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Becoming a Data-Driven Organization:
Aligning Business & Data Strategy
Donna Burbank & Nigel Turner
Global Data Strategy Ltd.
DATAVERSITY DAMA Webinar, January 19th 2016
Global Data Strategy, Ltd. 2016
Donna Burbank
Donna is a recognized industry expert in
information management with over 20
years of experience in data
management, metadata management,
and enterprise architecture. Her
background is multi-faceted across
consulting, product development,
product management, brand strategy,
marketing, and business leadership.
She is currently the Managing Director at
Global Data Strategy, Ltd., an
international information management
consulting company that specializes in
the alignment of business drivers with
data-centric technology. In past roles,
she has served in key brand strategy and
product management roles at CA
Technologies and Embarcadero
Technologies for several of the leading
data management products in the
market.
As an active contributor to the data
management community, she is a long
time DAMA International member and is
the President of the DAMA Rocky
Mountain chapter. She was also on the
review committee for the Object
Management Group’s Information
Management Metamodel (IMM) and a
member of the OMG’s Finalization
Taskforce for the Business Process
Modeling Notation (BPMN).
She has worked with dozens of Fortune
500 companies worldwide in the
Americas, Europe, Asia, and Africa and
speaks regularly at industry
conferences. She has co-authored two
books: Data Modeling for the
Business and Data Modeling Made
Simple with CA ERwin Data Modeler r8.
She can be reached at
donna.burbank@globaldatastrategy.com
Donna is based in Boulder, Colorado,
USA.
2
Follow on Twitter @donnaburbank
Global Data Strategy, Ltd. 2016
Nigel Turner
Nigel Turner has worked in Information
Management (IM) and related areas for over 20
years. This experience has embraced Data
Governance, Information Strategy, Data Quality,
Data Governance, Master Data Management, &
Business Intelligence.
He spent much of his career in British
Telecommunications Group (BT) where he led a
series of enterprise wide IM initiatives which
brought huge benefits to BT. He also created and led
large IM & CRM consultancy & delivery practices
which served BT Global Services’ customers,
including Emirates Airlines, the UK Ministry of
Defence, Intel, HBOS, Belgacom and others.
After leaving BT in 2010 Nigel became VP of
Information Management Strategy at Harte Hanks
Trillium Software, a leading global provider of Data
Quality & Data Governance tools and consultancy.
Here he engaged with over 150 customer
organisations from all parts of the globe,
undertaking extensive engagements with HSBC,
Progress Software, British Gas, HBOS, EDF Energy,
Severn Trent Water, British Airways, Telefonica O2
and others,
Currently Principal Consultant for EMEA at Global
Data Strategy, Ltd, he has been a principal consultant
at such firms as FromHereOn and IPL, where he has
led Data Governance engagement with customers
such as First Great Western.
Nigel is a well known thought leader in Information
Management and has run tutorials and presented at
many international conferences. He has also written
several white papers and is a frequent blogger on
Information Management topics. He has also
lectured part time at Cardiff University, where he
taught Data Governance modules to both
undergraduate and graduate students. In addition
he was a part time Associate Lecturer at the UK
Open University where he taught Systems &
Management. He also authored the Data Quality
Strategy module of the Institute of Data Marketing’s
Data Management Award.
Nigel is very active in professional Data Management
organisations and is an elected Data Management
Association (DAMA) UK Committee member. He was
the joint winner of DAMA International’s 2015
Community Award for the work he initiated and led
in setting up a mentoring scheme in the UK where
experienced DAMA professionals coach and support
newer data management professionals.
Nigel is a great advocate for keeping Information
Management as simple and business focused as
possible, and feels that a key role of Information
Management professionals is to help business
people relate IM to real business benefits. Much of
his consultancy work has been to enable
organisations to do this by helping them to build
compelling IM business cases, Data Governance
processes and structures that complement existing
business models, and demonstrating and delivering
the benefits of improved Data Quality and data
exploitation.
3
Follow on Twitter @NigelTurner8
Global Data Strategy, Ltd. 2016
Agenda
• Lay out the primary components of a Data Strategy
• Define Business and Data Strategies
• Show how to align Data Strategy with Business Motivations & Drivers
• Highlight reasons why Business & Data strategies can become misaligned
• Outline real life case studies of Data Strategies:
• Consumer Energy company
• Professional Development & Certification organization
• Global Telecommunications company
• International Pharmaceutical company
4
What we’ll cover today
5
Components of a Global
Data Strategy
Where do we start?
Global Data Strategy, Ltd. 2016
DAMA DMBOK Framework
• The DAMA Data Management Body of Knowledge
(DMBOK) is a helpful guideline to follow for industry
best practices
• Modeled after other BOK documents:
• PMBOK (Project Management Body of Knowledge)
• SWEBOK (Software Engineering Body of Knowledge)
• BABOK (Business Analysis Body of Knowledge)
• Outlines core data management functions:
• Data Architecture Management
• Data Development
• Database Operations Management
• Data Security Management
• Reference & Master Data Management
• Data Warehouse & Business Intelligence Management
• Document & Content Management
• Meta data Management
• Data Quality Management
• Data Governance is the central “hub” which controls
the various functions
Industry Best-Practices & Guidelines
DATA
ARCHITECTURE
MANAGEMENT
DATA
DEVELOPMENT
DATABASE
OPERATIONS
MANAGEMENT
DATA
SECURITY
MANAGEMENT
REFERENCE &
MASTER DATA
MANAGEMENT
DATA
QUALITY
MANAGEMENT
META DATA
MANAGEMENT
DOCUMENT &
CONTENT
MANAGEMENT
DATA
WAREHOUSE
& BUSINESS
INTELLIGENCE
MANAGEMENT
DATA
GOVERNANCE
6
Global Data Strategy, Ltd. 2016
Building an Enterprise Data Strategy
7
A Successful Data Strategy links Business Goals with Technology Solutions
“Top-Down” alignment with
business priorities
“Bottom-Up” management &
inventory of data sources
Managing the people, process,
policies & culture around data
Coordinating & integrating
disparate data sources
Leveraging & managing data for
strategic advantage
Global Data Strategy, Ltd. 2016
Basic Definitions
8
Business & Data Strategies
A BUSINESS STRATEGY is a medium to long term business plan which
details the aims & objectives of a business and how it means to
achieve them
A DATA STRATEGY is a medium to long term plan for the improvement,
management & exploitation of data across a business, and how it is to
be achieved
Global Data Strategy, Ltd. 2016
Business & Data Strategy – the Interdependency
9
Business Strategy Data Strategy
Sets Requirements for
Informs & Guides
Business Strategy
Global Data Strategy, Ltd. 2016
Getting it Wrong
10
What can cause business and data strategies to become misaligned?
Lack of a clearly
articulated business
strategy
Lack of cross-business / IT
collaboration &
communication
Absence of Business & IT
senior leadership in
strategy formulation &
execution
Complexity and lack of
priority, focus &
deliverable milestones
Business fails to take
ownership of the data
and hence the data
strategy
Poor change control & lag
Lack of skills and
expertise to realize the
strategy
Data strategy is viewed as
a technology roadmap,
led by IT
Global Data Strategy, Ltd. 2016
Getting it Right – the Key Features of an Effective Data Strategy
11
ALIGNED Directly
Connected to
Business Drivers.
ACTIONABLE with
clear activities &
milestones
EVOLUTIONARY to
meet changing
business needs &
new technology
UNIQUE to the
specific organization
Global Data Strategy, Ltd. 2016
Business & IT Roles in Data Strategy
BUSINESS ROLES IT ROLES JOINT ROLES
Establish a clear and communicable
Business Strategy
Understand the Business Strategy & its
potential technology implications and
requirements
Formulate & execute the Data Strategy
Prioritize key data areas and problems
Evaluate, select & procure appropriate data
technologies to support the Data Strategy
Ensure both business & IT senior management
play an active role to ensure its success
Own the data through the establishment
of Data Governance principles & practices
Understand and introduce Data
Management industry best practices and
methodologies
Exercise rapid and effective change control
Estimate, measure & realize potential and
actual benefits delivered by the Data
Strategy
Be aware of technology innovations and
propose how these can be introduced to
enhance the Data Strategy, e.g. Big Data,
Cloud, Internet of Things
Monitor Data Strategy deliverables and instigate
corrective action as required
Effect the business process and behavioral
changes required to implement the Data
Strategy
Proactively propose how new technologies
can improve and enrich the Business Strategy
Ensure constant cross-business & IT dialogue and
collaboration to ensure continued alignment of
Business & Data Strategies
13
Alignment of Business &
Data Strategies
Getting it right
Global Data Strategy, Ltd. 2016
How can we transform the business through data?
Optimization: Becoming a Data-Driven Company
• Making the Business More Efficient
• Better Marketing Campaigns
• Higher quality customer data, 360 view of customer, competitive info, etc.
• Better Products
• Data-Driven product development, Customer usage monitoring, etc.
• Better Customer Support
• Linking customer data with support logs, network outages, etc.
14
Transformative: Becoming a Data Company
• Changing the Business Model via Data – data becomes the product
• Monetization of Information: examples across multiple industries including:
• Telco: location information, usage & search data, etc.
• Retail: Click-stream data, purchasing patterns
• Social Media: social & family connections, purchasing trends &
recommendations, etc.
• Energy: Sensor data, consumer usage patterns, smart metering, etc.
Global Data Strategy, Ltd. 2016
The Motivation Model
• There is benefit in formally documenting the motivations for the project.
• Commonly-agreed upon guidelines for project tasks & deliverables
• Reminder of “why we’re doing this” - Neutral arbitrator for disagreements
• Components of the Motivation Model include:
• Corporate Mission: describes the aims, values and overall plan of an organization.
• e.g. To be provide the most comprehensive, customer-driven online shopping experience in the market
• Corporate Vision: describes the desired future state
• e.g. To transform the way consumers purchase goods through social-media-driven connections.
• External Drivers: What market forces are driving this initiative?
• e.g. Cultural shift to online retail
• Internal Drivers: What internal pressures or initiatives are key for this project?
• e.g. Disparate systems require need for an integrated view of customer
• Project Goals: high level statement of what the plan will achieve.
• e.g. To improve customer satisfaction with over 90% satisfaction rating in 2 years.
• Project Objectives: outcome of projects improving capabilities, process, assets, etc.
• e.g. To link consumer purchase history with social media activity.
15
Common Set of Goals & Guidelines
Global Data Strategy, Ltd. 2016
Sample Business Motivation Model
16
Corporate Mission Corporate Vision
Goals & Objectives
To provide a full service online retail experience
for art supplies and craft products.
To be the respected source of art products worldwide,
creating an online community of art enthusiasts.
Artful Art Supplies ArtfulArt
C
External Drivers
Digital Self-Service
Increasing
Regulation Pressures
Online Community &
Social Media
Customer Demand
for Instant Provision
Internal Drivers
Cost Reduction
Targeted Marketing
360 View of
Customer
Brand Reputation Community Building
Revenue Growth
C
Accountability
• Create a Data Governance
Framework
• Define clear roles &
responsibilities for both
business & IT staff
• Publish a corporate
information policy
• Document data standards
• Train all staff in data
accountability
C
Quality
• Define measures & KPIs for
key data items
• Report & monitor on data
quality improvements
• Develop repeatable
processes for data quality
improvement
• Implement data quality
checks as BAU business
activities
C
Culture
• Ensure that all roles
understand their
contribution to data quality
• Promote business benefits
of better data quality
• Engage in innovative ways
to leverage data for
strategic advantage
• Create data-centric
communities of interest
• Corporate-level Mission & Vision
• May already be created or may
need to create as part of project.
• Project-level, Data-Centric Drivers
• External Drivers are what you’re
facing in the industry
• Internal Drivers reflect internal
corporate initiatives.
• Project-level, Data-Centric Goals
& Objectives
• Clear direction for the project
• Use marketing-style headings
where possible
Global Data Strategy, Ltd. 2016
From Cruise Ship to Life Raft
With a common motivation, disparate skills, personalities and roles become an
asset, not an annoyance
18
Case Studies
What’s worked in the
real world?
Global Data Strategy, Ltd. 2016
The Importance of “Right-Sizing” your Data Strategy
19
The Right Data Strategy Depends on a Number of Factors
• The following case studies illustrate examples from a variety of organizations
• From a large international telco
• To a small professional development organization
• There are many common elements required by any organization
• Alignment with business motivation & drivers
• Core data management fundamentals (data quality, metadata, etc.)
• While some elements are unique based on size & scale of organization
• Global inventory needed for large international firm
• Geographic considerations: legal, cultural, technological, etc.
• Smaller organizations often need to be more tactical in their approach
Global Data Strategy, Ltd. 2016
Consumer Energy Company
• For the consumer energy sector Big Data and Smart Meters are transforming the ways of
doing business and interacting with customers.
• Moving away from traditional data use cases of metering & billing.
• Smart meters allow customers to be in control of their energy usage.
• Control over energy usage with connected systems
• Custom Energy Reports & Usage
• Smart Billing based on usage times
• As energy usage declines, data is becoming the true business asset for this energy company.
• Monetization of non-personal data is a future consideration.
• While the Big Data Opportunity is crucial, equally important are the traditional data sources
• New Data Quality Tools in place for operational and DW data
• Data Governance Program analyzing data in relation to business processes & roles
• Business-critical data elements identified and definitions created
Business Transformation via Data
Global Data Strategy, Ltd. 2016
Data-Driven Business Evolution
21
Data is a key component for new business opportunities
New Business Model
• Consumer-Driven Smart
Metering
• Connected Devices, IoT
• Proactive service monitoring
• Monetization of usage data
Traditional Business Model
• Usage-based billing
• Issue-driven customer service
More Efficient Business Model
• More efficient billing
• Faster customer service
response
• More consumer information
re: energy efficiency, etc.
Databases Big DataData
Quality
Data
Governance
Metadata Management
Global Data Strategy, Ltd. 2016
Defining Key Business Data Elements
Focusing on the data that really matters to the business
Evaluate CommunicateInvoke Act
Identify required
Data Definition(s)
Group related
Definitions
Identify Stakeholders
Socialize with key
stakeholders
Conduct initial
impact assessment
Draft initial Data
Definition
Conduct full impact
assessment
Obtain & review
approvals
Build profiling &
monitoring rules
Update metadata
locations
Notify all
stakeholders
Complete Data
Definition process
Global Data Strategy, Ltd. 2016
Professional Development Organization
• As a leading professional development & certification organization,
customers/members and customer service are critical to the success of the organization.
• The corporate goal was to move to more modern, online processes
• Online, Community-based member services
• Centralized CRM system
• Automated processes
• In order to reach this goal, a Data Governance initiative was implemented to improve data quality
and streamline IT processes
• Both business and IT staff were involved Existing systems and processes had developed in an organic,
ad-hoc manner over the years resulting in:
• Duplicate member records
• Incomplete or incorrect member records
• Wasted time and money from IT resources working to rectify bad data
23
Data Governance & Data Quality critical to member management
Global Data Strategy, Ltd. 2016
DataQuality & Data Governance – the Interdependency
The need for better data quality was a key driver for the data governance program
Retail
What is Data Quality?
Data that is demonstrably fit for
business purposes
Data Governance
Provides the means to
deliver
Data Quality
Drives the need for
What is Data Governance?
A continuous process of managing
and improving data for the benefit
of all stakeholders
Global Data Strategy, Ltd. 2016
Data Governance for Data Quality Improvement
• Processes were put in place for both
• Data Governance
• Data Improvement
• Tools were selected to help automate
manual processes
• Data quality
• Data profiling
• Both business and IT stakeholders
were involved in governance
• Identifying key data elements
• Defining business rules & standards
for data
25
Best Practices defined for Data Governance & Data Improvement
DATA
GOVERNANCE
DATA
IMPROVEMENT
• Data Standards
• Data Capture
• Data Cleansing
• People
• Processes
• Policies
• Communication
Global Data Strategy, Ltd. 2016
Global Telco Company
• An international telco company was looking to leverage data as a corporate asset.
• Data is seen as their most strategic asset and corporate focus
• Telecommunications is a secondary goal – becoming a commodity
• Opportunities in Leveraging Big Data
• New Product & Service Development
• Data is Anonymized & sent to digital arm for new product and development
• Data-driven prototyping – using analytics to see what products are working best and used most
• Customer Value Management
• Marketing with Opt-in, e.g. ads for bolt-on roaming when enter a new country—before they use a competitor
platform
• Sentiment Analysis (via call logs & social media)
• Operational Performance & Maintenance
• Network Optimization
• Integrating call failure information and location information with survey data
• Monetization
• Resell anonymized data to Retail, City Planners, etc.
• Footfall with integrated geospatial location data
Business Transformation to “Becoming a Data Company”
Global Data Strategy, Ltd. 2016
The Motivation Model
• Motivations from both
Business & IT
• Business focused on gaining
value from data
• IT focused on cost-savings &
reuse
• Common Goals &
Objectives
• Use Case Patterns
• Clear definition of Big Data
(& what it’s not)
• Common Service Catalog
• Reusable Technology &
Architecture
Global Data Strategy, Ltd. 2016
Big Data Use Cases
Customer Value
Management
Products and Services
Innovation
Data Monetization
KPI Reporting – Device
Analytics
Troubleshooting
Operational Analytics
Footfall Analytics
Product & Service Usage
Patterns
Network Usage PatternsNetwork Analytics
Storage Monitoring
Data Centre Monitoring
Customer Experience
Optimisation
Customer Insight
Consumer Marketing
Sentiment Analysis
(Social Media)
Family Identification
New
Insights
• New insights & increased value
from data as a corporate asset
• Better alignment between BI and
Infrastructure teams
• Improved education & governance
for Big Data use cases
• Clearer understanding of value
propositions for Big Data and BI
The Results
Global Data Strategy, Ltd. 2016
International Pharmaceutical Company
• An international Pharmaceutical company was looking to make better use of its
data to streamline its Clinical Development, Commercial Processes, and R&D.
• Business alignment was a key first step
• Created “blueprints” of how the business runs—then how data maps to that
• Data models, process models, & mappings
• Identified opportunities for business efficiencies
• Greater understanding how data was used by and critical to key business activities
• Streamlining IT Services was a core parallel activity
• Solution Planning & Definition
• Defining “who we are & what we do” , aka “Marketing”
• Best Practices & Architecture
• New best practices around MDM, data modeling, etc.
• Governance
• Models & architecture required for each new project
29
Business Alignment and IT Strategy
Global Data Strategy, Ltd. 2016
Business View Process View
PTS Discovery
Research
Discover the molecule
Technical
Makethe medicine
Safety
Test the medicine
Medical
Perform clinical trials
Commit to Target
Approval for
First Time in Human
Commit to
Product Development
Commit to
Phase III
Commit to
File and Launch
Commit to Candidate
PRECLINICAL DEVELOPMENT
VOLUNTEER
STUDIES PHASE I
(20+ volunteers)
CLINICAL TRIALS
PHASE II
(50+ volunteers)
CLINICAL TRIALS PHASE III
(1000+ volunteers)
PHASE IIIB
Answering questions on
submissions
Target identification
Target to
Start of Chemistry
Start of Chemistry to
Candidate Selection
Candidate selection to
First Time in Human
First Time in Human to Proof
of Concept (Phase I & IIa)
Proof of Concept to Commit
to Phase III (Phase IIb) Phase III
File ( Approval,
Reimbursement & Launch)
Life Cycle
Management
Product Discovery, Development, Reimbursement, Marketing and Supply
(Version 4, Aug-06)
PHASE IV
Start of Chemistry
Transfer to mature
supply
Product & process
performance
verified, including
QC methodology
and specifications
(control strategy)
Ongoing Change
and Risk
Management
Distribution of
products
Line extension
opportunities
Exploratory disease based
research
Informatics
Intellectual Property acquisition
and Partnership activities
Human genetics
Genomics
Functional analysis
Vaccines/biological targets
Biomarkers for predictive
medicine
Developing target knowledge
Create/identify chemical
libraries
Create robust (screen) assays
High throughput screening
Creation of pharmacological &
animal models
Disease genetics
Information from clinical
genetics
Create physical form -
biological or chemical
extraction/synthesis of
active substance at
laboratory scale (mg of
material)
BP: Target for Biopharm
development accepted
Patent strategy
development
Validation of hits by re-
screening
Basic enzyme/ receptor
potency and selectivity
In vitro/In vivo
metabolism,
pharmacological and
pharmacokinetic
screening of potentially
active compounds
Lead tractability
BP: Humanise MAb/Dab
generation
Make range of lead
compounds screen against
targets
Preliminary safety
pharmacology and
pharmacokinetics
BP: Target validation, Initiate
biomarker and immunoassay
development
Preliminary metabolism
Assay development
Screening toxicity
High throughput screening
genotoxicity
Preliminary
genotoxicity
in vivo toxicology
selection studies
Investigative
toxicogenomics
Acute toxicity – single
administration to two
animal species
Dose range finding
toxicology Formal repeat
dose toxicity studies
(medium duration) in two
species
Detailed safety
pharmacology studies
(CVS, CNS and other
major systems)
Genotoxicity studies
Pharmacokinetics:
absorption, distribution,
metabolism & elimination
(ADME)
BP: Tissue cross reactivity,
repeat dose tox studies,
assay development, PK/PD
studies
Long-term toxicity studies
Reproductive toxicology
studies
- fertility & embryo foetal
Chronic toxicity – repeat
administration (long term)
Continuing animal
pharmacology studies
Oncogenicity studies
Reproductive
toxicology studies
- peri-postnatal
New Indications
Further comparative trials
with competitor products
Trials in support of
Marketing
New line extensions
Initiate long-term
safety/health outcomes
studies
Demonstration of
therapeutic advantage,
e.g. vs. competitors
Healthcare Outcomes
- Quality of Life
- pharmacoeconomics
Develop communications
publication strategy (e.g.
congresses & workshops)
Knowledge transfer to
operating companies
International large-scale multi-centre
trials with different patient populations to
demonstrate proof of safety and efficacy
- initiate health outcomes studies
Expand disease and product knowledge
- Use of predictive medicine agents in
prognosis and diagnosis
- Validation of surrogate markers and
pharmacogenetic tests
Establishment of the therapeutic
profile/product labelling claims:
- indications
- dosage and routes of administration
- contra-indications
- side effects
- precautionary measures
Paediatric development plans
(or waivers)
First controlled trials in patients
to establish proof of concept -
indication of efficacy & clinical
benefit
Confirmation of safety
bioavailability & bioequivalence
of different formulations
Development of clinical genetics
databases
Evaluation of biomarkers for
pharmacogenetics and applied
diagnostics
Evaluation of surrogates
Evaluation of potential product
differentiators
Safety and tolerability in
healthy volunteers
- highest tolerable dose
- smallest effective dose
- mode of action
- dose/effect relationship
- duration of effect
- side effects
- QTc drug interaction study
Pharmacokinetics in man
BP: Early clinical studies
may be in patients rather
than volunteers
Medical development strategy
- differentiation
- evidence based
Definition of clinical options for
progression to proof of concept
Regulatory
Register the medicine
Commercial
Market the medicine
Support theproduct
REGISTRATION WITH GOVERNMENT
LICENSING AUTHORITIES
Preparation for Advisory
meetings
Presentation of data
safety review prior to
clinical evaluation
Select (genetic)
biomarker for
development in
predictive medicine
Evaluation of surrogates for
early clinical trials
Evaluation of potential
biomarkers for use in predictive
medicine
Outline Clinical Development
Plan
PROFILE OPTIMISATION
8-10 year horizon
Identify current and future unmet needs: patients, prescribers, payors,
regulators
Indication assessment and prioritisation
Define target product profile and fit with disease area strategy
Pricing and reimbursement environment analysis and trends
Future competitive landscape
Identify drivers of treatment decisions
Assess future treatment trends, pivotal trials, guideline changes
Prepare early economic model and burden of disease
External Expert identification
Global forecast and scenarios with value drivers
In-licensing
Development of broad
Regulatory strategies Preparation of
dossiers/ summary
documents of all
relevant data and
clinical trials protocol
for application to
licensing authority to
conduct clinical trials
(CTX/IND)
Disease
Opportunity
Assessment
Identifying, defining and
valuing disease areas
5 - 15 year horizon
Opportunity mapping
Define Target Product Profile
Initial profile identification
Integration of research
investment with long-term
commercial goals
Potential for predictive
medicine in disease diagnosis
Regulatory approval
for biomarkers,
surrogates etc
Draft summary of
product characteristics
& labelling
CTX/CTA variations /
IND amendments
Documentation of all relevant data
for submission for registration
(MAA/NDA)
- expert opinion on clinical trials
results
- expert opinion on toxicological
& pharmacological data
- expert opinion on analytical,
pharmaceutical & chemical data
Summary of product
characteristics/labelling
Risk management
assessment
Pharmacogenetics and
applied diagnostics
New markets
Possible
conversion to
OTC
Generics
RETURN MAXIMISATION
1-10 year horizon
Launch plan execution
Local operating companies sales and marketing
implementation
Monitor performance
Review and revise promotional campaigns
PR & issues management
Indication assessment and prioritisation
Line extensions
Enhance label
Support Intellectual property
VALUE PROPOSITION
3-8 year horizon
Draft scientific advantage and differentiation strategy
Draft and evaluate core claims, message elements
Develop market access value proposition
Set pricing and reimbursement targets
Commission primary market research to support decision-making and insight generation
Refine asset profile and label requirements
Engage regions to gain input to payor, prescriber, patient perspectives across regions
Prepare detailed forecasts and assumptions with regions
Define strategic options for clinical development and gain senior management agreement for progression
Develop Scientific Communications, publications, PR strategies
Begin External Expert advisory boards
Draft Health Outcomes plan and begin trials
Obtain and register generic name
Define market shaping strategy
Opportunities to change
medical practice through
use of
pharmacogenetics and
applied diagnostics
End of Phase II /
Scientific Advisory
meetings
- Regulator advise
on Phase III plans
External Clinical trials
reporting register
Advance predictive
medicine
Utilization of
pharmacogenetics
Phase IV commitments
Pharmacovigilence &
post-marketing
Surveillance
New Line Extension
approvals
- new formulations
- new indications
- label changes
Annual safety
reporting to
Regulators
License renewal
processes
Development of diagnostics for personalised medicines
LAUNCH OPTIMISATION
1-3 year horizon
Agree countries, sequence to file and launch
Regions and local operating companies take lead
- Regional message development, including communications plans
Detailed launch planning and promotional strategy development
Campaign development and testing
Sales force and resourcing decisions
Local pricing and reimbursement negotiations
Health outcomes data defined
Form local operating company launch teams
Set distribution strategy
Register trade names
Congresses and External Experts plans implemented
DESIGN FOR MANUFACTURE
Key:
API: Active Pharmaceutical Ingredient
BP: Biopharmaceuticals (i.e. indicating where Biopharm activities (or their timing) differs from small molecule development
Version 8: Oct08
Analytical characterisation of drug substance (Active
Pharmaceutical Ingredient; API)
Small scale synthesis of API (grams) for dose-ranging
toxicology studies
Confirm molecule is developable
Pre-formulation studies to determine formulation
constraints and possibilities
Develop route to provide dose ranging & 28 day toxicology
supplies
Define predictive medicine agent characteristics
BP: 1st Molecular Design to evaluate Developability
Salt/polymorph definition &
scale up
Complete campaign for 28
day toxicology studies &
FTIH clinical studies
Develop analytical methods
for API and release batches
Develop formulation for FTIH
and prepare clinical supplies
Development of analytical
methodology for stability
testing of dosage forms
Synthesis of radiolabelled
compounds for
pharmacokinetic studies
Selection of technology
platforms for biomarkers in
predictive medicine
BP: Cell line and process
development, initiate
production of materials for
toxicology and early clinical trial
API and Drug Product
Design Targets finalised
Preparation of API for mid-
phase toxicology and
clinical studies.
API final synthetic route
and crystallisation
selected
Preparation of drug
product clinical supplies
Stability testing of API and
drug product
Intellectual Property
evaluation and application
Biomarkers assay
development
BP: Finalise & transfer
manufacturing process,
compatibility and
comparability studies;
manufacture materials for
long term toxicity studies
Drug Product Definition for
Commercial Supply & pivotal
Clinical Trials, incl.
formulation, process and
packaging materials
Preparation of API for long
term oncology studies and
Phase III supplies using final
route (or biosynthetic route)
Site of manufacture for
pivotal clinical, regulatory
stability and commercial
product selected
Production of pivotal Clinical
Trials drug product,
including placebo
Manufacturing process
scale-up strategy defined,
incl. waste treatment &
recycling measures
Change and Late-stage Risk
Management commenced
BP: Manufacturing campaign
for Phase III materials using
final process in final site.
Confirm supply chain
Product & Process understanding
Commencement of validation of defined
manufacturing processes for API and
drug product at sites of manufacture
NDA/MAA batch stability confirmed for
API and drug product in final packaging
Manufacture of drug product for
ongoing clinical supplies
Development, validation and finalisation
of QC methodology and specifications
(control strategy)
Initial design development for new
manufacturing plant initiated (if required)
Design and preparation of packaging
materials
Ordering, scheduling and
production of the API and
the final drug product, incl.
packaging and product
literature
Quality control for release
of commercial API and
drug product
Validation for launch of
product & manufacture
process validation batches
Continuing
research into
the disease
Key
Bold Purple text – PTS accountability
Green – Not sure if in PTS
Light Blue – Key interfaces
Q1. Does PTS include biopharm ? No, but
PTS does do work for them. Need to
clarify BP interfaces on wave.
Q2. Don’t IP acquisition & partnership
activities occur across many phases?
N1. Both DD & PTS contribute to the
Candidate Selection document.
Q3. When are patents filed?
PTS Discovery PTS Development PTS Commercial
Slide 7
Business-Driven “Blueprinting”
• Detailed “blueprints” (aka models) outlined several views of the organization.
Data View Mapping Data to Process
• Motivation Model
• Business Capability Models
• Solution Planning
• High-Level Process Models
• Detailed Process Models
• Conceptual Data Models
• Business Glossary
• Logical Data Models
• Physical Data Models
• Process to Data Mapping
• Process to System & Data Mapping
Global Data Strategy, Ltd. 2016
Roles & Culture
• Business Acceptance: Clinical Scientists
had data models on their office walls
• “Blueprints” describing their clinical
development
31
• Architecture team had clear direction
• “Who we are and what we do” clearly articulated
to the business
• Best Practices for data management made
processes more efficient
• Governance driving architecture as a “must-have”
for each new initiative.
Global Data Strategy, Ltd. 2016
Customize your Enterprise Data Strategy
32
Elements are universal, but priorities & focus vary by organization
Global Data Strategy, Ltd. 2016
Summary
• More & more organizations are transforming the way they do business through data
• More efficient ways of doing business
• New business models
• Documenting Business Drivers & Motivations is key to success
• Mission & Vision
• External & Internal Drivers
• Goals & Objectives
• While it’s important to “right size” your data strategy, core components are common to all:
• Aligning business strategy with data strategy
• Data Governance
• Data Management
• Coordinating & integrating disparate data sources
• Management and inventory of source data systems
33
Business Transformation through Data
Global Data Strategy, Ltd. 2016
About Global Data Strategy, Ltd.
• Global Data Strategy is an international information management consulting company specializing in
the alignment of business drivers with data-centric technology.
• Our passion is data, and helping organizations enrich their business opportunities through data and
information.
• Our core values center around providing solutions that are:
• Business-Driven: We put the needs of your business first, before we look at any technological solution.
• Clear & Relevant: We provide clear explanations using real-world examples, not technical jargon.
• Customized & Right-Sized: Our implementations are based on the unique needs of your organization’s
size, corporate culture, and geography.
• High Quality & Technically Precise: We pride ourselves in excellence of execution, and we attract high-
quality professionals with years of technical expertise in the industry.
34
Data-Driven Business Transformation
Business Strategy
Aligned With
Data Strategy
Global Data Strategy, Ltd. 2016
Contact Info
• Email: donna.burbank@globaldatastrategy.com
nigel.turner@globaldatastategy.com
• Twitter: @donnaburbank
@NigelTurner8
• Website: www.globaldatastrategy.com
• Linkedin: https://www.linkedin.com/in/donnaburbank
https://uk.linkedin.com/in/nigelturnerdataman
35

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Becoming a Data-Driven Organization - Aligning Business & Data Strategy

  • 1. Becoming a Data-Driven Organization: Aligning Business & Data Strategy Donna Burbank & Nigel Turner Global Data Strategy Ltd. DATAVERSITY DAMA Webinar, January 19th 2016
  • 2. Global Data Strategy, Ltd. 2016 Donna Burbank Donna is a recognized industry expert in information management with over 20 years of experience in data management, metadata management, and enterprise architecture. Her background is multi-faceted across consulting, product development, product management, brand strategy, marketing, and business leadership. She is currently the Managing Director at Global Data Strategy, Ltd., an international information management consulting company that specializes in the alignment of business drivers with data-centric technology. In past roles, she has served in key brand strategy and product management roles at CA Technologies and Embarcadero Technologies for several of the leading data management products in the market. As an active contributor to the data management community, she is a long time DAMA International member and is the President of the DAMA Rocky Mountain chapter. She was also on the review committee for the Object Management Group’s Information Management Metamodel (IMM) and a member of the OMG’s Finalization Taskforce for the Business Process Modeling Notation (BPMN). She has worked with dozens of Fortune 500 companies worldwide in the Americas, Europe, Asia, and Africa and speaks regularly at industry conferences. She has co-authored two books: Data Modeling for the Business and Data Modeling Made Simple with CA ERwin Data Modeler r8. She can be reached at donna.burbank@globaldatastrategy.com Donna is based in Boulder, Colorado, USA. 2 Follow on Twitter @donnaburbank
  • 3. Global Data Strategy, Ltd. 2016 Nigel Turner Nigel Turner has worked in Information Management (IM) and related areas for over 20 years. This experience has embraced Data Governance, Information Strategy, Data Quality, Data Governance, Master Data Management, & Business Intelligence. He spent much of his career in British Telecommunications Group (BT) where he led a series of enterprise wide IM initiatives which brought huge benefits to BT. He also created and led large IM & CRM consultancy & delivery practices which served BT Global Services’ customers, including Emirates Airlines, the UK Ministry of Defence, Intel, HBOS, Belgacom and others. After leaving BT in 2010 Nigel became VP of Information Management Strategy at Harte Hanks Trillium Software, a leading global provider of Data Quality & Data Governance tools and consultancy. Here he engaged with over 150 customer organisations from all parts of the globe, undertaking extensive engagements with HSBC, Progress Software, British Gas, HBOS, EDF Energy, Severn Trent Water, British Airways, Telefonica O2 and others, Currently Principal Consultant for EMEA at Global Data Strategy, Ltd, he has been a principal consultant at such firms as FromHereOn and IPL, where he has led Data Governance engagement with customers such as First Great Western. Nigel is a well known thought leader in Information Management and has run tutorials and presented at many international conferences. He has also written several white papers and is a frequent blogger on Information Management topics. He has also lectured part time at Cardiff University, where he taught Data Governance modules to both undergraduate and graduate students. In addition he was a part time Associate Lecturer at the UK Open University where he taught Systems & Management. He also authored the Data Quality Strategy module of the Institute of Data Marketing’s Data Management Award. Nigel is very active in professional Data Management organisations and is an elected Data Management Association (DAMA) UK Committee member. He was the joint winner of DAMA International’s 2015 Community Award for the work he initiated and led in setting up a mentoring scheme in the UK where experienced DAMA professionals coach and support newer data management professionals. Nigel is a great advocate for keeping Information Management as simple and business focused as possible, and feels that a key role of Information Management professionals is to help business people relate IM to real business benefits. Much of his consultancy work has been to enable organisations to do this by helping them to build compelling IM business cases, Data Governance processes and structures that complement existing business models, and demonstrating and delivering the benefits of improved Data Quality and data exploitation. 3 Follow on Twitter @NigelTurner8
  • 4. Global Data Strategy, Ltd. 2016 Agenda • Lay out the primary components of a Data Strategy • Define Business and Data Strategies • Show how to align Data Strategy with Business Motivations & Drivers • Highlight reasons why Business & Data strategies can become misaligned • Outline real life case studies of Data Strategies: • Consumer Energy company • Professional Development & Certification organization • Global Telecommunications company • International Pharmaceutical company 4 What we’ll cover today
  • 5. 5 Components of a Global Data Strategy Where do we start?
  • 6. Global Data Strategy, Ltd. 2016 DAMA DMBOK Framework • The DAMA Data Management Body of Knowledge (DMBOK) is a helpful guideline to follow for industry best practices • Modeled after other BOK documents: • PMBOK (Project Management Body of Knowledge) • SWEBOK (Software Engineering Body of Knowledge) • BABOK (Business Analysis Body of Knowledge) • Outlines core data management functions: • Data Architecture Management • Data Development • Database Operations Management • Data Security Management • Reference & Master Data Management • Data Warehouse & Business Intelligence Management • Document & Content Management • Meta data Management • Data Quality Management • Data Governance is the central “hub” which controls the various functions Industry Best-Practices & Guidelines DATA ARCHITECTURE MANAGEMENT DATA DEVELOPMENT DATABASE OPERATIONS MANAGEMENT DATA SECURITY MANAGEMENT REFERENCE & MASTER DATA MANAGEMENT DATA QUALITY MANAGEMENT META DATA MANAGEMENT DOCUMENT & CONTENT MANAGEMENT DATA WAREHOUSE & BUSINESS INTELLIGENCE MANAGEMENT DATA GOVERNANCE 6
  • 7. Global Data Strategy, Ltd. 2016 Building an Enterprise Data Strategy 7 A Successful Data Strategy links Business Goals with Technology Solutions “Top-Down” alignment with business priorities “Bottom-Up” management & inventory of data sources Managing the people, process, policies & culture around data Coordinating & integrating disparate data sources Leveraging & managing data for strategic advantage
  • 8. Global Data Strategy, Ltd. 2016 Basic Definitions 8 Business & Data Strategies A BUSINESS STRATEGY is a medium to long term business plan which details the aims & objectives of a business and how it means to achieve them A DATA STRATEGY is a medium to long term plan for the improvement, management & exploitation of data across a business, and how it is to be achieved
  • 9. Global Data Strategy, Ltd. 2016 Business & Data Strategy – the Interdependency 9 Business Strategy Data Strategy Sets Requirements for Informs & Guides Business Strategy
  • 10. Global Data Strategy, Ltd. 2016 Getting it Wrong 10 What can cause business and data strategies to become misaligned? Lack of a clearly articulated business strategy Lack of cross-business / IT collaboration & communication Absence of Business & IT senior leadership in strategy formulation & execution Complexity and lack of priority, focus & deliverable milestones Business fails to take ownership of the data and hence the data strategy Poor change control & lag Lack of skills and expertise to realize the strategy Data strategy is viewed as a technology roadmap, led by IT
  • 11. Global Data Strategy, Ltd. 2016 Getting it Right – the Key Features of an Effective Data Strategy 11 ALIGNED Directly Connected to Business Drivers. ACTIONABLE with clear activities & milestones EVOLUTIONARY to meet changing business needs & new technology UNIQUE to the specific organization
  • 12. Global Data Strategy, Ltd. 2016 Business & IT Roles in Data Strategy BUSINESS ROLES IT ROLES JOINT ROLES Establish a clear and communicable Business Strategy Understand the Business Strategy & its potential technology implications and requirements Formulate & execute the Data Strategy Prioritize key data areas and problems Evaluate, select & procure appropriate data technologies to support the Data Strategy Ensure both business & IT senior management play an active role to ensure its success Own the data through the establishment of Data Governance principles & practices Understand and introduce Data Management industry best practices and methodologies Exercise rapid and effective change control Estimate, measure & realize potential and actual benefits delivered by the Data Strategy Be aware of technology innovations and propose how these can be introduced to enhance the Data Strategy, e.g. Big Data, Cloud, Internet of Things Monitor Data Strategy deliverables and instigate corrective action as required Effect the business process and behavioral changes required to implement the Data Strategy Proactively propose how new technologies can improve and enrich the Business Strategy Ensure constant cross-business & IT dialogue and collaboration to ensure continued alignment of Business & Data Strategies
  • 13. 13 Alignment of Business & Data Strategies Getting it right
  • 14. Global Data Strategy, Ltd. 2016 How can we transform the business through data? Optimization: Becoming a Data-Driven Company • Making the Business More Efficient • Better Marketing Campaigns • Higher quality customer data, 360 view of customer, competitive info, etc. • Better Products • Data-Driven product development, Customer usage monitoring, etc. • Better Customer Support • Linking customer data with support logs, network outages, etc. 14 Transformative: Becoming a Data Company • Changing the Business Model via Data – data becomes the product • Monetization of Information: examples across multiple industries including: • Telco: location information, usage & search data, etc. • Retail: Click-stream data, purchasing patterns • Social Media: social & family connections, purchasing trends & recommendations, etc. • Energy: Sensor data, consumer usage patterns, smart metering, etc.
  • 15. Global Data Strategy, Ltd. 2016 The Motivation Model • There is benefit in formally documenting the motivations for the project. • Commonly-agreed upon guidelines for project tasks & deliverables • Reminder of “why we’re doing this” - Neutral arbitrator for disagreements • Components of the Motivation Model include: • Corporate Mission: describes the aims, values and overall plan of an organization. • e.g. To be provide the most comprehensive, customer-driven online shopping experience in the market • Corporate Vision: describes the desired future state • e.g. To transform the way consumers purchase goods through social-media-driven connections. • External Drivers: What market forces are driving this initiative? • e.g. Cultural shift to online retail • Internal Drivers: What internal pressures or initiatives are key for this project? • e.g. Disparate systems require need for an integrated view of customer • Project Goals: high level statement of what the plan will achieve. • e.g. To improve customer satisfaction with over 90% satisfaction rating in 2 years. • Project Objectives: outcome of projects improving capabilities, process, assets, etc. • e.g. To link consumer purchase history with social media activity. 15 Common Set of Goals & Guidelines
  • 16. Global Data Strategy, Ltd. 2016 Sample Business Motivation Model 16 Corporate Mission Corporate Vision Goals & Objectives To provide a full service online retail experience for art supplies and craft products. To be the respected source of art products worldwide, creating an online community of art enthusiasts. Artful Art Supplies ArtfulArt C External Drivers Digital Self-Service Increasing Regulation Pressures Online Community & Social Media Customer Demand for Instant Provision Internal Drivers Cost Reduction Targeted Marketing 360 View of Customer Brand Reputation Community Building Revenue Growth C Accountability • Create a Data Governance Framework • Define clear roles & responsibilities for both business & IT staff • Publish a corporate information policy • Document data standards • Train all staff in data accountability C Quality • Define measures & KPIs for key data items • Report & monitor on data quality improvements • Develop repeatable processes for data quality improvement • Implement data quality checks as BAU business activities C Culture • Ensure that all roles understand their contribution to data quality • Promote business benefits of better data quality • Engage in innovative ways to leverage data for strategic advantage • Create data-centric communities of interest • Corporate-level Mission & Vision • May already be created or may need to create as part of project. • Project-level, Data-Centric Drivers • External Drivers are what you’re facing in the industry • Internal Drivers reflect internal corporate initiatives. • Project-level, Data-Centric Goals & Objectives • Clear direction for the project • Use marketing-style headings where possible
  • 17. Global Data Strategy, Ltd. 2016 From Cruise Ship to Life Raft With a common motivation, disparate skills, personalities and roles become an asset, not an annoyance
  • 18. 18 Case Studies What’s worked in the real world?
  • 19. Global Data Strategy, Ltd. 2016 The Importance of “Right-Sizing” your Data Strategy 19 The Right Data Strategy Depends on a Number of Factors • The following case studies illustrate examples from a variety of organizations • From a large international telco • To a small professional development organization • There are many common elements required by any organization • Alignment with business motivation & drivers • Core data management fundamentals (data quality, metadata, etc.) • While some elements are unique based on size & scale of organization • Global inventory needed for large international firm • Geographic considerations: legal, cultural, technological, etc. • Smaller organizations often need to be more tactical in their approach
  • 20. Global Data Strategy, Ltd. 2016 Consumer Energy Company • For the consumer energy sector Big Data and Smart Meters are transforming the ways of doing business and interacting with customers. • Moving away from traditional data use cases of metering & billing. • Smart meters allow customers to be in control of their energy usage. • Control over energy usage with connected systems • Custom Energy Reports & Usage • Smart Billing based on usage times • As energy usage declines, data is becoming the true business asset for this energy company. • Monetization of non-personal data is a future consideration. • While the Big Data Opportunity is crucial, equally important are the traditional data sources • New Data Quality Tools in place for operational and DW data • Data Governance Program analyzing data in relation to business processes & roles • Business-critical data elements identified and definitions created Business Transformation via Data
  • 21. Global Data Strategy, Ltd. 2016 Data-Driven Business Evolution 21 Data is a key component for new business opportunities New Business Model • Consumer-Driven Smart Metering • Connected Devices, IoT • Proactive service monitoring • Monetization of usage data Traditional Business Model • Usage-based billing • Issue-driven customer service More Efficient Business Model • More efficient billing • Faster customer service response • More consumer information re: energy efficiency, etc. Databases Big DataData Quality Data Governance Metadata Management
  • 22. Global Data Strategy, Ltd. 2016 Defining Key Business Data Elements Focusing on the data that really matters to the business Evaluate CommunicateInvoke Act Identify required Data Definition(s) Group related Definitions Identify Stakeholders Socialize with key stakeholders Conduct initial impact assessment Draft initial Data Definition Conduct full impact assessment Obtain & review approvals Build profiling & monitoring rules Update metadata locations Notify all stakeholders Complete Data Definition process
  • 23. Global Data Strategy, Ltd. 2016 Professional Development Organization • As a leading professional development & certification organization, customers/members and customer service are critical to the success of the organization. • The corporate goal was to move to more modern, online processes • Online, Community-based member services • Centralized CRM system • Automated processes • In order to reach this goal, a Data Governance initiative was implemented to improve data quality and streamline IT processes • Both business and IT staff were involved Existing systems and processes had developed in an organic, ad-hoc manner over the years resulting in: • Duplicate member records • Incomplete or incorrect member records • Wasted time and money from IT resources working to rectify bad data 23 Data Governance & Data Quality critical to member management
  • 24. Global Data Strategy, Ltd. 2016 DataQuality & Data Governance – the Interdependency The need for better data quality was a key driver for the data governance program Retail What is Data Quality? Data that is demonstrably fit for business purposes Data Governance Provides the means to deliver Data Quality Drives the need for What is Data Governance? A continuous process of managing and improving data for the benefit of all stakeholders
  • 25. Global Data Strategy, Ltd. 2016 Data Governance for Data Quality Improvement • Processes were put in place for both • Data Governance • Data Improvement • Tools were selected to help automate manual processes • Data quality • Data profiling • Both business and IT stakeholders were involved in governance • Identifying key data elements • Defining business rules & standards for data 25 Best Practices defined for Data Governance & Data Improvement DATA GOVERNANCE DATA IMPROVEMENT • Data Standards • Data Capture • Data Cleansing • People • Processes • Policies • Communication
  • 26. Global Data Strategy, Ltd. 2016 Global Telco Company • An international telco company was looking to leverage data as a corporate asset. • Data is seen as their most strategic asset and corporate focus • Telecommunications is a secondary goal – becoming a commodity • Opportunities in Leveraging Big Data • New Product & Service Development • Data is Anonymized & sent to digital arm for new product and development • Data-driven prototyping – using analytics to see what products are working best and used most • Customer Value Management • Marketing with Opt-in, e.g. ads for bolt-on roaming when enter a new country—before they use a competitor platform • Sentiment Analysis (via call logs & social media) • Operational Performance & Maintenance • Network Optimization • Integrating call failure information and location information with survey data • Monetization • Resell anonymized data to Retail, City Planners, etc. • Footfall with integrated geospatial location data Business Transformation to “Becoming a Data Company”
  • 27. Global Data Strategy, Ltd. 2016 The Motivation Model • Motivations from both Business & IT • Business focused on gaining value from data • IT focused on cost-savings & reuse • Common Goals & Objectives • Use Case Patterns • Clear definition of Big Data (& what it’s not) • Common Service Catalog • Reusable Technology & Architecture
  • 28. Global Data Strategy, Ltd. 2016 Big Data Use Cases Customer Value Management Products and Services Innovation Data Monetization KPI Reporting – Device Analytics Troubleshooting Operational Analytics Footfall Analytics Product & Service Usage Patterns Network Usage PatternsNetwork Analytics Storage Monitoring Data Centre Monitoring Customer Experience Optimisation Customer Insight Consumer Marketing Sentiment Analysis (Social Media) Family Identification New Insights • New insights & increased value from data as a corporate asset • Better alignment between BI and Infrastructure teams • Improved education & governance for Big Data use cases • Clearer understanding of value propositions for Big Data and BI The Results
  • 29. Global Data Strategy, Ltd. 2016 International Pharmaceutical Company • An international Pharmaceutical company was looking to make better use of its data to streamline its Clinical Development, Commercial Processes, and R&D. • Business alignment was a key first step • Created “blueprints” of how the business runs—then how data maps to that • Data models, process models, & mappings • Identified opportunities for business efficiencies • Greater understanding how data was used by and critical to key business activities • Streamlining IT Services was a core parallel activity • Solution Planning & Definition • Defining “who we are & what we do” , aka “Marketing” • Best Practices & Architecture • New best practices around MDM, data modeling, etc. • Governance • Models & architecture required for each new project 29 Business Alignment and IT Strategy
  • 30. Global Data Strategy, Ltd. 2016 Business View Process View PTS Discovery Research Discover the molecule Technical Makethe medicine Safety Test the medicine Medical Perform clinical trials Commit to Target Approval for First Time in Human Commit to Product Development Commit to Phase III Commit to File and Launch Commit to Candidate PRECLINICAL DEVELOPMENT VOLUNTEER STUDIES PHASE I (20+ volunteers) CLINICAL TRIALS PHASE II (50+ volunteers) CLINICAL TRIALS PHASE III (1000+ volunteers) PHASE IIIB Answering questions on submissions Target identification Target to Start of Chemistry Start of Chemistry to Candidate Selection Candidate selection to First Time in Human First Time in Human to Proof of Concept (Phase I & IIa) Proof of Concept to Commit to Phase III (Phase IIb) Phase III File ( Approval, Reimbursement & Launch) Life Cycle Management Product Discovery, Development, Reimbursement, Marketing and Supply (Version 4, Aug-06) PHASE IV Start of Chemistry Transfer to mature supply Product & process performance verified, including QC methodology and specifications (control strategy) Ongoing Change and Risk Management Distribution of products Line extension opportunities Exploratory disease based research Informatics Intellectual Property acquisition and Partnership activities Human genetics Genomics Functional analysis Vaccines/biological targets Biomarkers for predictive medicine Developing target knowledge Create/identify chemical libraries Create robust (screen) assays High throughput screening Creation of pharmacological & animal models Disease genetics Information from clinical genetics Create physical form - biological or chemical extraction/synthesis of active substance at laboratory scale (mg of material) BP: Target for Biopharm development accepted Patent strategy development Validation of hits by re- screening Basic enzyme/ receptor potency and selectivity In vitro/In vivo metabolism, pharmacological and pharmacokinetic screening of potentially active compounds Lead tractability BP: Humanise MAb/Dab generation Make range of lead compounds screen against targets Preliminary safety pharmacology and pharmacokinetics BP: Target validation, Initiate biomarker and immunoassay development Preliminary metabolism Assay development Screening toxicity High throughput screening genotoxicity Preliminary genotoxicity in vivo toxicology selection studies Investigative toxicogenomics Acute toxicity – single administration to two animal species Dose range finding toxicology Formal repeat dose toxicity studies (medium duration) in two species Detailed safety pharmacology studies (CVS, CNS and other major systems) Genotoxicity studies Pharmacokinetics: absorption, distribution, metabolism & elimination (ADME) BP: Tissue cross reactivity, repeat dose tox studies, assay development, PK/PD studies Long-term toxicity studies Reproductive toxicology studies - fertility & embryo foetal Chronic toxicity – repeat administration (long term) Continuing animal pharmacology studies Oncogenicity studies Reproductive toxicology studies - peri-postnatal New Indications Further comparative trials with competitor products Trials in support of Marketing New line extensions Initiate long-term safety/health outcomes studies Demonstration of therapeutic advantage, e.g. vs. competitors Healthcare Outcomes - Quality of Life - pharmacoeconomics Develop communications publication strategy (e.g. congresses & workshops) Knowledge transfer to operating companies International large-scale multi-centre trials with different patient populations to demonstrate proof of safety and efficacy - initiate health outcomes studies Expand disease and product knowledge - Use of predictive medicine agents in prognosis and diagnosis - Validation of surrogate markers and pharmacogenetic tests Establishment of the therapeutic profile/product labelling claims: - indications - dosage and routes of administration - contra-indications - side effects - precautionary measures Paediatric development plans (or waivers) First controlled trials in patients to establish proof of concept - indication of efficacy & clinical benefit Confirmation of safety bioavailability & bioequivalence of different formulations Development of clinical genetics databases Evaluation of biomarkers for pharmacogenetics and applied diagnostics Evaluation of surrogates Evaluation of potential product differentiators Safety and tolerability in healthy volunteers - highest tolerable dose - smallest effective dose - mode of action - dose/effect relationship - duration of effect - side effects - QTc drug interaction study Pharmacokinetics in man BP: Early clinical studies may be in patients rather than volunteers Medical development strategy - differentiation - evidence based Definition of clinical options for progression to proof of concept Regulatory Register the medicine Commercial Market the medicine Support theproduct REGISTRATION WITH GOVERNMENT LICENSING AUTHORITIES Preparation for Advisory meetings Presentation of data safety review prior to clinical evaluation Select (genetic) biomarker for development in predictive medicine Evaluation of surrogates for early clinical trials Evaluation of potential biomarkers for use in predictive medicine Outline Clinical Development Plan PROFILE OPTIMISATION 8-10 year horizon Identify current and future unmet needs: patients, prescribers, payors, regulators Indication assessment and prioritisation Define target product profile and fit with disease area strategy Pricing and reimbursement environment analysis and trends Future competitive landscape Identify drivers of treatment decisions Assess future treatment trends, pivotal trials, guideline changes Prepare early economic model and burden of disease External Expert identification Global forecast and scenarios with value drivers In-licensing Development of broad Regulatory strategies Preparation of dossiers/ summary documents of all relevant data and clinical trials protocol for application to licensing authority to conduct clinical trials (CTX/IND) Disease Opportunity Assessment Identifying, defining and valuing disease areas 5 - 15 year horizon Opportunity mapping Define Target Product Profile Initial profile identification Integration of research investment with long-term commercial goals Potential for predictive medicine in disease diagnosis Regulatory approval for biomarkers, surrogates etc Draft summary of product characteristics & labelling CTX/CTA variations / IND amendments Documentation of all relevant data for submission for registration (MAA/NDA) - expert opinion on clinical trials results - expert opinion on toxicological & pharmacological data - expert opinion on analytical, pharmaceutical & chemical data Summary of product characteristics/labelling Risk management assessment Pharmacogenetics and applied diagnostics New markets Possible conversion to OTC Generics RETURN MAXIMISATION 1-10 year horizon Launch plan execution Local operating companies sales and marketing implementation Monitor performance Review and revise promotional campaigns PR & issues management Indication assessment and prioritisation Line extensions Enhance label Support Intellectual property VALUE PROPOSITION 3-8 year horizon Draft scientific advantage and differentiation strategy Draft and evaluate core claims, message elements Develop market access value proposition Set pricing and reimbursement targets Commission primary market research to support decision-making and insight generation Refine asset profile and label requirements Engage regions to gain input to payor, prescriber, patient perspectives across regions Prepare detailed forecasts and assumptions with regions Define strategic options for clinical development and gain senior management agreement for progression Develop Scientific Communications, publications, PR strategies Begin External Expert advisory boards Draft Health Outcomes plan and begin trials Obtain and register generic name Define market shaping strategy Opportunities to change medical practice through use of pharmacogenetics and applied diagnostics End of Phase II / Scientific Advisory meetings - Regulator advise on Phase III plans External Clinical trials reporting register Advance predictive medicine Utilization of pharmacogenetics Phase IV commitments Pharmacovigilence & post-marketing Surveillance New Line Extension approvals - new formulations - new indications - label changes Annual safety reporting to Regulators License renewal processes Development of diagnostics for personalised medicines LAUNCH OPTIMISATION 1-3 year horizon Agree countries, sequence to file and launch Regions and local operating companies take lead - Regional message development, including communications plans Detailed launch planning and promotional strategy development Campaign development and testing Sales force and resourcing decisions Local pricing and reimbursement negotiations Health outcomes data defined Form local operating company launch teams Set distribution strategy Register trade names Congresses and External Experts plans implemented DESIGN FOR MANUFACTURE Key: API: Active Pharmaceutical Ingredient BP: Biopharmaceuticals (i.e. indicating where Biopharm activities (or their timing) differs from small molecule development Version 8: Oct08 Analytical characterisation of drug substance (Active Pharmaceutical Ingredient; API) Small scale synthesis of API (grams) for dose-ranging toxicology studies Confirm molecule is developable Pre-formulation studies to determine formulation constraints and possibilities Develop route to provide dose ranging & 28 day toxicology supplies Define predictive medicine agent characteristics BP: 1st Molecular Design to evaluate Developability Salt/polymorph definition & scale up Complete campaign for 28 day toxicology studies & FTIH clinical studies Develop analytical methods for API and release batches Develop formulation for FTIH and prepare clinical supplies Development of analytical methodology for stability testing of dosage forms Synthesis of radiolabelled compounds for pharmacokinetic studies Selection of technology platforms for biomarkers in predictive medicine BP: Cell line and process development, initiate production of materials for toxicology and early clinical trial API and Drug Product Design Targets finalised Preparation of API for mid- phase toxicology and clinical studies. API final synthetic route and crystallisation selected Preparation of drug product clinical supplies Stability testing of API and drug product Intellectual Property evaluation and application Biomarkers assay development BP: Finalise & transfer manufacturing process, compatibility and comparability studies; manufacture materials for long term toxicity studies Drug Product Definition for Commercial Supply & pivotal Clinical Trials, incl. formulation, process and packaging materials Preparation of API for long term oncology studies and Phase III supplies using final route (or biosynthetic route) Site of manufacture for pivotal clinical, regulatory stability and commercial product selected Production of pivotal Clinical Trials drug product, including placebo Manufacturing process scale-up strategy defined, incl. waste treatment & recycling measures Change and Late-stage Risk Management commenced BP: Manufacturing campaign for Phase III materials using final process in final site. Confirm supply chain Product & Process understanding Commencement of validation of defined manufacturing processes for API and drug product at sites of manufacture NDA/MAA batch stability confirmed for API and drug product in final packaging Manufacture of drug product for ongoing clinical supplies Development, validation and finalisation of QC methodology and specifications (control strategy) Initial design development for new manufacturing plant initiated (if required) Design and preparation of packaging materials Ordering, scheduling and production of the API and the final drug product, incl. packaging and product literature Quality control for release of commercial API and drug product Validation for launch of product & manufacture process validation batches Continuing research into the disease Key Bold Purple text – PTS accountability Green – Not sure if in PTS Light Blue – Key interfaces Q1. Does PTS include biopharm ? No, but PTS does do work for them. Need to clarify BP interfaces on wave. Q2. Don’t IP acquisition & partnership activities occur across many phases? N1. Both DD & PTS contribute to the Candidate Selection document. Q3. When are patents filed? PTS Discovery PTS Development PTS Commercial Slide 7 Business-Driven “Blueprinting” • Detailed “blueprints” (aka models) outlined several views of the organization. Data View Mapping Data to Process • Motivation Model • Business Capability Models • Solution Planning • High-Level Process Models • Detailed Process Models • Conceptual Data Models • Business Glossary • Logical Data Models • Physical Data Models • Process to Data Mapping • Process to System & Data Mapping
  • 31. Global Data Strategy, Ltd. 2016 Roles & Culture • Business Acceptance: Clinical Scientists had data models on their office walls • “Blueprints” describing their clinical development 31 • Architecture team had clear direction • “Who we are and what we do” clearly articulated to the business • Best Practices for data management made processes more efficient • Governance driving architecture as a “must-have” for each new initiative.
  • 32. Global Data Strategy, Ltd. 2016 Customize your Enterprise Data Strategy 32 Elements are universal, but priorities & focus vary by organization
  • 33. Global Data Strategy, Ltd. 2016 Summary • More & more organizations are transforming the way they do business through data • More efficient ways of doing business • New business models • Documenting Business Drivers & Motivations is key to success • Mission & Vision • External & Internal Drivers • Goals & Objectives • While it’s important to “right size” your data strategy, core components are common to all: • Aligning business strategy with data strategy • Data Governance • Data Management • Coordinating & integrating disparate data sources • Management and inventory of source data systems 33 Business Transformation through Data
  • 34. Global Data Strategy, Ltd. 2016 About Global Data Strategy, Ltd. • Global Data Strategy is an international information management consulting company specializing in the alignment of business drivers with data-centric technology. • Our passion is data, and helping organizations enrich their business opportunities through data and information. • Our core values center around providing solutions that are: • Business-Driven: We put the needs of your business first, before we look at any technological solution. • Clear & Relevant: We provide clear explanations using real-world examples, not technical jargon. • Customized & Right-Sized: Our implementations are based on the unique needs of your organization’s size, corporate culture, and geography. • High Quality & Technically Precise: We pride ourselves in excellence of execution, and we attract high- quality professionals with years of technical expertise in the industry. 34 Data-Driven Business Transformation Business Strategy Aligned With Data Strategy
  • 35. Global Data Strategy, Ltd. 2016 Contact Info • Email: donna.burbank@globaldatastrategy.com nigel.turner@globaldatastategy.com • Twitter: @donnaburbank @NigelTurner8 • Website: www.globaldatastrategy.com • Linkedin: https://www.linkedin.com/in/donnaburbank https://uk.linkedin.com/in/nigelturnerdataman 35