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UVU Data & Analytics Strategic Discussion

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Data & Advanced Analytics Higher Ed by David Gonzalez, ZIFF winners of the Big Mountain Data Competition [email protected] , @ziffio
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Page 1: UVU Data & Analytics Strategic Discussion

Data & Advanced Analytics

Higher Edby

David Gonzalez, ZIFFwinners of the Big Mountain Data Competition

[email protected], @ziffio

Page 2: UVU Data & Analytics Strategic Discussion
Page 3: UVU Data & Analytics Strategic Discussion

Agenda

1. Competing with data & analytics in Higher Ed

2. Identify where best to test a POC in your enterprise

3. Identify requirements for a POC4. Wrap up5. Lunch (sponsored by: ___ )

Page 4: UVU Data & Analytics Strategic Discussion

Upfront Contract

YESBusiness value

Specific to your needs

Getting started and next stepsWell formed problem

Collaboration

NOMe-too

Latest hotness

What the big guys are doingUnrealistic/overly complex

Long lectures

Page 5: UVU Data & Analytics Strategic Discussion

introductionswhat you hope to gain

Page 6: UVU Data & Analytics Strategic Discussion
Page 7: UVU Data & Analytics Strategic Discussion

Engagement MAP

Page 8: UVU Data & Analytics Strategic Discussion

Data Products

Data Aggregation

Analytics

Recommended Action

Activity

Page 9: UVU Data & Analytics Strategic Discussion

Research Innovation/Research

Leadership

Student Relationships/Individualized

Instruction

Infrastructure Mgmt/Operational Excellence

Economics Early market entry enables charging premium prices and acquiring large market share; speed-to-patent/funding is key

High cost of customer acquisition makes it imperative to gain large wallet share; economies of scope are key

High fixed costs make large volumes essential to achieve low unit costs; economies of scale are key

Competition Battle for talent; low barriers to entry; many small players thrive

Battle for scope; rapid consolidation; a few big players dominate

Battle for scale; rapid consolidation; a few big players dominate

Culture Employee centered; coddling the creative stars

Highly service oriented; student-comes-frist mentality

Cost focused; stresses standardization, predictability, and efficiency

Education* Core Competencies

Business Model GenerationAdapted from Business Core Competencies

early draft

Page 10: UVU Data & Analytics Strategic Discussion

Engagement MAP OperationsWho are our students?Who succeeds?Who fails?Which delivery methods work best?Outliers: Students? Faculty? TAs?What can be done?

Ready for the next course?

Paired with the right instructor/TA?

Going to complete the track?

Best suited for this major?

Likelihood of graduation?

How soon can we know if they will

succeed?

What’s working?Resources utilization?Scheduling

optimization?Cost centers?

How long with X last?

When will X fail/break?

Forecast price

Competing on analytics

Page 11: UVU Data & Analytics Strategic Discussion

Key Partnerships

Key Activities

PLATFORM MGMT

MANAGING SERVICES

EXPANDING REACH

Value Propositions

TARGETED ADS

FREE SEARCH

MONETIZING CONTENT

Customer Relationships

Customer Segments

ADVERTISERS

WEB SURFERS

CONTENT CREATORS

Key Resources

SEARCH PLATFORM

Channels

Cost Structure

PLATFORM COSTS

Revenue Structure

KEYWORD AUCTIONS

FREE

Business Model Canvas: GoogleBusiness Model Generation

Page 13: UVU Data & Analytics Strategic Discussion

UVU

FearsBenefits

Experience Wants

NeedsFeatures

Substitutes

Page 14: UVU Data & Analytics Strategic Discussion

What must be true?

What must be true in order to detect/identify/catch ___________________?● know● be able to● profile (story)

Page 15: UVU Data & Analytics Strategic Discussion

Inputs

twitter followers:Skullcandy & Competitors

Facebook page “likers”:page post likes & some profile

information

Reviews:Skullcandy & Competitors;

by store, brand, product

Insights

Segments

(e.g. geo, “snowboarders”,

bought, influential, etc.)

Sentiment /Reasons

(e.g. good/bad, quality, innovation, birthday, back-to-school, etc.)

Clusters

(predictive e.g. personas, buyers,

users, switchers, loyal, etc.)

Recommended Actions

(alerts, “mailing” lists, offers, etc.)

these are al la carte data points even within source

insights are cumulative (e.g. clusters are built from segments and/or reasons) but need not be comprehensive (e.g. a single datapoint can be the basis of

recommended actions)

Page 16: UVU Data & Analytics Strategic Discussion

… in order to get the info

● people● data points● resources

Page 17: UVU Data & Analytics Strategic Discussion

… in order to use it

● metric(s) or key results● tools● technology● expertise

Page 18: UVU Data & Analytics Strategic Discussion

Wrap up

HopeYou & your organization will incorporate

advanced analytics as part of your competitive strategy in your market.

Page 19: UVU Data & Analytics Strategic Discussion

Wrap up

BeliefData & Advanced Analytics can be key in better aligning your institution with strategic objectives

and can be ideal for helping to measure key results in the course of meeting those

objectives.

Page 20: UVU Data & Analytics Strategic Discussion

Wrap up

Dare to dream● Innovation as accepted norm in your institution● Employees feel greater autonomy, mastery, purpose● Shared, well defined, measurable vision and objectives● Leaner, more productive organizations● Delight students, faculty, and the administration


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