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What Spotfire Can
Do for You

Exploring the first 90 minutes, 60 hours, and 30 days
of adopting data visualization.

By Tibco Spotfire
If yours is a typical business,
whether large and small,
you’re faced with a tsunami of data.
Deep in this data are potentially transformative insights that could help you:

  • Make smarter decisions
  • Quickly develop better products
  • Create amazing customer experiences
  • Invent completely new services
Being able to capture, analyze, and interpret
your data has gone from “That would be great” to
“I needed that information yesterday!”
Inevitably, a whole host of software solutions
has sprung up to satisfy this thirst for all
things data, and ‘data scientist’ was even
touted as the sexiest job in the 21st Century ¹.
(And about time too, if you ask us.)
But really, you don’t need to have
a PhD to make data start working
The prevailing trend among data analytics
tool vendors is to take the science out
of preparing and analyzing data.
The prevailing trend among makers of
data analytics tools vendors is to
take the science out of preparing
and analyzing data.
Gartner predicted that
by 2017 the number of
so called "citizen data
scientists" (business
users, rather than data
specialists) will grow
five times faster than the
number of highly skilled
data scientists.
¹ Harvard Business Review
This short guide show that a
quick time-to-value is achievable
with the latest data analytics tools.
And that, with a better understanding of your data,
you can take the speculation and intuition
out of decision-making and become a
smarter business.
The first 90 minutes
"It still amazes us that what
used to take days in Excel
can now be done in less
than an hour".
Nuno Neto
Manager of Planning and Control,
Portugal Telecom
Ten years ago, data analytics was
for serious players only.
... so before companies could invest there
would have to be a rigorous justification
process and some expensive recruitment.
IT would need to become your new best
friend, too – it would take months or
years to get any business insights out of
corporate data, the process was error-
prone, and the results were often stale.
Fast forward today
Analytics software now does most of the hard work.
It easily connects and prepares data and machine
learning techniques to automatically identify
the best metric or visualization for particular
data sets.
Answer: it shouldn’t.
This line of thinking has led to
analytics solutions where the software
does most of the hard work... easily connecting and preparing
data and machine learning techniques
to automatically identify the best metric
or visualization for particular data sets.
For example,...
You load three csv files, and
the analytics tool notices there is a
time stamp for each transaction.

The tool recommends a time-series chart
be used to show sales performance over time.
It can also provide a click-through of each
metric to help you see the underlying details.
show sales performance over time. It can
also provide a simple click-through of each
metric to help the user see the underlying
details. Interactive analysis at the speed
of thought gives any user the ability to ask
questions and get answers, quickly.

By creating a self-service experience at
the front-end, non-specialists can use data
analytics to ask and answer questions,
with little or no specialist support.
Data scientists might
still create and manage
complex analytics applications
for your company,
but the goal is to make these
applications available to business
users so they themselves
can solve real business problems.
So, in your first 90 minutes with your
new data analytics tool, IT can go out
to lunch while you
find business insights –
in seconds.
Try creating a dashboard for a data set
you use frequently, like weekly sales figures,
to see what’s interesting. When it’s done,
change a couple filters and see what it tells you.
Self-service analytics is trial and error.
It lets you explore and find the unexpected.
For example, your marketing team
is hosting a 10-city tour to meet
prospective and current customers.
A dashboard can quickly display
the top 10 cities by customer count.
But let’s say there is a three-city
tie for the final city. In a few clicks,
your analysis can show which city has
the most revenue opportunity or,
if your tool has built-in GeoAnalytics,
you can click and see the proximity of
your customers to the city centers to determine
the likely location of the event.
Is the data you’re
working with correct?
Spend at least a few of
your first 90 minutes
checking you’re working
with the right numbers.
That’s an early lesson
you don’t want to learn
the hard way.
Perfection doesn’t exist
in BI, but having
confidence in your
numbers can’t be
Just because you can analyze
and visualize data doesn’t
necessarily mean you should.
It’s tempting, but creating pretty
visualizations for anything and everything
opens the door to ‘analysis paralysis’.

Creating visualizations for everything
can zap productivity.
Better to develop the right way of thinking.
What, exactly, are you trying to discover?
How will it help you add business value?
Are you trying to improve a process
or understand it?
Keeping your objective clear prevents you
and your team from chasing shadows.
Use data visualization
3 steps to follow
Use data visualization responsibly!
3 steps to follow:
Know what you’re looking for. Don’t start without a clear goal.
If you find something interesting, what are you going to do about it? Can team members also interact with the data to test or build on your findings?
If you develop an insight you think is important, share it with colleagues through collaborative analysis tools, like bookmarks and comments that engage your team.
Analysis paralysis or
paralysis by analysis
is the state of over-
analyzing (or over-
thinking) a situation so
that a decision or action
is never taken, in effect
paralyzing the outcome.

Primary symptom: an
acute sensation of what-
again? Only known cure:
ripping everything up
and starting again.
The data analytics
school of hard knocks
The first 60 hours

In the first 90 minutes,
you loaded data, visualized it,
and asked and answered
some questions.
You got meaningful business results in just
a few minutes, all on our own, without
support from IT.

But using visual analytics for local insights
is just the beginning.
In your first 60 hours,
you’ll need to deal
with messy data,
generate deeper
insights, and share
them with your team
though analytical
dashboards and
Dealing with Messy Data and Data Wrangling
Garbage in, garbage out.
Bad data leads to bad insights.
Your visual analytics tool can help you solve
the problem of bad data with self-service
data preparation and data wrangling.
Let’s say your sales data from
a partner is missing profit for a particular
quarter, or use product IDs instead of
product names.
Your analysis tool should be able to
visually help you merge, rename,
group, or exclude data as you go.
Each change to the source data should
also be tracked for later re-use or to
help you and others remember what
you changed.
You should have a
repeatable process for
connecting to, cleaning,
and importing data..

Heads up
If a solution is truly
self-service, it should
take care of a lot of
the data preparation
and cleaning. We call it
data wrangling, rounding up
the useful information and
discarding the superfluous
to hasten time-to-insight.
Broadcast Your Great Ideas
The power of the dashboard.
A well-designed dashboard is important
for successful data analysis.
Here are some great interactive
examples of best practices,
based on actual data
. Some of them
are interactive too, "so you can get a
sense of how data visualization works."
The first 30 days (and beyond)
More users,
more questions,
more insights

"As your data visualization
journey passes the 30-day mark,
you’ll likely try to tackle harder
Your analytics tool should guide you,
not force you to figure it out on your own.
Years ago, the only people who could
predict an outcome with data were
statisticians and math geeks.
Today, analytic tools do the hard work for you.
With built-in statistics and regression models,
non-experts can see what may lie ahead,
all in a single click.
Sales data by region and
time periods is useful for
showing performance trends . . .
...but imagine if you could combine it with
marketing campaign data – such as what
promotions were running, when, where,
and for how long.

That context adds a new
dimension and gives
both sales and marketing something
powerful to work with.
‘‘We were looking for
the ability to bring
together lots of
different information
and present it in a
format where it was
easy for all of our end-
users to access in one
centralized place.’’

Matt Samost
Business analyst at the
Tampa Bay Lightning
NHL team
Twenty people all making the
same dashboard would be
bad for your blood pressure.
But bringing more users into your analytics
processes, and allowing them to blend
data and ask new questions,
can lead to deeper analyses.

To make this happen, create a self-service
process whereby people can build their
own dashboards.
What might this look like? Perhaps a wiki or
a shared series of dashboards. But who can see
and do that, and why? Think about what would
best suit your processes, your data security
requirements, and what platforms or systems
you have to help you collaborate.
At 30 days and beyond,
the kind of analytics tool you
selected at the beginning starts
to become really important.
Understanding your ambitions
before you start can be hard.
Do you want to blend more data,
analyze geographically, collaborate,
or predict an outcome?
You need a solution that can grow with you.
Organizations that are successful with data
analytics, visualization, processes,
and well socialized insights, always have a
spectrum of requirements."
An executive needs to
know that it works, not how.
And have easy access
to the conclusions.
Data scientists want the freedom
to use the tools they already
have―and easily transfer those
analyses to a tool that business
users understand.

Big differences among data
discovery tools lie in how
well they handle growing
analytics needs.
Many tools let you quickly design
interactive dashboards, but become
increasingly complex when used
for more advanced analyses, messy
data, or the needs of data scientists..

Everyone’s analysis project is different,
so you need a reactive solution; one tool
everyone can use is the nirvana of business
The power of why,
and the pressure of what if.
Analytics software might be easily accessible
and simple to use, but what happens if you
find something interesting? Can you explore
the data further to answer the questions
that are certain to arise?
Rapid innovation in decision-making is a
key competitive advantage driven by the
influx of data. Data-driven decision-making
based on a solid understanding of the data is
no longer a by-product of doing business.
It’s the key to being in business
It starts today
Hit the launch button on your data driven
transformation with a no-cost trial of TIBCO Spotfire.
Start trial now
Need more reasons to use Spotfire?
          Webinar from Harvard Business
          Review Analytic Services

Watch this interactive Harvard Business
Review webinar with visualization expert
Scott Berinato, author of Good Charts and
multiple Harvard Business Review articles.
Hear how dataviz has become imperative
for competitive companies, what’s required
to build a great visualization, and how
adopting a new approach can create a
new organizational competency leading
to making better, faster decisions.
            Watch: Empowering
            the “analytical workforce”

Here Aberdeen Group VP and Principal
Analyst Michael Lock discussing findings
from his latest research into analytics and
serving a new generation of customers.
Topics include the top strategies for
understanding and meeting the needs of
today’s customers, and how to empower
the “analytical workforce”.
Watch Now
Watch the webinar
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