Storytelling drives usefullness in business intelligence

Storytelling Drives Usefulness in Business Intelligence
© 2016 HiredBrains Inc. All Rights Reserved
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Storytelling Drives Usefulness in
Business Intelligence
By Neil Raden
Hired Brains, Research
August2016

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Table of Contents
Executive Summary ........................................................................................................1
Ease of use......................................................................................................................... 4
Relevance and Understanding...................................................................................................................7
Going From One to Many: Storytelling........................................................................9
Metaphor.......................................................................................................................................................1112
Using Storytelling withVisual Analysis.............................................................................................13
Conclusion ......................................................................................................................16
ABOUT THE AUTHOR....................................................................................................18

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Executive Summary
“The real voyage of discovery consists not in seeking new landscapes, but in having
new eyes.” Marcel Proust 
Any individual exploration or examination of the data must be easily conducted and
shared, communicated and subject to group collaboration and consensus that
characterizes decision-making in most cases. Ease of use has to be evaluated in a
broader context of “ease of usefulness” to the audience of stakeholders, not a single
set of eyes. A key competency for moving analysis from the frontal lobes of an
analyst to other principals in the process is the ability to tell a story with data.
Data Discovery is a recent innovation in Business Intelligence that can bypass the
structure of a data warehouse and allow people to create their own viewpoints by
assembling analyses and visualizations, animating them and sharing them.
While performance and “ease of use” are necessary qualities in this field, they are far
from sufficient. There is no measure for ease of use, except the one that shows in
low adoption rates. A colorful GUI does not perform if the underlying actions are
not understood clearly by the user. Tools must be relevant to the work that people
do (not additive or complementary). The underlying data, models and assumptions
must be understood.
In this paper, we examine three concepts that are needed to succeed:
 A realistic model of “ease of use”
 A needed competency to weave a story from data as a means to achieve
positive results from analytical work

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 Examining the benefit derived when people are able to ask questions as
needed
Background
Numerous names have been used to describe the technology and methods that
allow people to draw insight from various sources of data: decision support
systems, business intelligence, business analytics, predictive analytics and business
discovery. For the sake of brevity, we refer to this class of technology as business
intelligence or BI. It is clearly an industry segment with fuzzy edges. In addition, the
names are somewhat misleading as, in fact, these approaches are used more widely
than just for business: in science, government, non-profit sectors and others.
Over thirty years or so, much that has been written about these subjects focused on
technology, features and poorly defined aspects such as performance and ease of
use. Useful to a point, these writings often avoid the well-documented fact that
uptake of BI in organizations is historically less than 20%, often much less. To
remedy this, various initiatives take place by BI vendors to achieve “pervasive BI”
but except in rare cases, it does not achieve the objective. The blame is usually
placed on not enough executive support or on IT control that fails to meet the needs
of the expected audience. What is rarely addressed is that people in organizations
make a rational decision to apply other techniques. Spreadsheets are the most
common course, but from an organizational point of view, they are not an optimal
solution.
The emergence of e-Business and web-oriented applications surfaced an
appreciation for user “experience” and “engagement,” very different from the
engineering and “human factors” approach of user interface design of enterprise
systems. BI vendors “ported” their user-facing applications to web-based interfaces,

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but unfortunately, the underlying models remained the same. A new interface did
not bring more “experience” or “engagement.” On the contrary, they were the same
old tools in a new wrapper which failed to engage the wider audience more
accustomed to true web-based applications designed for the web.
What had been lacking in the overall discourse about BI is how can people make use
of the tools effectively, regardless of the technology. For example, what exactly is
needed for people to not only be informed by these systems, through their own
efforts or presentation of material from others, but to use the insight to make well-
informed and actionable decisions? Most people who are not technologists are
unimpressed with features; they are interested in finding ways to be more effective.
It isn’t automatic.
In short, ease of use on an individual level pales in importance to how well a given
application contributes to the overall ease of use of the group. While any BI system
must be engaging, performant, fault tolerant and helpful, findings have to be
communicated and explained to others. Making copies, pointing at a screen and
developing presentations are of limited use..
The most effective method for communicating ideas and insights to others, and making
them stick, is the needed competence of telling a story with data.

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Ease of use
Even on an individual basis, ease of use is a pretty complex idea. Some things just
aren’t easy. Analyzing data, navigating through it, looking for patterns, choosing the
right visual display – no matter how much assistance a product can provide, analysis
still requires some effort. For those who find exerting this kind of effort tedious,
ease of use is not acknowledged. But for those so inclined, tools that handle the
tedious, repetitive and obvious work are considered easy to use.
For a long time, it was assumed that ease of use was not an issue for those involved
in the production of information technology: programmers, designers, analysts, etc.
The understanding was that they were so conversant in their cryptic (or verbose)
languages, scripts and configurations, that any attempt to make it easier to use was
a sort of affront to their sensibilities. That assumption was wrong. With the
development of IDE’s (Integrated Development Environments), Version Control,
Higher-Level Languages and a host of other innovations, the process of creating and
maintaining systems became much easier, not necessarily with the uses of GUI and
mouse and colors and buttons, but by fundamentally changing and streamlining the
way developers work. Much of development these days is configuration of applets,
reliance on tools such as relational databases and programming in object-oriented
or even functional languages. Even more importantly, these tools it made it
considerably easier for large projects to spread across groups, or even oceans, not
only for development, but for maintenance and enhancement too.
Unfortunately, much of what passes for ease of use on the user (or analytical) side
has not addressed a fundamental change in the operation of business intelligence.
It’s been focused on simplifying things that aren’t simple, masking complexity and
providing a pleasing interface (at best) to the individual using the system. For

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example, a major research report summed up ease of use for Business Intelligence
as follows:
The solution is easy to use when:
 It is familiar because it works as expected and is similar to another tool with
which a user has experience.
 It takes less time and fewer clicks to accomplish the ultimate goal. Routine
tasks may be automated and personalized.
 It is intuitive and obvious in how a task can best be performed.
From Ease of Use and Interface Appeal in Business Intelligence Tools
By Cindi Howson, BiScorecard
This one-person-at-a-time concept of ease of use has not addressed the central issue
– how do you change the way analytical work is done in an organization?
How do you get beyond one person and a display of information, to a seamless
environment where analyses, ideas and conclusions are shared?
Masking complicated requests with pleasing interfaces doesn’t make them easier. In
fact, it often does just the opposite. Those who couldn’t learn the lower level
interface, such as a scripting language, still can’t and those who could learn the
lower-level interface often find the “helpful” interface an impediment. The way to
make something complicated easy to use is to make it less complicated.
For instance, in business discovery software like Tableau (among others), people
seeking information usually start with what they easily grasp then incrementally
explore, like feeling for the first step in the dark with your foot, then moving more

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surely with subsequent ones1. But it is too easy for BI to complicate and hinder this
process by forcing the driver to make all the decisions in detail with each step
instead of anticipating what they might be, learning from the person’s habits. Ten
years ago this may have seemed like a dream, but it is quite common in customer-
focused applications today, but BI has trailed.
To illustrate the gap between what is perceived as ease of use, and the reality,
consider a vacuum sweeper:
Ease of use has to be couched in terms of doing the whole job. Ease of use first and
foremost requires that BI be useful to people and is relevant to the work they do. It
must promote understanding through shared ideas and discussion.
With respect to BI, a proper test of ease of use has to include:
 Obviously, elements of individual ease of use, especially an interface that
exposes functionality in a way that is understandable
 Performance cannot be separated from ease of use; it’s a “right now” world
today
 It is not sufficient for BI to inform an analyst; being informed does not
necessarily lead to better decisions, or even making them
Measures of ease of use are irrelevant. All that matters are that people use the tools
productively. They vote with their time and participation.
1 This statement is a metaphor. Its use in storytelling is explained in a further section
Presumed ease of use A robotic vacuum cleaner than runs on its own, vacuuming
the floor in an unattended way.
Actual Experience The small bag has to be changed frequently, doesn’t
thoroughly vacuum completely and usually requires bringing
out the conventional sweeper to finish the job
Actual Ease of Use A sweeper with exceptional suction that vacuums in one
sweep and has an easy to empty canister with no bag.

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Relevance and Understanding
What does it mean for a BI environment to be relevant? Do the tools and
information provide a degree of utility great enough to warrant modifying your
work processes to incorporate them? For the most part, BI in general has not. This
of course begs the question, “Why are these efforts not relevant?” The irritating
refrain from past Presidential elections, “Are you better off now than you were four
years ago,” is a good metaphor2. Has the implementation of your analytical
environment, typically a data warehouse and/or data marts plus a BI tool made
things better? Is your organization more effective? Are you making better decisions?
Do you have a better grasp on the elements that drive your success?
One element that is prominent in our research is understandability. One shouldn’t
confuse this with Ease of Use. People don’t just float out of their smokestacks, they
have to be rescued. What is understandable to a data modeler is not necessarily
understandable to anyone else. In many cases, people are staring at a schema that is
loaded with relational and/or multidimensional terms, like keys, joins, dimensions,
attributes and slices.
In her landmark book, “In the Age of the Smart Machine,” 3 a volume that no
practitioner in this business should leave unread, Shoshanah Zuboff offers some
insight into the relationship between learning and control in organizations:
“A commitment to intellective skill development is likely to be hampered when an
organization’s divisionof labor continuously replenishes the felt necessity of imperative
2 ibid
3 Shoshana Zuboff, In the Age of the Smart Machine: The Future of Work and Power,
(New York: Basic Books, 1988) 391-392

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control. Managers who prove and defend their own legitimacy do not easily share knowledge
or engage in inquiry. Workers who feel the requirements of subordinates are not
enthusiastic learners. New roles cannot emerge without the structures to support them. If
managers are to alter their behavior, then the methods of evaluation and reward that
encourage them to do so must be put in place. If employees are to learn to operate in new
ways and to broaden their contribution to the lifeof the business, then career ladders and
reward systems reflecting that change must be designed. In this context, access to
information is critically important; the structure of access to information expresses the
organization’s underlying conception of authority.”
The implications are clear: We cannot force success with BI without a desire and
commitment on the part of the organization to change and improve the flow of
information, the optimization of work processes and the breakdown of artificial
barriers that serve certain participants, but not the organization as a whole. This is a
much greater challenge than “change management,” a nebulous term that is applied
with no rigor.
Despite our best efforts, all too often, projects fail to reach their goals for reasons
that are not at all obvious. It’s easy to pinpoint the usual suspects, such as mid-
project organization realignment and killer politics, our inability as consultants to
convince our clients that certain decisions are sub-optimal and a host of others, well
documented in the literature (“Ten Mistakes to Avoid…”). But there are also many
cases where everything goes well, yet the initiative never gets traction in the
organization, penetration stays at a very low level and the ROI projections are not
met. In many of those cases, this failure to thrive has been something of mystery.
Part of the answer is that, despite good intentions, it may not be possible in some
organizations to make BI relevant without a concerted effort to help people change
their habits. Developing a spreadsheet or personal database is a singular effort and
in those organizations described by Zuboff above, the collaboration needed to make
BI successful is just not possible. A collection of singular efforts, inefficient and

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potentially inaccurate as it is, simply has a greater chance of being used as it skirts
the lines of authority and control. Another less ominous, but still dysfunctional
problem, is that is it simply too difficult to actually build models in most BI tools,
which are primarily designed for ad hoc query and analysis using pre-built
relationships.
So the first step is in our court, as an industry, to learn how to bundle the
appropriate organizational transformations into the technology implementation.
But there is more. Our offerings must be more aligned with the actual work that
people do.
Going From One to Many: Storytelling
Before humans knew how to write, probably before they even had language, the
means for passing wisdom from one person or one generation to the next was
storytelling. Most likely our brains are wired to respond to and retain stories
(though, oddly, not necessarily proficient at telling them). Nevertheless, it remains
perhaps our most powerful tool of communication. To put it bluntly, people are
generally more interested in a story than in the storyteller. If you want to get your
point across, you need to learn how to condense the data into a good story.
But how do you tell a story that can convince and compel?
To begin a discussion of telling a story with data, it’s a good idea to start with a
(real) story. This happened in the early days of OLAP technology:
Consultant: So we’ve built this facility for you so that you can align promotional
spend with results using time series analysis. You navigate on any of the
dimensions, or pivots, aggregating, filtering or even drilling into detail in this point-

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and-click interface. You have the facility, when you’re ready, to perform some
ARIMA analysis to do some forecasting.
Client: You don’t get it, do you?
Consultant: I’m sorry?
Client: I don’t want to navigate or drill or whatever you call it, that doesn’t help me
at all. This is a $5 billion company and we spend $1 billion a year on promotions. I
don’t know if one dollar of that is spent wisely. I want you to tell me where to focus
my promotional spend next year.
Consultant: Oh.
This is like a story in a story. The inner story is that what the client was saying,
which wasn’t grasped right away by the consultant, was that they failed to tell a
story and no matter how informative the facility, it didn’t solve the client’s problem.
The outer story is what Steven Denning4 calls a “springboard story,” Which he
defines as:
1. Must be a “story” with a beginning, middle and end that is relevant to the
listeners.
2. Must be highly compressed
3. Must have a hero – the story must be about a person who accomplished
4 http://www.amazon.com/exec/obidos/ASIN/0750673559/qid=984605028/103-
7657515-1436633

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something notable or noteworthy.
4. Must include a surprising element – the story should shock the listener out of
their complacency. It should shake up their model of reality.
5. Must stimulate an “of course!” reaction – once the surprise is delivered, the
listener should see the obvious path to the future.
6. Must embody the change process desired, be relatively recent and “pretty much”
true.
7. Must have a happy ending.
In Stephen Denning's words, "When a springboard story does its job, the listeners'
minds race ahead, to imagine the further implications of elaborating the same idea
in different contexts, more intimately known to the listeners. In this way, through
extrapolation from the narrative, the re-creation of the change idea can be
successfully brought to birth, with the concept of it planted in listeners' minds, not
as a vague, abstract inert thing, but an idea that is pulsing, kicking, breathing,
exciting - and alive.”
That may be a little too much excitement on a daily basis, something you save for
the really important things, but it matters nonetheless that turning data into a story
is a valid and necessary skill. But is it for everyone?
Not really. Actual storytelling is a craft. Not everyone knows how to do it or can even
learn it. But everyone can tell a story. It just may not be of the caliber of storytelling.
But to get a point across and have it stick (even if it’s just in your own mind, not to
an audience), learn to apply metaphor.
Metaphor
Metaphor is our most powerful tool for conveying information. While a tabular

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report may be a representation of data and derivations, visualization is a metaphor.
It is so much easier to see and understand. Comparative volume, gradients of color
or depth represent a continuum of values. Lines going up mean “up,’ and lines going
down mean “down” No need to scan a range of values to decipher the direction.
Metaphor and analogy are often used interchangeably, but an analogy is more
concrete and detailed, with the two things being compared having obvious
similarities, while metaphor is more literary, with the two things being further
apart. For example, comparing the way protons and neurons move around the
nucleus in an atom with our solar system is an analogy, while using the term black
hole for a very dense mass in space from which light cannot escape is metaphorical.
Data visualization is metaphorical.
The best most effective metaphors are ones that are new and creative. To say one is
“sick as a dog” conveys very little. To say, “this systems is as slow as a rainy Sunday,”
is likely to convey a more vivid image. The whole point of using metaphor in
storytelling is to illustrate, educate, convince and compel.
Metaphors can be powerful and clever ways of communicating findings. A great deal
of meaning can be conveyed in a single phrase with a powerful metaphor. Moreover,
developing and using metaphors can be fun, both for the analyst and for the listener.
It is important, however, to make sure that the metaphor serves the data and not
vice versa.
The creative analyst who finds a powerful metaphor may be tempted to manipulate
the data to fit the metaphor. In addition, because metaphors carry implicit
connotations, it is important to make sure that the data fit the most prominent of
those connotations so that what is communicated is what the analyst wants to
communicate. Finally, one must avoid concretizing metaphors and acting as if the
world were really the way the metaphor suggests it is.

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Using Storytelling with Visual Analysis
Workers can develop intensely useful analyses using data visualization tools with a
snappy in-memory database, agnostic about the data schema of the source data, ,
and can share them with their colleagues with some stories. Here are a few
examples:
Market basket analysis: Explaining a complicated analysis with results that can be
visualized in an easy-to-understand way often encounters resistance because the
evaluators do not understand the technique underneath. Some storytelling usually
helps:
“I was in Home Depot one day and when I was walking through the aisles, I wondered,
how could they ever understand what products customers looked at, or even picked up
and put back, but didn’t purchase? How could they modify their merchandising in a
way to understand what products are most often purchased together, or, even at a
short time later? In fact, how could we figure out how to do something like that here?
We have detailed information about customers’ behavior on our website, we know
what they pick up and purchase or not, or in what sequence they do. We have
information about the journey they take through the aisles [notice use of metaphor].”
The application looks at each item purchased and determines a list of
complementary items to recommend to purchasers. The market basket application
uncovered insightful new data about the items that customers typically purchase
together, enabling one organization to present these items as complementary
offerings to online customers.

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Figure1: Heat map to visualizemarket basket analysis
“We did some A/B testing in the way we arranged things, and, as a result, the average
order size for orders with the market basket pairings is more than twice the average
order size for orders without pairings. In addition, the information has helped this
company better serve its customers, with a deeper understanding of purchasing
preferences.”
Data never speaks for itself. As the visualization is shared in a discussion format,
there may be stories about suggesting bundles that did or did not work, or about
anomalies in the data that render it misleading. Further analysis and discussion
continues until a consensus is reached. However, everyone involved will have a
mental picture of people picking up products and how things group together, mental
images that would be lacking without storytelling and metaphor.
Visualization of detailed demographicdata to spot trends and correlations:

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Understanding and analyzing demographic data such as age, income, and home
ownership status of millions of individual users cannot always be done at an
aggregate level. Specifying groups of individuals on the fly, based on the detailed
records allows for greater insight. In Figure 2, it’s easy to see at a glance that
homeowners in the 35-54 range have the highest balances and that those with
incomes above $150K lead the pack. Some of the individual details of the customers
in that group are also included in the display.
Some analyses are useful in the way they slice and dice information without
revealing any sort of causality, simply a rendering of what is. In this example, it
might have been interesting to see how the numbers break out, but the storytelling
part of it would be focused on what to do about it.
“I was having some problems with my second car, a pick-up truck, that was randomly
losing power. It seemed to only affect the right side of the engine. I assumed it had to
be either a fuel problem or an ignition problem. I ruled out the “brain” because the V8
engine has two brains, one for each side. I swapped those to no effect. I checked fuel
lines, checked fuel pressure, and checked injectors one at a time. All normal. I did the
usual ignition check by removing wires one at a time and visually checking for a spark.
Then it occurred to me that I was wasting time on obvious things and had to think out
of the box. What one thing was most likely to account for these symptoms? In the end,
it turned out to be the sparkplug wires themselves.
In our marketing campaigns, we seem to try too many things and focus and too many
market segments. What that data shows, clearly, is that only one or two segments are
the most likely to result in sales and we need to focus on them.”

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Figure2: Dashboard profilingthe age, income, market and homeownership status of a financial
institution's 30+ million401k holders
Conclusion
Training, and to a larger extent, the culture of the organization toward learning, is a
key indicator of BI success. Equally important is the need to deliver tools in a bundle
of learning and cooperation that have a high degree of relevance to the work people
do, something that is often trivialized by Information Technology, focusing on the
work of a collection of individuals, not a collaborative group. There is much work to
do, but one thing we’ve learned from this research is that stepping back a little from
the technology of BI reveals a very complicated landscape strewn with hazards.
Many industry analysts, vendors, journalists and practitioners are not well equipped
for dealing with the challenges of making BI successful. We need to strengthen our
practice portfolios and partner with our clients to implement programs that
encompass technology and organizational development. Ease of use is only a
component of ease of usefulness to the enterprise.

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ABOUT THE AUTHOR
Neil Raden, based in Santa Fe, NM, is an active consultant, widely published author
and speaker and the founder of Hired Brains, Inc., http://www.hiredbrains.com.
Hired Brains provides advisory services, consulting, systems integration and
implementation services in Data Warehousing, Business Intelligence, Decision
Automation and Advanced Analytics for clients worldwide. Hired Brains Research
provides consulting, market research, product marketing and advisory services to
the software industry.
Neil was a contributing author to one of the first (1995) books on designing data
warehouses and he is more recently the co-author of Smart (Enough) Systems: How
to Deliver Competitive Advantage by Automating Hidden Decisions, Prentice-Hall,
2007. He welcomes your comments at nraden@hiredbrains.com or at his blog at
Competing on Decisions.

Storytelling drives usefullness in business intelligence

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