Minggu-02 Big Data Business Model Maturity Index.pdf

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1. Dr. Djadja AchmadSardjana S.T., M.M.
Big Data Business Model Maturity Index
[email protected]
IG: djadjasardjana

2. Dr. Djadja AchmadSardjana S.T., M.M.
Business Model Maturity
[email protected]
IG: djadjasardjana

3. Industry 4.0 Attributes
•Interoperabilityor the ability to achieve results by different means, to perform the same
functions, despite possible exchange of equipment and manufacturers.
•Decentralization, which corresponds to the ability to make decisions without dependence on a
data processing center or a decision-making body of human resources.
•Virtualization, reproduction resource or simulation of the real world in virtual mode.
•Modularity, capacity for change, to make processes more comfortable and adherent
depending on environmental configurations and the need for variations in product design.
•Real-timereaction through analysis of large volumes of data that allow the identification of
profiles and even subtle changes in scenarios.
•Orientation to servicesmade possible by the integration of processes, since they present
themselves as adequate means to mediate the relationship of the consumer market with the
companies, as an opportunity for improvements in the final use of the product.

4. Business Model
•Technology
•Internet of Things (IoT)
•Cloud Computing
•Data Science
•Big Data Analytics
•Artificial Intelligence
•Machine learning
•Virtualization
•Prototype and simulation
•Augmented reality
•3D Printing
•Robotics systems
•Process and project based
management
•Human-computer collaboration
•Equipment integration
•Collective Intelligence
•Sustainability
•Economic
•Security
•Environmental

5. Reconfiguration traditional plans
•Definition or review of strategic parameters:
•Scope and boundaries of organizational objectives
•Mission, vision and values
•SWOT (strengths, weaknesses, opportunities and threats)
•Mapping and optimization of processes
•Analysis of return on investment (ROI)
•Planning
•Execution
•Monitoring, evaluation and continuous improvement.

6. Reconfiguration plans reviewed
•Readiness assessmentsare evaluation and analysis tools that aim
to determine the level of preparedness of an organization in terms of
conditions, attitudes and resources.
•Frameworksare collections of procedures, methods and tools
focused on the design of an organizational architecture or a system.
•Roadmapsare "plans that match short-term and long-term goals
with specific technology solutions to help to meet those goals".
•Maturity modelsare models that help organizations achieve
expected skills in specific dimensions such as culture, processes,
resources, etc., through continuous improvement processes.

7. Process-based management
The ultimate goal is to become an agile
company, capable of continuous and
agile adaptation to a changing
environment.
Environment
System
Organization
(Business Process)
People
(Organizational
Culture)
Technology
(Resources)
The company as
a complex system
Source: Laudon, Kenneth C.; Laudon, Jane P. (2019).
InformationSystems. Pearson.

8. Scope
•Generic Model
•They are generic and their application depends on consulting and external evaluations
of the processes to identify the level of maturity.
•Specific Model
•Another way to address the demand for maturity assessment is by choosing to build a
model to meet a specific condition. It is common in such cases to use an existing model
as a reference. In any case, the identification of success factors that should be
considered is a fundamental element.

9. Generic Maturity Model
• Capability Maturity Model (CMM)& Capability Maturity Model Integration (CMMI)
• IMPULS Industrie 4.0 Readiness Model, by VDMA, RWTH Aachen and IW Consult
• Manufacturing Value Modeling Methodology (MVMM), by Gartner Maturity Model
• Industrie 4.0 Maturity Index, by Acatech
• PwC Industry 4.0–Enabling Digital Operations and Self Assessment;
• BCG–Digital Acceleration Index;
• The Maturity Model for Assessing Industry 4.0 Readiness and Maturity of Manufacturing, by Fraunhofer Austria;
• Minnosphereand Hochschule Neu-Ulm–University of Applied Sciences, online-assessment, digitalereadinesof companies;
• Federal Ministry for Economic Affairs and Energy Germany (BMWi), Industrie 4.0 –Checkliste: KommtIndustrie 4.0 fürunser
Unternehmenin Frage;
• DeutscherIndustrie-und Handelskammertag(DIHK) –SelbsttestzumdigitalenReifegrad;
• The Connected Enterprise Maturity Model, Rockwell Automation;
• Industry 4.0/Digital Operations Self-Assessment, PricewaterhouseCoopers.

10. Maturity levels (Acatech)
•Computerization-use of information technology;
•Connectivity-integration of IT tools;
•Visibility-sensors allow processes to be monitored from end to end;
•Transparency-digital shadow indicates the current situation;
•Predictive capacity-ability to simulate scenarios;
•Adaptability-ability to adapt continuously.

11. Maturity levels (Acatech)

12. Maturity index (Acatech)

13. Dr. Djadja AchmadSardjana S.T., M.M.
Big Data Maturity
[email protected]
IG: djadjasardjana

14. Big Data Business Model Maturity Index
^How Effective is Your
Organization at Leveraging
Data and Analytics to
Power your Business?
Key Business
Processes
Big Data
Economics
BUSINESS
OPTIMIZATION
BUSINESS
MONITORING
BUSINESS
INSIGHTS
Prescriptive
Recommendations
INSIGHTS
MONETIZATION
BUSINESS
METAMORPHOSIS
Source: Bill Schmarzo "Big Data MBA” Course Curriculum

15. Digital Transformation: Start with the Business
BUSINESS INITIATIVE
STAKEHOLDERS
KEY DECISIONS
PREDICTIVE ANALYTICS
BIG DATA ARCHITECTURE & TECHNOLOGIES
Source: Bill Schmarzo "Big Data MBA” Course Curriculum

16. WhatisBigDataand
WhyisitImportant?
Big Data is the Fuel for Data Monetization

17. Every Two Days We
Create As Much
Information As We Did
From The Dawn Of
Civilization Until 2003
-Eric Schmidt, Google CEO
A Billion People Visit
Facebook Every Day
-Business Insider
By 2018, Leading
Enterprises Will Support
1,000-10,000 TIMES More
Customer Touch Points
-1 DC

19. 30 Billion
CONNECTED DEVICES
44 Zettabytes
OF DATA
7 Billion
CONNECTED PEOPLE
Millions
OF NEW BUSINESSES
IIIn
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And*""
Customer Experience Changing Customer Expectations
Recommends movies, restaurants, friends, spouses, books, routes, etc.
Source: Bill Schmarzo "Big Data MBA” Course Curriculum
Customers Who Bought This Item Also Bought
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21. Big Data Isn’t About Big... it’s About Small
AFFINITIES
PASSIONS
INTERESTS
BIASES
ASSOCIATIONS
INCLINATIONS
PROPENSITIES
AFFILIATIONS
TASTES
PREFERENCES
BEHAVIORS
TENDENCIES
Source: “Me, Myself and Digital Twins”, University of San Francisco School of Management Big Data MBA

22. Big Data isn’t about Big.
AFFINITIES
INCLINATIONS BIASES
PATTERNS
TENDENCIES

23. it’s about Small
PROPENSITIES
AFFILIATIONS
TRENDS
BEHAVIORS

24. Demystifying Data Science
Data Science: The Data Monetization Engine

25. What is Data Science?
DataScienceisaboutidentifyingthosevariablesandmetricsthatmightbebetter
predictorsofperformance
Source: Bill Schmarzo "Big Data MBA” Course Curriculum 12

26. Analytics Value Chain
Learning to “Think Like a Data Scientist'
DescriptiveQuestions
(Whathappened?)
What were revenues and
profits last year?
How much fertilizer did I use
last planting season?
How much downtime did I
have last month due to
unplanned equipment
maintenance?
How many workers did I use
last month?
Predictive Analytics (What is
likely to happen?)
What will revenues and profits
be next year?
How much fertilizer will I need
next planting season?
When will my equipment need
maintenance next month?
How many workers will I need
next month and when will I
need them?
PrescriptiveActions
(Whatshouldwedo?)
Plant X and Y crops across N
acres
Pre-order X amount of
fertilizer at 5% discount
Service your harvester and
tractor #2 in January
Hire X number of workers for
Y days
Source: Bill Schmarzo "Big Data MBA” Course Curriculum

27. Business Intelligence Engagement Process
Step 4:BI tool creates SQL
|
Step 5:SQL is run against data warehouse
to create report
Source: Bill Schmarzo "Big Data MBA” Course Curriculum

28. REPEAT
Data Science Engagement Process
Supports rapid exploration, rapid testing, continuous learning “Scientific Method”
Google Physician Local CDC
Trends Notes Events
Source: “Scientific Method: Embrace the Art of Failure”, University of San Francisco School of Management Big Data MBA

29. Analytics Level 1: Insights and Foresights
Level 1 Advanced Analytics quantify cause-
and-effect (strength of relationships) and
goodness of fit (model accuracy)
• Statisticsareusedtosupporthypothesis
(decision)testingandprovidecredibilityto
modeloutcomes(confidencelevels,p-values)
• Predictive Analytics and Data Mining include
anomaly detection, clustering, classification,
regression and association rule learning
Predictive analytics and data mining
uncover statistically significant patterns and
relationships in large data sets to quantify
risks and opportunities
Graph Analytics to Identify
HMl
■0.0i<W9 EMI 2
*
ffi
m
Source: Bill Schmarzo "Big Data MBA” Course Curriculum
Regression to Quantify Cause-and-
Effect

30. Analytics Level 2: Augment Human Decision-making
Level 2 Advanced Analytics prescribe actions
(recommendations) to improve human decision-making
• Deep Learning (Neural Networks) recognizes
“things” out of complex data formats -images,
photos, voice, audio, video, text, handwriting
• Machine Learning identifies relationships and
patterns in the data, builds models, and predicts
things without programming
-Supervised Machine Learning identifies known
unknowns relationships and patterns from
“labeled” outcomes (e.g., fraud, attrition)
-Unsupervised Machine Learning identifies
unknown unknowns relationshipsand
patterns from data with no known outcomes
Clustering to Codify Similarities
Source: Bill Schmarzo “Big Data MBA” Course Curriculum
Deep Learning to Identify Things

31. Analytics Level 3: Learning
Level 3 Advanced Analytics learn and
adapt within continuously changing
environments (robots, autonomous
vehicles)
• Reinforcement Learning takes actions within
controlled environment to maximize rewards
while minimizing costs; uses trial-and-error to
map situations to actions to maximize
rewards (Hotter/Colder game)
• Artificial Intelligence algorithms acquire
knowledge about specific environment, apply
the knowledge to successfully interact within
that environment, and learn from the
resulting interactions so that subsequent
interactions get more effective
Source: Bill Schmarzo "Big Data MBA” Course Curriculum
and Intelligent Enterprise
Reinforcement Learning Train Autonomous Vehicles
Artificial Intelligence to win GO

32. Advanced Analytics Simplified
•Statistics & Predictive Analytics quantifies cause-and-effect (correlation
coefficient) and goodness of fit (Chi-squared test)
•Deep Learning (Neural Networks) recommends thingsfrom complex data
formats (images, audio, video, text, handwriting)
•Supervised Machine Learning identifies known unknowns relationships
that drive “discrete” outcomes (e.g., fraud, attrition, maintenance,)
•Unsupervised Machine Learning identifies unknown unknowns
relationships -clusters, segments, associations hidden in the data
•Reinforcement Learning & Artificial Intelligence learns and adapts
operating within continuously changing environment (e.g., chess, cars)
Source: Bill Schmarzo “Big Data MBA” Course Curriculum

33. Advanced Analytics Simplified (Even More!)
•Statistics&PredictiveAnalyticsquantifiescause-and-effect
•DeepLearning(NeuralNetworks)recognizesthingsfromcomplexdata
•SupervisedMachineLearningidentifiesknownunknownsrelationships
•Unsupervised Machine Learning identifies unknown unknowns
relationships -clusters, segments, associations hidden in the data
•ReinforcementLearning&ArtificialIntelligencelearnsandadapts
Source: Bill Schmarzo "Big Data MBA” Course Curriculum

34. Determining
Economic Value
of Data
University of San Francisco
Economic Value of Data Research
Project
APPLYING ECONOMIC
CONCEPTS TO BIG DATA TO
DETERMINE THE FINANCIAL
VALUE OF THE
ORGANIZATION’S DATA AND
ANALYTICS, AND
UNDERSTANDING THE
RAMIFICATIONS ON THE
ORGANIZATIONS'FINANCIAL
STATEMENTS AND IT
OPERATIONS AND BUSINESS
STRATEGIES
Abstract
CompaniesarecontemplatingtheorgaratanonaJandbusinesschallengesof
accountingfordataasa'corporateasset’DataisnowseenasacurrencyThis
researchpaperdeepdimintotheeconomicsofdataandanalyticsanddefines
these analogies.

35. “Data is an unusual currency. Most currencies exhibit a one-to-one
transactional relationship. For example, the quantifiable value of a
dollar is considered to be finite -it can only be used to buy one item or
service at a time, or a person can only do one paid job at a time. But
measuring the value of data is not constrained by transactional
limitations.
In fact, data currency exhibits a network effect, where data can be used
at the same time across multiple use cases thereby increasing its
value to the organization. This makes data a powerful currency in which to
invest.”
Source: “Determining the Economic Value of Data”
Source: Bill Schmarzo “Big Data MBA” Course Curriculum

36. Economic Multiplier Effect: Data
Where an increase in spending produces an
increase in national income and consumption
greater than the initial amount. Every time there
is an injection of money into the economy, there
is an economic multiplier effect.
Customer point of
sales data
Sales
Promotional
effectiveness
+2.5%
Marketing
Customer
acquisition
+2.0%
Call Center Product Dev
Customer
retention
+3.5%
New product
intro
~ i
JMhi
+2.6%
Source: Bill Schmarzo "Big Data MBA” Course Curriculum

37. Intellectual Capital “Rubik’s Cube” Challenges
•Howdoestheorganizationdeterminetheeconomicvalue
of its data in order to drive prioritization and investment
decisions?
•Howdoestheorganizationavoiddatasilos,shadowIT
spend and unmanaged data proliferation that thwart the
potential value of data?
How does the organization avoid the disillusionment of
“orphaned analytics ”?
How do you re-toolthe organization to establish a technical
and cultural environment for collaborative value creation?
How does one leverage assets that appreciate (not depreciate) with
usage, and can be used simultaneously across multiple business
processes?
Source: Bill Schmarzo “Big Data MBA” Course Curriculum

38. Intellectual Capital “Rubik’s Cube” Solution
USE CASES
Clusters of decisions around common subject area in
support of organization’s key business initiatives
a
01
101
0
10001
DATA ANALYTICS
DetailedhistoricaltransactionscoupledDatatransformedintoactionableanalytic
withinternalunstructuredandpublicly-insights(scores,rules,propensities,
availabledatasourcessegments,recommendations)
Source: Bill Schmarzo “Big Data MBA” Course Curriculum

39. Start with a Business Initiative
Chipotle 2012 Annual Report
DearSharedciders.
Wearepleased«vithChipotle'sperformancein2012.andareconfidentthatthecontinuingstrengthofoutbusinessisadiedresultofourfocusonthehey
elementsthatdriveourbusiness,primarilyouruniquefoodandpeoplecultures.Together,theseprioritiesareatdieheartofourvisiontochangetheway
peoplethinkaboutandeatfastfood.
Our food culttae sets us apart hem other restaurants We have always used great quality ingredients and prepared the food we sene using classic
cooking techniques in open kitchens. We a re proud ot the way we sou ice the finest ingredenS we can find and skillfully prepare and cook them with great
care, because we know it results in exceptional tasting food that our custtmeis appreciate. We believe this bond with our customers only deepens as they
become more curious about where their food comes from, and they discover the special war we source our food. We believe the more people care
about their food, and where it comes frcxn, the more likely they are to become loyal customers of Chipotle.
Throughout 20 L2. we continued to push ourselves to find better, more sustainable sources forthe ingredients we use and to refine our
cookingtechniquessc thiat we are continually offering orrearstomers the very best food we can. Our local produce program exceeded our expectations
as we served more than 15 million pounds of produce from local farms across the country last year, exceeding our goal of 10 million pounds. We also
increased our use of cheese and sour cream made with milk bon dairy cattle given access to pasture, finally, we continued to sene meat raised in a
responsible way (from animals that semised in a himane manna’ and vwhout the use of subtherapeutic antibiotics or added hormones) in afl of our
restaurants, except when we have experienced periodic short-term disru ptions to our supply. We are proud of the unique supply chain we have built over
the years arid vre wil continueto identify additional suppliers, and ffmour existing ones, to meet the growing demandfor these high-quality
We continue to build a people culture that attract and empowers top performeis. We now have more empowered tup performers than ever developing
from crewpositicfis in our company, bi 2012, we promoted 190 new Restaurateurs, giving us a total of 421 of these eite leaders including field leadeis who
were promoted from Restaurateur positions. Also, we are seeing a higher percentage of candidates promoted to the Restaurateur position than erer
before, demonstrating that our field teams better understand how to create these special cultures, and that the quality of managers in our restaurants ts
getting better all the time. At our September All Managers' Conference, we brought together an extraordinary group of2.000 leaders to share details
about our vision, and to provide opportunities for them to leatr about programs that will help them rut our restaurants even baler This inspiring experience
made us feel as confident as arer that the future 0(0(1-business is in good hant£.
last year we opened 133 restaurants, grew out revenue by 203% to $2.73 billion, and saw comparable restaurant sales grow 7.1% fcr the year . Our
restart rant-level mar^nswere among the highest in the industry at 27.1%. We are pleased with our performance, and anticipate continued growth and
success. We plan to open an additional 1G&-180 restaurants in 2013, and are confident that we are developing great leaders to run these restaurants in
a way that we can feel proud of. We are also planting seeds fen future growth in Europe and Canada, where we currently operate 12 restaurants, and
with ourShopHouse Southeast Asian Kitchen concept which iscurrentfy open in Washington DC.
bi 2012. our marketing focused on building the Chipotle brand and engaging with nur customers in ways that create stronger, deeper bonds than is
possible with 'limited time offers" and on connecting with people emebonafyin a way that is both true and meaningful. With programs like our award-
winning "Backto the Start' animated short film and our Cultivate toed and music festivals, we are creating a lasting connection with our customers and
continuing a tradition of building our brand in unconventional
During 2013 we will celebrate C hiped e's 20 anniversary. We are grateful fcr our past success, but even mere excited about what is yet to ctme.lhere is
tremendous opportunity for Chipatie in the years ahead. We are confident that we are well positioned for continued success.
Sincerely.
Stevefils
Founder,Chairman,&Co-CEO
Chipotle Business Initiatives
S Build people culture that attracts and empowers top
performers
S Grow revenues (up 20.3% in 2012) opening new
stores (opened 183 in 2012)
S Increase comparable restaurant sales
growth (7.1% in 2012)
S Marketing building Chipotle brand and engaging
with our customers
Source: Bill Schmarzo "Big Data MBA” Course Curriculum

40. Identify Use Cases
Business initiative: Increase same store sales
Increase shopping bag
revenue
Increase corporate
catering
Increase
noncorporate
catering
Decision
Decision
Deci
Decision
Deci
Improve promotional
effectiveness
Increase Store Traffic via
Loyalty program
Decision
Decision
Decision
Decision
Decision
Decision
Decisi
Increase Store Traffic
via local events
marketing
Decision
Decision
Decision
Improve New Product
Introduction Effectiveness
C
Decision
D
—*
Decision
_A
Source: Bill Schmarzo "Big Data MBA” Course Curriculum

41. Map Data Sources to Use Cases
Data is the fuel of the modern, intelligent organization -an asset to be gathered, enriched and
re-used across multiple Use Cases
Data Sources
Increase
Store Traffic
Local Events
Increase Store
Traffic Loyalty
Increase
Shopping Bag
Revenue
Increase
Corporate
Catering
Increase
Noncorporat
e Catering
Improve New
Product
Introductions
Improve
Promotional
Effectiveness
$62M $56M $26M $24M $14M $18M $27M
Point of Sales V V V V V V
Market Baskets V V V V V
Store Demographics V V V V
Local Competition V V
Store Manager Demo V V V
Consumer Comments V
Social Media V V V V V
Weather V V V
Local Events V V V V
T raffic V V
Source: Bill Schmarzo "Big Data MBA” Course Curriculum

42. Analytic Profiles Capture Analytics for Re-use
Analytic Profiles standardize the collection and re-application of analytics about Business
Entities across multiple Use Cases
Bill Schmarzo Chipotle
Analytic Profile
NCE
Score Var Trend
Source: Bill Schmarzo "Big Data MBA” Course Curriculum

43. Analytic Profile: Customer
Create Scores that support the Decisions that comprise each Use Case, and store those
scores in the Analytic Profile
Bill Schmarzo Chipotle
Analytic Profile
NCE
Score VarTrend
Demographic segments 1.0
92 1.85 ▲
Behavioral segments 1.0 67 3.25 ▼
Loyalty Index 2.0
82
2.25 ▲
Frequency Index 1.0 65 1.90 ▼
Recency Index 1.0 92 1.89 ▼
Use Case #1
Improve campaign
effectiveness
Use Case #2
Increase customer
loyalty
Use Case #3
Increase customer store
visits
Source: Bill Schmarzo "Big Data MBA” Course Curriculum
Use Case #4
Reduce customer
attrition

44. Analytic Profile: Customer
Over time as more data is available, the analytics stored in the Analytic Profiles get refined
and fine-tuned across multiple use cases
TraditionalData
•Purchases
•ProductPreferences
•Add-onPreferences
•DrinkPreferences
•VisitFrequency
•VisitRecency
•VisitMonetary
•MarketBasket
•GroupSize
•Coupons
•ConsumerComments
•StoreManagerNotes
Bill Schmarzo Chipotle Analytic
Profile
NCE
Score Var Trend
Demographic segments 3.2 92 1.85 ▲
Behavioral segments 3.1 67 3.25 ▼
Loyalty Index 2.0
82
2.25 ▲
Frequency Index 1.0 65 1.90 ▼
Recency Index 1.0 92 1.89 ▼
Lifetime Value Calc 1.0 99 1.05 ▲
Event Propensity 1.0 14 1.74
Promotion Propensity 1.1
02
1.15
Advocacy Propensity 2.1
08 1.20
Attrition Propensity 1.2 09 1.25
Non-traditionalData
•SocialMediaPosts
•HomeValue
•Employmenthistory
•JobChangeFrequency
•JobChangeRecency
•Industrycertifications
•Industryawards
•SocialMediaConnections
•Educationdegrees
•Rankofcollege
•Collegedonations
•Volunteeractivities
•Parkingtickets
Source: Bill Schmarzo "Big Data MBA” Course Curriculum

45. Data Lake 3.0: Collaborative Value Creation Platform
Improve Campaign
Effectiveness
Improve Manager
Retention
OptimizeStoreIncreaseCustomer
Remodeling^p\<\\ytlCS3S3S^Loyalty
/'DataasaService,/
x
\
lager/ ''\
|n
n--------______\
Improve Hiring
Effectiveness
Increase Customer
Store Visits
Reduce Customer
Attrition
Improve New Product
Introductions
Increase Customer
Cross-sell
Increase Customer
Advocacy
Source: Bill Schmarzo "Big Data MBA” Course Curriculum

46. Creating the
Intelligent Enterprise

47. Big Data Business Model Maturity Index
^How Effective is Your
Organization at Leveraging
Data and Analytics to
Power your Business?
Key Business
Processes
Big Data
Economics
BUSINESS
OPTIMIZATION
BUSINESS
MONITORING
BUSINESS
INSIGHTS
Prescriptive
Recommendations
Source: Bill Schmarzo "Big Data MBA” Course Curriculum
INSIGHTS
MONETIZATION
BUSINESS
METAMORPHOSIS

48. Advanced Analytics Drives Business Transformation
The Learning & Intelligent Enterprise
Self-diagnosis and Self-learning Augmenting Enterprise Intelligence
(Reinforcement Learning, Artificial Intelligence, Cognitive
Computing)
\Uirtr \//"M ■-^
Optimized Human Decision-making
Prescribing Actions & Recommendations
(Neural Networks, Deep Learning,
Machine Learning)
^rscesses
Insights and Foresight
Quantify Cause-and-Effect
(Statistics, Clustering,
Classification, Regression)
BUSINESS
OPTIMIZATION
B> .i\IES
MONITORING
BUSINESS
INSIGHTS
Prescriptive
RecommendatfSis
Source: Bill Schmarzo "Big Data MBA” Course Curriculum
r1
Clognitiv
e
Aanalytics
LA
.ESS
RPHOSI
S

49. 49
Assess your current state of data governance
•To implement data governance, if not to sell it
to senior leadership, assessment of the current
state is important.
•Extends beyond the informal list
•Uses a maturity model to quantify the existing
state; allows for measurement of progress in a
future state

50. 50
Activity 4: Data Governance Maturity Model
Level 1 Level 2 Level 3 Level 4 Level 5
Informal Developing
Adopted and
Implemented
Managed and
Repeatable
Integrated and
Optimized
Organizational
Structures
Attention to Data Governance
is informal and incomplete.
There is no formal governance
process.
Data Governance Program is
forming with a frameworkfor
purpose, principles, structures
and roles.
Data Governancestructures,
roles and processes are
implemented and fully
operational.
Data Governance structures,
roles and processes are
managed and empowered to
resolve data issues.
Data Governance Program
functions withproven
effectiveness.
Culture
Limitedawareness about the
value of dependable data.
General awareness of thedata
issues and needsfor business
decisions.
There is active participation and
acceptance of the principles,
structures and roles required to
implement a formal Data
Governance Program.
Data is viewed as a critical,
shared asset. There is
widespread support,
participation and endorsement
of theData Governance
Program.
Data governance structures
and participants are integral
to the organizationand
critical across all functions.
Data Quality
Limited awareness that data
quality problems affect
decision-making.Data clean-
up is ad hoc.
General awareness of data
quality importance. Data quality
procedures are being developed.
Data issues are captured
proactively through standard
data validation methods. Data
assets are identified and
valuated.
Expectations for data quality
areactively monitored and
remediation is automated.
Data quality efforts are
regular, coordinated and
audited.Data are validated
prior to entry into the source
system wherever possible.
Communication
Information regarding data is
limited through informal
documentation or verbal
means.
Written policies, procedures,
data standards and data
dictionaries may exist but
communication and knowledge
of it is limited.
Data standards and policies are
communicatedthrough written
policies, procedures and data
dictionaries.
Data standards and policies
are completely documented,
widely communicated and
enforced.
Allemployees are trained
and knowledgeable about
data policies and standards
and where to find this
information.
Roles &
Responsibilities
Roles and responsibilities for
data managementare informal
and loosely defined.
Roles and responsibilities for
data management areforming.
Focus is on areas where data
issues are apparent.
Roles and responsibilities are
well-defined and a chain of
command exists for questions
regarding data and processes.
Expectations of data
ownership and valuation of
data are clearly defined.
Roles,responsibilities for
data governance are well
established and the lines of
accountability are clearly
understood.

51. 51
Activity 4: Data Governance Maturity Model
Enter rating 1 through 5 based on maturitymodel rubric
Finance dataStudent data HR data ResearchdataFacilities dataOverall
Organizational
Structures
Culture
Data Quality
Communication
Roles &
Responsibilities

52. 52
Stony Brook Data Governance Maturity Model
Initial Results –Spring 2016

53. 53
Baseline
53
Data Governance
Culture
Data Quality
Communication
Roles & Responsibilities
Integrated & Optimized
Managed & Repeatable
Adopted & Implemented
Developing
Informal
Dimensions Maturity
K
Target
2017
Current
2015

54. Dr. Djadja AchmadSardjana S.T., M.M.
Case Studies: Lawson Data
[email protected]
IG: djadjasardjana

55. How Do We Leverage
Big Data ?

56. BIG DATA BUSINESS MODEL MATURITY INDEX
BUSINESS
OPTIMIZATION
BUSINESS
INSIGHTSBUSINESS
MONITORING
INSIGHTS
MONETIZATION
BUSINESS
METAMORPHOSIS
Learnings
(Best Practices)
Real-time Optimized
Decisions
Measures effectiveness of
leveraging data and
analytics to power business
models
Data Analytic
Platform
Capture Data
Sources
Create
Analytic
Models
Data & Analytics as
Corporate Assets
Data as
Currency

57. Enabling Lawson Big Data Transformation
Capture data
e.g.
EMR, Lawson,
Kronos, Claims,
Scheduling, 3
rd
party, public
Store
everything in
standardized
environment:
Data Profile,
Describe &
Enhancewith
metadata
INGEST STORE
Use Big Data
analytics
to discover
predictive
patterns –clinical
/ business ops
ANALYZE
Share across
the Enterprise
Clinicians,
Executives,
Analysts &
Patients
SURFACE
Project future
opportunities:
ACO, Patient
Engagements,
Population
Health, Precision
Medicine
ACT
Enable Self
Service
Reporting

58. Pre-project
Prep
Data
Exploration
Vision Workshop Timeline
•Confirm targeted
business
initiative
•Identify business
and IT workshop
participants
•Identify
appropriate data
source(s)
•Schedule
interview and
workshop dates
•Output –
Targeted
business
initiative
Day of
Interview
s
Ideation
Workshop
Recommend
-ations
•Conduct business
interviews
•Conduct IT
interviews
•Secure sample
data set (5GB)
•Collect
supporting
materials (file
structures,
sample reports)
•Output –Key
decisions and
business
questions
•Collaborate with
data owners to
capture subset
of relevant data
•Acquire, prepare
and explore data
•Explore external
data sources
•Build Analytic
model
•Develop
mockups
•Output –“Art of
the possible”
data
•Review targeted
business
initiative
•Review analytics
and mockups
•Brainstorm use
cases
•Output –
Prioritized
feasible Big Data
business
opportunities
•Present business
opportunities
and prioritization
•Output –Score
data sources
•Output –
Recommend
next steps
1 Day 2 Weeks 1 DayPrep Post

59. Example Use Cases

60. @nogalisinc

61. Some Questions to Frame your Approach
to Big Data
•How well does your organization leverage big data and analytics
to run the business?
•Can you quantify the economic value of your data and how that
affects your technology and business investments?
•Do you understand how to create a platform that exploits the
economic value of your data?

62. T E R I M A K A S I H