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

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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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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Near-Real-Time or Die

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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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

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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The Dashboard Bubble

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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Some Leaders In Doing It Yourself

• Do it yourself solutions 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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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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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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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. Not easy to change once

they’re built and people don’t use 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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Smart Analytics Is a Killer

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.15

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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Building a Data Dream Team

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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• “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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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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Let’s talk…

The Conversation Doesn’t Have to End Here…

luciano@emperitas.com / 801-810-5869/ EmperitasSG / 4609 South 2300 East Suite 204, Holladay, UT 84117