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.

Data Scientist vs. Data Engineer: Who should you hire?

We’ve already discussed the hype surrounding data science (Are Data Scientists Real Scientists?) and dissected what makes a good data scientist (How To Tell If You’re A Data Scientist Worth Hiring).

Today, we’ll talk about a decision modern businesses need to make: hiring data scientists vs. data engineers.

While data science is clearly the clearly the sexier job, data engineering is more in demand with five times more job openings, as per Glassdoor. This may come as a surprise, especially since much of the fanfare surrounding data has to do with data scientists.

Ooh, big data!

This article will compare and contrast data scientists and data engineers, and, hopefully, provide some insight on which one your company needs more.

Hot take: many businesses want data scientists when they clearly need data engineers.

Data Science vs. Data Engineering

Data science is the discipline of cleaning, organizing, and modeling (big) data. It makes use of advanced mathematical, statistical, and algorithmic techniques to extract insight and perform analysis on different data types including numbers, text, images, audio, and video.

In the last decade, we’ve seen data science skyrocket in popularity, as commercial use-cases for predictive analytics, machine learning, and statistical modeling have become more mainstream.

Data engineering, also called data architecture, is the practice of generating, storing, processing, and curating data. It involves a variety of programming languages to design databases, build data pipelines, manage data warehouses, and optimize data processing.

Like data science, we’ve seen data engineering rise in popularity in recent years as businesses have begun to handle more data in terms of volume, velocity, and variety.

Data Scientist vs. Data Engineer: Education

Data scientists often come from mathematical and quantitative backgrounds and data engineers, from programming backgrounds.

Popular courses among data scientists include statistics, mathematics, economics, and physics, although we’ve seen data scientists come from other courses like computer science and IT. However, it’s important to note that many data scientist jobs require post-graduate education in advanced mathematics, statistical modeling, machine learning, or other similar programs.

We may see these requirements change in the near future as colleges and universities continue to open data science specific degrees for undergraduates.

Data engineers, on the other hand, usually hold degrees in computer science, computer engineering, or IT. Moreover, most data engineering jobs require proficiency in multiple programming languages like Java, C#, SAP, Python, SQL, and NoSQL.

Companies hiring data engineers may also ask for professional certifications in Hadoop, Apache Spark, MapReduce, HIVE, and other similar systems.

Data scientist vs data engineer tools and software
Image from DataCamp

Since data science and data engineering are both relatively new (well, they really aren’t but data-specific education is), many of today’s data scientists and data engineers are products of MOOCs and other short certificate programs.

Data Scientist vs. Data Engineer: Skills

In general, data scientists have stronger analytical skills, and data engineers have stronger programming skills.

The former use mathematics, statistics, advanced analytics, machine learning, and artificial intelligence to formulate hypotheses, run tests, analyze data, and translate and/or visualize the results.

The latter employ advanced programming, database architecture, and distributed systems to deliver data to data scientists, data analysts, and business users.

Data Scientist vs. Data Engineer: Job Function

A data scientist’s main job is to analyze and model data in order to make decisions, and a data engineer’s is to source, clean, manage, and deliver that data for analysis and modeling.

Quick aside: At CirroLytix, there’s something we call the Data Value Chain. The Data Value Chain is a framework that outlines the process by which raw data is transformed to data-driven decisions, not unlike other value chains, where, a raw material, say, a cow, is undergoes a series of steps that transform it into a finished product, i.e. a world-class dish.

The Data Value Chain by CirroLytix
The Data Value Chain by CirroLytix

The data engineers come in during the earlier stages of the data value chain. They’re in charge of turning raw data, which may contain human, machine, or instrument errors, into neatly arranged data that can be digested and understood by a data storage system.

The responsibility of a data engineer is to design and implement solutions that improve data reliability, quality, and efficiency. In other words, they are in charge of getting useable data to data scientists, data analysts, and business users.

In an operation, this includes retrieving data from various sources, merging multiple tables, deduplicating data entries, and storing data in the proper format (e.g. transforming images into numerical pixel codes or separating full names into first, middle, and last names).

Data scientists come in much later, after the data has passed initial cleaning and manipulation. While they might do some data cleaning and manipulation of their own, they are more concerned with exploring data to extract insights and model predictions using statistical techniques and machine learning algorithms.

Additionally, a data scientist may be asked to visualize data and present findings to various stakeholders for consideration and decision-making, a task that requires business domain expertise and knowledge of data visualization and data storytelling techniques.

As you might’ve guessed, data engineers and data scientists are both key to a cohesive data value chain, and their jobs sometimes overlap.

This infographics from DataCamp sum it up well (data engineer is on the left and data scientist, on the right):

Job responsibilities: data scientist vs. data engineer
Image from DataCamp

Data Scientist vs. Data Engineer: Salary

Part of what makes data science so lucrative is the pay. Obviously, people (especially smart people) tend to flock towards high-powered, high-paying jobs.

However, when you look at the data, data engineers get the better end of the salary stick. Here’s a salary comparison from major listing sites:


  • Data Engineer: $63K – $131K
  • Data Scientist: $79K – $120K


  • Data Engineer: $172K
  • Data Scientist: $80K – $130K


  • Data Engineer: $43K – $364K
  • Data Scientist: $34K – $341K

Data Scientist vs. Data Engineer: One or both?

A data scientist will give you the insights and analysis you need to make sound, data-driven decisions, but without a data engineer to deliver and manage the data, a data scientist might not even have any data to work with in the first place.

Our view is that most mature businesses need a healthy mix of both in order to successfully manage a company’s data value chain. However, we suggest hiring data engineers first to build your data infrastructure and data scientists later on, once there is already data to model and analyze.


The data scientist vs. data engineer debate really depends on your company’s needs. If your immediate need is better data quality and delivery (which is the main data problem businesses face), then hiring a data engineer might make more sense. However, if your data warehouses and data pipelines work and you’re ready for advanced analytics, it may be the right time to invest in a data scientist.

That said, for most businesses, a data scientist is a luxury, not a necessity. Unless you’re doing advanced predictive modeling and machine learning, a couple data engineers and data analysts may do the trick.

Data science gets all the love, but, watch out, because data engineering may just be the new sexiest job in town.

Find data science and data engineering challenging? Contact us here and let us know how we can help.

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.

Are Data Scientists Real Scientists?



Data scientist is The Sexiest Job of the 21st Century, said Harvard Business Review in 2012.

Data Scientist vs Data Engineer Google Trends
Google Search Trends for Data Scientist vs. Data Engineer
Borrowed from the Analytics Association of the Philippines (AAP)

Seven years later, and we haven’t quite worked it out: Are data scientists real scientists? What do they really do? Is the work really as sexy as it sounds?

Much like Fatal Attraction, we begin our data science journey with doe-eyed infatuation and all kinds of butterflies—until we realize that data science is a psycho bitch who kidnaps our kid, boils our pet bunny (fluffy fur and all), and hacks us bloody with a blunt kitchen knife; we try to drown her in a tub but she…Just. Won’t. Die.

But I’m getting way ahead of myself.

Basically, the point I’m trying to make is that everyone wants to be a data scientist (or hire one) without fully understanding what data science is and what we really want out of it.

I think Dan Ariely, a psychology and behavioral economics professor at Duke University, hits the nail on the head:

“Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it…”

Part of the reason could be that data science is still in its confused adolescence. The field has only been around since 2008, and it has a ways to go in terms of defining and organizing itself.

It certainly doesn’t help that majority of data projects fail (see: 85-Percent of Big Data Projects Fail) and companies don’t know how to utilize data science talent (see: Why Data Scientists Are Leaving Their Jobs).

There are two issues I want to address: first, that data science has more to do with algorithms and statistical techniques than formal scientific work; and second, that most data scientist jobs are less sexy that people imagine.

Science, schmience

Neil Degrasse Tyson and Bill Nye Do You Even Science Meme
Do you even??

The dividing line between data science and real science is research methods—if you design experiments, and prove and propose formal hypotheses, your work is closer to a scientific role. Even generalizing conclusions from empirical data using algorithms can qualify as science when used  to augment research.

However, let’s not confuse that with people who draw pretty charts and run Python or R scripts for a living, without any research involved.

This isn’t to be exclusionary or pedantic about “real scientists” is, but the abuse of the term “data scientist” for recruitment, staffing, or marketing purposes does place a stain on the practice.

Let’s get real and call a spade, a spade.

Majority of a data scientist’s working hours are spent on arguably the least scientific parts of the job: cleaning and organizing data (60%) and collecting data sets (19%).

Incidentally, these are also the least enjoyable parts of the job.

What data scientists spend the most time doing infographic
Grab a magnifying glass to see the “science” part
Borrowed from the Analytics Association of the Philippines (AAP)
What is the least enjoyable part of data science infographic
Real data science isn’t as sexy as it sounds
Borrowed from the Analytics Association of the Philippines (AAP)

The sexy “science” stuff comes much, much later.

More marriage than sex

The idea of data science is sexy—especially when we hear stories about the Ubers, Netflixes, and Amazons of the world. But real data science work is a lot less so.

When data scientists take on jobs and companies hire data scientists, the expectation is to produce mind-blowing insight from vast amounts of data (read: Big Data). Truth be told, the road to data-driven disruption is not as straightforward as many think.

It takes a lot of work to get data that works.

Before a data scientist can even arrive at said mind-blowing insight, they need to make it through the mundane: hours and hours of ensuring the data sets are available and prepped for analysis, scripts are thoroughly debugged, and libraries are compatible—not to mention the professional Googling and GitHubbing involved before we can find the right algorithms.

If you recall the charts above, data scientists are actually spending most of their time on the least enjoyable parts of the job (see comparison below).

Tasks data scientists spend the most time on vs Least enjoyable tasks
Oh, so it’s just like a real job…*sad*

Anyone looking to pursue data science or employ a data scientists ought to consider the work, especially the mundane mind-numbing aspect of it, before leaping. If you’re not crazy about data, data science will drive you crazy.

The Future of Data Science

This isn’t to discourage aspiring “data scientists”—quite the contrary. As the global ocean of data expands, we need more data experts with the skills and knowledge to navigate it.

That said, we still have a ways to go in terms of defining the whos, hows, and whats of data work. In the process of figuring it all out, we should also avoid sugar-coating data job titles lest we get disappointed (this goes for both employers and potential employees).

A decade from now, we may well witness the extinction of the data scientist. Data jobs will only get increasingly specific, and catchall data scientists (the ones who juggle 8 different programming languages and 3 different job functions) may no longer be enough to meet the narrower and deeper demands of future data work. This is the same reason we rarely see job openings for “computer expert” or “business manager” anymore.

As data continues to stretch the limits of our imagination, we can’t possibly expect a handful of people to handle all of it.

Not even real scientists do that.


If you’re less concerned about job titles and more concerned about real, applicable skills, why not book a Business Analytics Masterclass. It’s not exactly science, but it works!

Predictive Lead Scoring 101

A Marketer’s Guide by CirroLytix

Not all customers are created equal. Some are practically begging to be sold to, while others won’t give you the time of day even if their life depended on it.

Any seasoned marketer will agree.

In this regard, it’s absolutely important to prioritize—to focus fire on leads likely to enjoy your product and save yourself the trouble of convincing everyone else. Marketers call this lead scoring.

Lead scoring is the practice of ranking leads according to their likelihood to perform a favorable (or unfavorable) action, usually purchasing a product.

Marketers use it for setting priorities (read: budgeting).

Whatever the size of your budget, it’d be borderline criminal to waste any amount on low-priority leads when there are better alternatives available.

This article will walk you through the ins and outs of lead scoring and provide a clear instructional reference for practicing it yourself.

Traditional vs Predictive Lead Scoring

There are two common methods to lead scoring, each with its own strengths and weaknesses.

Traditional lead scoring is a manual effort done by marketers to sort their leads into two categories: those who qualify, and those who don’t. Qualified leads are those worth chasing—they get the attention, the slice of the budget, and with luck, the place among your closed sales.

They do this by identifying key factors that correlate with an inclination (or disinclination) to convert, and deriving scores based on the weights of those factors.

Traditional Lead Scoring Sample Table
Borrowed from SalesForce

Example: BiroLytix

BiroLytix is a training service dedicated to helping stand-up comedians hone their craft.

They have a compilation of leads taken from various sources, and fine data on each of those leads such as their sex, age, professional background, and past training experience.

As per their lead scoring model, they assign people between the ages of 20 and 40 an extra +15 points, they give people in creative industries +10 points, and deduct 500 points from anyone working in data analytics.

Lead scoring is a matter of identifying your most and least promising leads, so it stands to reason that  the more important a value, the higher the bonus or penalty that comes along with it.

Predictive lead scoring is functionally the same as traditional lead scoring, with one major difference: while it’d be a marketer’s job to calculate for scores in the traditional method, predictive scoring relies on algorithms to do the heavy lifting.

Good predictive lead scoring algorithms make use of statistical techniques like logistic regression and multivariate regression to rank leads. In English, these model likelihood by assigning weights to each factor depending on importance. Some advanced lead algorithms use machine learning to “learn” and improve as more leads pour in.

Predictive Lead Scoring on MS Excel

Building a Leads Database

You can’t perform lead scoring without data. How much data depends on your appetite for precision: on one hand, total precision is near impossible; on the other hand, a faulty model can confuse good leads and bad leads.

We suggest taking as deep of a dive as possible into your data sources, and gathering as much useful information as you can without getting creepy.

Start by doing an inventory of existing customer databases—your website, CRM, social media pages, etc. This will allow you to do some preliminary data analysis and clue you in on how much more data you need to collect.

This task is definitely easier than it sounds (for most cases). A simple Contact Us form, for example, can be the foundation for a customer database. That said, we suggest you squeeze content gates for all they’re worth by asking for specifics like age, sex, birthday, and occupation.

Once you are able to correlate that personal data with behavioral data such as on-page behavior, social media activity, and previous purchases, you’ll be well on your way to building your own lead scoring machine.

It bears mentioning at this point that lead scoring isn’t a solution for everyone. Naturally, you’d have to be managing a large number of leads for this to make sense. Moreover, predictive lead scoring is only possible with a large enough data set to feed into an algorithm –if you’ve got data but need to process it with intuition and observation, stick to traditional.

How Lead Scoring Ends in Sales

Lead scoring would be pointless if it didn’t contribute to greater ROI on marketing efforts. Needless to say, it does—by over 70% according to a report by Marketing Sherpa.

There are two likely reasons for this: a quicker response time to urgent leads, and a quicker time with trend-spotting.

Urgent leads who are closer to the purchase stage have done their homework on your solution, which means they’ve probably considered your competitors as well. Time is of the essence, so knowing which of your leads are worth your time can go a long way.

With big, shiny numbers, lead scoring points you to late-stage customers who are ripe for the attention, and who are at the biggest risk of falling into the clutches of a rival business.

Conversion Funnel by Moz
Conversion Funnel
Image borrowed from Moz

Trend-spotting is also easier with lead scoring, because the mere process alone of refining a winning formula will lead you to great insight.

As mentioned, lead scoring depends on your ability to spot factors that influence the likelihood of closing a lead. Getting into the habit of identifying signs of a closer and weighing them against each other is invaluable practice in mindfulness when it comes to marketing.

In practice, lead scoring makes life significantly easier for any given marketing team. Targeted CTAs, emails, and alerts to schedule call can be set to fire for your most promising leads. All it takes is to determine a threshold, and program your automation software accordingly.

Neil Patel has an article covering a handful of automation techniques that align perfectly with a lead scoring strategy, and they’re worth looking into for inspiration as you write your own scoring playbook.

The cardinal rule for lead scoring is to follow the data. It can and will be tempting to abandon the method in favor of good, old fashioned intuition, but it pays to remember that numbers never lie. It may take some trial and error, but the persistence pays off when you’re closing leads like never before.

Lead scoring is a powerful marketing technique that helps you turn a lead list into a conversion machine. It’s one of many tools worth learning, and we’ll be covering plenty more in our upcoming Business Analytics Masterclass series. If you like what you see so far, you’ll love what we keep in the back.

Enroll in a class to reserve your spot.

5 Cybersecurity Tips For The Average Human

Not everyone can outsmart a hacker, but most of us can certainly out-dumb the average guy. It’s statistics, duh.

Avoiding a hacker is like The Running of the Bulls—keeping safe is, for the most part, not being the slowest schmuck in the game. If there are a handful of less secure people around you, chances are the hacker will go for them instead of you.

The Running of the Bulls in Pamplona, Spain

On a more serious note, cybersecurity is among today’s most pressing concerns, especially as everything—including our most sensitive information (we’re looking at you, Facebook)—moves to the digital realm.

The FBI recently reported that ransomware attacks number 4,000 each day, and a study by cybersecurity firm PandaLabs estimated over 230,000 samples of malware produced in each day of 2015 (their prognosis said the number should be much larger today).

The situation being what it is, it’s about time most of us developed the skills needed to navigate life online. Here are six handy tips to make sure you don’t get hacked. Think of these as your very own digital rape whistle.

1. Know that everyone is a target

The very first step to being cyber-secure is the awareness that it can happen to anyone. Even the Amish have hackers among their ranks! But I digress.

Truth be told, most people won’t even bother with the necessary precautions unless they’ve already been compromised or someone close to them has. I implore everyone to be just a tad more paranoid, especially as today’s hackers try to penetrate anything and everything—smartphones, webcams, ATMs, oven toasters, nuclear power plants, you name it.

2. Your password is your life

Most men won’t even share their passwords to their wives and girlfriends. It baffles me that many of them don’t exercise the same vigilance against hackers, who, frankly, can do much, much worse. Remember Ashley Madison?

Three things to remember:

  • Don’t use an easy password—especially not “password”
  • Change your passwords regularly—as often as you change sheets
  • Never use the same password repeatedly—keep threats contained
Laptop password is incorrect Steve Carell
Borrowed from Windows Stuff

3. Watch your Wi-Fi

Treat public Wi-Fi the way you would public toilets–only go if you absolutely have to and use everything you can to stay clean.

Hackers love public Wi-Fi because, one, there’s a lot of information to steal, and, two, public networks have vulnerabilities they can exploit. Once a hacker is able to penetrate a public network, they can intercept the information flowing in and out of it, including browser activity, personal data, and passwords. This is what’s called a “Man in the Middle” or  MITM approach.

The next level to a MITM attack is rogue Wi-Fi, also called an Evil Twin.

Hackers often set up rogue Wi-Fi in public places to steal information. They usually name the access point similar (e.g. Mall Wi-Fi Free) to the legitimate hotspot (hence, evil twin). Once you connect to a hacker’s network, they own you—your apps, emails, passwords, everything. They can even send out all kinds of filth from your device.

If you absolutely need to go online, at least have a VPN installed. A VPN is basically a service that encrypts your data (i.e. turns your data into jumbled up stuff that hackers can’t read) before it even reaches a Wi-Fi network or access point.

4. SSL or bust

If you continue to access a website that says “Not Secure”, you’re pretty much begging for it. An SSL (or Secure Sockets Layer) is a security technology used to encrypt data entered in browsers before it is sent out to servers, like a secure passageway to make sure the hackers can’t get to you. Sites with SSLs usually have an SSL certificate to prove that they have SSL installed.

Image result for website not secure
What’s the worst that could happen?

Not much thinking involved here. Most modern browsers will alert you if a website isn’t secure, so what you need to do is really simple: don’t enter any information, especially not sensitive information, in a site without an SSL Certificate.

5. Share at your own risk

As much as we try to protect ourselves, hackers will always find new ways to exploit our data. The most important thing we need to acknowledge is that anything we put online is at risk (of course, to varying degrees).

Always be vigilant that your data can be stolen, and keep your eye out for any kind of harm that might come your way. You may not be an expert, but as long as you’re alert and aware, you’ve already solved 90-percent of the problem.

Remember, you don’t have to be a genius to keep yourself cyber-secure. You just need to be just a bit more difficult to hack than the average Internet user.

Data Scientist or Know-It-All?

The importance of domain expertise in data practice.

There’s a short yet wonderful story that perfectly encapsulates how many of today’s businesses use data. It’s a simple parable that all of us can learn from, regardless of our background, level of experience, or field of practice. In fact, if you’re already working with data, you might have a similar story to share. It goes like this:

A data scientist holds up a chart.
Everyone believes him.
End of story.

In today’s data-supercharged world, data is the law and the data practitioner is taken as the de facto expert. Ignore the fact that Ben just got hired last week—he has a MA in Statistics and a PhD in Machine Learning, so he must have all the answers, right?

(To clarify, said Ben is a hypothetical person. If you happen to know a Ben or are one, we apologize in advance. If you happen to be a woman, please don’t take our usage of a traditionally male name as a vote in favor of the patriarchy. This is purely for emphasis. We support all women, especially women in data. With everything cleared up, let’s get back to the matter at hand…)

Of course people will listen to the data guy. Numbers are compelling, especially when presented in chart form. Who are we mortals to question an interactive, multi-colored bubble chart? What power does one man hold over a regression line with an R-square well over 0.90?

In the modern boardroom, data is gospel truth. Everything else is mere conjecture.

We seldom stop to consider whether the data is flawed, or if the data guy understands the subject matter enough to draw insights or conclusions. Maybe the regression model is accurate, but what if it uses the wrong variables, or maps out the wrong features? What if the chart displays absolute figures in places where a logarithmic scale is more appropriate? What if the time series shows periods that are either too long or too short? What if the final analysis is inconsequential to the use case at hand?

When all is said and done, data can be just as flawed as the people who work with it.

Consider the infamous case of the NASA’s $125 million Mars Climate Orbiter. A simple conversion mishap—the failure to convert pound-force (lbf) to Newtons (N)—had the spacecraft flying within 37 miles of the Martian surface, dangerously below the 53-mile minimum. What followed was an epic fail of astronomic (no pun intended) proportions: Mars’ atmospheric friction burned the poor thing to a crisp before hurling its ashes deep into a cratery abyss.

Eyewitness reports allege the fire started with a contentious bar chart

Crash and burn—or, rather, burn then crash.

Mind you, this blunder happened with Lockheed Martin’s and NASA’s top brass, arguably the best domain experts in their respective fields, on the job. If even they can make mistakes like this, what makes us think we regular folk are exempt?

The next example is more down-to-earth… literally.

Applying domain knowledge could be as simple as choosing between a FIFO (first-in-first-out) and LIFO (last-in-first-out) approach, as detailed in this SuperDataScience podcast.

To explain FIFO and LIFO briefly: If element A arrives first, B second, and C last, FIFO dictates that they leave in that same order. LIFO is the complete opposite, wherein the last element, in this case C, leaves first, followed by B then A.

As you might already predict, the “right” choice varies greatly among industries.

For example, a business dealing in perishable goods like vegetables or fresh meat might prefer a FIFO approach, wherein an earlier element, say Monday’s shipment, is sent out before Tuesday’s or Wednesday’s. Conversely, a steel manufacturer may opt for convenience and use a LIFO approach wherein the steel bars at the top of the pile (i.e. the last ones in) get shipped out first. Caveat: we are experts in neither the perishable goods nor steel industries, so this is, again, purely for illustration purposes.

Yes, data skills can be applied to nearly every domain. However, we cannot discount the fact that data practitioners need domain expertise in order to truly be effective (or at least to avoid $125 million blunders). Data in retail can differ from data in healthcare or economics or agriculture or any other industry.

This is no different from other jobs. In the same manner we demand industry experience from management professionals and sub-specializations from doctors and engineers, we need to push for domain expertise and domain knowledge in the data practice.

What does this entail?

For the data practitioner, this means building years of experience and knowledge in a specific domain. Go deep rather than broad.

For the company looking to fill a data position, this means hiring a data practitioner with an industry background, or grooming one from the existing workforce (the second is an option we highly encourage).

For schools and institutions offering data courses, this means creating industry-specific courses and tracks, or encouraging students to pursue a minor in a field of interest.

Parting thoughts

Data does not exist in a vacuum and neither do data experts. To make data impactful, we need to encourage data practitioners to look beyond the spreadsheet and out into the real world.

Doing so might just save all of us from another epic crash and burn.