or defensive about your tools, odds are they aren’t as qualified as you claim to be. Like any professional worth the name, a data scientist worth hiring would stick to tools which they’ve mastered, and which prove comfortable for them as well as the clients they’re servicing.

The closer you look to an arrogant nerd with a chip on your shoulder, the farther you are from being an asset to a project or company.

You Never Stop Learning

There’s a lot to qualify here. Learning isn’t a cut-and-dried matter of having a monthly reading list, or a quota for MOOC certifications. Those things matter, but they aren’t all that matters.

The kind of learning you should be after is a healthy mix of theoretical and practical (with a moderate inclination towards the latter).

Staying familiar with theory keeps you sharp, and grounds you in the logic underpinning your algorithms, models, and programs. Jobs get repetitive, and this is especially true for jobs you’re good at; beat the call of complacency by paying mind to the innovators at work.

Conversely, theory is at its best when it’s paired with positive action. There are many so-called data scientists who find their self-assurance in what they think they can do, and neglect to prove that they can actually accomplish anything. If you’re a data scientist looking to prove your value in the corporate world, your best shot is to keep yourself busy with hands-on projects.

You Do the Dirty Work

Data scientist is a sexy job title, but a good chunk of that work is far from glamorous. Before anyone can save millions by following your brilliant suggestion, odds are you’d have to make sure all your sexy data is in order.

Data is a lot more marriage than sex: before you can have all the good bits, you need to put in the work (read: the sometimes difficult, bang-your-head-on-the-wall kind of work). That includes deduplicating entries, turning numbers to string, separating first names from last names, doing fuzzy matches, and a bunch of other unsexy things.

It’s a long winding road from data to insight. The question is, are you willing to stick it out for better or for worse?

Remember, being a data scientist doesn’t exempt you from being a data janitor.

You aren’t Obsessed with the Title

If you’ve lived in the Philippines for an extended period of time, then you know that people get defensive about their titles. There’s no force in the universe that could absolve you for calling the wrong person mister instead of attorney.

We’ve all met that guy.

As a rule of thumb, the kinds of people who obsess over titles are likely to tend to their ego before tending to their clients. It’s a distinct form of arrogance that stunts growth. After all, what mediocre fool would have the gall to suggest that a bona fide data scientist do better?

A good data scientist doesn’t need the title to do good work with data science. They need the tools for the job, and they need the drive to see a project thrive. They know full well that leaving a trail of happy clients in their wake says more than a title ever could.


It bears mentioning that there are whole discussions we’ve glossed over in the course of this article. There might be One Best Programming Language out there, and a seamless model is still a thing of beauty in the end.

However, the growing hype behind data science means that anyone who calls themselves an expert is inherently responsible for their clients’ outcomes. Whether we like it or not, data professionals are shaping the image of an entire industry in its relatively early days.

As such, a good, long look in the mirror can save you a lot of folded contracts and burnt bridges. There are habits and routines that every professional is at risk of developing over the course of a career, so don’t kick yourself if you fall short of Employee of the Month material.

Stay practical, learn as much as you can, and for God’s sake, take a page from Kendrick.

Survival of the Sh*ttest: 3 Reasons Corporate Mediocrity Endures

Business is a constant process of innovation and adaptation. With business technology changing every handful of years, staying competitive means keeping abreast of trends and best practices.

If you’ve been working for at least a handful of years, then you know that the sensible thing for a business to do is to stay competitive.

If you’ve been working in the Philippines for at least a handful of years, then you know that there’s always one crap reason or another for businesses to stick to the Old Ways.

People around a bonfire in a cave
“This is fine.” (Image from Blogspot)

There’s a very good chance that the business you own or work for sucks (even just a little bit) for reasons concerning innovation. It isn’t necessarily anyone’s fault; after all, history tells us that structural change isn’t a natural inclination for our species.

Yet, like my mother would always say, “We build homes to perfect them over time, anak. So please, for God’s sake, clean your f*cking room.”

Or something to that effect.

Let’s have a look at the common organizational roadblocks to innovation–things you’d likely hear when someone shuts down the idea of opening up a new department, or signing up for a helpful new training program.

Reason 1: “If it Ain’t Broke, Don’t Fix It”

The first roadblock to innovation is the “If it Ain’t Broke, Don’t Fix It“ mentality.

You’ve heard it before: the excuse that your department or company has a perfectly fine way of doing things, so there’s never a need to shake things up.

Heck, you might even be making a lot of money sticking to the status quo, and besides: if growth is at a plateau, then it’s your own damned fault for not working harder.

To the Luddite, improvement isn’t worth the effort for so long as the status quo gets the job done.

While things could be quicker and easier for the people who need to slog through an outdated way of doing business, the current model is here to stay for as long as it isn’t impossible to work with.

Example: File Storage

Here’s a history lesson for the kids reading: before Google Drive, people had to rely on local file storage—that is, keeping all your documents and media stored on one or more computers. If people needed to access their work files from home, then they had to carry around USB flash drives or email themselves before clocking out.

They were dark times indeed, but humanity trudged through to a beautiful, cloud-hosted future.

Or, most of us did, anyway.

Some of today’s businesses continue to rely solely on local file storage.

In a handful of truly surprising cases, businesses with the funds to shift to cloud storage refuse to digitize their files at all, opting instead for clunky filing cabinets full of critical data and information (they’d better pray that there isn’t a fire hazard anywhere near).

One such business, pictured here. Probably.

If you’re dead set on introducing your workplace to a key innovation, find a way to get it into people’s hands without being insubordinate.

Find a compelling product review online, download a free trial on your personal laptop, or submit a copy of your next report made using whatever tools or formulae you’ve got your eyes on.

It’s like Prometheus bringing fire to mankind, without the punishment of being fed to ravenous birds.

Reason 2: Cost

The second possible reason why your company sucks is because it’s afraid of cost. People tend to be skeptical about the value of an investment –whether it’s in opening a new department, or adopting a new practice or technology.

Not every business can afford to upgrade every quarter, and that’s well and fine. But companies and departments with the resources to make a much-needed change are a far too common sight, much to the dismay of professionals who wish they could do more on the job.

In many cases, vital business upgrades are seen as frivolous expenses or risky gambles. The reality is that many investments like cloud hosting or a robust data infrastructure are well-known for their steep ROI.

Upon closer inspection, the cause of this line of thinking is closer to ignorance than it is to frugality.

We can understand being skeptical about the merits of today’s chatbots or even blockchain, but people continue to second-guess technology that’s been propelling businesses since 2005.

It’s rock-bashing traditionalism at its finest, and it’s holding our enterprises back.

Example: Working With Data

Every business can benefit from working with data.

That’s not a sales pitch, it’s a fact: when used properly, having a functionalthe data value chain can help an organization eliminate guesswork, predict business outcomes, and reach new heights of efficiency to the tune of over $10 in ROI for every dollar spent.

Data analytics, however, isn’t a simple matter of plug-and-play: it takes time, money, and mental bandwidth to build an infrastructure and collect the kinds of data that can be used for significant improvements.

Plenty of businesses are content with letting their data rot in filing cabinets, and any extra spending is taken as a deal-breaker. Apparently, they missed the memo that it takes money to make money.

“Like out of five stars??”

Communicating the benefits of an investment can be difficult, especially when trying to convince someone who clearly wants to remain skeptical. Our best advice is to be patient, be systematic in making the case for adopting a key innovation, and be persistent.

One way or another, you’ll either find yourself delivering the right pitch to get it through your superior’s thick skull, or find yourself killing time as you wait for your next career shift to greener pastures.

Reason 3: The Learning Curve

By its nature, innovation comes with a learning curve. For many adopters, it’s a necessary speed bump on the road to better business. For others, it’s a brick wall.

Organizations that reject innovation on account of the learning curve fall into one of three categories.

First, you have groups that can manage the challenge, but can’t be bothered to throw in any effort (the lazy).

Second, you have groups that can’t manage the challenge because they’ve hired people who simply aren’t up to par (the inept).

Finally, there are people who could learn but lack the funds or skills to hold training sessions (the unfortunate).

Example: Automation

It’s possible to automate common business tasks like managing files, organizing schedules, and even taking phone calls. If you name it, the chances are good that someone’s already taught a computer how to do it for you.

Of course, introducing a workplace to automation comes with an adjustment period –and the fact of that alone is enough to discourage some local businesses from literally kicking back and letting manual tasks perform themselves.

If you find yourself among the unfortunate, know that there are plenty of affordable training options for many of the latest innovations. You don’t need to cut a department to teach another how to work with automation software.

If you’re surrounded by the lazy and inept, however, we wish you all the best. There’s little to do when your managers and team members lack the initiative or mental power to stay ahead of the curve. Your best shot is to perform well, and keep your eyes open for a smarter career setting.

Case in Point: Blockbuster vs Netflix

In 2004, the American video rental service Blockbuster reported six billion dollars in revenue.

In 2010, Blockbuster declared bankruptcy.

Today, in 2019, your youngest work colleagues would scratch their heads at the very idea of renting videos only to return them to a store after some time has passed.

Conversely, there’s barely a soul alive today who hasn’t heard of Netflix, which is probably why most people cringe hard when they learn that back in the year 2000, Blockbuster declined an offer to purchase Netflix for a measly $50 million.

I’d bet my subscription that you or the people at your workplace feel for Blockbuster. Nothing was broke that needed fixing, $50 million is a hefty sum by anyone’s standards, and who the hell could have explained video streaming back in 2000 anyway?

Before you leave a flower on the grave of the world’s greatest home entertainment failure, know this: when Netflix tried selling itself to Blockbuster, the young streaming company was already raking in billions in revenue.

They knew they were sitting on gold, and the signs of continued success were plain as day. Why they even tried to sell themselves off is beyond me, but whatever motivations each party may have held, the outcome is clear: Netflix knew how to ride the tides of change.

For Blockbuster, innovation was optional, until it suddenly wasn’t.


Things aren’t all doom and gloom, and believe it or not, you can teach an old dog new tricks. In our Business Analytics Masterclass, we teach some best practices and techniques so that you can be more hands-on with your data.

We guarantee you’ll leave armed with the tools for transformative innovation.

Bonus points if you can bring any knuckle-dragging supervisors along with you—we have a thing for communicating the power of innovation to people who don’t want to hear it.

Using Data for Human Resource Management

How to Stop Losing Good People to Hidden Figures

How do we encourage higher productivity?
What makes for a valuable employee?
What makes our valuable employees leave?

The problems at the heart of human resource management remain constant across time, and today’s HR professionals are busy answering yesterday’s questions. Every time a promising answer arrives, economic, cultural, or generational variables change to wipe the board clean.

Still, we persist in finding new solutions and developing better tools to manage our employees and provide them with every advantage in aligning with our business’ goals.

Or, at least, we should be.

A failure to innovate makes it harder for businesses to thrive. Circumstances may change and people may be the trickiest subject to master, but nothing kills growth quite like a refusal to get with the times.

This is especially true in the field of HR. Recent waves of technological progress have added great complexity to the average employee’s life and values, and technology provides a great advantage when sorting it all out—but only to the willing.

People Analytics

Just as the name suggests, big data has grown to encompass a wide range of applications. People analytics, defined by Google re:Work as the use of “a data-driven approach to inform your people practices, programs and processes,” is one of these applications.

Consider the oldest HR problem in the book: how do I encourage higher productivity?

If you were to ask a random person on the street, they might give an intuitive answer: start by finding out what motivates your employees and then work from there. Call each member of your organization over for a quick interview, and make changes based on the ideas they give you.

The direction of the kind stranger’s advice is a form of people analytics. You gather qualitative data about your employees, analyze the data, and use what you learn to make smarter business decisions. The approach of interviewing your staff members one by one may be a bit low-tech compared to what we’re capable of today, but you can see the core of the idea in action.

With people analytics, you can spot the variables that make for top performers, high-power teams, and flight risks. You can find out what your employees are thinking and feeling, as well as which of those thoughts and emotions make for the best investments. You can master your organization’s internal processes before taking it in a new, more efficient direction.

Data Engineering in HR

People analytics is a technique, but it’s one that relies on a particular frame of mind. In theory, you can pull up a spreadsheet and start running analysis on whatever data you have available. It’s possible, but unless you’re thinking from the perspective of a data engineer, the results aren’t likely to hold significant value.

There’s more to people analytics than analytics. To make the most out of HR’s latest and greatest innovation, you need to consider the following:

  1. How do we collect data, and where from?
  2. How do we store and organize data?
  3. How do we make sense of the data?
  4. How do we act based on what we’ve learned?

There are four parts to what we at Cirrolytix call the data value chain: a process by which raw data is harvested and refined into the kind of information that can save industries. Think of it as a factory line for data-driven decision making.

A data analyst is involved in the third step, or data consumption. This means that even the most skilled analyst is nothing without a proper infrastructure for gathering data.

A data engineer’s perspective is concerned with the whole enchilada. It leaves no stone unturned, and helping businesses wield data from the moment they start to collect it.

HR Analyst

When you realize everyone is taking their sick leaves on the same days every year

Hidden Figures, Clear Risk

HR professionals who’ve tapped into people analytics and other big data techniques have spotted factors they never imagined would have an effect on their KPIs. There’s a large risk in letting these factors go unnoticed; if you weren’t aware, employee turnover in the Philippines has recently hit a five-year high.

To show you what tapping into people analytics looks like, let’s refer to the Harvard Business Review’s study on the kinds of behavior that employees exhibit before quitting. HBR referred to hundreds of data points and determined that lowered productivity, relatively poorer teamwork, and drops in motivation are signs that an employee is soon to leave.

The discovery is great news for any HR manager who knows how to act on it, but therein lies the problem: how does a company with ranks upon ranks of employees monitor behavior with the necessary level of precision?

The answer would be to build a sound infrastructure for collecting and analyzing data. Tap into expert knowledge and quantify those highly subjective variables (create scores for motivation, for example, or track teamwork to the decimal). Data science allows HR professionals to shine a light on the hidden figures that can make or break their years of effort.

Moving Forward

Like it or not, there are lines of influence that run through every business. Invisible to the untrained eye, these variables warn of fortune and disaster long before the point of no return.

For most Filipino businesses, today’s problems were solved yesterday by data specialists and the tools they’ve developed. Those who plan to dominate tomorrow have to get the ball rolling and engage with emerging technology.


If you’re interested in facing the demands of today’s market head-on, sign up for our Masterclass series on business analytics. We’ll equip you with the tools you need to make data-driven decisions and spot the hidden figures that flow through the professional world.

AI vs. Creativity

Why the “Fight” Only Hurts Marketers.

“Do our computer pundits lack all common sense? The truth is no online database will replace your daily newspaper, no CD-ROM can take the place of a competent teacher and no computer network will change the way government works.”

The above is a quote from author Clifford Stoll.

You’ve likely never heard of him, and here’s why:

Good Old-Fashioned Naysaying

The early days of the internet were fraught with imperfection. Web development was in its cluttered infancy, e-commerce was a matter of distant science fiction, and searching for information was a nightmare.

Like any new technology, the internet was a bit of a fixer-upper.

But the reason why you’ve never heard of Clifford Stoll is because Clifford Stoll, like many of today’s influencers, spent a lot of time kicking the newborn invention. Where some worked to improve, he spoke to diminish. Where some saw potential, he saw an opportunity to take shots at those who saw potential.

When Nicholas Negroponte, then director of the MIT Media Lab, predicted the rise of ebooks and digital news media, Clifford Stoll called bullshit. When asked about the future of online commerce, Clifford Stoll scoffed and said, “Even if there were a trustworthy way to send money over the Internet—which there isn’t—the network is missing a most essential ingredient of capitalism: salespeople.” I wonder if he’s ever shopped on Amazon.

(Today, Clifford spends his time selling blown glass Klein bottles out of a basement crawlspace in his home. Ironically, none of us would even know that if it weren’t for the internet.)

It’s a tale as old as tech, and folks like Clifford will enjoy their freedom of inconsequential speech until the end of the human story.

It’s a case of good old fashioned naysaying, and it’s currently one of the marketing world’s greatest roadblocks.

Doom and Duh: The Case of AI vs. Marketing

There are two general categories of naysayers when it comes to AI and marketing.

First are the doomsayers who believe that AI will be the death of creativity in marketing. To their understanding, machine learning is less of a decision-making tool and more of a robotic overlord —literally, a tool that makes the decisions.

The workflow goes:

  1. Machine says jump.
  2. Hapless marketer jumps.
  3. Profit.

Second are those who won’t go as far as to say AI will be the death of marketing, but take a lot of care to remind us that AI should be used in moderation.

As if we needed telling. As if the power of machine learning might corrupt even the most virtuous marketer into forgetting that there’s an art to sales and marketing.

#Digicon2018 saw a lot of these types: visionaries who single-handedly prevented the decay of the marketing profession by reminding people that it takes emotion to sell a product. To their credit, it’s hard enough to become a Captain of Industry —but these people achieved the rank of Captain Obvious along the way.

As a result, the landscape for discussing the future of marketing is full of doom and duh. Half the room is shouting No hope! and the other half is begging to be told, No shit!

For marketers, this all poses a major problem: the idea that firms should take it slow and approach AI with caution.

Working in a country where SEO and data-driven marketing have only just begun to gain traction, I can say definitively that caution is the least of our problems.

We aren’t flying too close to the sun —on the contrary, we’ve barely taken off. If you look at the global landscape, we’re falling far behind other countries in terms of what we can (and should) accomplish. While comparable businesses in technologically progressive countries are securing their advantages for 2020, we’re stuck debating the problems of 2010.

Overpopulation on Mars

Don’t get me wrong, it pays to be cautious when developing new technology. There are ethical concerns and externalities to consider, but that’s all far beyond the scope of the “Creativity vs AI” debate.

The thought leaders worrying about AI undermining creativity need to take a look at yesterday’s news: creativity is expected to be in high demand in 2020 (right behind critical thinking and complex problem solving), as per the World Economic Forum.

This can’t be right. The algorithm said to use our feelings.

The voices of professional reason lecturing us on the importance of creativity in the face of data should take a page from any of the dozens of disruptors who’ve made compelling –and yes, creative– ads and products through the strategic use of data.

Put simply, the rest of the world is so far past the problems dreamed up by today’s Clifford Stolls that the hesitation to upskill is doing us more harm than good.

Our legacy industries are suffering from a wide competitive gap, and it’s only a matter of time before stubborn marketing agencies lose business to younger, more agile firms who know how to get with the times.

It’s a sad story, told into being by people who are either afraid of change, or comfortable being the biggest fish in a stagnating pond.

I’d say that the debate between creativity and AI is moot and academic, but that would be insulting to academics. They’re worrying about overpopulation on Mars when we’ve barely made it past the moon, so to speak. The ultimate irony is, the creatives most afraid of being left behind by AI are those who lack the creativity to use it.

Failure to launch?

Try failure to think.


If you think learning is a better use of time than shouting at imaginary problems, why not sign up for our Masterclass? We’ll teach you how to solve problems instead of complaining about them.

Left-brained or Right-brained?

At some point in your life, you probably believed that the left and right halves of your brain determined your skill set. If you were a left-brained individual, you were logical and better at maths and the hard sciences. If you were right-brained, then you were creative and artistic.

It’s a compelling idea, but it’s wrong.

While the hemispheres of the brain have their own unique characteristics and patterns of behavior, the reality of how the brain works is far more complex than a simple binary.

Language, problem-solving, and creativity aren’t confined to either half of our gray goo. Instead, the brain works through a great deal of coordination among various, specialized parts.

This article, for example, would have been impossible without the parts of my brain that store information, the parts that analyze information, the parts that play a role in language, and the parts that compel my hands to type (to put things very simply).

Despite these facts, people continue to work around things that fall beyond the scope of their “dominant brains,” much like how some of today’s top marketers draw a line between themselves and data.

They’re just as wrong, and here’s why:

Bozoma St. John quote
Feelings man…
Image credit: Rappler

A Problem of Misidentification

“We’re in the feelings business, not in the data business,” said Bozoma “Boz” Saint John as she kicked off Digicon 2018 on the 3rd of October.

The former head marketer of Apple Music and Uber delivered what most would consider an insightful talk on the importance of emotion in today’s marketing landscape. To Boz’ credit, it was largely that: insightful and correct. However, she took the reminder a few steps too far into unproductive territory.

She argued that a genuine connection with a market of consumers is emotional. “It’s not logic,” she said, “It’s not based on the algorithm. It is based on how you feel.”

Here we see a classic example of a false dichotomy: logic or emotion. Tapping into the emotional core of marketing means setting aside your spreadsheets and practicing empathy. You can trust a machine to think, and a person to feel.

As any marketer will tell you, this isn’t the case. You find the pulse of a market, that emotional register, by running surveys and gathering data –it’s qualitative data, but data nonetheless.

Granted, an algorithm isn’t the first thing that comes to mind when we think of processing data about the emotional links people make with products on the market, but our algorithms are growing smarter and more comprehensive.

The concerns shared by Saint John, however, remain fixed. Take this dated article from the Harvard Business Review as a case in point. Every wave of technology comes with those who advise against dependence, and we’ve already covered how extreme cases of naysaying have led to people eating their words in the most dramatic of ways.

What was served to the attendees of this year’s Digicon, then, was a problem of misidentification. Logic and emotion work closer than many realize, and realizing this can set the terms for a marketing firm’s success or failure in the near future.

The Whole Damn Brain

Data scientists are well aware of the limitations of their models and programs, and like any halfway decent problem-solver, they’ve set to mending these gaps in their craft.

They’ve made leaps and bounds in bridging logic with emotion, and the first example that comes to mind is how Netflix built the acclaimed House of Cards using patterns and trends in data. If influencers like Boz Saint John are after emotionally-charged marketing, what better argument can we make for big data than a critically acclaimed, multi-season work of art born from data analytics?

To say that forming a strong connection with an audience is a simple matter of leaning on emotion is a discredit to people and their complexity. Feelings of joy, relief, comfort, and association can mean brand loyalty, but finding a way to tap into these emotions leaves more room for logic and systematic thinking than some would have you think.

It takes the whole damn brain to form a sentence, no matter how technical or artistic. Likewise, it takes the whole damn brain to come up with a marketing campaign worth launching.

This leaves the average marketer at a critical decision point.

On one hand, we can spend our time discussing the best ways to keep the world of logic from leaking into the emotional. Be like Boz, and place emotion on a high pedestal. Leave data for end-month reports, and creativity for the heavy lifting.

On the other hand, we can dive headfirst into the complexity and adapt our thinking to make the most out of creativity and logic. Use the whole damn brain, and look for ways to turn our tools to our advantage –without sacrificing the art behind the profession.

Two options, weighed and measured for your convenience. One door leads to a neat and tidy world, and the other leads to a more difficult, more complex version of reality –one where you’re forced to confront data and emotion to create campaigns bigger than the sums of their parts.

The latter is a frighteningly difficult option, but seeing as Halloween is just around the corner, I see no better time to commit to doing something that scares you.


Data is scary, we get that. For a guided tour through the world of data analytics, sign up for our Masterclass. We teach practical data analytics for businesses, grounded in examples you’re sure to relate to. Go ahead, we don’t bite.