1. Dashboards Are Dumb Data:
Why Smart Analytics Will Kill Your KPIs
Emperitas Webinar May 19th
2016
www.emperitas.com / 801.810.5869 / 4609 South 2300 East Suite 204, Holladay, UT 84117

2. Hi, I’m Luciano Wheatley Pesci…
Founder & Director, Utah Community Research Group (UTAHCRG), Univ. of Utah
• Teach microeconomics, statistics, applied research & data analytics, and American
economic development & history.
Co-Founder and CEO, EMPERITAS
• Team of analysts, data scientists, and economists who find actionable business
intelligence through marketing analytics and agile research, to help our clients beat out
their competitors for the most profitable customers.
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3. My Basic Argument Today…
• Near-Real-Time or Die: The pace of competition requires
organizations see near-to-real-time information.
• The Dashboard Bubble: Solutions ranging from “do it yourself” to
“turn key” all have pros & cons, but most aren’t what they claim to be.
• Smart Analytics Is a Killer: Smart Analytics provides a different,
complementary option, but it requires creating a data culture.
• Building a Data Dream Team: “Data Scientist” is a catchall term
covering multiple job functions, knowing which to hire will determine
the success of your organization.
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4. Near-Real-Time or Die

5. It’s Pretty Competitive Out There
• You need to be watching your competitors because
they’re definitely watching you.
• Constant battle over brand recognition, market share,
sourcing, and innovation.
• Data has become the main differentiator in the struggle.
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6. Example Competitive Intelligence Tools
• Competitive Intelligence tools
are a booming market, and
most use a dashboard.
• NUVI (Social)
• SpyFu (SEO/PPC)
• Moat (Banner Ads)
• Glassdoor (HR)
• CI Radar (Operational Insights)
• Quid (Market Trends) 6

7. The Motivation For Real-Time
• With access to all this data & information, speed is becoming the next differentiator.
• The need for speed has created a bubble of real-time & near-real-time dashboards.
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8. The Dashboard Bubble

9. Different Flavors of Dashboards
• Dashboards can be tied to a SaaS product (AdWords,
HubSpot, NUVI) or they can aggregate and display different
data sources (Tableau, Grow).
• They can also be grouped by the size and type of
organization they serve, specifically Enterprise vs SMB.
• More generally though, they can be grouped into “do it
yourself” or “turn key” solutions.
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10. Some Leaders In Doing It Yourself
• Do it yourselfsolutions are the better option
when you have the skills necessary to use them.
• R Shiny (FREE)
• Clear Analytics (FREE)
• Microsoft BI (Free/$120)
• Qlik (Free/$500+)
• Tableau ($1.5k+)
• Adaptive Insights ($5k+)
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11. Interesting Turn Key Solutions
• Turn key solutions are mostly aimed at the Enterprise
customers, but there are a few solutions available
to SMBs.
• Yellow Fin ($3k+)
• Grow ($6k+)
• Domo (your soul)
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12. Dashboards Straight Talk (Pros)
• People are visual learners,dashboards make data insights more
actionable since people understand them.
• Information is always available and in one place.
• Some of the solutions allow you to explore the data.
• Simplifies the process of sharing insights across your team.
• Starts laying the foundation to create a “data culture.”
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13. Dashboards Straight Talk (Cons)
• Most are being sold as a total solution,rather than a
necessary part of operating in the new economy.
• Can have difficult, costly integration.Noteasy to change once
they’re built and people don’tuse them once they have them.
• Usually provide descriptive insights.Few have predictive
analytics,and even those mostly use linear modeling.
• They can create tunnel vision.NPS and other “silver bullet
metrics” overpromise and under deliver.
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14. Smart Analytics Is a Killer

15. What is Smart Analytics?
• Combines secondary research (what already exists in public domain)
with qualitative and quantitative primary research.
• Iterative and adaptive process best done
in 2-week sprints using agile methods.
• Moves beyond single metrics and uses data-mining to find predictive connections and
meaningful differences that can drive better strategy decisions.
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16. AI & Data Detectives
• AI may take over (smart analytics), but right now it's a human art to blend theory,
evidence, and prediction in a visually rich way others can understand.
• It takes a team. There are no data science “unicorns”
that can outperform a team of 5 expert analysts.
• Analysts are not statisticians, they’re Data Detectives.
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17. Building a Data Dream Team

18. It’s about Building a Culture
• Effectively using data requires a team because the
data sources, and expertise, are spread all across
the organization.
• A willingness to constantly learn is a prerequisite of
creating a data culture; what you did this year won’t
be what you do next year.
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19. • “Data Scientist” is being used to describe three uniquely different roles:
• Dealing with Storing Data & Infrastructure (Architects, IT & DBAs).
• Dealing with Retrieving & Processing Data (ETL & Coders).
• Dealing with Statistics & Visualizations/Story-Telling (Data Detectives & Statisticians).
• Data Scientists rarely have the organizational expertise needed to recommend action on
the data. It’s on you to know what insights are needed and how you’re going to use them.
What Kind of Data Talent Do You Need?
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20. Next Webinar: How to Grow Your Own Data Detectives
• In our next webinar, I’ll explain how I’ve trained hundreds
of data detectives at the University of Utah, and how
I’ve grown my own team at Emperitas.
• It will teach you everything you need to grow your own
internal talent and create a data culture at your organization.
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21. Let’s talk…

22. The Conversation Doesn’t Have to End Here…
luciano@emperitas.com / 801-810-5869/ EmperitasSG / 4609 South 2300 East Suite 204, Holladay, UT 84117