China Wine Company is developing a big data strategy to capitalize on industry trends and opportunities in the Chinese wine market. They have identified three key steps: 1) defining objectives aligned with business goals; 2) assessing current analytics capabilities; and 3) implementing a big data roadmap. The document outlines specific objectives such as customer acquisition and segmentation. It also provides a framework to evaluate current data, team expertise, and infrastructure maturity. Finally, it proposes a multi-year roadmap to evolve capabilities from descriptive to predictive and prescriptive analytics, along with estimated timelines and costs. The overall strategy aims to leverage big data to drive growth in the dynamic Chinese wine industry.
4. Wine industry trends
• Wine buyers are now wine drinkers. The government campaign against gifts and extravagant spending started to take hold
in China in 2013 and consumption shifted from gifting to self
• Shifting trend towards white wine - perceived health benefits are driving customers to white wine, specially the health
conscious younger female customers
• Younger and diversifying wine drinking population, 48% customers are new into wine drinking. With increasing affordability
and greater product information, new consumers are moving from beer to wine
• While taste of wine is the universal deciding factor for purchase, preferences differ by regions within china
• Continuous shift to online purchase with a customer base who enjoys wine in a more genuine, everyday setting
Key Facts & Figures
• Total Wine sales in China grew by 5% in 2016 reaching 441 Million RMB projected to 606 Million RMB by 2021
• Red wine contributes highest to overall wine sales with 51% share (CAGR 4.5%); while white wine is second highest with
18% share (CAGR 5.3%)
• Customers have higher willingness to pay for white wine. Red wine sells 64% in higher price segment (>90 RMB), while
white sells 89% in >90 RMB category
Current Trends
Source: Euromonitor international: Passport – Wine in China, May 2017
4
6. Three Key Steps In Big Data Transformation For Wine Industry
Include
Define What You Want
To Achieve ?A
Implement Steps To
Achieve TransformationC
Understand Where You
Currently StandB
Overall Business Objectives
Define the target areas and opportunities that align with the
overall Business objectives and long term company strategy
Analytics Layer
Hire and develop resources capable of linking business problems
with analytical solutions
Technology Layer
Equip and upgrade technologies that work through the analytics
supply chain for data capture, storage, processing & distribution
Current Capability
Understand current analytical maturity of the organization in
terms of its readiness for change and digitization
Business Layer
Convert Analytical outputs to business solutions and enable on
ground implementation, with a feedback loop
6
8. Big Data Objectives Should Closely Sync With Overall Business
Strategy Of The Firm
Identifying key focus area is critical to success of the engagement
Customer
Organizations focusing on
customer acquisition and
loyalty development
Eg. VR Games
Operations
Companies predominantly
competing on price by
reducing cost
Eg. Airlines
Product
Industries focused on
product innovation and
category expansion
Eg. Technology
Marketing
Organizations focused on
creating product awareness
and driving market through
promotions
Eg. FMCG
Objectives Capability Enablement
8
9. Sales Data
Marketing
Customer
Ecommerce
Social Media
Wine Events
Wine Forums
Apps
Retailers
Restaurants
Wine Clubs
Having The Customer At The Center Of The Business Steering
Shall Enable A Smooth Journey For A Wine Company
Objectives Capability Enablement
9
Customer
Marketing
ProductChannel
Price
10. Objectives Should Be Further Scoped In Terms Of Key Business
Questions That Need To Answered
Strategic Focus ** Key Questions / Solutions Business Priority
Customer Acquisition
Customer Segmentation
Customer Loyalty
Lifetime Value
Churn Prediction
How to increase customer acquisition in the most efficient way?
How do your customers differ in their tastes and preferences?
Who are my most loyal customers and how profitable are they?
How much value do customers bring to me over their lifetime?
Which customers are likely to churn from my brand? How to stop them?
High
High
Medium
Low
Low
Pricing Analytics
Label Analytics
Bottle Shape Analytics
Ingredient Mix Analytics
What is the optimal price for my product? How elastic is it?
What kind of labels are most effective for sales?
How does shape of the wine bottle impact the sales?
What kind of ingredient mix result in best quality?
Low
Low
High
Medium
Next Best Product
Media Mix Analytics
Market Segmentation
Campaign Effectiveness
What product should be recommended next to a customer for max. value?
What differentiates markets across China?
What is the right mix of marketing media required for optimal conversion?
How effective was the campaigns run for different product promotions
Medium
High
High
Medium
Demand Forecasting
Supply Chain Optimization
Vineyard Productivity
Inventory Management
Can I accurately predict my demand at an actionable level?
How can I improve my supply chain efficiency?
Which are the high productivity vineyards and how well do they yield?
How can I manage inventory better to reduce operational costs?
Medium
Medium
High
Low
Objectives Capability Enablement
** Not Exhaustive
10
11. In An Early Growth Stage Industry, Customer Acquisition Is Key
To Success Of The Brand
Objectives Capability Enablement
Customer Visits Wine Page
online but does not make a
purchase
Customer Shops for seafood
& citrus fruits but does not
buy white wine
Customer is offered an invite
to ABC restaurant for wine
tasting event
The customer is offered XYZ
wine which matches his taste
preference at the event
Makes a purchase online
or offline
Estimate the right product
to be offered to the
customer
Evaluate marketing ROI and
campaign effectiveness
Identify potential customers
by analyzing online
purchase behavior
Select the customer most
likely to convert, select the
right location, select the
right event to promote
Understand general
products that are purchased
/ preferred with white win
Analytics Backend Enablement
Customer Purchase Journey
11
13. Understand You Current Capability In 6 Dimensions
Objectives Capability Enablement
Data Capture & Quality
1. Poor Quality
2. In Silos
3. Key Data Present
4. Centralized Data
5. Diversity Volume & Speed
Data
Team & Expertise
1. No dedicated Resource
2. Limited Manpower
3. Distributed Team
4. Multidisciplinary
5. Experienced & Empowered
People Resource
Commitment & Buy-in
1. Poor Awareness
2. Local Leadership
3. Senior Leadership
4. Strategic Planning
5. Competitive
Organization
Depth & Breadth
1. Initiation
2. Adaptation
3. Specific Activity
4. Improvisation
5. Competing on Analytics
Scope
Policy & Administration
1. No Governance
2. Policies
3. Structure
4. Compliance
5. Security & Privacy
Governance
Data Processing
1. No Central System
2. Unconnected
3. Integrated
4. Enterprise
5. Fully connected
Infrastructure
4
2
3
1
4
3
Wine
Company
13
14. Understand you current capability – Explain different stages of
maturity
Objectives Capability Enablement
Stage 1 Stage 2 Stage 3 Stage 4 Stage 5
Increasing Maturity
Analytically
Impaired
Localized
Analytics
Analytical
Aspirations
Analytical
Companies
Analytical
Competitors
Not Data Driven
Use some reporting
and measurements
Leveraging basic
analytical decisioning
Critical at identifying
and utilizing
analytics across
functions
Analytics stands
as a core part of
company strategy
14
16. Technology Plays A Critical Role In Setting Up The Base For Big
Data Enablement
Technology
Roadmap
Data Capture
ü Capture data across all
essential areas
ü Sales, Marketing, CRM
ü Procure external data
ü Integrate 3rd party sources
Data Storage
ü Identify the right technology
for data storage
ü Small data vs. big data
ü Real time transactional
storage
ü Update frequency
Distribution
ü Enabling business processes
and business applications
ü Integrated system to distribute
processed outputs
ü Digital enablement
ü Security and privacy
Data Process
ü Centralized system linking all
data sources and storage
ü High computing power wrt.
Data volume
ü Localized and global functioning
Objectives Capability Enablement
16
17. Multiple Technologies Work In Sync to Generate Value Added
Solutions
Non-operational dataOperational data
Data
flow
Partner
interface
External
Information
& Data Exchange
Integration
Layer
Data Storage
Data
processing
Workload management
Transform, Metadata, Security
Machine learning Stream processing Search engine Transaction
Distributed (analytic cluster)
Data Integration (leverage existing ETL technology or build big-data native ETL)
Distributed (production cluster)
Batch processing
BigDataPlatform
API/Web Service Self service Pub/Sub
DataManagement
SystemManagement
CDM layer
Consolidated BPM solution
Preferences
Production Environment
Workflow mgt
Customer interface
Decision engines Offers
Application, Card mgmt, etc.
Risk
ESBData transport
Analytic Environment
Analytics tools
Data Marts and direct data access
Data access
& compilation
Data
analysis
Devices & Network
DataConsolidationEnvironment
Objectives Capability Enablement
17
18. Analytics Is Defined By Business, Mathematics And Technology
Working Together
Identifying relevant business
Problems
Collecting and processing
relevant data & Variables
Identifying the right
analytical technique
Generating relevant insights
and validating with business
Supply Chain for Solving an Analytics Problem
ü Identify problems across business functions that
can be solved using analytics
ü Translate problems to analytical questions
ü Realize the data required for analysis
ü Use internal and external data sources to collate
required variables
ü Process and clean data to have a single view of
the central entity
ü Identify relevant data & analytical models to be
applied (ML, Deep Learning etc.)
ü Understand the most feasible platform to
perform the analysis
ü Chart the road map for solution validation
ü Synchronize with business teams on approach,
data and solution feasibility
ü Constantly engrave business feedback to
solutions
Objectives Capability Enablement
Analytics
Business
Math Technology
18
19. Business Layer Translates Analytics Insights to Actions
Objectives Capability Enablement
Business Validation Digital Integration Implementation Feedback
Key Tasks:
• Review analytical variables
for relevancy with current
business scenario
• Provide update on
irrelevant and outdated
practices
• Define the granularity level
of analysis suited for
business consumption
Output:
Close sync between business
and analytics facilitating better
consumption
Owners:
Analytical Business Partners
Business Leads
Key Tasks:
• Decide on the most
feasible platform for
implementation (Mobile/
cloud/excel)
• Get required support from
technology team to build
frontend
• Ensure analytics sanity in
implied solutions
Output:
Transformation of analytics
outputs to digital platform for
easy consumption
Owners:
Business Leads
Technology Leads
Key Tasks:
• Lay out implementation
plan for solutions
• Organize training as might
be needed for business
owners
• Ensure adherence and
compliance on analytical
recommendation
Output:
Timely and accurate
implementation to ensure best
business outcomes
Owners:
Business Leads
Key Tasks:
• Calculate ROI impact on
implemented solution
• Get real time feedback
from the business owners
and translate it to
analytical modifications
• Sync between business and
analytics team to resolve
potential issues
Output:
Subsequent improvement in
analytical solutions to be more
specific and usable for business
Owners:
Analytical Business Partners
Business Leads
19
20. BAT Layers Need To Actively Communicate Through A Well
Defined Organization Structure
Business Owners
Analytics Leads
Technology Heads
Objectives Capability Enablement
20
24. Long And Short Term Goals Are Realized By Accurate Time And
Cost Estimation
Task 6 Months Approx. Cost RMB*
Defining Long/Short term Objectives
Setting Scope of Big Data Team
110,000 (2P x 7Hr)
81,000 (2P x 5Hr)
Selecting BAT Leads
Business Enablement
Analytics Enablement
Technology Enablement
Subject to capability
assessment
Defining Assessment Scope
IT & Infrastructure Check
People and Management Check
Policy & Scope Check
45,000 (3Pr x 3Hr)
400,000 (2A x 80Hr)
500,000 (2A x 100Hr)
450,000 (2A x 90Hr)
Descriptive: Reporting
Inquisitive: Problem Areas
Predictive: Opportunity Areas
Optimization
By Project
*Assuming Hourly cost as below:
Partner (P): RMB 8100 ($1300)
Principal (Pr): RMB 5000 ($800)
Associates (A): RMB 2500 ($400)
Analyst (An): RMB 1250 ($200)
24
12 Months 18 Months 24 Months 30 Months 36 MonthsStage
Scope &
Objectives
Enablement
Capability
Assessment
Roadmap
27. Understand you current capability – Explain different stages of
maturity
Objectives Capability Enablement
Area Stage 1
Analytically Impaired
Stage 2
Localized Analytics
Stage 3
Analytical Aspirations
Stage 4
Analytical Companies
Stage 5
Analytical Competitors
Data
Inconsistent, poor quality and
organization; difficult to do
substantial analysis; no groups
with strong data orientation.
Much data useable, but in
functional or process silos
Identified key data domains and
data repositories across
functions or processes
Integrated, accurate, common
data in central warehouse; data
still mainly an IT matter; little
unique data.
Relentless search for new data and
metrics; organization separate from IT
oversees information; data viewed as
strategic asset.
Infrastructure
Poorly integrated systems which
lack enterprise perspective on
data
Islands of data, technology, and
expertise deliver local value.
Process or business unit focus for
analytics. Infrastructure for
analytics beginning to coalesce.
Key data, technology and
analysts are managed from an
enterprise perspective.
Key analytical resources focused on
enterprise priorities and
differentiation.
Organization
Little awareness of or interest in
analytics
Local leaders emerge, but don’t
discuss analytics as a group
Senior leaders recognizing
importance of analytics and
developing analytical capabilities
Senior leaders developing
analytical plans and building
analytical capabilities.
Strong leaders behaving analytically
and showing passion for analytical
competition.
Scope
Limited targeting of
opportunities
Multiple metrics are tracked
lacking synchronization between
regions & hierarchy
Analytical efforts coalescing
behind a small set of important
targets.
Analytics centered on a few key
business domains with explicit
and ambitious outcomes.
Analytics integral to the company’s
distinctive capability and strategy.
People
Limited analytics skills, and
attached to specific functions
Pockets of analysts in regions;
unmanaged mix of skills
Analysts recognized as key talent
and focused on important
business areas.
Highly capable analysts explicitly
recruited, developed, deployed,
and engaged.
World-class professional analysts;
cultivation of analytical amateurs
across the enterprise.
Governance
No governance in place to
manage data and technology
Basic policies have been defined
with respect to data gathering
Governance policies are well
structured to cover all critical
aspects
High degree of compliance is
emphasized for the policies
Competitive and frequently updated
standards
27
