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Markus Breunig & Godehard Gerling Hochschule Rosenheim und go3consulting PartG Big Data – A Dowsing Rod to Locate Innovations?

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Page 1: Big Data – A Dowsing Rod to Locate Innovations? · • Innovation can be modeled in 3 horizons – Big Data can be instrumental in all 3 of them! " „DDD“ – Adjacent Innovation

Markus Breunig & Godehard Gerling!Hochschule Rosenheim und go3consulting PartG!

Big Data – "A Dowsing Rod to Locate Innovations?!

Page 2: Big Data – A Dowsing Rod to Locate Innovations? · • Innovation can be modeled in 3 horizons – Big Data can be instrumental in all 3 of them! " „DDD“ – Adjacent Innovation

Modern Dowsing Rods – pure High-Tech!!

DDS - Salzburg!

Foto:%augsburger-allgemeine.de%

Page 3: Big Data – A Dowsing Rod to Locate Innovations? · • Innovation can be modeled in 3 horizons – Big Data can be instrumental in all 3 of them! " „DDD“ – Adjacent Innovation

Big Data is ...!

...like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it. (Dan Ariely – on his facebook page)!

DDS - Salzburg!

Foto:%telegraph.co.uk%

Page 4: Big Data – A Dowsing Rod to Locate Innovations? · • Innovation can be modeled in 3 horizons – Big Data can be instrumental in all 3 of them! " „DDD“ – Adjacent Innovation

Agenda!

•  Recap - Big Data!•  Three Horizons of Innovation & Big Data!•  Methodical Approaches for the Individual Horizons!–  Incremental!–  Adjacent!

–  Disruptive!

•  Conclusions!

DDS - Salzburg!

Page 5: Big Data – A Dowsing Rod to Locate Innovations? · • Innovation can be modeled in 3 horizons – Big Data can be instrumental in all 3 of them! " „DDD“ – Adjacent Innovation

DDS - Salzburg!

Volume'

Velocity' Variety'

3'Vs''of''

Big'Data'

GB,'TB'or'PB'Records/Transac<ons'

Tables/Files'

Batch'NearA/RealA<me'

Streaming'

Structured'SemiAStructured'Unstructured'

Page 6: Big Data – A Dowsing Rod to Locate Innovations? · • Innovation can be modeled in 3 horizons – Big Data can be instrumental in all 3 of them! " „DDD“ – Adjacent Innovation

DDS - Salzburg!

Type'of'Innova<on:' Incremental% Adjacent% Radical%-%Disrup=ve%

Management'Objec<ve:'

Efficiency,%DDD% Op=miza=on% Business%Transforma=on%

Tools'&'Methods:'

Score%Cards,%Cockpits%

Canvas%Modeling,%10-Types%

Ethnographic,%Exploratory,%Agile%Project%Management%

Impact%

Innova=on%

Big Data & Business Model Innovation!

Page 7: Big Data – A Dowsing Rod to Locate Innovations? · • Innovation can be modeled in 3 horizons – Big Data can be instrumental in all 3 of them! " „DDD“ – Adjacent Innovation

DDS - Salzburg!

Type'of'Innova<on:' Incremental% Adjacent% Radical%-%Disrup=ve%

Management'Objec<ve:'

Efficiency,%DDD% Op=miza=on% Business%Transforma=on%

Tools'&'Methods:'

Score%Cards,%Cockpits%

Canvas%Modeling,%10-Types%

Ethnographic,%Exploratory,%Agile%Project%Management%

Impact%

Innova=on%

Big Data & Business Model Innovation: "Incremental Innovation!

Page 8: Big Data – A Dowsing Rod to Locate Innovations? · • Innovation can be modeled in 3 horizons – Big Data can be instrumental in all 3 of them! " „DDD“ – Adjacent Innovation

DDS - Salzburg!

Phase%3%Advancing'

the'Goals'

Phase%2%Boos<ng'

the'Insights'

Phase%1%Improving'

the'Reports'

Which%reports%are%currently%used%to%monitor%

the%business?%

Which%ques=ons%do%the%recipients%of%the%reports%answer%based%on%these%

reports?%

Which%goals%do%the%report%recipients%pursue%by%

asking%these%ques=ons?%

(How)%can%Big%Data%improve%the%accuracy%of%the%numbers%in%these%

reports?%

(How)%can%these%ques=ons%be%answered%beXer%using%

Big%Data?%

(How)%can%these%goals%be%supported%beXer%using%Big%

Data?%

Incremental Innovation: "Optimizing the existing Business!

Page 9: Big Data – A Dowsing Rod to Locate Innovations? · • Innovation can be modeled in 3 horizons – Big Data can be instrumental in all 3 of them! " „DDD“ – Adjacent Innovation

DDS - Salzburg!

Type'of'Innova<on:' Incremental% Adjacent% Radical%-%Disrup=ve%

Management'Objec<ve:'

Efficiency,%DDD% Op=miza=on% Business%Transforma=on%

Tools'&'Methods:'

Score%Cards,%Cockpits%

Canvas%Modeling,%10-Types%

Ethnographic,%Exploratory,%Agile%Project%Management%

Impact%

Innova=on%

Big Data & Business Model Innovation: "Adjacent Innovation!

Page 10: Big Data – A Dowsing Rod to Locate Innovations? · • Innovation can be modeled in 3 horizons – Big Data can be instrumental in all 3 of them! " „DDD“ – Adjacent Innovation

Adjacent Innovation: "Optimizing the business model!

DDS - Salzburg!

Business%Model%

The Business Model Canvas

Revenue Streams

Channels

Customer SegmentsValue PropositionsKey ActivitiesKey Partners

Key Resources

Cost Structure

Customer Relationships

Designed by: Date: Version:Designed for:

designed by: Business Model Foundry AGThe makers of Business Model Generation and Strategyzer

This work is licensed under the Creative Commons Attribution-Share Alike 3.0 Unported License. To view a copy of this license, visit:http://creativecommons.org/licenses/by-sa/3.0/ or send a letter to Creative Commons, 171 Second Street, Suite 300, San Francisco, California, 94105, USA.

What are the most important costs inherent in our business model? Which Key Resources are most expensive? Which Key Activities are most expensive?

is your business moreCost Driven (leanest cost structure, low price value proposition, maximum automation, extensive outsourcing)Value Driven (focused on value creation, premium value proposition)

sample characteristicsFixed Costs (salaries, rents, utilities)Variable costsEconomies of scaleEconomies of scope

Through which Channels do our Customer Segments want to be reached? How are we reaching them now?How are our Channels integrated? Which ones work best?Which ones are most cost-efficient? How are we integrating them with customer routines?

channel phases1. Awareness

How do we raise awareness about our company’s products and services?2. Evaluation

How do we help customers evaluate our organization’s Value Proposition?3. Purchase

How do we allow customers to purchase specific products and services?4. Delivery

How do we deliver a Value Proposition to customers?5. After sales

How do we provide post-purchase customer support?

For what value are our customers really willing to pay?For what do they currently pay? How are they currently paying? How would they prefer to pay? How much does each Revenue Stream contribute to overall revenues?

For whom are we creating value?Who are our most important customers?

Mass MarketNiche MarketSegmentedDiversifiedMulti-sided Platform

What type of relationship does each of our Customer Segments expect us to establish and maintain with them?Which ones have we established? How are they integrated with the rest of our business model?How costly are they?

examplesPersonal assistanceDedicated Personal AssistanceSelf-ServiceAutomated ServicesCommunitiesCo-creation

What Key Activities do our Value Propositions require?Our Distribution Channels? Customer Relationships?Revenue streams?

catergoriesProductionProblem SolvingPlatform/Network

What Key Resources do our Value Propositions require?Our Distribution Channels? Customer Relationships?Revenue Streams?

types of resourcesPhysicalIntellectual (brand patents, copyrights, data)HumanFinancial

Who are our Key Partners? Who are our key suppliers?Which Key Resources are we acquairing from partners?Which Key Activities do partners perform?

motivations for partnershipsOptimization and economy Reduction of risk and uncertaintyAcquisition of particular resources and activities

What value do we deliver to the customer?Which one of our customer’s problems are we helping to solve? What bundles of products and services are we offering to each Customer Segment?Which customer needs are we satisfying?

characteristicsNewnessPerformanceCustomization“Getting the Job Done”DesignBrand/StatusPriceCost ReductionRisk ReductionAccessibilityConvenience/Usability

typesAsset saleUsage feeSubscription FeesLending/Renting/LeasingLicensingBrokerage feesAdvertising

fixed pricingList PriceProduct feature dependentCustomer segment dependentVolume dependent

dynamic pricingNegotiation (bargaining)Yield ManagementReal-time-Market

strategyzer.com

! Business%Model%Canvas%!  Itera=ve%&%Incremental%

! Doblin‘s%10-Types%

Page 11: Big Data – A Dowsing Rod to Locate Innovations? · • Innovation can be modeled in 3 horizons – Big Data can be instrumental in all 3 of them! " „DDD“ – Adjacent Innovation

Alexander Osterwalder: "Business Models can be Described in 9 Key Aspects!

Source:businessmodelgenera=on.com%

DDS - Salzburg!

Page 12: Big Data – A Dowsing Rod to Locate Innovations? · • Innovation can be modeled in 3 horizons – Big Data can be instrumental in all 3 of them! " „DDD“ – Adjacent Innovation

Keely‘s 10 Types of Innovation can be Matched to Key Aspects of Business Models!

•  Result of investigating > 3000 innovations!•  Combinations of types may be present in any

innovation!

•  Successful & sustainable innovations "combine > 3.5 innovation types!

cf.%Keely,%L.%et%al.%Ten%Types%of%Innova=on.%New%York:%Wiley;%2013.!

DDS - Salzburg!

Page 13: Big Data – A Dowsing Rod to Locate Innovations? · • Innovation can be modeled in 3 horizons – Big Data can be instrumental in all 3 of them! " „DDD“ – Adjacent Innovation

Each Innovation Type is Comprised of "Multiple Innovation Tactics (112 in Total)!

cf.%Keely,%L.%et%al.%Ten%Types%of%Innova=on.%New%York:%Wiley;%2013.!

DDS - Salzburg!

Page 14: Big Data – A Dowsing Rod to Locate Innovations? · • Innovation can be modeled in 3 horizons – Big Data can be instrumental in all 3 of them! " „DDD“ – Adjacent Innovation

DDS - Salzburg!

We have Identified the Data-Driven Innovation Tactics!(Example: 1st Four Types of Innovation) !

Profit Model""

Network" Structure" Process"

o  AdASupported'o  Auc=on%o  Bundled'Pricing'o  Cost%Leadership%o  Disaggregated'Pricing'o  Financing%o  Flexible'Pricing'o  Float%o  Forced'Scarcity'o  Freemium'o  Installed%Base%o  Licensing%o  Membership'o  Metered'Use'o  Microtransac=ons%o  Premium%o  Risk%Sharing%o  Scaled%Transac=ons%o  Subscrip<ons'o  Switchboard'o  UserADefined'

o  Alliances%o  Collabora=on%o  Complementary%

Partnering%o  Consolida=on%o  Coopera=on%o  Franchising'o  Merger/Acquisi=on%o  Open%Innova=on%o  Secondary%Markets%o  Supply'Chain'Integra<on

o  Asset%Standardiza=on%o  Competency%Center%o  Corporate%University%o  Decentralized%

Management%o  Incen<ve'Systems'o  IT%Integra=on%o  Knowledge%Management%o  Organiza=onal%Design%o  Outsourcing%

o  Crowdsourcing%o  Flexible'Manufacturing'o  Intellectual%Property%o  Lean%Produc=on%o  Localiza=on%o  Logis=cs%Systems%o  OnADemand'Produc<on'o  Predic<ve'Analy<cs'o  Process%Automa=on%o  Process%Efficiency%o  Process%Standardiza=on%o  Strategic%Design%o  User-Generated%

Source:%doblin.com%

Page 15: Big Data – A Dowsing Rod to Locate Innovations? · • Innovation can be modeled in 3 horizons – Big Data can be instrumental in all 3 of them! " „DDD“ – Adjacent Innovation

DDS - Salzburg!

Data-Driven Innovation Tactics – Examples!

Profit Model"Bundled Pricing!

Sell$in$a$single$transac-on$two$or$more$items$that$could$be$sold$as$standalone$offerings.$

Iden<fy'items'to'bundle'based'on'data'analysis.'Predict'the'resul<ng'changes'in'revenue.'Analyze'the'resul<ng'changes'in'revenue.''

Product Performance"Feature Aggregation!

Combine$a$number$of$exis-ng$features$from$disparate$sources$into$a$single$offering.$

Analyze'which'features'are'mostly'used'together'in'the'disparate'sources.'Predict'changes'in'usage.'Analyze'changes'in'feature'usage/revenue/profit.'

Service"Loyalty Programs!

Provide$benefits$and/or$discounts$to$frequent$and$high@value$customers.$

Compute'the'frequent/highAvalue'customers.'Analyze'usage'of'the'product'by'these'customers.'Decide'which'incen<ves'to'offer.'Model'cost/profit'changes.'

Page 16: Big Data – A Dowsing Rod to Locate Innovations? · • Innovation can be modeled in 3 horizons – Big Data can be instrumental in all 3 of them! " „DDD“ – Adjacent Innovation

DDS - Salzburg!

Type'of'Innova<on:' Incremental% Adjacent% Radical%-%Disrup=ve%

Management'Objec<ve:'

Efficiency,%DDD% Op=miza=on% Business%Transforma=on%

Tools'&'Methods:'

Score%Cards,%Cockpits%

Canvas%Modeling,%10-Types%

Ethnographic,%Exploratory,%Agile%Project%Management%

Impact%

Innova=on%

Big Data & Business Model Innovation: "Radical - Disruptive!

Page 17: Big Data – A Dowsing Rod to Locate Innovations? · • Innovation can be modeled in 3 horizons – Big Data can be instrumental in all 3 of them! " „DDD“ – Adjacent Innovation

Customer%

Disruptive Innovation: Develop new Business Models centered on Customers and their Behaviour!

DDS - Salzburg!

! Customer%Journey%

Stanford Innovation Masters Series • 10

Prototyping is Part of a Design Process

EMPATHIZEEMPATHIZE

TESTTEST

IDEATEIDEATE

PROTOTYPEPROTOTYPEDEFINEDEFINE

Stanford Innovation Masters Series • 11

3 Stages of Prototyping

# of Prototypes

What could be

What should be

What will be

INSPIRE

EVOLVE

VALIDATE

(from Moggridge)

Embrace failure

Build to think

Low resolution

Expect changes

Experiment

Targeted modelsManage Changes

Build to spec.

Integrated Models

Stanford Innovation Masters Series • 12

Not Just for Products

Interactions

Spaces

Experiences

Stanford Innovation Masters Series • 13

The Exercise

Roll up your sleeves and Prototype!

The Task: Get the ball from the top of the table into the basket as many times as possible in 45 seconds, subject to the rules

! Design%Thinking%

!  Itera=ve%&%Incremental%

! X-Func=onal%Team%

Page 18: Big Data – A Dowsing Rod to Locate Innovations? · • Innovation can be modeled in 3 horizons – Big Data can be instrumental in all 3 of them! " „DDD“ – Adjacent Innovation

Customer/User Journeys "Serve as the Foundation for New Insights!

DDS - Salzburg!

Observable%Behaviours%

Data%

Persona%

Source%1%Source%2%%Source...%

%

Data%Sources%

Page 19: Big Data – A Dowsing Rod to Locate Innovations? · • Innovation can be modeled in 3 horizons – Big Data can be instrumental in all 3 of them! " „DDD“ – Adjacent Innovation

Design Thinking Provides a Structured Discovery and Improvement Process!

The current broader use of the term Design Thinking denotes an interdisciplinary approach to problem solving based on radical collaboration. It promotes hands-on research (observations, enquiries, interviews, self-experiments), ethnological (v. statistical) methods, various ideation techniques (brainstorming, sketching, prototyping), and repeated user and reality feedback by way of prototypes to iteratively refine an approach or a solution. Design Thinking is biased towards action. It works under the assumption that all design activities are ultimately social in nature and that making ideas and concepts tangible will improve the discussion en route to solving a problem.!

DDS - Salzburg!

Stanford Innovation Masters Series • 10

Prototyping is Part of a Design Process

EMPATHIZEEMPATHIZE

TESTTEST

IDEATEIDEATE

PROTOTYPEPROTOTYPEDEFINEDEFINE

Stanford Innovation Masters Series • 11

3 Stages of Prototyping

# of Prototypes

What could be

What should be

What will be

INSPIRE

EVOLVE

VALIDATE

(from Moggridge)

Embrace failure

Build to think

Low resolution

Expect changes

Experiment

Targeted modelsManage Changes

Build to spec.

Integrated Models

Stanford Innovation Masters Series • 12

Not Just for Products

Interactions

Spaces

Experiences

Stanford Innovation Masters Series • 13

The Exercise

Roll up your sleeves and Prototype!

The Task: Get the ball from the top of the table into the basket as many times as possible in 45 seconds, subject to the rules

Page 20: Big Data – A Dowsing Rod to Locate Innovations? · • Innovation can be modeled in 3 horizons – Big Data can be instrumental in all 3 of them! " „DDD“ – Adjacent Innovation

X-Functional Team - Competence Profiles"!

DDS - Salzburg!

Business'

Data'Scien<sts' IT/Infrastructure'

How%to%use%which%data%most%beneficial%

to%further%my%business%model?%

What%can%be%inferred%from%the%data%using%

which%algorithm?%

How%and%where%will%the%data%be%collected,%stored%and%processed?%

%

!%Score%Cards%!%Canvas%Modeling%%%%%%10-Types%!%Design%Thinking%

! Sta=s=cs%! Data%Mining%

! Hadoop%Ecosystem%! Cloud%Storage%

Page 21: Big Data – A Dowsing Rod to Locate Innovations? · • Innovation can be modeled in 3 horizons – Big Data can be instrumental in all 3 of them! " „DDD“ – Adjacent Innovation

DDS - Salzburg!

Type'of'Innova<on:' Incremental% Adjacent% Radical%-%Disrup=ve%

Management'Objec<ve:'

Efficiency,%DDD% Op=miza=on% Business%Transforma=on%

Tools'&'Methods:'

Score%Cards,%Cockpits%

Canvas%Modeling,%10-Types%

Ethnographic,%Exploratory,%Agile%Project%Management%

Impact%

Innova=on%

Big Data & Business Model Innovation!

Page 22: Big Data – A Dowsing Rod to Locate Innovations? · • Innovation can be modeled in 3 horizons – Big Data can be instrumental in all 3 of them! " „DDD“ – Adjacent Innovation

Conclusions ! !!

•  Innovation can be modeled in 3 horizons – Big Data

can be instrumental in all 3 of them!

"  „DDD“ – Adjacent Innovation – Structured Disruption!

•  Each horizon requires a specific approach!

•  Success results from a structured combination of

well-established innovation methods and Big Data!

!

DDS - Salzburg!

Page 23: Big Data – A Dowsing Rod to Locate Innovations? · • Innovation can be modeled in 3 horizons – Big Data can be instrumental in all 3 of them! " „DDD“ – Adjacent Innovation

Thank you for your attention!!

Prof. Dr. Markus Breunig!+49 151 23344 657"[email protected]"[email protected] "www.fh-rosenheim.de"!Godehard Gerling !+49 160 975 23522"[email protected]"www.go3consulting.de"

DDS - Salzburg!