In this webinar, we will share findings and insights from the maturity scorecards we have completed with the world's leading retailers, how they used this to secure executive sponsorship to ensure the data technology and business requirements were in tandem, as well as the use cases typically pursued. We will discuss the typical organizational constructs we see applicable based on the different stages of maturity and also discuss some best practices for driving best in class process for data driven transformation.

1. 1 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
Understanding Big Data Maturity-
Retail and Consumer Goods
April 2017

2. 2 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
Contents
à Big Data in Retail
– Digital Revolution
– Explosion of Data
à Big Data Maturity Analysis
– Hortonworks Big Data Maturity Scorecard
– Retail and CPG Maturity Analysis
à Big Data Use Cases
– Retail Use Case Maturity Map
– Single View of Customer
à Big Data in Action
– Retail Case Study
– Call to Action

3. 45% of F 200 firms want to
become and Integrated
Digital eco-system provider
47% of firms will introduce a new digital product portfolio in 18 months
69% say improving their
data strategy will be key
to their relationship with
the customer
48% believe sustainability as a
key reason to change their
digital business model by 2019
78% of F 500 organizations
have a medium to poor
Big Data and Analytics
capabilities
63% of $10bn+ firms are
witnessing their core
business model disrupted
Only 36% CEOs have a shared a
digital transformation vision
although 93% of the employees
believe it is the right thing to do
Digital and the Fourth Industrial Revolution through the numbers lens …

4. 4 © Hortonworks Inc. 2011 – 2017 All Rights Reserved Hortonworks Confidential. For Internal Use Only.
Significance of Big Data in Retail
Source: McKinsey, Press Search
Improvement
potential in retail
operating margins
through Big Data
Improvement in
Marketing potential
through Big Data
Retail companies to
use Beacons in the
next 5 years
Estimated annual
economic impact of
IOT in Retail by
2025
Consumers now
use a device or in-
store technology
during shopping
Estimated cross-
channel retail
sales in the US by
2017
60% 70%80% $1.8T>$500B15-20%

5. 5 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
Contents
à Big Data in Retail
– Digital Revolution
– Explosion of Data
à Big Data Maturity Analysis
– Hortonworks Big Data Maturity Scorecard
– Retail and CPG Maturity Analysis
à Big Data Use Cases
– Retail Use Case Maturity Map
– Single View of Customer
à Big Data in Action
– Retail Case Study
– Call to Action

6. 6 © Hortonworks Inc. 2011 – 2017 All Rights Reserved Hortonworks Confidential. For Internal Use Only.
There are several challenges that are inhibiting companies adopt Big Data
– According to Gartner Value is #1
Source: Gartner
Determining how
to get value from
big data
Obtaining
skills and
capablities
needed
Risk and
governance
issues
Funding for big
data-related
initiatives
Defining our
strategy
Integrated
multiple data
sources
Integrating big
data technology
with existing
infrastructure
Infrastructure
and/or
architecture
Leadership or
organizational
issues
Understanding
what is "Big
Data"
Greater than 50%
30% to 49%
20% to 29%
Less than 19%
Only 15% firms
are able to
calculate ROI for
any Digital
Initiative
-Mckinsey Digital

7. 7 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
Tailor a Value-based Path to the Summit of Data-driven Excellence
Data Driven Futuristic Organization
• Stage 1: AWARE
Big data is discussed but not reflected in business strategies or
processes beyond historical analysis
•
• Stage 2: EXPLORE
Emerging consensus on the potential of big data
and localized experiments and results
• Stage 3: OPTIMIZING
Operational performance is optimized in up to
three dimensions: customer lifecycle, product
lifecycle, an facility lifecycle
• Stage 4: TRANSFORMING
Data embraced as currency, as business value
streams are created through predictive analytics
Hortonworks Big Data Maturity Scorecard helps you start that journey
Stages of Hortonworks
Big Data Maturity Scorecard

8. 8 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
Companies need to understand where they stand in terms of big data maturity so that they can progress and identify the required
initiatives.
Our Big Data Scorecard helps in assessing your company’s current state along
five key capability domains:
1) Sponsorship;
2) Data and Analytics;
3) Technology and Infrastructure;
4) Organization and Skills; and
5) Process Management.
Within each of these capability domains, we identify four key focus areas that
indicate maturity, and then assess each area according to their specific maturity
level
Five capability domains of Hortonworks Maturity Model
Although the purpose of our framework is to evaluate your company’s maturity level in these areas, we believe it’s far more important to
understand how to capitalize on your existing capabilities, and to invest in those focus areas where we can best maximize progress toward defined
business objectives.

9. 9 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
Sponsorship
Data & Analytics
Practices
Technology and
Infrastructure
Organization
and Skills
Process
Management
Overall Findings for Retail and Consumer Goods Sector
1.5
1.2
2.0
1.71.61.71.7
1.51.5
2.0
1.71.71.61.5
1.9
1.81.92.1
1.6
1.9
2.9
2.6
3.1
2.92.92.82.92.82.8
3.1
2.82.82.9
2.6
3.03.03.03.13.02.9
Cross Functional Practices
In house or Outsourced
Operations Security Gov
Planning and Budgeting
Functionality
Investment Focus
2.9
Data Processing
Data Storage
Data Collection
Hosting Strategy
Business Case
Advocacy
Funding
Data Analysis
Leadership Model
Vision Strategy
Program Measurement
Analytic Dev Skills
Integration
Analytic Tools
1.7
• Overall, firms are still in the
Exploration stage –
– Firms lack enterprise vision around Big
Data with little executive sponsorship
– Firms are primarily using structured data
and are outsourcing Big Data projects
– They are starting to adopt analytical
tools for project specific objectives
• In the next 2-3 years, firms are
expected to be in Optimizing phase
– With enterprise-wide vision and
alignment with sponsorship and funding
– Firms will have data lake with
unstructured data and integrated,
analytical tools on top of it
– Will leverage mix of in-house and
outsourced resources
Current In 2-3 YearsBig Data Maturity Scores (Average)
Key Takeaways
In the next few slides, we analyze the Big Data scorecard results along the 5 capability domains

10. 10 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
Capability Domain: Sponsorship
29%
6%
65%
35%35%
24%
6%
4321
6%
12%
53%
29%
41%
24%24%
12%
4321
6%
24%
41%
29%
41%
29%
24%
6%
4321
12%
47%
35%
6%
35%35%
24%
6%
321 4
• Vision and Strategy: Currently, most
of the firms are in early stages of
establishing enterprise-wide vision on
Big Data and in 2-3 years most of
them will have one
• Funding: Big data projects are
primarily driven by IT projects and
budget. But Big Data programs will be
part of cyclical budgeting process in
the near future
• Advocacy: There is some level of
executive sponsorship which will only
increase with better alignment
among the leadership
• Business Case: Most of the firms
although don’t have business case
right now, they plan to have one in
the next 2-3 years
Vision and Strategy Funding
Business CaseAdvocacy
Current In 2-3 Years
% of firms at a Maturity Level
Key Takeaways

11. 11 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
Capability Domain: Data and Analytics Practices
29%
35%35%
41%
24%
29%
6%
4321
6%
12%
35%
47%
35%
29%
35%
0%
4%3%2%1%
6%6%
18%
71%
18%
35%35%
12%
1 432
6%
12%
18%
65%
29%
35%29%
6%
4321
Data Collection Data Storage
Data AnalysisData Processing
Current In 2-3 Years
% of firms at a Maturity Level
• Data Collection: Although most of
the firms still use structured data,
they expect to make big strides and
will deploy automated mechanisms
to collect both structured and
unstructured data in 2-3 years
• Data Storage: Most still discard
majority of the data but are planning
to have “data lake” to keep their data
• Data Processing: Currently,
processing is manual but firms expect
to have enterprise-wide metadata
standards in the near future
• Data Analysis: Most of the firms
focus mainly on business metrics
reporting that is going change to
more advanced and predictive
analytics in the next 2-3 years
Key Takeaways

12. 12 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
Capability Domain: Technology and Infrastructure
6%
24%
59%
12%
29%29%29%
12%
4321
0%
24%24%
53%
24%
41%
29%
6%
4%3%2%1%
6%
24%
35%35% 35%35%
29%
0%
1 432
0%
6%
41%
53%
18%
53%
18%
12%
4321
Hosting Strategy Functionality
IntegrationTools
Current In 2-3 Years
% of firms at a Maturity Level
• Hosting Strategy: Most of the firms
currently store data on-premise but
expect to deploy hybrid hosting
infrastructure going forward
• Functionality: Majority of the firms
currently deploy EDW data
warehouses and are in the process of
complementing it with Hadoop-based
clusters
• Tools: Firms are starting to adopt
analytical tools for project specific
objectives and will increasingly have
central administration of these tools
• Integration: Currently, there is little
integration between the tools but
with the Hadoop deployment there
will be better integration and cross-
functional analysis
Key Takeaways

13. 13 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
Capability Domain: Organization and Skills
6%
12%
29%
53%
41%
29%
12%
18%
4321
0%
12%
29%
59%
18%
47%
35%
0%
4%3%2%1%
6%6%
41%
47%
24%
41%
29%
6%
1 432
6%6%
35%
53%
29%
41%
18%
12%
4321
Analytical and Development Skills In-house or Outsourced
Cross-functional PracticesLeadership Model
Current In 2-3 Years
% of firms at a Maturity Level
• Analytical and Development Skills:
Currently, the Big Data skills are
mostly located within the IT
organization but firms are investing a
lot to gain advanced analytical skills
across the organization
• In-house or Outsourced: For
majority of the firm, significant work
is being outsourced but firms will
deploy mix of in-house and
outsourced skill-set in the near future
• Leadership: Majority firms are also
expected to have a centralized
analytics group to help drive the Big
Data programs
• Cross-functional Practices: With
centralized group, firms will have
increasing capability for cross-
functional collaboration and analysis
Key Takeaways

14. 14 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
Capability Domain: Process Management
0%
35%
29%
35%
41%
35%
18%
6%
4321
6%
12%
29%
53%
35%
24%
41%
0%
4%3%2%1%
35%
0%0%
24%
76%
18%
35%
12%
4321
0%
18%18%
65%
29%
35%29%
6%
1 432
Planning and Budgeting
Operations, Security and
Governance
Investment FocusProgram Measurement
Current In 2-3 Years
% of firms at a Maturity Level
• Planning and Budgeting: Although
there is little formal planning and
budgeting for Big Data programs
currently, the firms expect to have
one in 2-3 years
• Operations, Security and
Governance: Firms vary in this
dimension – majority will have
enterprise-wide policy and protocol
in the near future
• Program Measurement: Majority of
the firms expect to monitor the
outcomes from Big Data programs
along with the formal planning
• Investment Focus: Although
investment is currently made on ad
hoc basis, it is expected to change to
find new sources of revenue and
business models moving forward
Key Takeaways

15. 15 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
Sponsorship
Data & Analytics
Practices
Technology and
Infrastructure
Organization
and Skills
Process
Management
Case Study: A leading European Retailer
1.01.0
3.03.0
2.02.0
3.03.03.03.03.0
2.0
3.0
1.0
3.0
2.0
3.03.03.03.0 3.03.0
4.04.04.04.04.04.04.04.04.04.04.0
3.0
4.04.04.04.04.04.0
2.5
3.9
Investment Focus
Program Measurement
Operations Security Gov
Planning and Budgeting
Cross Functional Practices
Business Case
Advocacy
Funding
Vision Strategy
Leadership Model
In house or Outsourced
Analytic Dev Skills
Integration
Analytic Tools
Functionality
Hosting Strategy
Data Analysis
Data Processing
Data Storage
Data Collection
• Retailer is already in Optimizing stage
and will attain the highest maturity,
Transforming, in Big Data in 2-3 years
– Has an enterprise-wide vision and
strategy – on which rest of the key
elements of Big Data depend
– Has started to ingest unstructured data
and rarely discard data
– Has adopted Hadoop to accomplish
different workloads with integration of
analytical tools across the organization
– Is investing in Big Data skills for
advanced analytics and leverages both
inhouse and outsourced resources
– Has already included Big Data programs
in its budgeting and planning cycle
Current In 2-3 Years
Big Data Maturity Scores
Key Takeaways

16. 16 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
Contents
à Big Data in Retail
– Digital Revolution
– Explosion of Data
à Big Data Maturity Analysis
– Hortonworks Big Data Maturity Scorecard
– Retail and CPG Maturity Analysis
à Big Data Use Cases
– Retail Use Case Maturity Map
– Single View of Customer
à Big Data in Action
– Retail Case Study
– Call to Action

17. 17 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
Transformation
--- Maturity Stages à
OptimizationExplorationAwareness
---MaturityStagesà
Marketing
Merchandising
IT Ops
Digital
Store Operations
Purchasing &
Logistics
2
6
7
4
10
11
13
1a
1b
12
8
3
95
15
14
16
17
Peer Competitive Scale
Standard among peer group
Common among peer group
Strategic among peer group
New Innovations
Retail Industry – Use Case Maturity Roadmap
No Use Case Name
1a Single View of Customer
1b Single View of Customer
2 Basket Analysis
3 Social Listening
4 Enriched Basket Analysis
5 Clickstream Analysis
6 Recommendation Engine
7 Price Optimization
8
Beacon/Sensor Monitoring
and Ingest
9 Store Communications
10 Email Management
11 EDW Enhancement
12 Inventory Optimization
13 Path to Purchase
14 Supply Chain Telemetry
15 Customer Service Analysis
16 Preventative Maintenance
17 Machine Learning / AI
Discussed in
subsequent slides

18. 18
Use Case: Single View of Customer
• Ability to identify # unique customers which directly impacts both the top-line ROI measurement and bottom–line optimization
• Increased customer loyalty, LFL sales, average basket size, redemption propensity on promotional activity, listing fees
• Dynamic real time targeted pricing which results in better margins from your most loyal customers
Business
Value
• Better customer experience leading to increased loyalty and customer advocacy
• Increased Marketing Effectiveness leading to higher ROI on every £ spent
• Cross selling and predictive promotional propensity means greater number of manufacturer partnerships
Why
Do It?
• Currently, Retailers, CPG firms and other manufacturers create shopper profiles based on historical data, SKU level data and
Basket data – They however struggle to marry that data with the behavioral data from multiple other channels (mobile, Social
Media, etc.) to map out the DNA of the customer and fail to predict futuristic buying patterns of customers across categories
and products
• Single View of the customer not only allows organizations the ability to create targeted campaigns based on shopping patterns
but also opens up new avenues of revenue streams through advanced marketing efforts such as cross device marketing,
beacon sensing, proximity mktg. etc.
Idea
Summary
The Single View of Customer combines historical sales data from structured systems with new, unstructured and semi-structured
data from social media, sentiment analysis, web activity, and blog posts. Single View of the customer helps create the DNA of the
consumer that can be used to target, re-target, personalize messaging to help address issues around loyalty, churn, cross-selling,
increasing the top line etc.
Innovate –Grow & Enable

19. 19
Contents
à Big Data in Retail
– Digital Revolution
– Explosion of Data
à Big Data Maturity Analysis
– Hortonworks Big Data Maturity Scorecard
– Retail and CPG Maturity Analysis
à Big Data Use Cases
– Retail Use Case Maturity Map
– Single View of Customer
à Big Data in Action
– Retail Case Study
– Call to Action

20. Ø Quick Facts
Quick Facts
• For direct marketing, the lack of visibility into a customer’s credit and financial situation
restricted retailer's ability to pre-screen “right” customers to send the mailers
• Mismatch between Inventory Merchandising Ad Planner and Warehouse Inventory led
to incomplete sales
• Generation of various business reports took days to complete and even after that, not all
the information was available to the Business stakeholders
Situation Analysis
Innovation Strategy
• Retailer built an Enterprise Analytics platform based on Hortonworks Data Platform, breaking-
down silos and increasing historical depth of data available for analysis
• Drove targeted marketing strategy with insight driven customer segmentation analysis,
leveraging new data sources, including the available history
• Implemented near-real time simulation of new Credit Strategy with respect to approval or
decline of application process by collecting exhaustive set of variables needed for credit policy
coding for all customers
Business Impact
• Reduced Spend on Direct Mailers by optimizing mailing by Customer Segment: $3M in first 10
months of 2016 ($4.5 to $5.0M expected run rate savings)
• Reduced ads effectiveness analysis in Product Performance report: 300x improvement in
turnaround
• Reduced associate time in coding for red-flags and lookups for decline rules: 500x time
reduction in implementing credit policy
$3M
Marketing dollars saved to-
date from trimming the direct
mailers
Up to 500x
Time improvement in
implementing credit policy
Up to 45x
Time improvement in
generating Inventory
Merchandising Ad Planner

21. 21 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
Call to Action
Take Big Data Scorecard Survey on
Hortonworks website
Collaborate with Hortonworks to
create value-based Big Data roadmap