Human Factors of XR: Using Human Factors to Design XR Systems
Accenture: Outlook What C Suite Should Know About Analytics 2011
1. This article originally appeared
in the 2011, No. 1, issue of
The journal of
high-performance business
O
n the Edge
W
hat the C-suite should
know about analytics
By Kishore S. Swaminathan
Chief Scientist
Accenture
A
nalytics is a transformational phenomenon that will
fundamentally change how business discourse will be
conducted and decisions will be made.
2. Case study after case study has buy more of our services or express
confirmed the value proposition their “better impression” in any other
for analytics across a wide range way. Not unexpectedly, I was asked
of business functions, including by the powers that be if this really
pricing, demand prediction, targeted was a battle that I wanted to fight.
marketing, supply chain optimiza-
tion, CRM and HR. In my view, I chose this example to illustrate how
analytics is something much more average, mundane decisions are made
than a technology with an ROI; it’s in organizations daily based on well-
a transformational phenomenon intentioned, plausible yet armchair
that will fundamentally change how theories—those that, like Aristotle’s,
business discourse will be conducted lack any empirical evidence. While
and decisions made. An analogy highly specialized functions such
may help in understanding why. as pricing or customer segmentation
may be based on sophisticated
If you drop a feather and a rock models and empirical data, my con-
at the same time from the same tention is that the long-term impact
height, which will hit the ground of analytics will be in instilling
first? At one point in history, this a culture of data-driven decision
was a question for philosophers to making at all levels of an enterprise.
resolve. Aristotle opined that the Or, put more bluntly, business
rock, because it was heavier, would proposals and decisions—big or
fall faster and hit the ground first. small—will have to provide satis-
Aristotle’s armchair wisdom was not factory answers to this question:
questioned until the 16th century, “Do we think this is true or do we
when Galileo, through cleverly know?” (This particular formulation
designed experiments, proved him is attributed to Gary Loveman, CEO
wrong and established an empirical of Harrah’s Entertainment.)
basis for answering such questions
about the physical world. A sophisticated and analytically
oriented enterprise of the future
Much the same way that an em- will behave and operate differently
pirically based scientific method from today’s enterprise along five
became the basis of our understand- major dimensions.
ing of the world around us, analytics
will eventually bring empiricism High analytical literacy
into business discourse and dethrone Data is a double-edged sword. When
many of today’s business practices. properly used, it can lead to sound
and well-informed decisions. When
Mundane decisions improperly used, the same data can
Recently, I received a memo saying lead not only to poor decisions but
that all employees at my location to poor decisions made with high
would be required to keep their confidence that, in turn, could lead
offices clean, subject to inspection to actions that could be erroneous
every other Friday. I wanted an and expensive. Let’s consider some
explanation, so I asked if there was specific examples.
any data to show that clean offices
lead to higher productivity. When one has access to real-time
data, it’s tempting to make real-time
My question, of course, was side- decisions. For instance, if you are a
stepped, and I was told that clean retailer and you have real-time access
offices would make a better impres- to sales data from cash registers from
sion on clients. Undeterred, I asked all your stores and real-time access
2
Outlook 2011
if there was any data to show that to your inventory in your warehouse,
Number 1 clients walking through our offices you could be tempted to run sales
3. promotions on the fly and manage When you have fine-grained vis-
your supply chain in tandem to sup- ibility into your processes, customers,
port your real-time promotions. suppliers and competitors, you have
the ability to make very fine-grained
However, this is unlikely to work decisions. In fact, your decision rules
because three types of events—your can capture subtleties such as “stock
decisions, the ensuing customer more beer on Sunday nights in loca-
behavior and supply chain events— tions where the home football team is
operate in different timeframes, so on a winning streak.” Such decisions
making decisions any faster than the are highly context-sensitive and can
slowest-moving event could be use- change as rapidly as the fortunes of
less at best and dangerous at worst. the football team.
Another problem with data and ana-
lytics is that they give you very fine- Volatility—or rapidly changing
grained visibility into your business decisions that are context- and
processes, and you could be tempted time-sensitive—will be a big chal-
to overoptimize the processes. Highly lenge for enterprises. Decisions
optimized processes—just-in-time are no longer easily explainable;
inventory being an example—are capital investments cannot be
very fragile because circumstances based on mass repeatability but
beyond your control could arise, and must cater to endemic volatility.
there is little room for error.
Integrated awareness
A third problem is what’s known as Today’s enterprises have more in-
“oversteering,” or making decisions formation than they can act upon
when none is needed. So, for example, because the information is siloed in
your data could tell you that a project so many ways: technologically (data
is behind schedule, which, in turn, in different systems that cannot be
may lead you to berate the project brought together), organizationally
manager or tell your stakeholders (data in different governance units
that the project will be delayed. that cannot be brought together) or
Yet neither of these actions may be by ownership (inside versus outside
necessary if the project has contin- the enterprise). The enterprise of
gency built in, if the status update the future will be (or will be forced
has a different frequency than your to be) “conscious” in the sense that
sampling frequency or if perhaps the it will know that it must integrate
employees who are aware of the proj- everything it has access to.
ect delay will put in more work time
to get the project back on schedule. As an extreme example of “inte-
grated awareness,” let’s consider
Volatility pharmaceuticals, an industry that
Businesses thrive on stability and has traditionally relied on clinical
repeatability. Stable and repeatable trials data as a means of estab-
processes justify large-scale capital lishing the efficacy and the side
expenses; they justify large-scale effects of a drug.
employee training; and they reduce
cognitive overhead because processes A pharmaceuticals company today
and decisions do not change and can legally and morally claim im-
hence their rationale does not have munity from any adverse effect of
to be explained repeatedly. a drug that was not revealed during
clinical trials—in other words, any
By contrast, an analytically based information that it did not explicitly
enterprise of the future will have to collect as part of a clinical trial pro-
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Outlook 2011
be designed around volatility rather tocol. But in a world of blogs and
Number 1 than repeatability. social networks, where people share
4. this information unprompted and in The third cause of analysis-paralysis
public, it will become both a respon- is the fact that most companies
sibility and an obligation of phar- do not know or articulate their risk
maceuticals companies to monitor tolerance clearly and are much
public sources and integrate the public more likely to penalize failed
information with their own clini- action than inaction. As a result,
cal data. (For more on the business many managers do not act unless
impact of social media, see Outlook there is enough data to assure
2011, No. 1.) them of successful outcomes. An
analytically literate organization
“I should have known” (either for will have a firm grasp of its risk
regulatory or competitive reasons) tolerance. With guidelines and
will be the new normal, replacing models for action under uncertainty,
the “I did not know” or “I could not it will restore the symmetry be-
have known” approach to awareness tween how it treats failed action
and information integration. and inaction.
The end of analysis-paralysis Intuition’s new pulpit
In the future, businesses will likely Empiricism and analytics sound
be run by managers and leaders who a death knell for such vaunted
are no-nonsense empiricists; they business traits as intuition, gut feel,
won’t move a finger until after all the killer instinct and so forth, right?
relevant data has been gathered and
analyzed. A recipe for organizational Wrong.
“analysis-paralysis”? This is not an
unreasonable fear. But though it may Science is purely empirical and
seem counterintuitive, an empirical dispassionate, but scientists are not.
enterprise with high analytical Science is objective and mechanical,
literacy is less likely to fall prey to but it also values scientists who are
this malady than today’s enterprises. creative, intuitive and can take a
leap of faith.
There are three very distinct ways
that organizations can fall into Data, by itself, can be interpreted in
the analysis-paralysis trap. One is many ways. Imagine a physical or
a managerial tendency to “over-fit business phenomenon that produces
the curve”—a statistical term that the following sequence of data: 1, 2,
refers to the diminishing value of 6, 24, 33. Perhaps it’s a factorial
additional data once a pattern (or sequence with 33 as noise, or a
curve, in the graphic sense) has been sequence where every fourth term
found. Data collection has a price, is twice the multiple of the previous
inaction has a price and an analyti- three. Or perhaps every fifth terms
cally literate organization will clearly if the sum of the previous four.
understand the cost of over-fitting.
All are indeed correct. To prove or
The second cause of analysis-paraly- disprove any theory, you need the
sis is waiting for data that simply does next several terms of the sequence.
not exist, which reflects an inability A good scientist knows when there
to design experiments to generate is enough data to warrant a theory,
the needed data. As mentioned above, when there isn’t, what new data to
experimentation has a price and in- gather and how to design an experi-
action has a price, so an analytically ment to gather the right data.
literate organization will be charac-
terized by a clear understanding Apple’s Steve Jobs is known to ex-
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Outlook 2011
of data gaps and the value of experi- plicitly discount the value of surveys
Number 1 mentation to break the logjam. and focus groups for designing new
