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.

The Business Analytics Masterclass

Practical analytics for better business outcomes.

What separates us from a generic training program? For the marketers who attended our Masterclass, the answer was nuance.

On the 3rd to 5th of September, we held the marketing course of our Business Analytics Masterclass at the Forum at Solaire. We were joined by by freelancers, managers, strategists, and executives from across various industries, including:

  • Academe
  • Banking
  • BPO Shared Services
  • Brand Activation
  • Creatives
  • FMCG
  • Hospitality
  • Media
  • Retail
  • Technology

As promised, the masterclass was a practical and business-oriented learning program designed to help our attendees make data-driven decisions.

Our participants were introduced to the concept of the data value chain, and the importance of taking a comprehensive approach to data. They left with an appreciation for collecting and storing data, as well as recognizing what it can and can’t do for marketing.

The Data Value Chain by CirroLytix

Likewise, they were equipped with the necessary skills to collect, organize, examine, and implement data in their respective fields.

Thanks to the extensive corporate backgrounds of facilitators Claire San Juan (our Chief Data Scientist) and Doc Ligot (our Chief Technology Officer), the lectures were grounded in examples from real marketing data and actual use cases, such as:

  1. RFM (Recency, Frequency, Monetary) Segmentation
  2. Customer Segmentation
  3. Product Affinity & Customer Satisfaction
  4. A/B Testing & Campaign Analysis
  5. Retail In-Store Positioning
  6. Marketing Mix & Attribution Models
  7. Purchase Propensity & Lead Scoring

On the second day, we ran our participants through a number of our custom-made exercises designed to familiarize them with the different tools and tactics for data analytics, taking home guides and presentations to revisit at their respective workplaces.

We planned the Masterclass to be an intensive learning event first and foremost, but a number of our participants found themselves leaving with more than a set of ideas.

To sum it up, the first module of our Masterclass was a success. We’re looking forward to seeing new faces at the upcoming module for HR which you can sign up for here.

Business Analytics Masterclass – Kind words from our graduates

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.

Financial Analysts vs. Data Scientists

Financial analysts could’ve been the first data scientists…

Earlier this year, the Chartered Financial Analyst (CFA) Institute announced a decision to include artificial intelligence, automated investment services, and mining unstructured data in its 2019 exams (Reuters). Stephen Horan, the Institute’s managing director for credentialing, says that the expanded coverage will help future analysts “…distinguish between structured and unstructured data analytic methods, as well as identify characteristics of robust investment algorithms.”

It’s been a long time coming.

Using data for business is nothing new. Financial analysts have been at it for decades, long before the first data scientists popped onto the scene, and, unfortunately, long before data became cool. Like hipsters before hipster things hit mainstream.

Like data scientists, financial analysts have good analytical skills and a nose for business problem solving: What data should we measure? What types of analysis can we perform? How do we use data to drive action? The only big difference between the two is technology and statistics.

But, clearly, data scientists are getting the better end of the deal.

A quick search on GlassDoor shows that, on average, data scientists earn nearly twice as much as financial analysts. What is more, they’re taking jobs from financial analysts—apparently, data science is now the hottest job in Wall Street.

Why is this the case?

Financial Analyst Average Salary (Glass Door)
Financial Analyst Average Salary (Glass Door)
Data Scientist Average Salary (Glass Door)
Data Scientist Average Salary (Glass Door)

Mo data, mo problems

Back in the day, data was simple. We didn’t have too much of it, and what we did have could easily fit into neatly arranged rows and columns in spreadsheets. Excel was more than enough in most cases.

Then the Internet, relational databases, and their concomitant technologies happened. All of a sudden, even the most esoteric phenomena were turned into data points. We started collecting data on everything and anything we could get our hands on—online activity, transportation patterns, food consumption, exercise habits, even cats in movieswhich animals fart, and abandoned trolleys in Bristol river.

A study by IBM showed that over 90-percent of the world’s data was created post-2010, much of it unstructured data from sensors, websites, and the ubiquitous Internet of Things (IOT). We have reached a point where data has grown so vast in terms of volume, velocity, and variety that the old rules no longer apply. We have effectively outgrown the spreadsheet.

This posed a conundrum for financial analysts. Companies were no longer content with financial reports and balance sheets; they wanted to extract and analyze all kinds of data from all kinds of sources, and get feedback in real-time. This called a new set of skills altogether.

The most prepared to meet this demand were software engineers, computer scientists, and statisticians. Not necessarily people who understood business but who, at least, knew how to source, publish, and analyze data. Over time, these people picked up on the business side of things, and data science was born.

We should ask ourselves why the inverse did not occur—that is, why didn’t financial analysts pick up data skills to meet the new demand? Some attribute this to the general slowness of financial professions, others, to the lack of the right incentives (and disincentives) to encourage upskilling.

Whichever the case, it was an opportunity missed. Had financial analysts learned to manage new types of data, they would’ve easily been more relevant today, especially that companies now demand faster reporting, smarter metrics, and, of course, more data.

Unfortunately, much of that is already beyond the purview of the average financial analyst.

A word on computers

It can be hard to imagine, but computers used to be people. In the early 1940s, firms like NASA employed human computers (mostly women) to solve complex math problems—such as the number of engines needed to keep a plane airborne, or the optimal trajectory of a rocket.

Human computers - the women of NASA
The lady on the right reminds me of my mother… board
Good Housekeeping

Quo vadis?

Our data-supercharged future isn’t all doom and gloom for financial analysts. If they keep the status quo, the pay is still pretty decent; and, if they want to adapt to today’s data, the gap isn’t as wide as many believe.

Many data skills can be learned fairly quickly, and there are a lot of free data courses to take advantage of. A PhD in computational statistics or machine learning might be nice, but it isn’t mandatory. Starting with something simple, say SQL or Python, can already open a lot of doors; the important part is to combine those new things with some good old-fashioned critical thinking and problem solving skills.

All that said, the recent CFA decision is a veritable sign that a major shift is in order. Old tools and techniques in financial analysis can’t possibly handle the volume, velocity, and variety of today’s data.

The jury has spoken: financial analysts need to adapt to the changing data landscape if they want to remain relevant…or at least get paid more.

Building Better Analytics Teams

What makes a good analytics team?

I have had a number of opportunities to build and manage teams of domain experts in my career, and true to form — I have noticed some common patterns.

Rather than a “how-to”, I instead want to highlight the traits and scenarios I have seen make some teams succeed better than others.

The Better Teams Define Their Success Parameters

A touchy subject and oftentimes a source of criticism is the purpose of the analytics team. I have built teams within corporations and a common expression is: “avoid science experiments, give us actionable results.” Although the advice seems like sound “corporate-speak” — what I found is the pendulum actually swings in both directions. You actually need an intersection of at least 4 things:

  • Analytic purpose: Is it targeted business insights, or data-driven discovery?
  • Is the team’s composition and objectives aligned to the analytic purpose?
  • Business maturity curve: what is the most appropriate capability your company requires at the moment?
  • Is the team able to execute on its mandate?

The problem arises when one or more of these areas are out of sync with the rest, which results in sloppy results, lack of focus, and worse: disillusionment of management on the value of analytics and a backslide back to gut-feel decision-making.

Define The Box, To Look Out Of The Box

In some cases you will encounter teams that segment themselves by analytical process, or segmenting analytics teams into procedures: e.g. the data quality team, the reporting team, the analysis team, the forecasting team, the modeling team, etc.

Analytics is actually the aggregation of all these processes. While there is occasional argument for and against this “commoditized” approach, the more successful teams are able to holistically integrate various parts of the analytical process to drive outcomes, regardless of their place in the analytical process. In some cases it does help to have distinct teams handling different processes, but each team should be able to contribute and interact with other processes: i.e. the “reporters” can help drive analysis, the “analysts” help police data quality, the “forecasters” refer to reporting for drivers of their models, etc.

Some of the most interesting expressions I have heard uttered in the workplace are multiple versions of: “I don’t write commentary, I just produce reports” and “I just comment on the numbers, go ask the reporting team why the number looks funny.”

Quite tragic.

The Better Teams Hire Character, Then Train Skill

We are probably at the height of the trend (or hype) in “analytical occupations”. Roles like Data Scientist, Data Engineer, Chief Data Officer and Chief Digital Officer are now the hot jobs under recruitment and most job descriptions for these roles put a high emphasis on technical competencies and skillsets required from candidates. After going through repeated recruitment, hundreds of CVs and interviews, in my experience, the successful analysts are not so much a function of skill but of their attitudes and values.

A baseline of technical capacity is definitely required to handle the analytical tools and make sense of data trends and patterns — but the stellar analysts also possess the drive and ambition to see projects through, the courage to point out anomalies or insights, and the initiative to take ownership over their data (warts and all) to drive business outcomes. Analysts who are naturally collaborative — and willing to share effort and credit on tasks also end up achieving more than the solo jockeys, regardless of brilliance.

There’s also the self-deprecating attitude — despite the body of knowledge they will usually possess, all the high-performing analysts I’ve met are humble enough to admit they don’t know everything — and this drives them to a path of curiosity and lifelong learning. All of them are voracious consumers of information, new trends, technology, and happily interact with other analysts to swap techniques. Contrast this with the occasional “big-wig” complex of some experts and academics who let ego get in their way — some of whom I’ve seen crash and burn miserably in the workplace.

Executive Leader
Leaders create an encouraging but intense environment that requires people’s best work

An Expert Leader Is First And Foremost A Leader

A follow-up point to hiring is the characteristic of a good analytical leader — and the typical assumption is that the big data boss needs to be the smartest, most-academically-decorated genius in the room. Technical skill and academic qualification tend to help with tackling analytical scenarios, but successful analytical leaders are still distinguished by their soft skills. The best characteristics (including those I have learned myself, the hard way) I think can be summarized by the Multipliers framework by the Wiseman Group — because they are true for analytics leaders:

Good Analytical Leaders (Multipliers):

  • Attract talented people and maximize them
  • Create an encouraging but intense environment that requires people’s best work
  • Define success and areas for people to stretch themselves
  • Drive sound decisions through rigorous debate
  • Give people ownership of results and invests in their development

Bad Analytical Leaders (Diminishers):

  • Hoard resources and underutilize and lose talent to boredom
  • Suppress people’s creativity and thinking
  • Must always be the smartest person in the room
  • Make centralized decisions and disempowers
  • Micromanage everyone

The Better Teams Encourage the Qualitative As Much As the Quantitative

Analytics roles generally attract individuals from quantitative disciplines — computer sciences, economics, statistics, mathematics — and generally the ability to crunch numbers and formulae will not be in short supply. However, especially in a work environment, analysts will not just be called upon to process data, but also to present this in meaningful form to peers and management. This requires a set of disciplines closer to the arts and humanities: public speaking, 2D and 3D visualization, colors and representations, and written communication — all of which will usually come counter-intuitive to the quants.

In forming analytics teams, a healthy balance between science and humanities (reminiscent of the Apple philosophy) is needed — through deliberate recruitment of mixed backgrounds and complimentary training to fill out the gaps for each analyst. The successful teams I’ve handled had good mixes of Statistics and Accounting, Mathematics and Finance, Economics, Business Management, and Computer Science.

The Better Teams Never Let Technology Define Capability

Depending on where they were minted, many analysts tend to be rabidly loyal to a particular tool, or software, or framework. We have all seen the nth iteration of the “Is R better than SAS or SPSS” or “SQL vs. NoSQL” or “RDBMS vs. Hadoop” debates but the reality I have seen is technology is rarely a differentiator of results. Granted some forms of analysis are easier to perform on certain platforms than others — the results come from teams that are able to formulate a business question or problem in terms that technology (whatever available) can interpret, process, and consequently resolve.

When I moved from being a business user into IT consulting, I was appalled at the number of instances teams or companies I encountered acquired tools they rarely use, or use incorrectly — and since most commercially available analytical software and hardware do not come cheap, this can become an invisible sinkhole of profits for many organizations. In these cases it is the analytics teams more than IT that should drive the applicability of their current or desired technology toolkit to their business environment.

Analytics Maturity
Executive commitment to analytics drives maturity
Source: Davenport and Harris

The Better Teams Recognize And Manage The Politics Of Information

Over the years I remained in contact with many prior colleagues who worked in the teams I’ve set up and am always humbled to hear about their present successes and how they traced much of their learning to those formative years spent together working with me. What I’ve found is the key to longevity in an analytics career is the deceptively simple recognition of the saying: information is power.

Careers, companies, and strategies have been built and destroyed on the back of information. Oddly, most analytical teams prefer to be “apolitical” or “amoral” about information — they will tend to dwell on the “how” rather than the “why”. I think this is both a costly mistake and an opportunity loss. Analytics has the ability to transform organizations, and analytics teams should take ownership of that power and use it, but be conscious of the massive power on their hands.

I am not suggesting analytics teams become active and malicious power brokers (some do) but going back to the first trait above: in defining success parameters become a trusted advisor to all parties in the organization.

Final Thought: The Best Analytical Teams Cease To Exist

According to Davenport and Harris’ analytical maturity stages: true maturity in analytics happens when the analytical functions no longer exist as a centralized capability, but become a natural part of each organizational function. This implies that a truly successful analytical team actually ceases to exist independently — and effectively each team in the organization becomes an analytical team.

Or to put it another way, the truly successful teams in any organization are the ones that become truly analytical.

I like the sound of that.