RediscoveringBI | June 2013

Radiant Advisors Publication

rediscoveringBI

BEYOND THE END USER TAKING A

THE CLOUD

BEHAVIORAL APPROACH

FROM SKY GAZING TO

DETERMINISTIC VS. PROBABILISTIC STORYTELLING REDISCOVERED

DATA STORAGE

COLLABORATE AND SHARE 4 STRATEGIES FOR DATA SHARING

06 JUNE 2013 ISSUE 9

COLLABORATIVE BUSINESS INTELLIGENCE

rediscoveringBI

June 2013 Issue 9

SPOTLIGHT

[P3] Beyond The End User

There is a better,

behavioral approach to decision support than traditional business intelligence models.

[By Donald Farmer] FEATURES

[P7] The Cloud

The cloud

is the next step in business intelligence maturity, but are we ready?

[By Laura Madsen]

[P9]

[P13]

telling is fundamentally at odds with the

nology, it is people that make a project

deterministic storycraft we've practiced for

work.

all of human history.

[By Lyndsay Wise]

Storytelling Rediscovered Statistical story-

Strategies for Collaboration Aside from tech-

[By Stephen Swoyer]

EDITOR’S PICK

SIDEBAR

What You're Really Meant To Do

Motive For Metaphor Storytelling is

[P6]

[P15]

What you're really meant to do is

hard-coded into the ways in which

a lifelong process of self-discovery,

business structure and represent

according to Robert Kaplan.

themselves.

[By Lindy Ryan]

[By Stephen Swoyer]

SIDEBAR

1 • rediscoveringBI Magazine • #rediscoveringBI

FROM THE EDITOR Many of us still hear the echoes of familiar, if sharp-edged, business witticisms (i.e. “winner takes all”). We do so because they are largely true: business is tough, and competition even more so. It’s not even necessarily a bad thing to be a business “shark” -- think of business magnate Donald Trump, social media pioneer Mark Zuckerberg, or

Radiant Advisors Publication

rediscoveringBI

BEYOND THE END USER TAKING A

THE CLOUD

BEHAVIORAL APPROACH

fictional Kabletown exec Jack Donaghy. They might not be the most beloved names in the business world (the exception being 30 Rock’s Jack), but they are inarguably some of the most well known.

FROM SKY GAZING TO

DETERMINISTIC VS. PROBABILISTIC STORYTELLING REDISCOVERED

As challenging as business today is, the warm, fuzzy feelings evoked by softer corporate buzzwords – like teamwork, leadership, or collabora-

COLLABORATE AND SHARE 4 STRATEGIES FOR

tion -- are not without their practical benefits, too.

Our focus this month, collaboration, is more than the just the action of working together to complete a task. It’s a recursive process wherein

DATA STORAGE

DATA SHARING

06

COLLABORATIVE BUSINESS INTELLIGENCE

JUNE 2013 ISSUE 9

two or more people (or teams or organizations) partner to share knowledge, collect ideas, and realize shared goals. It empowers decision-

Editor In Chief

making, develops communication, and ultimately, fosters innovation.

Lindy Ryan

Research today is consistently proving Bratton and Tumin’s 2012 thesis:

lindy.ryan@radiantadvisors.com

organizations that cannot adapt to the increasingly collaborative environment of today’s highly networked world will perish.

However necessary collaboration is, it isn’t a plug-and-play process: it requires practice and tools. We in the BI world especially know the value of technology, and the collaborative tools we employ are no different. In fact, they could be even more important, especially if you consider the BI industry the stewards of the world’s data.

In this month’s edition of RediscoveringBI, authors Donald Farmer, Laura

Contributor Donald Farmer donald.farmer@qlikview.com

Contributor Laura Madsen lmadsen@lancetsoftware.com

Madsen, Lyndsay Wise, and Stephen Swoyer explore the function of

Contributor

collaboration, pointing out the human factor in data interpretation;

Lyndsay Wise

the possibility of over-collaborating, or collaborating prematurely;

wise@wiseanalytics.com

the importance of communication and interpretation of information; and, finally, strategies for cultivating a collaborative culture in today’s organizations.

Lindy R yan Lindy Ryan Editor in Chief

Distinguished Writer Stephen Swoyer stephen.swoyer@gmail.com

Art Director Brendan Ferguson brendan.ferguson@radiantadvisors.com

For More Information info@radiantadvisors.com

Radiant Advisors

rediscoveringBI Magazine • #rediscoveringBI •

2

BEYOND THE END USER

[There is a better, behavioral approach to decision support.]

DONALD FARMER

A

few weeks ago, a friend -- in his role as CIO at a medi-

Perhaps trickier is a second problem. Even with the right data,

um-sized manufacturer -- invited me to sit in on his

choices are not strictly rational. I should note: my friend’s team

quarterly team leader’s review. He asked me to watch

is not irrational (they certainly did not behave irrationally).

how his group used dashboards for real. He wanted

However, there was a distinct emotional content to their deci-

some advice.

sions. Though leading sales indicators looked weak, managers

The dashboards -- well-built, informative, and good-looking

were sure the next quarter would finish well, so did not hold

tools -- presented very few design problems. Likewise, neither

back on manufacturing. The leaders felt good and the general

did I hear any of the expected misunderstandings or tensions

positivity greatly influenced them, even without the data to

about metadata and metrics. I thought for a while I might even

support their choices.

encounter that mythical beast, the “single version of the truth.”

Even what we believe to be our most rational moments break

They were close to having a single version of something.

down under the press of emotions. A study found that judges

For sure, the technical team had understood the message

grant 65% of parole requests immediately after a meal break,

of business intelligence (BI): the right information at the

but often near 0% just before lunch or at the end of the day.

right time in the right format will enable better decisions.

Feeling tired (or hungry) literally impaired their judgment. In

Nevertheless, in the review meeting, the fatal flaws in this

another study, judges set bail eight times higher in some test

approach soon appeared.

cases when the questionnaires setting out the cases included

The “right information” presents a fickle target, changing most

a question possibly reminding them of their own mortality.

notably whenever an indicator appears troublesome, even if

Remember, these are judges -- trained to be objective and care-

the benchmark looks correct. For example, assembly of one

ful in weighing evidence! With that in mind, what chance did

SKU had dropped by 15% for the quarter. A shortfall is a bad

my CIO friend of being truly data-driven?

thing, but a manager had the reason: a short delay in the sup-

How can we design our information systems to allow for these

ply chain pushed a large run to finish two days over the end of

human factors, or even to compensate for them? Mostly we

the quarter. If the batch had completed on-time, the indicator

can’t, if we follow the existing formal methods of BI. Our cog-

would look much better. So, the right data turned out not quite

nitive biases -- our evolved heuristics, our instincts -- are just

right. Some similar un-captured caveat affected almost every

too ingrained in our thinking.

benchmark they looked at.

For example, you may be tempted to add more metrics to paint

3 • rediscoveringBI Magazine • #rediscoveringBI

SPOTLIGHT

I recommend building systems influenced by how decision-makers really make decisions." a more complete picture. This should make for better informed

tems exist today. You should find it easy to search through

and more rational decisions, don’t you think? In the case of

your business data model with the flexibility and comfort of

the missed manufacturing target, perhaps we should include

the best Internet engines, while still respecting the formalized

additional numbers for manufacturing runs completed just

underlying logic -- that’s already possible. Touch technologies

after the quarter end date, or just before. I expect we would

encourage interactivity in surprising ways; people simply

only end up with a confusing mess of metrics, rather than a

enjoy working with touch

clearer picture.

to explore with a tactile interface than with a mouse-driven

More important, refining the benchmarks further (or adding

environment.

more benchmarks) reinforces the formalistic approach, which

Most critically, business users, with few technical skills, must

I believe created the trouble in the first place. Formalism

be able to discover and to explore new hypotheses. These

defines the problem, not the answer.

users should find new associations between data -- and outli-

I suggest a new approach, one that is behavioral rather than

ers within their data -- without having to revisit the source

formal. In other words, I recommend building systems influ-

systems and without constructing new visualizations and

enced by how decision-makers really make decisions. Today, our

dashboards.

systems impose on users an idealized model of the enterprise.

These non-technical business thinkers were often described in

The ideal is not real.

the past as “end users” because we in the BI industry regarded

A full account of such systems stretches beyond the scope of

them as passive consumers of our wisdom and data-driven

this article, but let me share a simple piece of advice I sug-

insights. No more. The business expert should not be at the

gested to help my friend’s dashboard-driven meetings.

end of anything -- they represent the center of decision-mak-

Do not merely provide a dashboard of metrics and benchmarks.

ing, and their needs should center the information architec-

However accurate the data, however good the visualizations,

ture. We need to see an end of the end user.

however well laid out: the dashboard will be incomplete. Yes,

My friend the CIO has made some important changes to his

benchmarks can provide a very useful high-level synopsis, but

production meetings and the information that supports them.

you should also provide your decision-makers with tools that

The dashboards still circulate, but the business users now

enable them to explore the data themselves. If they can try out

have them on their iPads to encourage them to explore the

the effect of alternative selections, they will feel much more

data. With every dashboard, an associated application enables

confident in the data itself. If the tool enables them to pick up

the user to browse the underlying data themselves, and more

good “information scent,” suggesting new trails of reasoning to

advanced applications give access to data sources with a

follow, they will lead themselves to useful insights.

broader scope and more historical information.

We must also remember that the simple power to browse data

I am looking forward to see how these new users develop their

-- rather than analyzing -- can be very potent. After all, with

decision making with tools, which, for the first time, let them

Internet search engines, even the technologically naive can

be themselves.

browse with ease through vast tangles of data, difficult for

Share your comments >

they appear to be more willing

even an advanced data scientist to evaluate with more technical tools.

Browsing and scanning lead us quickly and easily to expected results if we search in a goal-oriented manner, though browsing can also present us with unexpected insights and anoma-

Donald Farmer is VP of Product

lies. Even better, we can find results outside the scope of our

Management for QlikView and has

planned research, but which turn out to be interesting.

toiled 25+ years in some very diverse

The capabilities to build more behaviorally appropriate sys-

fields of data management. rediscoveringBI Magazine • #rediscoveringBI •

4

UPCOMING INDUSTRY

EVENTS

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EDITOR’S PICK

WHAT YOU'RE REALLY MEANT TO DO LINDY RYAN

W

hen I was younger (much

he says, is a lifelong one: a constant

younger), I wanted to be a

process that requires self-examination,

ballerina. A handful of disas-

hard word, and building relationships

trous ballet lessons later, I

with others.

still couldn’t even perform a turnout

“Part of reaching your true potential,”

(wherein, heels together, the knees and

says Kaplan, “is understanding your life

toes point in opposite directions) with-

story – craft it and keep refining it.”

out promptly tumbling over my own

This sounds intuitive, but if you sit back

two feet and landing, the epitome of

and really think about that message,

disgrace, flat on my face. This, as I

perhaps it’s not. After all, we all struggle

quickly discovered, was a deal-breaker

with reaching our internal definition of

to my hopeful career as a ballerina,

“success,” of finding happiness in our

seeing as how it’s one of the first (read:

work, and of feeling like we’ve really

fundamental) movements of ballet. For

accomplished something.

all intents and purposes, I was a failure.

In his book, What You’re Really Meant to

As it turns out, I make a much better

Do, Kaplan outlines a rigorous process

editor than I do a ballerina (in fact,

for developing the mind-set and habits

I make a much better “lot of things”

to understand yourself better, improve

than do I a ballerina!). Of course, even

your capabilities, and follow your con-

that discovery didn’t come quickly –

victions – a defined method, if you will,

or easily. After a few changed majors

for developing strategies to navigate

in college, followed by a spontaneous

your life and career. And, while the

departure from a halfway completed

mechanics of his process are somewhat

graduate program and several years

straightforward, it’s not – as Kaplan

invested in the wrong industry, I finally

says – a “touchy-feely” process: like any

discovered my potential and my passion

good lesson, it comes with its share of

– what I was “meant to do”.

hard knocks.

But, even this – the place I am in

“Success is personal,” says Kaplan. “Take

now – isn’t truly an end point. It’s

ownership and define what yours looks

what Harvard Business School’s Robert

like.”

Kaplan might call a resting place on a

Share your comments >

journey of self-discovery. This journey,

What You're Really Meant to Do is available on Amazon and the Radiant Advisors eBookshelf: www.radiantadvisors.com/ebookshelf What You’re Really Meant to Do: A Road Map for Reaching Your Unique Potential published by Harvard Business Review Press, ISBN-13: 978-1422189902, $25.00.

Lindy Ryan is Editor in Chief of Radiant Advisors. rediscoveringBI Magazine • #rediscoveringBI •

6

FEATURES

THE CLOUD

[The cloud is the next step in business intelligence maturity, but are we ready?]

LAURA MADSEN

W

hen I was a small girl growing up in the Wisconsin country-side I would spend long

summer

afternoons

lying in the grass imagining shapes in the clouds high above me. The wonderful thing about a child’s imagination is that a cloud can take on any form, from a unicorn to an elephant to…a data center.

I had no way of knowing then that the term “cloud” or the idiom “to the cloud!” would become such a ubiquitous phrase in my adult work-life as it was in my childhood days gazing at the sky.

Cloud computing is not new. It is not a panacea; it won’t make you richer, smarter, or better looking – but I suspect most of you already know that.

The trouble is you wouldn’t know it if you paid any attention to the marketing hype: off-the-charts claims about simplicity, turn-around, and scalability have become the gold standard for cloud computing these days. It wasn’t that way when we called them server farms or “software as a service,” (or some other variation), but put a catchy phrase on it and stand back!

Marketing hype aside, the cloud is the next step on our maturity in business intelligence (BI). As data grows (and don’t even get me started on big data!), the need for larger and larger storage

7 • rediscoveringBI Magazine • #rediscoveringBI

capacity grows with it. The truth is, there is no real value

and off-board staff?

to an organization to keep a very large data center up and

When assessing using cloud services, it’s not enough to have

running, but there is an awful lot of risk associated with it.

a couple of conversations. A trip to the location and a tour of

Between disaster recovery and high usability, an organization

the facility, including all documentation of security practices,

could find they are becoming an “IT heavy” shop by just trying

should be required as you weigh your decision.

to support the businesses’ growing need for data. A couple of years ago a CIO said to me, “There is no value to me to keep full-time employees around to support servers. I’d rather use their skills elsewhere.”

To be fair: the same could be said for deploying to the cloud.

Weighing Your Options

Much of the decision as to whether or not to use “the cloud” for storage will be

The Economics of Cloud Computing

A number of academic articles have attempted to answer the question “is it financially better to use in-house data centers or cloud computing?” I won’t go into any detail here, but suffice it to say that there is a lot of “it depends” in the answer.

The variables are staggering, including data growth, software license

based on the type of

organization

you are. In other words, are you an organization that is okay having the data out of your hands?

Do

you

work in an industry

the silver lining for all of that marketing hype is that the industry will either have to catch up with its own claims or slowly dissipate"

that has very specific requirements associated with data, data access, security, permission, and storage? Being in healthcare, I happen to work in an industry that does have a lot of those requirements, and going to the cloud is not a forgone conclusion. As a matter of fact, it has a lot of risk associated with it.

Today, you have two options. Keep a data center up and running with your own staff and budget, or use some variation of cloud services that will help you store and manage all of your data. In healthcare, some of those decision criteria have to be specific to the regulations associated with personal health information (PHI). I believe some due diligence is required to ensure that the vendor can meet all of the regulations governing the healthcare industry, and they vary by state.

I would not just ask the vendor about HIPAA compliance,

tions,

implicaregulatory

implications, and vertical horizontal

versus parti-

tioning – just to name a few. Most of the articles I reviewed looked at discuss a five- to ten-year timeline

for moving to the cloud, which makes a lot of sense considering how large of an effort it would be to move from in-house to cloud storage, even from one cloud provider to another.

The short answer is, if you decide to do it, don’t expect a quick return on your investment. Like the stock market, you will have to invest and hold before you will see a return.

Not Yet

I’m not ready to go to the cloud. I’m not completely opposed as to whether or not the due diligence is there, but right now I still see a lot of risk – particularly for healthcare – in cloud computing. The great news is that will likely change very soon, because the silver lining for all of that marketing hype is that the industry will either have to catch up with its own

but also about any state laws (such as needing to know the

claims or slowly dissipate like early morning clouds.

physical location of your data at all times) that could have

Share your comments >

impact. Also, what type of security practices do they follow?

How often are they audited? Is the building constructed with the appropriate materials to withstand high winds, drastic

Laura Madsen is the BI Evangelist

temperature swings, or flooding? Does the data center build-

and Healthcare Services Lead at Lancet

ing that services “the cloud” have electronic access control

Software, a BI Consulting firm based in

measures? Do they have a documented procedure to on-board

Minneapolis, MN. rediscoveringBI Magazine • #rediscoveringBI •

8

FEATURES

STORYTELLING REDISCOVERED [Statistical storytelling is probabilistic, rather than deterministic.]

STEPHEN SWOYER

W

e make sense of our world

the schema of a data model, embod-

what I'm talking about, however. We

by telling stories about it.

ies a kind of a priori representation or

can recognize and control for our sto-

We could probably even

understanding – and, for this reason, a

rytelling misprisions, after all. Nope:

speak of a human predispo-

misrepresentation and misunderstand-

the problem is much deeper than this;

sition to think and to know in terms of

ing – of the world? We're all aware of

it's fundamental; it has to do with the

stories: Storytelling is in our DNA.

the shortcomings of the data model as

structure of the story itself. To put it in

We use storytelling to communicate

a structure: of the trade-offs or com-

the language of probability theory, our

with and to make ourselves under-

promises that factor into its construc-

tendency to interpret events by telling

standable to others, be it in the context

tion. By imposing a rigid schema and

stories about them is a way of imposing

of a boardroom presentation, a cubicle

organizing data in specific ways, a data

a deterministic structure on something

brainstorming session, a 30-second

warehouse (DW) data model attempts

elevator ride, or a six-hour flight from

(our world) that isn't properly deter-

to optimize for most business intel-

SFO to IAD. It's more or less automatic:

ministic.

ligence (BI) workloads. It's able to tell a

When we're trying to share an idea or

certain kind of story – but it's much less

an experience with someone, we do so

adept at telling others. Have we ever

Houston, We Have a Problem

by telling them a story. It's worked pret-

thought about the similar trade-offs

For most of human history, this wasn't

ty well for us for thousands of years.

and optimizations that go into structur-

a problem. We simply didn't have the

But what if the very structure of the

ing the stories that we tell every day?

means to identify, qualify, or quantify

story itself -- with its beginning, middle,

Certainly, in telling a story – in making

its effects. The combination of statistics

and end; its neatly articulated sequenc-

a story our story – we've been known

and big data* gives us this means. One

ing; its emphasis on dramatic develop-

to exaggerate, to misrepresent, or to

upshot of this is the recognition that

ments – is problematic?

invent events. That's a given. It's the

human storytelling – as a tool for inter-

What if the structure of a story, like

very stuff of dramatic flair. This isn't

preting and understanding events – can

9 • rediscoveringBI Magazine • #rediscoveringBI

in some cases be unhelpful or disadvantageous.

created specifically to measure value. Cabrera's supporters

This isn't theoretical maundering. Look at last year's

gave disproportionate emphasis to three statistics – viz.,

Presidential contest, when we had two different kinds of

batting average, home runs, and runs batted in – that Trout's

storytelling – deterministic and probabilistic – telling two

backers insisted on interpreting in a much larger context.

very different stories about

Determinism

the state of the race: the

one.

former depicted an exciting, leapfrogging, neck-and-neck contest; the latter – plotted over a protracted period – suggested a mostly ho-hum race, with periodic volatility ultimately regressing over time. Or consider last year's American League (AL) MVP race, which once again pit-

We [likewise] have outsized eyes for things that actually are probabilistically exceptional: we give more weight to anomalies than we probably should."

won

that

And that's the point.

Probabilistic stories tend to be much less exciting – with fewer surprises, dramatic reversals, or spectacular developments – than the deterministic, narrativedriven

stories

we

know

and love. In a probabilistic universe, there's a vanishingly small chance (let's say

ted deterministic and probabilistic storytellers against one

0.00001 probability) that vampires or zombies can exist, let

another. Determinists championed Detroit Tigers first base-

alone live among us -- in the next bayou or burbclave over,

man Miguel Cabrera, who'd become the first player to win

no less.

baseball's Triple Crown in almost 50 years. Probabilists made

Some of us would look at this value and say: “Aha! So it is

the case for Los Angeles Angels outfielder Mike Trout, who'd

possible!” It is – but it isn't. We seem hard-wired to want to

led all AL players in wins above replacement (WAR) – the com-

accord more probability to possibility than is warranted. We

posite metric baseball statisticians (i.e., “Sabermetricians”)

like to think of ourselves and of the things that are important rediscoveringBI Magazine • #rediscoveringBI •

10

to us – our friends, families, organizations; our ideas and

want to translate the probabilistic into deterministic terms.

initiatives; our partners and associates – as special. As – in

We dislike open-ended-ness, indeterminacy: both/and. We

so many words – bucking the probabilistic trend; as, in some

want to resolve, fix, and close things; we want our stories to

way, exceptional.

have tidy conclusions: We want either/or.

We likewise have outsized eyes for things that actually are

We need to be especially mindful of these issues if we expect

probabilistically exceptional: we give more weight to anoma-

to use analytic technologies to meaningfully enrich business

lies than we probably should. Statistically, very few major

decision-making. Viewed narrowly through the lens of BI,

league baseball pitchers over the age of 35 have enjoyed

statistical numeracy – i.e., the application of statistical con-

lasting success after a shoulder injury; some of us want and

cepts, methods, and (most important) rigor to the analysis of

need to believe that Phillies pitcher Roy Halladay, who injured

data – promises to contribute to an improved understanding

his shoulder in early May, will be different, however. As the

of business operations, customer behaviors, cyclical changes,

greatest pitcher of his generation, surely Halladay can buck

seemingly random or anomalous events, and so on. If we know what we're doing with it.

this trend? Surely he's the

Our embrace of statis-

exception?

In Praise of True Probabilism

tics will count for lit-

Statistical storytelling is

complexity of our mod-

probabilistic, not deter-

els, or the power of our

ministic. In this sense, it's

algorithms – if we fail

fundamentally at odds

to account for that most

with the deterministic

critical of variables: the

storycraft we've prac-

human being who must

ticed (and honed) for all

first conceive of and then

of human history.

interpret (the results of)

The two are not irrecon-

an analysis. Professional

cilable, but they don't tell

statisticians pay scru-

quite the same story.

pulous attention to this

To put it another way,

problem: it's a critical

human beings are super

aspect of research and

bad at thinking proba-

preparation – of rigor –

bilistically – even when

in statistics.

the issue isn't something

But

fantastic, like the exis-

als can make mistakes.

tle – regardless of the size of our data sets, the

even

profession-

What's more, with the rise

tence of vampires or zombies. Most of us can't properly interpret a weather forecast,

of big data analytics, we're attempting to enfranchise non-

especially one that calls for precipitation. If a forecast for

professional users – e.g., business analysts, power users, and

the La Grange, Ga. area predicts a 90 percent chance of rain,

other savvy information consumers – by giving them more

this doesn't mean there's a 90 percent chance it's going to

discretion to interact with information. This usually means

rain across all or most of La Grange; nor does it mean that

letting them have a freer hand when it comes to the con-

90 percent of the La Grange region is likely to get rained on.

sumption, if not to the selection and preparation, of data

It is rather a prediction (with 90 percent probability) that

sources and analytic models. In a sense, then, practices such

rain will develop in some part of the forecast area during

as analytic discovery propose to enlist the business analyst

the stated period. This might well translate into widespread

as an armchair statistician, giving her access to larger data

precipitation. It might not. This isn't the way most of us tend

sets – compiled (or “blended”) from more and varied sources

to interpret a weather forecast, however.

of data – in the context of a discovery tool that exposes

The problem, then, is two-fold: (1) human beings seem to

algorithms, functions, visualizations, and other amenities as

have a (hard-wired) preference for narrative or deterministic

self-servicable selections.

storytelling; (2) human beings likewise tend to have real

To the extent that a user summarizes a data set or makes

difficulty with probabilistic thinking: in most cases, then, we

inferences about the results of an analysis, she's arrogating

11 • rediscoveringBI Magazine • #rediscoveringBI

unto herself the work of the statistician. She's practicing

users with statistical tools and techniques, we must attempt

applied statistics. But is she doing so with all due rigor? What

to control for “effects” like sensational tang. It won't be easy.

if she conducts her analysis in ways that distort it? And what if she's interpreting her own biases, customs, or preconceptions – to say nothing of her human preference for dramatic contrast – into her analysis?

This isn't to dismiss – or, for that matter, to undermine – the potential value of analytic discovery. It's rather to emphasize that our ability to make use

A Storytelling Epilogue

In sales- or marketing-speak, to tell a story is to make a pitch.

Life doesn't so much make pitches as throw pitches: fastballs, changeups, sliders, sinkers, curveballs, and so on. Some of these pitches – like Cliff Lee's baffling curveball – can be hard to spot. They're breaking balls, with lots of late movement, as distinct to big fat

of information is irreducibly

fastballs, fired right down

constrained by our capacity to interpret it: to the extent that we interpret ourselves into an analysis, we're projecting our own biases, desires, and preconceptions into the “insights” that we produce. We must be alert to the possibility of hidden or

We must develop and hone a new kind of business storytelling: a statisticsinformed storytelling."

non-obvious human factors in analysis: this as true of interpretation as it is of preparation and experimentation.

We must also control for our all-too-human love of a good story: this means recognizing the storytelling capacity of the tools – such as metaphor, analogy, and idea itself – that we use to interpret, frame, or flesh out our stories. Metaphor and analogy have the potential to mislead or confound. (See Sidebar: A Motive For Metaphor) Ideas, for that matter, can have irresistible power.

Consider the concept of the “meme,” which was conceived by Richard Dawkins to describe an idea that gets promoted and disseminated – in evolutionary terms, selected for – on the basis of its cultural or intellectual appeal. If you think memes can't mislead, look to that most versatile of memetic constructs: Malcolm Gladwell's “tipping point,” which (12 years on) has by now been interpreted into...almost everything. The “tipping point” even enjoys some currency among sales and marketing professionals, chiefly as a promotional tool. This is in spite of the fact that researchers Duncan Watts and Peter Dodds penned an influential paper (“Influentials, Networks, and Public Opinion Formation,” circa-2007) that raised serious questions about Gladwell's theory. From a sales or marketing perspective, the tipping-point-as-meme has unquestionable promotional power; its usefulness as an analytic tool is alto-

the middle of the plate.

They're like Roy Halladay's cut fastball: when it's on – I mean really on – it's practically unhittable. The baseball metaphor is suggestive: after all, Sabermetrics brought to baseball a new, self-reflective kind of storytelling. A kind of story-

telling that doesn't simply explain but which is also able to predict the performance of pitchers like Lee and Halladay.

Some of the biggest names in Sabermetrics – Bill James, Nate Silver, Bill Baer – have creditable statistical expertise. But the overwhelming majority of the folks who use Sabermetrics to interpret, explain, and tell stories about baseball – be it for their own enrichment, for the drafting of fantasy baseball teams, or (in some cases) just for the heck of it – aren't statisticians, proper. They're armchair analysts, doing what they love. Thanks to Sabermetrics, they're having a lot more success doing what they love.

We must develop and hone a new kind of business storytelling: a statistics-informed storytelling. This isn't going to be easy: baseball, after all, is a game, with well-defined parameters – including a deterministic beginning and a deterministic ending. In this sense, baseball lends itself more easily to Sabermetric storytelling.

Business, like life itself, has an infinite set of parameters, not all of which are well understood, and some of which have yet to be identified. But the success of Sabermetrics, its successful use by armchair analysts, provides an encouraging example.

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gether more suspect, however. No matter: as memes go, the “tipping point” is the very stuff of what American philosopher

Stephen Swoyer is a technology writer

William James dubbed “sensational tang:” the kind of idea

with more than 15 years of experience.

that's simply irresistible to the intellect, regardless of its

His writing has focused on business

validity or veracity.

intelligence and data warehousing for

It's an illustrative lesson: if we expect to arm non-traditional

almost a decade.

rediscoveringBI Magazine • #rediscoveringBI •

12

FEATURES

4 STRATEGIES TO INCREASE COLLABORATION AND DATA SHARING [Aside from technology, it is people that make a project work.]

LYNDSAY WISE

C

ollaboration is a big contributor to an organization’s

ness issues, and a corporate culture that encourages working

level of project success. Technology considerations,

together to solve issues and generate opportunities.

project management, and finishing within time and

Overall, building a collaborative infrastructure within an

on budget are factors that are given credit when

organization requires a group-oriented culture. The reality for

looking at project success. The reality, however, is that aside

most is that this may exist in pockets within the company, but

from technology, it is people that make a project work. Setting

may not exist on a broader level.

aside time to provide insights, gather requirements, translate

One of the most poignant examples I have is when I worked

business requirements into systems requirements, and ensure

for an automotive manufacturer. Each department had built

adoption after implementation all require various levels of

their own data marts and, in many cases, information over-

collaboration.

lapped across departments, even if it wasn’t technically shared

Collaborative technologies (and features within analytics

between them. Multiple departments used sensitive informa-

solutions) sometimes make it seem as if the level of col-

tion – such as parts warranty – but each department accessed

laboration within an organization will be dependent on the

this information separately, making it seem as if they owned it

collaborative capabilities within the solution itself. On the

exclusively. Consequently, communicating about information

one hand, adding features that support collaboration helps

was out of the question because although there was a high

enhance communication, but, on the other hand, it won’t

level of collaboration within individual departments, broader

encourage an environment of collaboration where one doesn’t

communication was limited at best – especially when it came

already exist. For instance, if people within various depart-

to discussing data assets. A lack of communication, such as

ments don’t willingly share information – and if people do not

in this case, can make it difficult to implement an analytics

normally work in teams to accomplish their tasks – then add-

solution and expect collaboration to spread within the orga-

ing a set of functionality within software will not change that

nization. There are, however, ways to increase the likelihood of

without additional changes to the corporate culture. Ensuring

business users taking advantage of collaborative capabilities

effective collaboration requires a combination of both: tech-

to help ensure communication that supports more effective

nology that supports information sharing and discussing busi-

decision-making.

13 • rediscoveringBI Magazine • #rediscoveringBI

1.

Know your Corporate Culture

levels of communication among disparate departments and teams so that they can work together. In order to do this more broadly, technology needs to be better aligned with business goals. Collaborative features – such as chat, notes, annota-

Each organization has a unique corporate culture. It is impor-

tions, assigning responsibility for tasks, and the like -- help

tant to understand what has worked in the past (and what

support overall collaboration across the organization. Making

hasn’t) in order to identify what needs to be done to improve

sure that people understand the tasks they are responsible

the collaboration outlook within the corporate culture itself.

for and how their role relates to other tasks across busi-

In many cases, people are already sharing information

ness processes can create a sense of importance and value.

through email, meetings, and regular discussions, but this

Overall, solution capabilities should support business goals

level of collaboration may not extend beyond specific work-

and be easy enough for people with various comfort levels of

groups or departments. For business intelligence (BI) and

technology to take advantage of.

analytics to benefit the organization as a whole, it needs to be shared and interacted with. Instead of having BI silos or different people looking at the same information separately, it can be more beneficial to collaborate across business units and to share information, as long as security requirements are

4.

Set Collaborative Expectations

taken into account. This may mean looking at how disparate

To make the best out of collaboration technology and

groups work and interact with each other and how informa-

use, organizations need to create an environment whereby

tion is shared more generally. A starting point may be the

using the features available are encouraged and rewarded.

development of a data governance (DG) initiative to identify

Additionally, training is required to make sure that users

various levels of responsibility for data assets, or by creating

understand the value of using collaborative toolsets within

status meetings to discuss projects with a larger number of

software solutions. This can include solutions that expand

people to gain broader insights and encourage idea sharing.

beyond BI and analytics as well. As long as business users

Once this occurs, expanding that into collaboration within

know that these types of capabilities exist – and how they can

current BI solutions become a natural next step.

support business decision-making – there is a better chance for organization-wide adoption. Also, if expectations of use

2.

Look at Stakeholder Involvement

Different people have different levels of insight into planning, what occurs within the organization, and how to best take advantage of performance opportunities. Consequently, identifying stakeholders and garnering their involvement can help with better execution of BI projects and help ensure broader involvement over time. This involvement will directly translate into taking advantage of collaborative capabilities within solutions, such as adding notes, sharing analytics, and discussing challenges and opportunities with team members and decision-makers. By empowering key stakeholders to make decisions, they are more likely to share their theories because they know that they are respected members of a team.

3.

Match Desired Collaboration to Solution Capabilities

As discussed, the key to collaboration is to support broader

are set initially, then it is more likely that adoption will occur on a broader level.

Bringing it back to Basics The reality remains that collaboration requires a combination of technology and people. Employees are constantly collaborating with one another and technology is slowly catching up to include some collaborative capabilities within software offerings. Although not truly “collaborative” in the traditional sense, these features can help support how people do their jobs and how they communicate with others. The ability to share information, make comments, assign responsibilities, and alert people of changes can help improve process efficiencies and help decision-makers act faster to better use their analytics.

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Lyndsay Wise is the president and founder of WiseAnalytics. She provides consulting services for SMBs and conducts research into BI mid-market needs. rediscoveringBI Magazine • #rediscoveringBI •

14

SIDEBAR

A MOTIVE FOR METAPHOR [STEPHEN SWOYER] Have you ever gone so much as a single day – be honest:

Now as ever, that's the case. Sometimes, it can be problematic

comas don't count – without using metaphor or analogy? At a

– even for the pros.

basic level, both comprise a kind of storytelling.

At this year's annual meeting of the American Academy for

This is particularly the case in the scientific and analytic dis-

the Advancement of Science in Boston, researcher Kathleen

ciplines, where metaphor and analogy tend to be vested with

Wilkie of the Tufts University School of Medicine discussed

critical explanatory power.

the use of mathematical models to study the human immuno-

As psychologist Julian Jaynes famously put it, “Understanding

response to cancer.

a thing is to arrive at a metaphor for that thing by substitut-

According to Wilkie, dominant research models assumed that

ing something more familiar to us.” The power of metaphor

an antagonistic relationship must obtain between cancer

is so deeply embedded in as to constitute the very basis of

cells and the immune system's “first responders” – i.e., neu-

language, according to Jaynes, who pointed to our tendency

trophils, platelets, and macrophages, along with the better

to use familiar metaphors to explain unfamiliar events. What's

known NK cells. (Indeed, the acronym “NK” is shorthand for

more, metaphor is the means by which language grows –

“natural killer,” which – in most scenarios – is an especially

i.e., gains descriptive and explanatory power: language and

apt description of how an NK cell behaves.) Wilkie discovered

understanding, he suggested, are compounded of metaphors.

that this interaction isn't strictly antagonistic, however; what

This is why even scientists use metaphor to make themselves

happens instead is that a tumor will actually co-opt at least

intelligible. In a sense, a scientific theory is itself a metaphor:

some of the body's immuno first-responders. In other words,

it's a way of describing a relation between a model and the

the inflammatory cells produced by the immune system tend

data it's supposed to represent. “Understanding in science

to segment into pro- and anti-tumor immunities; the former

is the feeling of similarity between complicated data and a

works to promote tumor growth, the latter to retard it.

familiar model,” Jaynes wrote.

Wilkie actually had to develop an entirely new model, incor-

Even though Jaynes was writing in the late-1970's, this aspect

porating non-traditional concepts and techniques (such as

of his argument are as true as ever. Consider his observation

“generalized logistic growth”) to account for this behavior.

that “a great deal of medicine is based upon the military met-

Her findings have salience for cancer treatment strategies –

aphor of defense of the body against attacks of this or that.”

e.g., in some cases, it might actually make sense to promote

15 • rediscoveringBI Magazine • #rediscoveringBI

tumor growth (if, as she writes, “it leads to a significantly

shift perspective and look at these distribution relationships

smaller tumor burden”) – and on the basis of this alone are

at the firm level,” explains Cooper, a principal with Deborah

of critical importance. But Wilkie's discovery also underscores

M. Cooper Consulting.

the ease with which we can project ourselves – i.e., our own

The upshot, then, is that Cooper has actually had to change

understanding of how things work (of how things should

her interpretive stance, discarding assumptions and interro-

work) – into an analysis.

gating metaphors in search of a better – more apposite, more

In 1976, Julian Jaynes adverted to the explanatory function

advantageous – predictive fit. “At one organization, where we

of “military” or antagonistic metaphors in medical science;

had manager-to-broker-level conversations, shifting to the

37 years later, Kathleen Wilkie discovered that the dominant

firm-level enabled CEO to CEO-level conversations,” she con-

models – which assumed a de facto antagonism – had got it

tinues. “At another organization, I found that the type of firm-

all wrong.

level distribution relationship was a better predictor of how

These kinds of D'oh! moments are by no means limited to

distributor employees would interact with our e-commerce

medical science. If anything, they're hard-coded into the ways

channel, as opposed to the employee's individual attributes

in which businesses structure and represent themselves. To

or past contact history.”

the extent that a business wants to leverage the power –

For analysts of any stripe -- from data scientists to armchair

or the promise – of big data analytics, it must be aware of,

statisticians -- context is key. This means understanding how a

anticipate, and control for the ways in which it projects a

business represents (and, by implication, misrepresents) itself

specific view or representation of its operations into its data.

and its market. “I try to make sure I understand the context [of

Deborah Cooper, a data scientist with extensive experience

the customer's business] so that I'm not only working within

in financial services, describes an issue she's encountered in

the data set, but I'm also bringing [in] market definition and

several marketing optimization engagements. “Organizations

market understanding [from the perspective of the business].

often work with distribution partners to get products or

I'm bringing business models and contextual information into

services to their customers. In financial services, distribu-

the data set with me,” Cooper concludes.

tion partners may work with an organization's front-line on a person-to-person basis. I've found that it's also useful to rediscoveringBI Magazine • #rediscoveringBI •

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